server: guard entire plugin load block

server: log plugin load failure
common: listenZeroSingleClient configurable timeout
2026-02-05 23:22:13 +00:00 · 2024-06-01 13:07:55 -07:00 · 2024-06-01 13:05:56 -07:00 · 2024-06-01 09:44:51 -07:00 · 2024-06-01 09:19:32 -07:00 · 2024-06-01 08:49:37 -07:00
118 changed files with 4325 additions and 1933 deletions
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -9,52 +9,28 @@ on:
  workflow_dispatch:
  
 jobs:
-  test_linux_local:
-    name: Test Linux local installation
-    runs-on: ubuntu-latest
+  test_local:
+    name: Test local installation on ${{ matrix.runner }}
+    runs-on: ${{ matrix.runner }}
+    strategy:
+      fail-fast: false
+      matrix:
+        runner: [ubuntu-latest, macos-14, macos-13, windows-latest]
    
    steps:
      - name: Checkout repository
        uses: actions/checkout@v3
-        
-      - name: Run install script
+
+      - name: Parse latest server release
+        id: parse_server
+        shell: bash
        run: |
-          cat ./install/local/install-scrypted-dependencies-linux.sh | sudo SERVICE_USER=$USER bash
-          
-      - name: Test server is running
-        run: |
-          systemctl status scrypted.service
-          curl -k --retry 20 --retry-all-errors --retry-max-time 600 https://localhost:10443/
-          
-  test_mac_local:
-    name: Test Mac local installation
-    runs-on: macos-latest
-    
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-        
-      - name: Run install script
-        run: |
-          mkdir -p ~/.scrypted
-          bash ./install/local/install-scrypted-dependencies-mac.sh
-          
-      - name: Test server is running
-        run: |
-          curl -k --retry 20 --retry-all-errors --retry-max-time 600 https://localhost:10443/
-          
-  test_windows_local:
-    name: Test Windows local installation
-    runs-on: windows-latest
-    
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-        
-      - name: Run install script
-        run: |
-          .\install\local\install-scrypted-dependencies-win.ps1
-          
-      - name: Test server is running
-        run: |
-          curl -k --retry 20 --retry-all-errors --retry-max-time 600 https://localhost:10443/
+          VERSION=$(cat ./server/package-lock.json | jq -r '.version')
+          echo "version=$VERSION" >> "$GITHUB_OUTPUT"
+          echo "Will test @scrypted/server@$VERSION"
+
+      - name: Install scrypted server
+        uses: scryptedapp/setup-scrypted@v0.0.2
+        with:
+          branch: ${{ github.sha }}
+          version: ${{ steps.parse_server.outputs.version }}
--- a/common/package-lock.json
+++ b/common/package-lock.json
@@ -74,7 +74,7 @@
    },
    "../sdk": {
      "name": "@scrypted/sdk",
-      "version": "0.3.4",
+      "version": "0.3.29",
      "license": "ISC",
      "dependencies": {
        "@babel/preset-typescript": "^7.18.6",
@@ -111,64 +111,57 @@
    },
    "../server": {
      "name": "@scrypted/server",
-      "version": "0.82.0",
+      "version": "0.106.0",
+      "hasInstallScript": true,
      "license": "ISC",
      "dependencies": {
        "@mapbox/node-pre-gyp": "^1.0.11",
-        "@scrypted/types": "^0.3.4",
-        "adm-zip": "^0.5.10",
+        "@scrypted/ffmpeg-static": "^6.1.0-build1",
+        "@scrypted/node-pty": "^1.0.10",
+        "@scrypted/types": "^0.3.28",
+        "adm-zip": "^0.5.12",
        "body-parser": "^1.20.2",
        "cookie-parser": "^1.4.6",
-        "debug": "^4.3.4",
+        "dotenv": "^16.4.5",
        "engine.io": "^6.5.4",
-        "express": "^4.18.2",
-        "ffmpeg-static": "^5.2.0",
-        "follow-redirects": "^1.15.4",
+        "express": "^4.19.2",
+        "follow-redirects": "^1.15.6",
        "http-auth": "^4.2.0",
-        "ip": "^1.1.8",
-        "level": "^8.0.0",
-        "linkfs": "^2.1.0",
+        "ip": "^2.0.1",
+        "level": "^8.0.1",
        "lodash": "^4.17.21",
-        "memfs": "^4.6.0",
-        "mime": "^3.0.0",
-        "nan": "^2.18.0",
+        "nan": "^2.19.0",
        "node-dijkstra": "^2.5.0",
        "node-forge": "^1.3.1",
-        "node-gyp": "^10.0.1",
+        "node-gyp": "^10.1.0",
+        "py": "npm:@bjia56/portable-python@^0.1.31",
        "router": "^1.3.8",
-        "semver": "^7.5.4",
-        "sharp": "^0.33.1",
+        "semver": "^7.6.2",
+        "sharp": "^0.33.3",
        "source-map-support": "^0.5.21",
-        "tar": "^6.2.0",
+        "tar": "^7.1.0",
        "tslib": "^2.6.2",
-        "typescript": "^5.3.3",
+        "typescript": "^5.4.5",
        "whatwg-mimetype": "^4.0.0",
-        "ws": "^8.16.0"
+        "ws": "^8.17.0"
      },
      "bin": {
        "scrypted-serve": "bin/scrypted-serve"
      },
      "devDependencies": {
        "@types/adm-zip": "^0.5.5",
-        "@types/cookie-parser": "^1.4.6",
-        "@types/debug": "^4.1.12",
+        "@types/cookie-parser": "^1.4.7",
        "@types/express": "^4.17.21",
        "@types/follow-redirects": "^1.14.4",
        "@types/http-auth": "^4.1.4",
        "@types/ip": "^1.1.3",
-        "@types/lodash": "^4.14.202",
-        "@types/mime": "^3.0.4",
+        "@types/lodash": "^4.17.1",
        "@types/node-dijkstra": "^2.5.6",
-        "@types/node-forge": "^1.3.10",
-        "@types/pem": "^1.14.4",
-        "@types/semver": "^7.5.6",
+        "@types/node-forge": "^1.3.11",
+        "@types/semver": "^7.5.8",
        "@types/source-map-support": "^0.5.10",
-        "@types/tar": "^6.1.10",
        "@types/whatwg-mimetype": "^3.0.2",
        "@types/ws": "^8.5.10"
-      },
-      "optionalDependencies": {
-        "node-pty-prebuilt-multiarch": "^0.10.1-pre.5"
      }
    },
    "node_modules/@cspotcode/source-map-support": {
@@ -453,53 +446,47 @@
      "version": "file:../server",
      "requires": {
        "@mapbox/node-pre-gyp": "^1.0.11",
-        "@scrypted/types": "^0.3.4",
+        "@scrypted/ffmpeg-static": "^6.1.0-build1",
+        "@scrypted/node-pty": "^1.0.10",
+        "@scrypted/types": "^0.3.28",
        "@types/adm-zip": "^0.5.5",
-        "@types/cookie-parser": "^1.4.6",
-        "@types/debug": "^4.1.12",
+        "@types/cookie-parser": "^1.4.7",
        "@types/express": "^4.17.21",
        "@types/follow-redirects": "^1.14.4",
        "@types/http-auth": "^4.1.4",
        "@types/ip": "^1.1.3",
-        "@types/lodash": "^4.14.202",
-        "@types/mime": "^3.0.4",
+        "@types/lodash": "^4.17.1",
        "@types/node-dijkstra": "^2.5.6",
-        "@types/node-forge": "^1.3.10",
-        "@types/pem": "^1.14.4",
-        "@types/semver": "^7.5.6",
+        "@types/node-forge": "^1.3.11",
+        "@types/semver": "^7.5.8",
        "@types/source-map-support": "^0.5.10",
-        "@types/tar": "^6.1.10",
        "@types/whatwg-mimetype": "^3.0.2",
        "@types/ws": "^8.5.10",
-        "adm-zip": "^0.5.10",
+        "adm-zip": "^0.5.12",
        "body-parser": "^1.20.2",
        "cookie-parser": "^1.4.6",
-        "debug": "^4.3.4",
+        "dotenv": "^16.4.5",
        "engine.io": "^6.5.4",
-        "express": "^4.18.2",
-        "ffmpeg-static": "^5.2.0",
-        "follow-redirects": "^1.15.4",
+        "express": "^4.19.2",
+        "follow-redirects": "^1.15.6",
        "http-auth": "^4.2.0",
-        "ip": "^1.1.8",
-        "level": "^8.0.0",
-        "linkfs": "^2.1.0",
+        "ip": "^2.0.1",
+        "level": "^8.0.1",
        "lodash": "^4.17.21",
-        "memfs": "^4.6.0",
-        "mime": "^3.0.0",
-        "nan": "^2.18.0",
+        "nan": "^2.19.0",
        "node-dijkstra": "^2.5.0",
        "node-forge": "^1.3.1",
-        "node-gyp": "^10.0.1",
-        "node-pty-prebuilt-multiarch": "^0.10.1-pre.5",
+        "node-gyp": "^10.1.0",
+        "py": "npm:@bjia56/portable-python@^0.1.31",
        "router": "^1.3.8",
-        "semver": "^7.5.4",
-        "sharp": "^0.33.1",
+        "semver": "^7.6.2",
+        "sharp": "^0.33.3",
        "source-map-support": "^0.5.21",
-        "tar": "^6.2.0",
+        "tar": "^7.1.0",
        "tslib": "^2.6.2",
-        "typescript": "^5.3.3",
+        "typescript": "^5.4.5",
        "whatwg-mimetype": "^4.0.0",
-        "ws": "^8.16.0"
+        "ws": "^8.17.0"
      }
    },
    "@tsconfig/node10": {
--- a/install/config.yaml
+++ b/install/config.yaml
@@ -1,6 +1,6 @@
 # Home Assistant Addon Configuration
 name: Scrypted
-version: "20-jammy-full.s6-v0.99.0"
+version: "v0.105.0-jammy-full"
 slug: scrypted
 description: Scrypted is a high performance home video integration and automation platform
 url: "https://github.com/koush/scrypted"
--- a/install/docker/docker-compose.yml
+++ b/install/docker/docker-compose.yml
@@ -35,7 +35,7 @@ services:
            # Avahi can be used for network discovery by passing in the host daemon
            # or running the daemon inside the container. Choose one or the other.
            # Uncomment next line to run avahi-daemon inside the container.
-            # See volumes section below to use the host daemon.
+            # See volumes and security_opt section below to use the host daemon.
            # - SCRYPTED_DOCKER_AVAHI=true

            # NVIDIA (Part 1 of 4)
@@ -71,11 +71,16 @@ services:
            # Ensure Avahi is running on the host machine:
            # It can be installed with: sudo apt-get install avahi-daemon
            # This is not compatible with running avahi inside the container (see above).
+            # Also, uncomment the lines under security_opt
            # - /var/run/dbus:/var/run/dbus
            # - /var/run/avahi-daemon/socket:/var/run/avahi-daemon/socket

            # Default volume for the Scrypted database. Typically should not be changed.
            - ~/.scrypted/volume:/server/volume
+        # Uncomment the following lines to use Avahi daemon from the host
+        # Without this, AppArmor will block the container's attempt to talk to Avahi via dbus
+        # security_opt:
+        #     - apparmor:unconfined
        devices: [
            # uncomment the common systems devices to pass
            # them through to docker.
--- a/install/docker/install-intel-graphics.sh
+++ b/install/docker/install-intel-graphics.sh
@@ -1,13 +1,35 @@
 if [ "$(uname -m)" = "x86_64" ]
 then
-    echo "Installing Intel graphics packages."
-    apt-get update && apt-get install -y gpg-agent &&
-    rm -f /usr/share/keyrings/intel-graphics.gpg &&
-    curl -L https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor --yes --output /usr/share/keyrings/intel-graphics.gpg &&
-    echo 'deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc' | tee  /etc/apt/sources.list.d/intel.gpu.jammy.list &&
-    apt-get -y update &&
-    apt-get -y install intel-opencl-icd intel-media-va-driver-non-free &&
+    # this script previvously apt install intel-media-va-driver-non-free, but that seems to no longer be necessary.
+
+    # the intel provided script is disabled since it does not work with the 6.8 kernel in Ubuntu 24.04 or Proxmox 8.2.
+    # manual installation of the Intel graphics stuff is required.
+
+    # echo "Installing Intel graphics packages."
+    # apt-get update && apt-get install -y gpg-agent &&
+    # rm -f /usr/share/keyrings/intel-graphics.gpg &&
+    # curl -L https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor --yes --output /usr/share/keyrings/intel-graphics.gpg &&
+    # echo 'deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc' | tee  /etc/apt/sources.list.d/intel.gpu.jammy.list &&
+    # apt-get -y update &&
+    # apt-get -y install intel-opencl-icd &&
+    # apt-get -y dist-upgrade;
+
+    # manual installation
+    # https://github.com/intel/compute-runtime/releases/tag/24.13.29138.7
+
+    rm -rf /tmp/neo && mkdir -p /tmp/neo && cd /tmp/neo &&
+    apt-get install -y ocl-icd-libopencl1 &&
+    curl -O -L https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.16510.2/intel-igc-core_1.0.16510.2_amd64.deb &&
+    curl -O -L https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.16510.2/intel-igc-opencl_1.0.16510.2_amd64.deb &&
+    curl -O -L https://github.com/intel/compute-runtime/releases/download/24.13.29138.7/intel-level-zero-gpu-dbgsym_1.3.29138.7_amd64.ddeb &&
+    curl -O -L https://github.com/intel/compute-runtime/releases/download/24.13.29138.7/intel-level-zero-gpu_1.3.29138.7_amd64.deb &&
+    curl -O -L https://github.com/intel/compute-runtime/releases/download/24.13.29138.7/intel-opencl-icd-dbgsym_24.13.29138.7_amd64.ddeb &&
+    curl -O -L https://github.com/intel/compute-runtime/releases/download/24.13.29138.7/intel-opencl-icd_24.13.29138.7_amd64.deb &&
+    curl -O -L https://github.com/intel/compute-runtime/releases/download/24.13.29138.7/libigdgmm12_22.3.18_amd64.deb &&
+    dpkg -i *.deb &&
+    cd /tmp && rm -rf /tmp/neo &&
    apt-get -y dist-upgrade;
+
    exit $?
 else
    echo "Intel graphics will not be installed on this architecture."
--- a/install/docker/install-scrypted-docker-compose.sh
+++ b/install/docker/install-scrypted-docker-compose.sh
@@ -61,6 +61,8 @@ then
    sudo apt-get -y install avahi-daemon
    sed -i 's/'#' - \/var\/run\/dbus/- \/var\/run\/dbus/g' $DOCKER_COMPOSE_YML
    sed -i 's/'#' - \/var\/run\/avahi-daemon/- \/var\/run\/avahi-daemon/g' $DOCKER_COMPOSE_YML
+    sed -i 's/'#' security_opt:/security_opt:/g' $DOCKER_COMPOSE_YML
+    sed -i 's/'#'     - apparmor:unconfined/    - apparmor:unconfined/g' $DOCKER_COMPOSE_YML
 fi

 echo "Setting permissions on $SCRYPTED_HOME"
--- a/install/docker/setup-scrypted-nvr-volume.sh
+++ b/install/docker/setup-scrypted-nvr-volume.sh
@@ -72,6 +72,7 @@ function removescryptedfstab() {
    grep -v "scrypted-nvr" /etc/fstab > /tmp/fstab && cp /tmp/fstab /etc/fstab
    # ensure newline
    sed -i -e '$a\' /etc/fstab
+    systemctl daemon-reload
 }

 BLOCK_DEVICE="/dev/$1"
@@ -95,7 +96,17 @@ then
    set +e

    sync
-    mkfs -F -t ext4 "$BLOCK_DEVICE"1
+    PARTITION_DEVICE="$BLOCK_DEVICE"1
+    if [ ! -e "$PARTITION_DEVICE" ]
+    then
+        PARTITION_DEVICE="$BLOCK_DEVICE"p1
+        if [ ! -e "$PARTITION_DEVICE" ]
+        then
+            echo "Unable to determine block device partition from block device: $BLOCK_DEVICE"
+            exit 1
+        fi
+    fi
+    mkfs -F -t ext4 "$PARTITION_DEVICE"
    sync

    # parse/evaluate blkid line as env vars
@@ -119,6 +130,7 @@ then
    mkdir -p /mnt/scrypted-nvr
    echo "PARTLABEL=scrypted-nvr     /mnt/scrypted-nvr    ext4   defaults,nofail 0 0" >> /etc/fstab
    mount -a
+    systemctl daemon-reload
    set +e

    DIR="/mnt/scrypted-nvr"
--- a/install/local/install-scrypted-dependencies-linux.sh
+++ b/install/local/install-scrypted-dependencies-linux.sh
@@ -97,7 +97,7 @@ echo "docker compose rm -rf"
 sudo -u $SERVICE_USER docker rm -f /scrypted /scrypted-watchtower 2> /dev/null

 echo "Installing Scrypted..."
-RUN sudo -u $SERVICE_USER npx -y scrypted@latest install-server
+RUN sudo -u $SERVICE_USER npx -y scrypted@latest install-server $SCRYPTED_INSTALL_VERSION

 cat > /etc/systemd/system/scrypted.service <<EOT

--- a/install/local/install-scrypted-dependencies-mac.sh
+++ b/install/local/install-scrypted-dependencies-mac.sh
@@ -121,7 +121,7 @@ then
 fi

 echo "Installing Scrypted..."
-RUN $NPX_PATH -y scrypted@latest install-server
+RUN $NPX_PATH -y scrypted@latest install-server $SCRYPTED_INSTALL_VERSION

 cat > ~/Library/LaunchAgents/app.scrypted.server.plist <<EOT
 <?xml version="1.0" encoding="UTF-8"?>
--- a/install/local/install-scrypted-dependencies-win.ps1
+++ b/install/local/install-scrypted-dependencies-win.ps1
@@ -11,7 +11,7 @@ iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/in
 choco upgrade -y nodejs-lts --version=20.11.1

 # Install VC Redist, which is necessary for portable python
-choco install vcredist140
+choco install -y vcredist140

 # TODO: remove python install, and use portable python
 # Install Python
@@ -26,7 +26,12 @@ $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";"
 py $SCRYPTED_WINDOWS_PYTHON_VERSION -m pip install --upgrade pip
 py $SCRYPTED_WINDOWS_PYTHON_VERSION -m pip install debugpy typing_extensions typing opencv-python

-npx -y scrypted@latest install-server
+$SCRYPTED_INSTALL_VERSION=[System.Environment]::GetEnvironmentVariable("SCRYPTED_INSTALL_VERSION","User")
+if ($SCRYPTED_INSTALL_VERSION -eq $null) {
+  npx -y scrypted@latest install-server
+} else {
+  npx -y scrypted@latest install-server $SCRYPTED_INSTALL_VERSION 
+}

 $USER_HOME_ESCAPED = $env:USERPROFILE.replace('\', '\\')
 $SCRYPTED_HOME = $env:USERPROFILE + '\.scrypted'
--- a/npm-install.sh
+++ b/npm-install.sh
@@ -1,10 +1,4 @@
 #!/bin/bash
-echo 'if (!process.version.startsWith("v18")) throw new Error("Node 18 is required. Install Node Version Manager (nvm) for versioned node installations. See https://github.com/koush/scrypted/pull/498#issuecomment-1373854020")' | node
-if [ "$?" != 0 ]
-then
-    exit
-fi
-
 echo ######################################
 echo "Setting up popular plugins."
 echo "Additional will need npm install manually."
@@ -15,7 +9,7 @@ cd $(dirname $0)
 git submodule init
 git submodule update

-for directory in sdk common server packages/client packages/auth-fetch
+for directory in sdk server common packages/client packages/auth-fetch
 do
    echo "$directory > npm install"
    pushd $directory
--- a/packages/auth-fetch/src/auth-fetch.ts
+++ b/packages/auth-fetch/src/auth-fetch.ts
@@ -1,4 +1,4 @@
-import { HttpFetchOptions, HttpFetchResponseType, checkStatus, fetcher, getFetchMethod, setDefaultHttpFetchAccept } from '../../../server/src/fetch';
+import { HttpFetchOptions, HttpFetchResponseType, checkStatus, createHeadersArray, fetcher, getFetchMethod, hasHeader, setDefaultHttpFetchAccept, setHeader } from '../../../server/src/fetch';

 export interface AuthFetchCredentialState {
    username: string;
@@ -74,15 +74,15 @@ export function createAuthFetch<B, M>(
 ) {
    const authHttpFetch = async <T extends HttpFetchOptions<B>>(options: T & AuthFetchOptions): ReturnType<typeof h<T>> => {
        const method = getFetchMethod(options);
-        const headers = new Headers(options.headers);
+        const headers = createHeadersArray(options.headers);
        options.headers = headers;
        setDefaultHttpFetchAccept(headers, options.responseType);

        const initialHeader = await getAuth(options, options.url, method);
        // try to provide an authorization if a session exists, but don't override Authorization if provided already.
        // 401 will trigger a proper auth.
-        if (initialHeader && !headers.has('Authorization'))
-            headers.set('Authorization', initialHeader);
+        if (initialHeader && !hasHeader(headers, 'Authorization'))
+            setHeader(headers, 'Authorization', initialHeader);

        const initialResponse = await h({
            ...options,
@@ -126,7 +126,7 @@ export function createAuthFetch<B, M>(

        const header = await getAuth(options, options.url, method);
        if (header)
-            headers.set('Authorization', header);
+            setHeader(headers, 'Authorization', header);

        return h(options);
    }
--- a/packages/cli/package-lock.json
+++ b/packages/cli/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "scrypted",
-  "version": "1.3.15",
+  "version": "1.3.16",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "scrypted",
-      "version": "1.3.15",
+      "version": "1.3.16",
      "license": "ISC",
      "dependencies": {
        "@scrypted/client": "^1.3.3",
--- a/packages/cli/package.json
+++ b/packages/cli/package.json
@@ -1,6 +1,6 @@
 {
  "name": "scrypted",
-  "version": "1.3.15",
+  "version": "1.3.16",
  "description": "",
  "main": "./dist/packages/cli/src/main.js",
  "bin": {
--- a/packages/cli/src/service.ts
+++ b/packages/cli/src/service.ts
@@ -14,8 +14,12 @@ const EXIT_FILE = '.exit';
 const UPDATE_FILE = '.update';

 async function runCommand(command: string, ...args: string[]) {
-    if (os.platform() === 'win32')
+    if (os.platform() === 'win32') {
        command += '.cmd';
+        // wrap each argument in a quote to handle spaces in paths
+        // https://github.com/nodejs/node/issues/38490#issuecomment-927330248
+        args = args.map(arg => '"' + arg + '"');
+    }
    console.log('running', command, ...args);
    const cp = child_process.spawn(command, args, {
        stdio: 'inherit',
--- a/plugins/amcrest/package-lock.json
+++ b/plugins/amcrest/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/amcrest",
-   "version": "0.0.150",
+   "version": "0.0.151",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/amcrest",
-         "version": "0.0.150",
+         "version": "0.0.151",
         "license": "Apache",
         "dependencies": {
            "@scrypted/common": "file:../../common",
--- a/plugins/amcrest/package.json
+++ b/plugins/amcrest/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/amcrest",
-   "version": "0.0.150",
+   "version": "0.0.151",
   "description": "Amcrest Plugin for Scrypted",
   "author": "Scrypted",
   "license": "Apache",
--- a/plugins/amcrest/src/amcrest-api.ts
+++ b/plugins/amcrest/src/amcrest-api.ts
@@ -134,7 +134,7 @@ export interface AmcrestEventData {
 export enum AmcrestEvent {
    MotionStart = "Code=VideoMotion;action=Start",
    MotionStop = "Code=VideoMotion;action=Stop",
-    MotionInfo  = "Code=VideoMotionInfo;action=State",
+    MotionInfo = "Code=VideoMotionInfo;action=State",
    AudioStart = "Code=AudioMutation;action=Start",
    AudioStop = "Code=AudioMutation;action=Stop",
    TalkInvite = "Code=_DoTalkAction_;action=Invite",
@@ -263,6 +263,8 @@ export class AmcrestCameraClient {
        // make content type parsable as content disposition filename
        const cd = contentType.parse(ct);
        let { boundary } = cd.parameters;
+        // amcrest may send "--myboundary" or "-- myboundary" (with a space)
+        const altBoundary = `-- ${boundary}`;
        boundary = `--${boundary}`;
        const boundaryEnd = `${boundary}--`;

@@ -286,7 +288,7 @@ export class AmcrestCameraClient {
                        this.console.log('ignoring dahua http body', body);
                    continue;
                }
-                if (ignore !== boundary) {
+                if (ignore !== boundary && ignore !== altBoundary) {
                    this.console.error('expected boundary but found', ignore);
                    this.console.error(response.headers);
                    throw new Error('expected boundary');
--- a/plugins/bticino/package-lock.json
+++ b/plugins/bticino/package-lock.json
@@ -1,29 +1,26 @@
 {
   "name": "@scrypted/bticino",
-   "version": "0.0.15",
+   "version": "0.0.16",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/bticino",
-         "version": "0.0.15",
+         "version": "0.0.16",
         "dependencies": {
            "@slyoldfox/sip": "^0.0.6-1",
            "sdp": "^3.0.3",
-            "stun": "^2.1.0",
-            "uuid": "^8.3.2"
+            "stun": "^2.1.0"
         },
         "devDependencies": {
            "@scrypted/common": "file:../../common",
            "@scrypted/sdk": "file:../../sdk",
            "@types/node": "^16.9.6",
-            "@types/uuid": "^8.3.4",
            "cross-env": "^7.0.3",
            "ts-node": "^10.9.1"
         }
      },
      "../../common": {
-         "name": "@scrypted/common",
         "version": "1.0.1",
         "dev": true,
         "license": "ISC",
@@ -39,8 +36,7 @@
         }
      },
      "../../sdk": {
-         "name": "@scrypted/sdk",
-         "version": "0.3.14",
+         "version": "0.3.29",
         "dev": true,
         "license": "ISC",
         "dependencies": {
@@ -89,18 +85,18 @@
         }
      },
      "node_modules/@jridgewell/resolve-uri": {
-         "version": "3.1.0",
-         "resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.0.tgz",
-         "integrity": "sha512-F2msla3tad+Mfht5cJq7LSXcdudKTWCVYUgw6pLFOOHSTtZlj6SWNYAp+AhuqLmWdBO2X5hPrLcu8cVP8fy28w==",
+         "version": "3.1.2",
+         "resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz",
+         "integrity": "sha512-bRISgCIjP20/tbWSPWMEi54QVPRZExkuD9lJL+UIxUKtwVJA8wW1Trb1jMs1RFXo1CBTNZ/5hpC9QvmKWdopKw==",
         "dev": true,
         "engines": {
            "node": ">=6.0.0"
         }
      },
      "node_modules/@jridgewell/sourcemap-codec": {
-         "version": "1.4.14",
-         "resolved": "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.14.tgz",
-         "integrity": "sha512-XPSJHWmi394fuUuzDnGz1wiKqWfo1yXecHQMRf2l6hztTO+nPru658AyDngaBe7isIxEkRsPR3FZh+s7iVa4Uw==",
+         "version": "1.4.15",
+         "resolved": "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.15.tgz",
+         "integrity": "sha512-eF2rxCRulEKXHTRiDrDy6erMYWqNw4LPdQ8UQA4huuxaQsVeRPFl2oM8oDGxMFhJUWZf9McpLtJasDDZb/Bpeg==",
         "dev": true
      },
      "node_modules/@jridgewell/trace-mapping": {
@@ -130,9 +126,9 @@
         }
      },
      "node_modules/@tsconfig/node10": {
-         "version": "1.0.9",
-         "resolved": "https://registry.npmjs.org/@tsconfig/node10/-/node10-1.0.9.tgz",
-         "integrity": "sha512-jNsYVVxU8v5g43Erja32laIDHXeoNvFEpX33OK4d6hljo3jDhCBDhx5dhCCTMWUojscpAagGiRkBKxpdl9fxqA==",
+         "version": "1.0.11",
+         "resolved": "https://registry.npmjs.org/@tsconfig/node10/-/node10-1.0.11.tgz",
+         "integrity": "sha512-DcRjDCujK/kCk/cUe8Xz8ZSpm8mS3mNNpta+jGCA6USEDfktlNvm1+IuZ9eTcDbNk41BHwpHHeW+N1lKCz4zOw==",
         "dev": true
      },
      "node_modules/@tsconfig/node12": {
@@ -148,27 +144,21 @@
         "dev": true
      },
      "node_modules/@tsconfig/node16": {
-         "version": "1.0.3",
-         "resolved": "https://registry.npmjs.org/@tsconfig/node16/-/node16-1.0.3.tgz",
-         "integrity": "sha512-yOlFc+7UtL/89t2ZhjPvvB/DeAr3r+Dq58IgzsFkOAvVC6NMJXmCGjbptdXdR9qsX7pKcTL+s87FtYREi2dEEQ==",
+         "version": "1.0.4",
+         "resolved": "https://registry.npmjs.org/@tsconfig/node16/-/node16-1.0.4.tgz",
+         "integrity": "sha512-vxhUy4J8lyeyinH7Azl1pdd43GJhZH/tP2weN8TntQblOY+A0XbT8DJk1/oCPuOOyg/Ja757rG0CgHcWC8OfMA==",
         "dev": true
      },
      "node_modules/@types/node": {
-         "version": "16.18.16",
-         "resolved": "https://registry.npmjs.org/@types/node/-/node-16.18.16.tgz",
-         "integrity": "sha512-ZOzvDRWp8dCVBmgnkIqYCArgdFOO9YzocZp8Ra25N/RStKiWvMOXHMz+GjSeVNe5TstaTmTWPucGJkDw0XXJWA==",
-         "dev": true
-      },
-      "node_modules/@types/uuid": {
-         "version": "8.3.4",
-         "resolved": "https://registry.npmjs.org/@types/uuid/-/uuid-8.3.4.tgz",
-         "integrity": "sha512-c/I8ZRb51j+pYGAu5CrFMRxqZ2ke4y2grEBO5AUjgSkSk+qT2Ea+OdWElz/OiMf5MNpn2b17kuVBwZLQJXzihw==",
+         "version": "16.18.96",
+         "resolved": "https://registry.npmjs.org/@types/node/-/node-16.18.96.tgz",
+         "integrity": "sha512-84iSqGXoO+Ha16j8pRZ/L90vDMKX04QTYMTfYeE1WrjWaZXuchBehGUZEpNgx7JnmlrIHdnABmpjrQjhCnNldQ==",
         "dev": true
      },
      "node_modules/acorn": {
-         "version": "8.8.2",
-         "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.8.2.tgz",
-         "integrity": "sha512-xjIYgE8HBrkpd/sJqOGNspf8uHG+NOHGOw6a/Urj8taM2EXfdNAH2oFcPeIFfsv3+kz/mJrS5VuMqbNLjCa2vw==",
+         "version": "8.11.3",
+         "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.11.3.tgz",
+         "integrity": "sha512-Y9rRfJG5jcKOE0CLisYbojUjIrIEE7AGMzA/Sm4BslANhbS+cDMpgBdcPT91oJ7OuJ9hYJBx59RjbhxVnrF8Xg==",
         "dev": true,
         "bin": {
            "acorn": "bin/acorn"
@@ -178,9 +168,9 @@
         }
      },
      "node_modules/acorn-walk": {
-         "version": "8.2.0",
-         "resolved": "https://registry.npmjs.org/acorn-walk/-/acorn-walk-8.2.0.tgz",
-         "integrity": "sha512-k+iyHEuPgSw6SbuDpGQM+06HQUa04DZ3o+F6CSzXMvvI5KMvnaEqXe+YVe555R9nn6GPt404fos4wcgpw12SDA==",
+         "version": "8.3.2",
+         "resolved": "https://registry.npmjs.org/acorn-walk/-/acorn-walk-8.3.2.tgz",
+         "integrity": "sha512-cjkyv4OtNCIeqhHrfS81QWXoCBPExR/J62oyEqepVw8WaQeSqpW2uhuLPh1m9eWhDuOo/jUXVTlifvesOWp/4A==",
         "dev": true,
         "engines": {
            "node": ">=0.4.0"
@@ -229,12 +219,18 @@
         }
      },
      "node_modules/call-bind": {
-         "version": "1.0.2",
-         "resolved": "https://registry.npmjs.org/call-bind/-/call-bind-1.0.2.tgz",
-         "integrity": "sha512-7O+FbCihrB5WGbFYesctwmTKae6rOiIzmz1icreWJ+0aA7LJfuqhEso2T9ncpcFtzMQtzXf2QGGueWJGTYsqrA==",
+         "version": "1.0.7",
+         "resolved": "https://registry.npmjs.org/call-bind/-/call-bind-1.0.7.tgz",
+         "integrity": "sha512-GHTSNSYICQ7scH7sZ+M2rFopRoLh8t2bLSW6BbgrtLsahOIB5iyAVJf9GjWK3cYTDaMj4XdBpM1cA6pIS0Kv2w==",
         "dependencies": {
-            "function-bind": "^1.1.1",
-            "get-intrinsic": "^1.0.2"
+            "es-define-property": "^1.0.0",
+            "es-errors": "^1.3.0",
+            "function-bind": "^1.1.2",
+            "get-intrinsic": "^1.2.4",
+            "set-function-length": "^1.2.1"
+         },
+         "engines": {
+            "node": ">= 0.4"
         },
         "funding": {
            "url": "https://github.com/sponsors/ljharb"
@@ -365,6 +361,22 @@
            "node": ">=0.10"
         }
      },
+      "node_modules/define-data-property": {
+         "version": "1.1.4",
+         "resolved": "https://registry.npmjs.org/define-data-property/-/define-data-property-1.1.4.tgz",
+         "integrity": "sha512-rBMvIzlpA8v6E+SJZoo++HAYqsLrkg7MSfIinMPFhmkorw7X+dOXVJQs+QT69zGkzMyfDnIMN2Wid1+NbL3T+A==",
+         "dependencies": {
+            "es-define-property": "^1.0.0",
+            "es-errors": "^1.3.0",
+            "gopd": "^1.0.1"
+         },
+         "engines": {
+            "node": ">= 0.4"
+         },
+         "funding": {
+            "url": "https://github.com/sponsors/ljharb"
+         }
+      },
      "node_modules/diff": {
         "version": "4.0.2",
         "resolved": "https://registry.npmjs.org/diff/-/diff-4.0.2.tgz",
@@ -382,6 +394,25 @@
            "is-arrayish": "^0.2.1"
         }
      },
+      "node_modules/es-define-property": {
+         "version": "1.0.0",
+         "resolved": "https://registry.npmjs.org/es-define-property/-/es-define-property-1.0.0.tgz",
+         "integrity": "sha512-jxayLKShrEqqzJ0eumQbVhTYQM27CfT1T35+gCgDFoL82JLsXqTJ76zv6A0YLOgEnLUMvLzsDsGIrl8NFpT2gQ==",
+         "dependencies": {
+            "get-intrinsic": "^1.2.4"
+         },
+         "engines": {
+            "node": ">= 0.4"
+         }
+      },
+      "node_modules/es-errors": {
+         "version": "1.3.0",
+         "resolved": "https://registry.npmjs.org/es-errors/-/es-errors-1.3.0.tgz",
+         "integrity": "sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==",
+         "engines": {
+            "node": ">= 0.4"
+         }
+      },
      "node_modules/filter-obj": {
         "version": "1.1.0",
         "resolved": "https://registry.npmjs.org/filter-obj/-/filter-obj-1.1.0.tgz",
@@ -402,9 +433,12 @@
         }
      },
      "node_modules/function-bind": {
-         "version": "1.1.1",
-         "resolved": "https://registry.npmjs.org/function-bind/-/function-bind-1.1.1.tgz",
-         "integrity": "sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A=="
+         "version": "1.1.2",
+         "resolved": "https://registry.npmjs.org/function-bind/-/function-bind-1.1.2.tgz",
+         "integrity": "sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==",
+         "funding": {
+            "url": "https://github.com/sponsors/ljharb"
+         }
      },
      "node_modules/generate-function": {
         "version": "2.3.1",
@@ -415,13 +449,29 @@
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-         "version": "1.2.0",
-         "resolved": "https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.2.0.tgz",
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+         "version": "1.2.4",
+         "resolved": "https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.2.4.tgz",
+         "integrity": "sha512-5uYhsJH8VJBTv7oslg4BznJYhDoRI6waYCxMmCdnTrcCrHA/fCFKoTFz2JKKE0HdDFUF7/oQuhzumXJK7paBRQ==",
         "dependencies": {
-            "function-bind": "^1.1.1",
-            "has": "^1.0.3",
-            "has-symbols": "^1.0.3"
+            "es-errors": "^1.3.0",
+            "function-bind": "^1.1.2",
+            "has-proto": "^1.0.1",
+            "has-symbols": "^1.0.3",
+            "hasown": "^2.0.0"
+         },
+         "engines": {
+            "node": ">= 0.4"
+         },
+         "funding": {
+            "url": "https://github.com/sponsors/ljharb"
+         }
+      },
+      "node_modules/gopd": {
+         "version": "1.0.1",
+         "resolved": "https://registry.npmjs.org/gopd/-/gopd-1.0.1.tgz",
+         "integrity": "sha512-d65bNlIadxvpb/A2abVdlqKqV563juRnZ1Wtk6s1sIR8uNsXR70xqIzVqxVf1eTqDunwT2MkczEeaezCKTZhwA==",
+         "dependencies": {
+            "get-intrinsic": "^1.1.3"
         },
         "funding": {
            "url": "https://github.com/sponsors/ljharb"
@@ -432,15 +482,26 @@
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+      "node_modules/has-property-descriptors": {
+         "version": "1.0.2",
+         "resolved": "https://registry.npmjs.org/has-property-descriptors/-/has-property-descriptors-1.0.2.tgz",
+         "integrity": "sha512-55JNKuIW+vq4Ke1BjOTjM2YctQIvCT7GFzHwmfZPGo5wnrgkid0YQtnAleFSqumZm4az3n2BS+erby5ipJdgrg==",
         "dependencies": {
-            "function-bind": "^1.1.1"
+            "es-define-property": "^1.0.0"
         },
+         "funding": {
+            "url": "https://github.com/sponsors/ljharb"
+         }
+      },
+      "node_modules/has-proto": {
+         "version": "1.0.3",
+         "resolved": "https://registry.npmjs.org/has-proto/-/has-proto-1.0.3.tgz",
+         "integrity": "sha512-SJ1amZAJUiZS+PhsVLf5tGydlaVB8EdFpaSO4gmiUKUOxk8qzn5AIy4ZeJUmh22znIdk/uMAUT2pl3FxzVUH+Q==",
         "engines": {
-            "node": ">= 0.4.0"
+            "node": ">= 0.4"
+         },
+         "funding": {
+            "url": "https://github.com/sponsors/ljharb"
         }
      },
      "node_modules/has-symbols": {
@@ -454,6 +515,17 @@
            "url": "https://github.com/sponsors/ljharb"
         }
      },
+      "node_modules/hasown": {
+         "version": "2.0.2",
+         "resolved": "https://registry.npmjs.org/hasown/-/hasown-2.0.2.tgz",
+         "integrity": "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==",
+         "dependencies": {
+            "function-bind": "^1.1.2"
+         },
+         "engines": {
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+         }
+      },
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         "resolved": "https://registry.npmjs.org/hosted-git-info/-/hosted-git-info-2.8.9.tgz",
@@ -468,9 +540,9 @@
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+         "version": "1.1.9",
+         "resolved": "https://registry.npmjs.org/ip/-/ip-1.1.9.tgz",
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@@ -486,11 +558,11 @@
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+         "version": "2.13.1",
+         "resolved": "https://registry.npmjs.org/is-core-module/-/is-core-module-2.13.1.tgz",
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-            "has": "^1.0.3"
+            "hasown": "^2.0.0"
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            "url": "https://github.com/sponsors/ljharb"
@@ -671,9 +743,9 @@
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+         "version": "1.13.1",
+         "resolved": "https://registry.npmjs.org/object-inspect/-/object-inspect-1.13.1.tgz",
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@@ -789,11 +861,11 @@
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         "dependencies": {
-            "side-channel": "^1.0.4"
+            "side-channel": "^1.0.6"
         },
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            "node": ">=0.6"
@@ -865,11 +937,11 @@
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         "integrity": "sha512-BYqTHXTGUIvg7t1r4sJNKcbDZkL92nkXA8YtRpbjFHRHGDL/NtUeiBJMeE60kIFN/Mg8ESaWQvftaYMGJzQZCQ=="
      },
      "ip": {
-         "version": "1.1.8",
-         "resolved": "https://registry.npmjs.org/ip/-/ip-1.1.8.tgz",
-         "integrity": "sha512-PuExPYUiu6qMBQb4l06ecm6T6ujzhmh+MeJcW9wa89PoAz5pvd4zPgN5WJV104mb6S2T1AwNIAaB70JNrLQWhg=="
+         "version": "1.1.9",
+         "resolved": "https://registry.npmjs.org/ip/-/ip-1.1.9.tgz",
+         "integrity": "sha512-cyRxvOEpNHNtchU3Ln9KC/auJgup87llfQpQ+t5ghoC/UhL16SWzbueiCsdTnWmqAWl7LadfuwhlqmtOaqMHdQ=="
      },
      "ip2buf": {
         "version": "2.0.0",
@@ -1521,11 +1648,11 @@
         "integrity": "sha512-zz06S8t0ozoDXMG+ube26zeCTNXcKIPJZJi8hBrF4idCLms4CG9QtK7qBl1boi5ODzFpjswb5JPmHCbMpjaYzg=="
      },
      "is-core-module": {
-         "version": "2.11.0",
-         "resolved": "https://registry.npmjs.org/is-core-module/-/is-core-module-2.11.0.tgz",
-         "integrity": "sha512-RRjxlvLDkD1YJwDbroBHMb+cukurkDWNyHx7D3oNB5x9rb5ogcksMC5wHCadcXoo67gVr/+3GFySh3134zi6rw==",
+         "version": "2.13.1",
+         "resolved": "https://registry.npmjs.org/is-core-module/-/is-core-module-2.13.1.tgz",
+         "integrity": "sha512-hHrIjvZsftOsvKSn2TRYl63zvxsgE0K+0mYMoH6gD4omR5IWB2KynivBQczo3+wF1cCkjzvptnI9Q0sPU66ilw==",
         "requires": {
-            "has": "^1.0.3"
+            "hasown": "^2.0.0"
         }
      },
      "is-plain-obj": {
@@ -1669,9 +1796,9 @@
         "integrity": "sha512-DlL+XwOy3NxAQ8xuC0okPgK46iuVNAK01YN7RueYBqqFeGsBjV9XmCAzAdgt+667bCl5kPh9EqKKDwnaPG1I7A=="
      },
      "object-inspect": {
-         "version": "1.12.3",
-         "resolved": "https://registry.npmjs.org/object-inspect/-/object-inspect-1.12.3.tgz",
-         "integrity": "sha512-geUvdk7c+eizMNUDkRpW1wJwgfOiOeHbxBR/hLXK1aT6zmVSO0jsQcs7fj6MGw89jC/cjGfLcNOrtMYtGqm81g=="
+         "version": "1.13.1",
+         "resolved": "https://registry.npmjs.org/object-inspect/-/object-inspect-1.13.1.tgz",
+         "integrity": "sha512-5qoj1RUiKOMsCCNLV1CBiPYE10sziTsnmNxkAI/rZhiD63CF7IqdFGC/XzjWjpSgLf0LxXX3bDFIh0E18f6UhQ=="
      },
      "p-limit": {
         "version": "1.3.0",
@@ -1760,11 +1887,11 @@
         "integrity": "sha512-IgjKyaUSjsROSO8/D49Ab7hP8mJgTYcqApOqdPhLoPxAplXmkp+zRvsrSQjFn5by0rhm4VH0GAUELIPpx7B1yg=="
      },
      "qs": {
-         "version": "6.11.1",
-         "resolved": "https://registry.npmjs.org/qs/-/qs-6.11.1.tgz",
-         "integrity": "sha512-0wsrzgTz/kAVIeuxSjnpGC56rzYtr6JT/2BwEvMaPhFIoYa1aGO8LbzuU1R0uUYQkLpWBTOj0l/CLAJB64J6nQ==",
+         "version": "6.12.1",
+         "resolved": "https://registry.npmjs.org/qs/-/qs-6.12.1.tgz",
+         "integrity": "sha512-zWmv4RSuB9r2mYQw3zxQuHWeU+42aKi1wWig/j4ele4ygELZ7PEO6MM7rim9oAQH2A5MWfsAVf/jPvTPgCbvUQ==",
         "requires": {
-            "side-channel": "^1.0.4"
+            "side-channel": "^1.0.6"
         }
      },
      "query-string": {
@@ -1812,11 +1939,11 @@
         }
      },
      "resolve": {
-         "version": "1.22.1",
-         "resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.1.tgz",
-         "integrity": "sha512-nBpuuYuY5jFsli/JIs1oldw6fOQCBioohqWZg/2hiaOybXOft4lonv85uDOKXdf8rhyK159cxU5cDcK/NKk8zw==",
+         "version": "1.22.8",
+         "resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.8.tgz",
+         "integrity": "sha512-oKWePCxqpd6FlLvGV1VU0x7bkPmmCNolxzjMf4NczoDnQcIWrAF+cPtZn5i6n+RfD2d9i0tzpKnG6Yk168yIyw==",
         "requires": {
-            "is-core-module": "^2.9.0",
+            "is-core-module": "^2.13.0",
            "path-parse": "^1.0.7",
            "supports-preserve-symlinks-flag": "^1.0.0"
         }
@@ -1836,6 +1963,19 @@
         "resolved": "https://registry.npmjs.org/semver/-/semver-5.7.2.tgz",
         "integrity": "sha512-cBznnQ9KjJqU67B52RMC65CMarK2600WFnbkcaiwWq3xy/5haFJlshgnpjovMVJ+Hff49d8GEn0b87C5pDQ10g=="
      },
+      "set-function-length": {
+         "version": "1.2.2",
+         "resolved": "https://registry.npmjs.org/set-function-length/-/set-function-length-1.2.2.tgz",
+         "integrity": "sha512-pgRc4hJ4/sNjWCSS9AmnS40x3bNMDTknHgL5UaMBTMyJnU90EgWh1Rz+MC9eFu4BuN/UwZjKQuY/1v3rM7HMfg==",
+         "requires": {
+            "define-data-property": "^1.1.4",
+            "es-errors": "^1.3.0",
+            "function-bind": "^1.1.2",
+            "get-intrinsic": "^1.2.4",
+            "gopd": "^1.0.1",
+            "has-property-descriptors": "^1.0.2"
+         }
+      },
      "shebang-command": {
         "version": "2.0.0",
         "resolved": "https://registry.npmjs.org/shebang-command/-/shebang-command-2.0.0.tgz",
@@ -1852,13 +1992,14 @@
         "dev": true
      },
      "side-channel": {
-         "version": "1.0.4",
-         "resolved": "https://registry.npmjs.org/side-channel/-/side-channel-1.0.4.tgz",
-         "integrity": "sha512-q5XPytqFEIKHkGdiMIrY10mvLRvnQh42/+GoBlFW3b2LXLE2xxJpZFdm94we0BaoV3RwJyGqg5wS7epxTv0Zvw==",
+         "version": "1.0.6",
+         "resolved": "https://registry.npmjs.org/side-channel/-/side-channel-1.0.6.tgz",
+         "integrity": "sha512-fDW/EZ6Q9RiO8eFG8Hj+7u/oW+XrPTIChwCOM2+th2A6OblDtYYIpve9m+KvI9Z4C9qSEXlaGR6bTEYHReuglA==",
         "requires": {
-            "call-bind": "^1.0.0",
-            "get-intrinsic": "^1.0.2",
-            "object-inspect": "^1.9.0"
+            "call-bind": "^1.0.7",
+            "es-errors": "^1.3.0",
+            "get-intrinsic": "^1.2.4",
+            "object-inspect": "^1.13.1"
         }
      },
      "signal-exit": {
@@ -1876,9 +2017,9 @@
         }
      },
      "spdx-exceptions": {
-         "version": "2.3.0",
-         "resolved": "https://registry.npmjs.org/spdx-exceptions/-/spdx-exceptions-2.3.0.tgz",
-         "integrity": "sha512-/tTrYOC7PPI1nUAgx34hUpqXuyJG+DTHJTnIULG4rDygi4xu/tfgmq1e1cIRwRzwZgo4NLySi+ricLkZkw4i5A=="
+         "version": "2.5.0",
+         "resolved": "https://registry.npmjs.org/spdx-exceptions/-/spdx-exceptions-2.5.0.tgz",
+         "integrity": "sha512-PiU42r+xO4UbUS1buo3LPJkjlO7430Xn5SVAhdpzzsPHsjbYVflnnFdATgabnLude+Cqu25p6N+g2lw/PFsa4w=="
      },
      "spdx-expression-parse": {
         "version": "3.0.1",
@@ -1890,9 +2031,9 @@
         }
      },
      "spdx-license-ids": {
-         "version": "3.0.13",
-         "resolved": "https://registry.npmjs.org/spdx-license-ids/-/spdx-license-ids-3.0.13.tgz",
-         "integrity": "sha512-XkD+zwiqXHikFZm4AX/7JSCXA98U5Db4AFd5XUg/+9UNtnH75+Z9KxtpYiJZx36mUDVOwH83pl7yvCer6ewM3w=="
+         "version": "3.0.17",
+         "resolved": "https://registry.npmjs.org/spdx-license-ids/-/spdx-license-ids-3.0.17.tgz",
+         "integrity": "sha512-sh8PWc/ftMqAAdFiBu6Fy6JUOYjqDJBJvIhpfDMyHrr0Rbp5liZqd4TjtQ/RgfLjKFZb+LMx5hpml5qOWy0qvg=="
      },
      "split-on-first": {
         "version": "1.1.0",
@@ -1942,9 +2083,9 @@
         "integrity": "sha512-MTBWv3jhVjTU7XR3IQHllbiJs8sc75a80OEhB6or/q7pLTWgQ0bMGQXXYQSrSuXe6WiKWDZ5txXY5P59a/coVA=="
      },
      "ts-node": {
-         "version": "10.9.1",
-         "resolved": "https://registry.npmjs.org/ts-node/-/ts-node-10.9.1.tgz",
-         "integrity": "sha512-NtVysVPkxxrwFGUUxGYhfux8k78pQB3JqYBXlLRZgdGUqTO5wU/UyHop5p70iEbGhB7q5KmiZiU0Y3KlJrScEw==",
+         "version": "10.9.2",
+         "resolved": "https://registry.npmjs.org/ts-node/-/ts-node-10.9.2.tgz",
+         "integrity": "sha512-f0FFpIdcHgn8zcPSbf1dRevwt047YMnaiJM3u2w2RewrB+fob/zePZcrOyQoLMMO7aBIddLcQIEK5dYjkLnGrQ==",
         "dev": true,
         "requires": {
            "@cspotcode/source-map-support": "^0.8.0",
@@ -1968,9 +2109,9 @@
         "integrity": "sha512-8yyRd1ZdNp+AQLGqi3lTaA2k81JjlIZOyFQEsi7GQWBgirnQOxjqVtDEbYHM2Z4yFdJ5AQw0fxBLLnDCl6RXoQ=="
      },
      "typescript": {
-         "version": "5.0.2",
-         "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.0.2.tgz",
-         "integrity": "sha512-wVORMBGO/FAs/++blGNeAVdbNKtIh1rbBL2EyQ1+J9lClJ93KiiKe8PmFIVdXhHcyv44SL9oglmfeSsndo0jRw==",
+         "version": "5.4.5",
+         "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.4.5.tgz",
+         "integrity": "sha512-vcI4UpRgg81oIRUFwR0WSIHKt11nJ7SAVlYNIu+QpqeyXP+gpQJy/Z4+F0aGxSE4MqwjyXvW/TzgkLAx2AGHwQ==",
         "dev": true,
         "peer": true
      },
@@ -1979,11 +2120,6 @@
         "resolved": "https://registry.npmjs.org/universalify/-/universalify-0.1.2.tgz",
         "integrity": "sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg=="
      },
-      "uuid": {
-         "version": "8.3.2",
-         "resolved": "https://registry.npmjs.org/uuid/-/uuid-8.3.2.tgz",
-         "integrity": "sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg=="
-      },
      "v8-compile-cache-lib": {
         "version": "3.0.1",
         "resolved": "https://registry.npmjs.org/v8-compile-cache-lib/-/v8-compile-cache-lib-3.0.1.tgz",
--- a/plugins/bticino/package.json
+++ b/plugins/bticino/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/bticino",
-   "version": "0.0.15",
+   "version": "0.0.16",
   "scripts": {
      "scrypted-setup-project": "scrypted-setup-project",
      "prescrypted-setup-project": "scrypted-package-json",
@@ -34,14 +34,12 @@
   "dependencies": {
      "@slyoldfox/sip": "^0.0.6-1",
      "sdp": "^3.0.3",
-      "stun": "^2.1.0",
-      "uuid": "^8.3.2"
+      "stun": "^2.1.0"
   },
   "devDependencies": {
      "@scrypted/common": "file:../../common",
      "@scrypted/sdk": "file:../../sdk",
      "@types/node": "^16.9.6",
-      "@types/uuid": "^8.3.4",
      "cross-env": "^7.0.3",
      "ts-node": "^10.9.1"
   }
--- a/plugins/bticino/src/bticino-camera.ts
+++ b/plugins/bticino/src/bticino-camera.ts
@@ -1,22 +1,22 @@
-import { closeQuiet, createBindUdp, createBindZero, listenZeroSingleClient } from '@scrypted/common/src/listen-cluster';
+import { createBindUdp, listenZeroSingleClient } from '@scrypted/common/src/listen-cluster';
 import { sleep } from '@scrypted/common/src/sleep';
 import { RtspServer } from '@scrypted/common/src/rtsp-server';
 import { addTrackControls, parseSdp } from '@scrypted/common/src/sdp-utils';
-import sdk, { BinarySensor, Camera, DeviceProvider, FFmpegInput, HttpRequest, HttpRequestHandler, HttpResponse, Intercom, MediaObject, MediaStreamUrl, MotionSensor, PictureOptions, Reboot, ResponseMediaStreamOptions, ScryptedDeviceBase, ScryptedMimeTypes, Setting, Settings, SettingValue, VideoCamera, VideoClip, VideoClipOptions, VideoClips } from '@scrypted/sdk';
+import sdk, { BinarySensor, Camera, DeviceProvider, FFmpegInput, HttpRequest, HttpRequestHandler, HttpResponse, Intercom, MediaObject, MediaStreamUrl, MotionSensor, PictureOptions, Reboot, ResponseMediaStreamOptions, ScryptedDeviceBase, ScryptedInterface, ScryptedMimeTypes, Setting, Settings, SettingValue, VideoCamera, VideoClip, VideoClipOptions, VideoClips } from '@scrypted/sdk';
 import { SipCallSession } from '../../sip/src/sip-call-session';
 import { RtpDescription, getPayloadType, getSequenceNumber, isRtpMessagePayloadType, isStunMessage } from '../../sip/src/rtp-utils';
 import { VoicemailHandler } from './bticino-voicemailHandler';
 import { CompositeSipMessageHandler } from '../../sip/src/compositeSipMessageHandler';
 import { SipHelper } from './sip-helper';
 import child_process, { ChildProcess } from 'child_process';
-import dgram from 'dgram';
 import { BticinoStorageSettings } from './storage-settings';
 import { BticinoSipPlugin } from './main';
 import { BticinoSipLock } from './bticino-lock';
-import { ffmpegLogInitialOutput, safeKillFFmpeg, safePrintFFmpegArguments } from '@scrypted/common/src/media-helpers';
+import { safePrintFFmpegArguments } from '@scrypted/common/src/media-helpers';
 import { PersistentSipManager } from './persistent-sip-manager';
 import { InviteHandler } from './bticino-inviteHandler';
 import { SipOptions, SipRequest } from '../../sip/src/sip-manager';
+import { startRtpForwarderProcess } from '../../webrtc/src/rtp-forwarders';
 import fs from "fs"
 import url from "url"
 import path from 'path';
@@ -37,8 +37,7 @@ export class BticinoSipCamera extends ScryptedDeviceBase implements MotionSensor

    private session: SipCallSession
    private remoteRtpDescription: Promise<RtpDescription>
-    private audioOutForwarder: dgram.Socket
-    private audioOutProcess: ChildProcess
+    private forwarder
    private refreshTimeout: NodeJS.Timeout
    public requestHandlers: CompositeSipMessageHandler = new CompositeSipMessageHandler()
    public incomingCallRequest : SipRequest
@@ -276,21 +275,27 @@ export class BticinoSipCamera extends ScryptedDeviceBase implements MotionSensor
    }    

    async takePicture(option?: PictureOptions): Promise<MediaObject> {
-        const thumbnailCacheTime : number = parseInt( this.storage?.getItem('thumbnailCacheTime') ) * 1000 || 300000 
-        const now = new Date().getTime()
-        if( !this.lastImageRefresh || this.lastImageRefresh + thumbnailCacheTime < now ) {
-            // get a proxy object to make sure we pass prebuffer when already watching a stream
-            let cam : VideoCamera = sdk.systemManager.getDeviceById<VideoCamera>(this.id)
-            let vs : MediaObject = await cam.getVideoStream()
-            let buf : Buffer = await mediaManager.convertMediaObjectToBuffer(vs, 'image/jpeg');
-            this.cachedImage = buf
-            this.lastImageRefresh = new Date().getTime()
-            this.console.log(`Camera picture updated and cached: ${this.lastImageRefresh} + cache time: ${thumbnailCacheTime} < ${now}`)
-
+        let rebroadcastEnabled = this.interfaces?.includes( "mixin:@scrypted/prebuffer-mixin")
+        if( rebroadcastEnabled ) {
+            const thumbnailCacheTime : number = parseInt( this.storage?.getItem('thumbnailCacheTime') ) * 1000 || 300000 
+            const now = new Date().getTime()
+            if( !this.lastImageRefresh || this.lastImageRefresh + thumbnailCacheTime < now ) {
+                // get a proxy object to make sure we pass prebuffer when already watching a stream
+                let cam : VideoCamera = sdk.systemManager.getDeviceById<VideoCamera>(this.id)
+                let vs : MediaObject = await cam.getVideoStream()
+                let buf : Buffer = await mediaManager.convertMediaObjectToBuffer(vs, 'image/jpeg');
+                this.cachedImage = buf
+                this.lastImageRefresh = new Date().getTime()
+                this.console.log(`Camera picture updated and cached: ${this.lastImageRefresh} + cache time: ${thumbnailCacheTime} < ${now}`)
+    
+            } else {
+                this.console.log(`Not refreshing camera picture: ${this.lastImageRefresh} + cache time: ${thumbnailCacheTime} < ${now}`)
+            }
+            
+            return mediaManager.createMediaObject(this.cachedImage, 'image/jpeg')
        } else {
-            this.console.log(`Not refreshing camera picture: ${this.lastImageRefresh} + cache time: ${thumbnailCacheTime} < ${now}`)
+            throw new Error("To enable snapshots, enable rebroadcast plugin or set a Snapshot URL in the Snapshot plugin to an external image.");
        }
-        return mediaManager.createMediaObject(this.cachedImage, 'image/jpeg')
    }

    async getPictureOptions(): Promise<PictureOptions[]> {
@@ -317,52 +322,31 @@ export class BticinoSipCamera extends ScryptedDeviceBase implements MotionSensor
            this.session = await this.callIntercom( cleanup )
        }
            
-
        this.stopIntercom();

-        const ffmpegInput: FFmpegInput = JSON.parse((await mediaManager.convertMediaObjectToBuffer(media, ScryptedMimeTypes.FFmpegInput)).toString());
-
-        const audioOutForwarder = await createBindZero()
-        this.audioOutForwarder = audioOutForwarder.server
+        const ffmpegInput = await sdk.mediaManager.convertMediaObjectToJSON<FFmpegInput>(media, ScryptedMimeTypes.FFmpegInput);
        let address = (await this.remoteRtpDescription).address
-        audioOutForwarder.server.on('message', message => {
-            if( this.session )
-                this.session.audioSplitter.send(message, 40004, address)
-            return null
-        });
-
-        const args = ffmpegInput.inputArguments.slice();
-        args.push(
-            '-vn', '-dn', '-sn',
-            '-acodec', 'speex',
-            '-flags', '+global_header',
-            '-ac', '1',
-            '-ar', '8k',
-            '-f', 'rtp',
-            //'-srtp_out_suite', 'AES_CM_128_HMAC_SHA1_80',
-            //'-srtp_out_params', encodeSrtpOptions(this.decodedSrtpOptions),
-            `rtp://127.0.0.1:${audioOutForwarder.port}?pkt_size=188`,
-        );
-
-        this.console.log("===========================================")
-        safePrintFFmpegArguments( this.console, args )
-        this.console.log("===========================================")
-
-        const cp = child_process.spawn(await mediaManager.getFFmpegPath(), args);
-        ffmpegLogInitialOutput(this.console, cp)
-        this.audioOutProcess = cp;
-        cp.on('exit', () => this.console.log('two way audio ended'));
-        this.session.onCallEnded.subscribe(() => {
-            closeQuiet(audioOutForwarder.server);
-            safeKillFFmpeg(cp)
+        this.forwarder = await startRtpForwarderProcess(this.console, ffmpegInput, {
+            audio: {
+                codecCopy: 'speex',
+                encoderArguments: [
+                    '-vn', '-sn', '-dn',
+                    '-acodec', 'speex',
+                    '-flags', '+global_header',
+                    '-ac', '1',
+                    '-ar', '8k',
+                    '-f', 'rtp',
+                ],
+                onRtp: rtp => {
+                        this.session?.audioSplitter?.send(rtp, 40004, address)
+                }
+            }
        });
    }

    async stopIntercom(): Promise<void> {
-        closeQuiet(this.audioOutForwarder)
-        this.audioOutProcess?.kill('SIGKILL')
-        this.audioOutProcess = undefined
-        this.audioOutForwarder = undefined
+        this.forwarder?.kill()
+        this.forwarder = undefined
    }

    resetStreamTimeout() {
@@ -572,12 +556,24 @@ export class BticinoSipCamera extends ScryptedDeviceBase implements MotionSensor
        // Call the C300X
        this.remoteRtpDescription = sip.callOrAcceptInvite(
            ( audio ) => {
-            return [
-                // this SDP is used by the intercom and will send the encrypted packets which we don't care about to the loopback on port 65000 of the intercom
-                `m=audio 65000 RTP/SAVP 110`,
-                `a=rtpmap:110 speex/8000`,
-                `a=crypto:1 AES_CM_128_HMAC_SHA1_80 inline:${this.keyAndSalt}`,
-            ]
+                let audioSection = [
+                    // this SDP is used by the intercom and will send the encrypted packets which we don't care about to the loopback on port 65000 of the intercom
+                    `m=audio 65000 RTP/SAVP 110`,
+                    `a=rtpmap:110 speex/8000`,
+                    `a=crypto:1 AES_CM_128_HMAC_SHA1_80 inline:${this.keyAndSalt}`,
+                ]
+                if( !this.incomingCallRequest ) {
+                    let DEVADDR = this.storage.getItem('DEVADDR');
+                    if( DEVADDR ) {
+                        audioSection.unshift('a=DEVADDR:' + DEVADDR)
+                    } else {
+                        if( sipOptions.to.toLocaleLowerCase().indexOf('c300x') >= 0 || sipOptions.to.toLocaleLowerCase().indexOf('c100x') >= 0 ) {
+                            // Needed for bt_answering_machine (bticino specific), to check for c100X
+                            audioSection.unshift('a=DEVADDR:20')
+                        }                           
+                    }
+                }
+                return audioSection
        }, ( video ) => {
                return [
                // this SDP is used by the intercom and will send the encrypted packets which we don't care about to the loopback on port 65000 of the intercom
--- a/plugins/core/package-lock.json
+++ b/plugins/core/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/core",
-   "version": "0.3.24",
+   "version": "0.3.25",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/core",
-         "version": "0.3.24",
+         "version": "0.3.25",
         "license": "Apache-2.0",
         "dependencies": {
            "@scrypted/common": "file:../../common",
--- a/plugins/core/package.json
+++ b/plugins/core/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/core",
-   "version": "0.3.24",
+   "version": "0.3.25",
   "description": "Scrypted Core plugin. Provides the UI, websocket, and engine.io APIs.",
   "author": "Scrypted",
   "license": "Apache-2.0",
--- a/plugins/core/src/platform/lxc.ts
+++ b/plugins/core/src/platform/lxc.ts
@@ -2,6 +2,7 @@ import fs from 'fs';
 import child_process from 'child_process';
 import { once } from 'events';
 import sdk from '@scrypted/sdk';
+import { stdout } from 'process';

 export const SCRYPTED_INSTALL_ENVIRONMENT_LXC = 'lxc';

@@ -41,6 +42,31 @@ export async function checkLxcDependencies() {
            sdk.log.a('Failed to daemon-reload systemd.');
    }

+    try {
+        // intel opencl icd is broken from their official apt repos on kernel versions 6.8, which ships with ubuntu 24.04 and proxmox 8.2.
+        // the intel apt repo has not been updated yet.
+        // the current workaround is to install the release manually.
+        // https://github.com/intel/compute-runtime/releases/tag/24.13.29138.7
+        const output = await new Promise<string>((r,f)=> child_process.exec("sh -c 'apt show versions intel-opencl-icd'", (err, stdout, stderr) => {
+            if (err)
+                f(err);
+            else
+                r(stdout + '\n' + stderr);
+        }));
+
+        if (output.includes('Version: 23')) {
+            const cp = child_process.spawn('sh', ['-c', 'curl https://raw.githubusercontent.com/koush/scrypted/main/install/docker/install-intel-graphics.sh | bash']);
+            const [exitCode] = await once(cp, 'exit');
+            if (exitCode !== 0)
+                sdk.log.a('Failed to install intel-opencl-icd.');
+            else
+                needRestart = true;
+        }
+    }
+    catch (e) {
+        sdk.log.a('Failed to verify/install intel-opencl-icd version.');
+    }
+
    if (needRestart)
        sdk.log.a('A system update is pending. Please restart Scrypted to apply changes.');
 }
--- a/plugins/coreml/package-lock.json
+++ b/plugins/coreml/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/coreml",
-   "version": "0.1.49",
+   "version": "0.1.54",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/coreml",
-         "version": "0.1.49",
+         "version": "0.1.54",
         "devDependencies": {
            "@scrypted/sdk": "file:../../sdk"
         }
--- a/plugins/coreml/package.json
+++ b/plugins/coreml/package.json
@@ -42,5 +42,5 @@
   "devDependencies": {
      "@scrypted/sdk": "file:../../sdk"
   },
-   "version": "0.1.49"
+   "version": "0.1.54"
 }
--- a/plugins/coreml/src/coreml/init.py
+++ b/plugins/coreml/src/coreml/init.py
@@ -26,14 +26,13 @@ predictExecutor = concurrent.futures.ThreadPoolExecutor(1, "CoreML-Predict")

 availableModels = [
    "Default",
+    "scrypted_yolov10m_320",
+    "scrypted_yolov10n_320",
+    "scrypted_yolo_nas_s_320",
    "scrypted_yolov9c_320",
-    "scrypted_yolov9c",
    "scrypted_yolov6n_320",
-    "scrypted_yolov6n",
    "scrypted_yolov6s_320",
-    "scrypted_yolov6s",
    "scrypted_yolov8n_320",
-    "scrypted_yolov8n",
    "ssdlite_mobilenet_v2",
    "yolov4-tiny",
 ]
@@ -77,6 +76,8 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv
                self.storage.setItem("model", "Default")
            model = "scrypted_yolov9c_320"
        self.yolo = "yolo" in model
+        self.scrypted_yolov10n = "scrypted_yolov10" in model
+        self.scrypted_yolo_nas = "scrypted_yolo_nas" in model
        self.scrypted_yolo = "scrypted_yolo" in model
        self.scrypted_model = "scrypted" in model
        model_version = "v7"
@@ -132,6 +133,8 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv
        self.loop = asyncio.get_event_loop()
        self.minThreshold = 0.2

+        self.faceDevice = None
+        self.textDevice = None
        asyncio.ensure_future(self.prepareRecognitionModels(), loop=self.loop)

    async def prepareRecognitionModels(self):
@@ -169,9 +172,11 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv

    async def getDevice(self, nativeId: str) -> Any:
        if nativeId == "facerecognition":
-            return CoreMLFaceRecognition(nativeId)
+            self.faceDevice = self.faceDevice or CoreMLFaceRecognition(nativeId)
+            return self.faceDevice
        if nativeId == "textrecognition":
-            return CoreMLTextRecognition(nativeId)
+            self.textDevice = self.textDevice or CoreMLTextRecognition(nativeId)
+            return self.textDevice
        raise Exception("unknown device")

    async def getSettings(self) -> list[Setting]:
@@ -211,6 +216,18 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv
        if self.yolo:
            out_dict = await self.queue_batch({self.input_name: input})

+            if self.scrypted_yolov10n:
+                results = list(out_dict.values())[0][0]
+                objs = yolo.parse_yolov10(results)
+                ret = self.create_detection_result(objs, src_size, cvss)
+                return ret
+
+            if self.scrypted_yolo_nas:
+                predictions  = list(out_dict.values())
+                objs = yolo.parse_yolo_nas(predictions)
+                ret = self.create_detection_result(objs, src_size, cvss)
+                return ret
+
            if self.scrypted_yolo:
                results = list(out_dict.values())[0][0]
                objs = yolo.parse_yolov9(results)
--- a/plugins/coreml/src/coreml/face_recognition.py
+++ b/plugins/coreml/src/coreml/face_recognition.py
@@ -3,6 +3,7 @@ from __future__ import annotations
 import concurrent.futures
 import os

+import asyncio
 import coremltools as ct
 import numpy as np
 # import Quartz
@@ -10,6 +11,7 @@ import numpy as np

 # import Vision
 from predict.face_recognize import FaceRecognizeDetection
+from PIL import Image


 def euclidean_distance(arr1, arr2):
@@ -29,6 +31,8 @@ predictExecutor = concurrent.futures.ThreadPoolExecutor(8, "Vision-Predict")
 class CoreMLFaceRecognition(FaceRecognizeDetection):
    def __init__(self, nativeId: str | None = None):
        super().__init__(nativeId=nativeId)
+        self.detectExecutor = concurrent.futures.ThreadPoolExecutor(1, "detect-face")
+        self.recogExecutor = concurrent.futures.ThreadPoolExecutor(1, "recog-face")

    def downloadModel(self, model: str):
        model_version = "v7"
@@ -51,23 +55,29 @@ class CoreMLFaceRecognition(FaceRecognizeDetection):
        inputName = model.get_spec().description.input[0].name
        return model, inputName
    
-    def predictDetectModel(self, input):
-        model, inputName = self.detectModel
-        out_dict = model.predict({inputName: input})
-        results = list(out_dict.values())[0][0]
+    async def predictDetectModel(self, input: Image.Image):
+        def predict():
+            model, inputName = self.detectModel
+            out_dict = model.predict({inputName: input})
+            results = list(out_dict.values())[0][0]
+            return results
+
+        results = await asyncio.get_event_loop().run_in_executor(
+            self.detectExecutor, lambda: predict()
+        )
        return results

-    def predictFaceModel(self, input):
-        model, inputName = self.faceModel
-        out_dict = model.predict({inputName: input})
-        return out_dict["var_2167"][0]
+    async def predictFaceModel(self, input: np.ndarray):
+        def predict():
+            model, inputName = self.faceModel
+            out_dict = model.predict({inputName: input})
+            results = out_dict["var_2167"][0]
+            return results
+        results = await asyncio.get_event_loop().run_in_executor(
+            self.recogExecutor, lambda: predict()
+        )
+        return results
    
-    def predictTextModel(self, input):
-        model, inputName = self.textModel
-        out_dict = model.predict({inputName: input})
-        preds = out_dict["linear_2"]
-        return preds
-
    # def predictVision(self, input: Image.Image) -> asyncio.Future[list[Prediction]]:
    #     buffer = input.tobytes()
    #     myData = NSData.alloc().initWithBytes_length_(buffer, len(buffer))
--- a/plugins/coreml/src/coreml/text_recognition.py
+++ b/plugins/coreml/src/coreml/text_recognition.py
@@ -1,8 +1,13 @@
 from __future__ import annotations

+import concurrent.futures
 import os

+import asyncio
+
 import coremltools as ct
+import numpy as np
+from PIL import Image

 from predict.text_recognize import TextRecognition

@@ -11,6 +16,9 @@ class CoreMLTextRecognition(TextRecognition):
    def __init__(self, nativeId: str | None = None):
        super().__init__(nativeId=nativeId)

+        self.detectExecutor = concurrent.futures.ThreadPoolExecutor(1, "detect-text")
+        self.recogExecutor = concurrent.futures.ThreadPoolExecutor(1, "recog-text")
+
    def downloadModel(self, model: str):
        model_version = "v7"
        mlmodel = "model"
@@ -32,14 +40,24 @@ class CoreMLTextRecognition(TextRecognition):
        inputName = model.get_spec().description.input[0].name
        return model, inputName

-    def predictDetectModel(self, input):
-        model, inputName = self.detectModel
-        out_dict = model.predict({inputName: input})
-        results = list(out_dict.values())[0]
+    async def predictDetectModel(self, input: Image.Image):
+        def predict():
+            model, inputName = self.detectModel
+            out_dict = model.predict({inputName: input})
+            results = list(out_dict.values())[0]
+            return results
+        results = await asyncio.get_event_loop().run_in_executor(
+            self.detectExecutor, lambda: predict()
+        )
        return results

-    def predictTextModel(self, input):
-        model, inputName = self.textModel
-        out_dict = model.predict({inputName: input})
-        preds = out_dict["linear_2"]
+    async def predictTextModel(self, input: np.ndarray):
+        def predict():
+            model, inputName = self.textModel
+            out_dict = model.predict({inputName: input})
+            preds = out_dict["linear_2"]
+            return preds
+        preds = await asyncio.get_event_loop().run_in_executor(
+            self.recogExecutor, lambda: predict()
+        )
        return preds
--- a/plugins/homekit/package-lock.json
+++ b/plugins/homekit/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/homekit",
-   "version": "1.2.54",
+   "version": "1.2.57",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/homekit",
-         "version": "1.2.54",
+         "version": "1.2.57",
         "dependencies": {
            "@koush/werift-src": "file:../../external/werift",
            "check-disk-space": "^3.4.0",
@@ -47,26 +47,20 @@
            "examples/*"
         ],
         "devDependencies": {
+            "@biomejs/biome": "^1.4.1",
            "@types/jest": "^29.5.11",
-            "@types/node": "^20.10.4",
-            "@typescript-eslint/eslint-plugin": "^6.14.0",
-            "@typescript-eslint/parser": "^6.14.0",
-            "eslint": "^8.55.0",
-            "eslint-config-prettier": "^9.1.0",
-            "eslint-plugin-prettier": "^5.0.1",
-            "eslint-plugin-simple-import-sort": "^10.0.0",
+            "@types/node": "^20.10.6",
            "jest": "^29.7.0",
-            "knip": "^3.7.0",
+            "knip": "^3.9.0",
            "node-actionlint": "^1.2.2",
            "organize-imports-cli": "^0.10.0",
-            "prettier": "^3.1.1",
            "process": "^0.11.10",
            "ts-jest": "^29.1.1",
            "ts-node": "^10.9.2",
            "ts-node-dev": "^2.0.0",
-            "typedoc": "0.25.4",
+            "typedoc": "0.25.5",
            "typedoc-plugin-markdown": "3.17.1",
-            "typescript": "5.0.4"
+            "typescript": "5.3.3"
         },
         "engines": {
            "node": ">=16"
@@ -127,7 +121,7 @@
      },
      "../../sdk": {
         "name": "@scrypted/sdk",
-         "version": "0.3.18",
+         "version": "0.3.29",
         "dev": true,
         "license": "ISC",
         "dependencies": {
@@ -1306,26 +1300,20 @@
      "@koush/werift-src": {
         "version": "file:../../external/werift",
         "requires": {
+            "@biomejs/biome": "^1.4.1",
            "@types/jest": "^29.5.11",
-            "@types/node": "^20.10.4",
-            "@typescript-eslint/eslint-plugin": "^6.14.0",
-            "@typescript-eslint/parser": "^6.14.0",
-            "eslint": "^8.55.0",
-            "eslint-config-prettier": "^9.1.0",
-            "eslint-plugin-prettier": "^5.0.1",
-            "eslint-plugin-simple-import-sort": "^10.0.0",
+            "@types/node": "^20.10.6",
            "jest": "^29.7.0",
-            "knip": "^3.7.0",
+            "knip": "^3.9.0",
            "node-actionlint": "^1.2.2",
            "organize-imports-cli": "^0.10.0",
-            "prettier": "^3.1.1",
            "process": "^0.11.10",
            "ts-jest": "^29.1.1",
            "ts-node": "^10.9.2",
            "ts-node-dev": "^2.0.0",
-            "typedoc": "0.25.4",
+            "typedoc": "0.25.5",
            "typedoc-plugin-markdown": "3.17.1",
-            "typescript": "5.0.4"
+            "typescript": "5.3.3"
         }
      },
      "@leichtgewicht/ip-codec": {
--- a/plugins/homekit/package.json
+++ b/plugins/homekit/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/homekit",
-   "version": "1.2.54",
+   "version": "1.2.57",
   "description": "HomeKit Plugin for Scrypted",
   "scripts": {
      "scrypted-setup-project": "scrypted-setup-project",
--- a/plugins/homekit/src/main.ts
+++ b/plugins/homekit/src/main.ts
@@ -166,10 +166,12 @@ export class HomeKitPlugin extends ScryptedDeviceBase implements MixinProvider,
            case MDNSAdvertiser.CIAO:
                break;
            default:
-                if (fs.existsSync('/var/run/avahi-daemon/'))
-                    advertiser = MDNSAdvertiser.AVAHI;
-                else
-                    advertiser = MDNSAdvertiser.CIAO;
+                advertiser = MDNSAdvertiser.CIAO;
+                // this avahi detection doesn't work sometimes? fails silently.
+                // if (fs.existsSync('/var/run/avahi-daemon/'))
+                //     advertiser = MDNSAdvertiser.AVAHI;
+                // else
+                //     advertiser = MDNSAdvertiser.CIAO;
                break;
        }
        return advertiser;
@@ -267,8 +269,6 @@ export class HomeKitPlugin extends ScryptedDeviceBase implements MixinProvider,
                    },
                        undefined, 'Pairing'));
                    storageSettings.settings.pincode.persistedDefaultValue = randomPinCode();
-                    // TODO: change this value after this current default has been persisted to existing clients.
-                    // changing it now will cause existing accessories be renamed.
                    storageSettings.settings.addIdentifyingMaterial.persistedDefaultValue = false;

                    const mixinConsole = deviceManager.getMixinConsole(device.id, this.nativeId);
--- a/plugins/homekit/src/types/camera.ts
+++ b/plugins/homekit/src/types/camera.ts
@@ -117,7 +117,7 @@ addSupportedType({
                },
                closeRecordingStream(streamId, reason) {
                    const r = openRecordingStreams.get(streamId);
-                    r?.throw(new Error(reason?.toString()));
+                    console.log(`motion recording closed ${reason > 0 ? `(error code: ${reason})` : ''}`);
                    openRecordingStreams.delete(streamId);
                },
                updateRecordingActive(active) {
--- a/plugins/homekit/src/types/camera/camera-recording.ts
+++ b/plugins/homekit/src/types/camera/camera-recording.ts
@@ -321,6 +321,9 @@ export async function* handleFragmentsRequests(streamId: number, device: Scrypte
        let moov: Buffer[];

        for await (const box of generator) {
+            if (!isOpen())
+                return;
+            
            const { header, type, data } = box;
            // console.log('motion fragment box', type);

@@ -352,6 +355,8 @@ export async function* handleFragmentsRequests(streamId: number, device: Scrypte
                    needSkip = false;
                    continue;
                }
+                if (!isOpen())
+                    return;
                const fragment = Buffer.concat(pending);
                saveFragment(i, fragment);
                pending = [];
@@ -361,8 +366,6 @@ export async function* handleFragmentsRequests(streamId: number, device: Scrypte
                    data: fragment,
                    isLast,
                }
-                if (!isOpen())
-                    return;
                yield recordingPacket;
                if (wasLast)
                    break;
@@ -370,7 +373,7 @@ export async function* handleFragmentsRequests(streamId: number, device: Scrypte
        }
    }
    catch (e) {
-        console.log(`motion recording completed ${e}`);
+        console.log(`motion recording error ${e}`);
    }
    finally {
        console.log(`motion recording finished`);
--- a/plugins/homekit/src/types/camera/camera-snapshot.ts
+++ b/plugins/homekit/src/types/camera/camera-snapshot.ts
@@ -24,8 +24,6 @@ export function createSnapshotHandler(device: ScryptedDevice & VideoCamera & Cam
                width: request.width,
                height: request.height,
            },
-            // wait up to 2 seconds for the snapshot image, fallback to cached image
-            timeout: 2000,
        })
        return await mediaManager.convertMediaObjectToBuffer(media, 'image/jpeg');
    }
--- a/plugins/homekit/src/types/camera/camera-streaming.ts
+++ b/plugins/homekit/src/types/camera/camera-streaming.ts
@@ -354,15 +354,11 @@ export function createCameraStreamingDelegate(device: ScryptedDevice & VideoCame
            if (twoWayAudio) {
                let rtspServer: RtspServer;
                let track: string;
-                let playing = false;
-                session.audioReturn.once('message', async buffer => {
+                let twoWayAudioState: 'stopped' | 'starting' | 'started' = 'stopped';
+
+                const start = async () => {
                    try {
-                        const decrypted = srtpSession.decrypt(buffer);
-                        const rtp = RtpPacket.deSerialize(decrypted);
-
-                        if (rtp.header.payloadType !== session.startRequest.audio.pt)
-                            return;
-
+                        twoWayAudioState = 'starting';
                        const { clientPromise, url } = await listenZeroSingleClient();
                        const rtspUrl = url.replace('tcp', 'rtsp');
                        let sdp = createReturnAudioSdp(session.startRequest.audio);
@@ -393,7 +389,7 @@ export function createCameraStreamingDelegate(device: ScryptedDevice & VideoCame
                            device.stopIntercom();
                            client.destroy();
                            rtspServer = undefined;
-                            playing = false;
+                            twoWayAudioState = 'stopped';
                        }
                        // stop the intercom if the client dies for any reason.
                        // allow the streaming session to continue however.
@@ -402,16 +398,17 @@ export function createCameraStreamingDelegate(device: ScryptedDevice & VideoCame

                        rtspServer = new RtspServer(client, sdp);
                        await rtspServer.handlePlayback();
-                        playing = true;
+                        twoWayAudioState = 'started';
                    }
                    catch (e) {
                        console.error('two way audio failed', e);
+                        twoWayAudioState = 'stopped';
                    }
-                });
+                };

                const srtpSession = new SrtpSession(session.aconfig);
                session.audioReturn.on('message', buffer => {
-                    if (!playing)
+                    if (twoWayAudioState === 'starting')
                        return;

                    const decrypted = srtpSession.decrypt(buffer);
@@ -420,6 +417,9 @@ export function createCameraStreamingDelegate(device: ScryptedDevice & VideoCame
                    if (rtp.header.payloadType !== session.startRequest.audio.pt)
                        return;

+                    if (twoWayAudioState !== 'started')
+                        return start();
+
                    rtspServer.sendTrack(track, decrypted, false);
                });
            }
--- a/plugins/objectdetector/package-lock.json
+++ b/plugins/objectdetector/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/objectdetector",
-   "version": "0.1.39",
+   "version": "0.1.42",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/objectdetector",
-         "version": "0.1.39",
+         "version": "0.1.42",
         "license": "Apache-2.0",
         "dependencies": {
            "@scrypted/common": "file:../../common",
--- a/plugins/objectdetector/package.json
+++ b/plugins/objectdetector/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/objectdetector",
-   "version": "0.1.39",
+   "version": "0.1.42",
   "description": "Scrypted Video Analysis Plugin. Installed alongside a detection service like OpenCV or TensorFlow.",
   "author": "Scrypted",
   "license": "Apache-2.0",
--- a/plugins/objectdetector/src/main.ts
+++ b/plugins/objectdetector/src/main.ts
@@ -1159,7 +1159,7 @@ export class ObjectDetectionPlugin extends AutoenableMixinProvider implements Se
  async releaseDevice(id: string, nativeId: string): Promise<void> {
    if (nativeId?.startsWith(SMART_MOTIONSENSOR_PREFIX)) {
      const smart = this.devices.get(nativeId) as SmartMotionSensor;
-      smart?.listener?.removeListener();
+      smart?.detectionListener?.removeListener();
    }
  }

--- a/plugins/objectdetector/src/smart-motionsensor.ts
+++ b/plugins/objectdetector/src/smart-motionsensor.ts
@@ -1,9 +1,10 @@
 import sdk, { Camera, EventListenerRegister, MediaObject, MotionSensor, ObjectDetector, ObjectsDetected, Readme, RequestPictureOptions, ResponsePictureOptions, ScryptedDevice, ScryptedDeviceBase, ScryptedDeviceType, ScryptedInterface, ScryptedNativeId, Setting, SettingValue, Settings } from "@scrypted/sdk";
 import { StorageSetting, StorageSettings } from "@scrypted/sdk/storage-settings";
-import type { ObjectDetectionPlugin } from "./main";
 import { levenshteinDistance } from "./edit-distance";
+import type { ObjectDetectionPlugin } from "./main";

 export const SMART_MOTIONSENSOR_PREFIX = 'smart-motionsensor-';
+export const SMART_OCCUPANCYSENSOR_PREFIX = 'smart-occupancysensor-';

 export function createObjectDetectorStorageSetting(): StorageSetting {
    return {
@@ -26,7 +27,7 @@ export class SmartMotionSensor extends ScryptedDeviceBase implements Settings, R
        },
        detectionTimeout: {
            title: 'Object Detection Timeout',
-            description: 'Duration in seconds the sensor will report motion, before resetting.',
+            description: 'Duration in seconds the sensor will report motion, before resetting. Setting this to 0 will reset the sensor when motion stops.',
            type: 'number',
            defaultValue: 60,
        },
@@ -71,9 +72,17 @@ export class SmartMotionSensor extends ScryptedDeviceBase implements Settings, R
            type: 'number',
            defaultValue: 2,
        },
+        labelScore: {
+            group: 'Recognition',
+            title: 'Label Score',
+            description: 'The minimum score required for a label to trigger the motion sensor.',
+            type: 'number',
+            defaultValue: 0,
+        }
    });

-    listener: EventListenerRegister;
+    detectionListener: EventListenerRegister;
+    motionListener: EventListenerRegister;
    timeout: NodeJS.Timeout;
    lastPicture: Promise<MediaObject>;

@@ -143,8 +152,10 @@ export class SmartMotionSensor extends ScryptedDeviceBase implements Settings, R

    trigger() {
        this.resetTrigger();
-        const duration: number = this.storageSettings.values.detectionTimeout;
        this.motionDetected = true;
+        const duration: number = this.storageSettings.values.detectionTimeout;
+        if (!duration)
+            return;
        this.timeout = setTimeout(() => {
            this.motionDetected = false;
        }, duration * 1000);
@@ -152,12 +163,14 @@ export class SmartMotionSensor extends ScryptedDeviceBase implements Settings, R

    rebind() {
        this.motionDetected = false;
-        this.listener?.removeListener();
-        this.listener = undefined;
+        this.detectionListener?.removeListener();
+        this.detectionListener = undefined;
+        this.motionListener?.removeListener();
+        this.motionListener = undefined;
        this.resetTrigger();


-        const objectDetector: ObjectDetector & ScryptedDevice = this.storageSettings.values.objectDetector;
+        const objectDetector: ObjectDetector & MotionSensor & ScryptedDevice = this.storageSettings.values.objectDetector;
        if (!objectDetector)
            return;

@@ -167,13 +180,25 @@ export class SmartMotionSensor extends ScryptedDeviceBase implements Settings, R

        const console = sdk.deviceManager.getMixinConsole(objectDetector.id, this.nativeId);

-        this.listener = objectDetector.listen(ScryptedInterface.ObjectDetector, (source, details, data) => {
+        this.motionListener = objectDetector.listen({
+            event: ScryptedInterface.MotionSensor,
+            watch: true,
+        }, (source, details, data) => {
+            const duration: number = this.storageSettings.values.detectionTimeout;
+            if (duration)
+                return;
+
+            if (!objectDetector.motionDetected)
+                this.motionDetected = false;
+        });
+
+        this.detectionListener = objectDetector.listen(ScryptedInterface.ObjectDetector, (source, details, data) => {
            const detected: ObjectsDetected = data;

            if (this.storageSettings.values.requireDetectionThumbnail && !detected.detectionId)
                return false;

-            const { labels, labelDistance } = this.storageSettings.values;
+            const { labels, labelDistance, labelScore } = this.storageSettings.values;

            const match = detected.detections?.find(d => {
                if (this.storageSettings.values.requireScryptedNvrDetections && !d.boundingBox)
@@ -208,13 +233,24 @@ export class SmartMotionSensor extends ScryptedDeviceBase implements Settings, R
                    return false;

                for (const label of labels) {
-                    if (label === d.label)
-                        return true;
+                    if (label === d.label) {
+                        if (!labelScore || d.labelScore >= labelScore)
+                            return true;
+                        this.console.log('Label score too low.', d.labelScore);
+                        continue;
+                    }
+
                    if (!labelDistance)
                        continue;
-                    if (levenshteinDistance(label, d.label) <= labelDistance)
+
+                    if (levenshteinDistance(label, d.label) > labelDistance) {
+                        this.console.log('Label does not match.', label, d.label, d.labelScore);
+                        continue;
+                    }
+
+                    if (!labelScore || d.labelScore >= labelScore)
                        return true;
-                    this.console.log('Label does not match.', label, d.label);
+                    this.console.log('Label score too low.', d.labelScore);
                }

                return false;
--- a/plugins/onnx/.vscode/settings.json
+++ b/plugins/onnx/.vscode/settings.json
@@ -3,16 +3,16 @@
    // docker installation
    // "scrypted.debugHost": "koushik-ubuntuvm",
    // "scrypted.serverRoot": "/server",
-    "scrypted.debugHost": "koushik-ubuntuvm",
-    "scrypted.serverRoot": "/home/koush/.scrypted",
+    // "scrypted.debugHost": "koushik-ubuntuvm",
+    // "scrypted.serverRoot": "/home/koush/.scrypted",
 
    // pi local installation
    // "scrypted.debugHost": "192.168.2.119",
    // "scrypted.serverRoot": "/home/pi/.scrypted",

    // local checkout
-    // "scrypted.debugHost": "127.0.0.1",
-    // "scrypted.serverRoot": "/Users/koush/.scrypted",
+    "scrypted.debugHost": "127.0.0.1",
+    "scrypted.serverRoot": "/Users/koush/.scrypted",
    // "scrypted.debugHost": "koushik-winvm",
    // "scrypted.serverRoot": "C:\\Users\\koush\\.scrypted",

--- a/plugins/onnx/package-lock.json
+++ b/plugins/onnx/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/openvino",
-   "version": "0.1.81",
+   "version": "0.1.93",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/openvino",
-         "version": "0.1.81",
+         "version": "0.1.93",
         "devDependencies": {
            "@scrypted/sdk": "file:../../sdk"
         }
--- a/plugins/onnx/package.json
+++ b/plugins/onnx/package.json
@@ -33,6 +33,7 @@
      "runtime": "python",
      "type": "API",
      "interfaces": [
+         "DeviceProvider",
         "Settings",
         "ObjectDetection",
         "ObjectDetectionPreview"
@@ -41,5 +42,5 @@
   "devDependencies": {
      "@scrypted/sdk": "file:../../sdk"
   },
-   "version": "0.1.81"
+   "version": "0.1.93"
 }
--- a/plugins/onnx/src/ort/init.py
+++ b/plugins/onnx/src/ort/init.py
@@ -1,37 +1,43 @@
 from __future__ import annotations

+import ast
 import asyncio
+import concurrent.futures
+import json
+import platform
+import sys
+import threading
+import traceback
 from typing import Any, Tuple

-import sys
-import platform
 import numpy as np
 import onnxruntime
 import scrypted_sdk
 from PIL import Image
-import ast
 from scrypted_sdk.other import SettingValue
 from scrypted_sdk.types import Setting
-import concurrent.futures

 import common.yolo as yolo
 from predict import PredictPlugin

-predictExecutor = concurrent.futures.ThreadPoolExecutor(1, "ONNX-Predict")
+from .face_recognition import ONNXFaceRecognition
+
+try:
+    from .text_recognition import ONNXTextRecognition
+except:
+    ONNXTextRecognition = None

 availableModels = [
    "Default",
+    "scrypted_yolov10m_320",
+    "scrypted_yolov10n_320",
+    "scrypted_yolo_nas_s_320",
    "scrypted_yolov6n_320",
-    "scrypted_yolov6n",
    "scrypted_yolov6s_320",
-    "scrypted_yolov6s",
    "scrypted_yolov9c_320",
-    "scrypted_yolov9c",
    "scrypted_yolov8n_320",
-    "scrypted_yolov8n",
 ]

-
 def parse_labels(names):
    j = ast.literal_eval(names)
    ret = {}
@@ -51,12 +57,14 @@ class ONNXPlugin(
                self.storage.setItem("model", "Default")
            model = "scrypted_yolov8n_320"
        self.yolo = "yolo" in model
+        self.scrypted_yolov10 = "scrypted_yolov10" in model
+        self.scrypted_yolo_nas = "scrypted_yolo_nas" in model
        self.scrypted_yolo = "scrypted_yolo" in model
        self.scrypted_model = "scrypted" in model

        print(f"model {model}")

-        onnxmodel = "best" if self.scrypted_model else model
+        onnxmodel = model if self.scrypted_yolo_nas else "best" if self.scrypted_model else model

        model_version = "v2"
        onnxfile = self.downloadFile(
@@ -66,34 +74,114 @@ class ONNXPlugin(

        print(onnxfile)

+        deviceIds = self.storage.getItem("deviceIds") or '["0"]'
+        deviceIds = json.loads(deviceIds)
+        if not len(deviceIds):
+            deviceIds = ["0"]
+        self.deviceIds = deviceIds
+
+        compiled_models = []
+        self.compiled_models = {}
+
        try:
-            sess_options = onnxruntime.SessionOptions()
+            for deviceId in deviceIds:
+                sess_options = onnxruntime.SessionOptions()

-            providers: list[str] = []
-            if sys.platform == 'darwin':
-                providers.append("CoreMLExecutionProvider")
-            
-            if 'linux' in sys.platform and platform.machine() == 'x86_64':
-                providers.append("CUDAExecutionProvider")
+                providers: list[str] = []
+                if sys.platform == 'darwin':
+                    providers.append("CoreMLExecutionProvider")

-            providers.append('CPUExecutionProvider')
+                if ('linux' in sys.platform or 'win' in sys.platform) and platform.machine() == 'x86_64':
+                    deviceId = int(deviceId)
+                    providers.append(("CUDAExecutionProvider", { "device_id": deviceId }))
+
+                providers.append('CPUExecutionProvider')
+
+                compiled_model = onnxruntime.InferenceSession(onnxfile, sess_options=sess_options, providers=providers)
+                compiled_models.append(compiled_model)
+
+                input = compiled_model.get_inputs()[0]
+                self.model_dim = input.shape[2]
+                self.input_name = input.name
+                self.labels = parse_labels(compiled_model.get_modelmeta().custom_metadata_map['names'])

-            self.compiled_model = onnxruntime.InferenceSession(onnxfile, sess_options=sess_options, providers=providers)
        except:
            import traceback

            traceback.print_exc()
            print("Reverting all settings.")
            self.storage.removeItem("model")
+            self.storage.removeItem("deviceIds")
            self.requestRestart()

-        input = self.compiled_model.get_inputs()[0]
-        self.model_dim = input.shape[2]
-        self.input_name = input.name
-        self.labels = parse_labels(self.compiled_model.get_modelmeta().custom_metadata_map['names'])
+        def executor_initializer():
+            thread_name = threading.current_thread().name
+            interpreter = compiled_models.pop()
+            self.compiled_models[thread_name] = interpreter
+            print('Runtime initialized on thread {}'.format(thread_name))
+
+        self.executor = concurrent.futures.ThreadPoolExecutor(
+            initializer=executor_initializer,
+            max_workers=len(compiled_models),
+            thread_name_prefix="onnx",
+        )
+
+        self.prepareExecutor = concurrent.futures.ThreadPoolExecutor(
+            max_workers=len(compiled_models),
+            thread_name_prefix="onnx-prepare",
+        )
+
+        self.faceDevice = None
+        self.textDevice = None
+        asyncio.ensure_future(self.prepareRecognitionModels(), loop=self.loop)
+
+    async def prepareRecognitionModels(self):
+        try:
+            devices = [
+                {
+                    "nativeId": "facerecognition",
+                    "type": scrypted_sdk.ScryptedDeviceType.Builtin.value,
+                    "interfaces": [
+                        scrypted_sdk.ScryptedInterface.ObjectDetection.value,
+                    ],
+                    "name": "ONNX Face Recognition",
+                },
+            ]
+
+            if ONNXTextRecognition:
+                devices.append(
+                    {
+                        "nativeId": "textrecognition",
+                        "type": scrypted_sdk.ScryptedDeviceType.Builtin.value,
+                        "interfaces": [
+                            scrypted_sdk.ScryptedInterface.ObjectDetection.value,
+                        ],
+                        "name": "ONNX Text Recognition",
+                    },
+                )
+
+            await scrypted_sdk.deviceManager.onDevicesChanged(
+                {
+                    "devices": devices,
+                }
+            )
+        except:
+            pass
+
+    async def getDevice(self, nativeId: str) -> Any:
+        if nativeId == "facerecognition":
+            self.faceDevice = self.faceDevice or ONNXFaceRecognition(self, nativeId)
+            return self.faceDevice
+        elif nativeId == "textrecognition":
+            self.textDevice = self.textDevice or ONNXTextRecognition(self, nativeId)
+            return self.textDevice
+        raise Exception("unknown device")

    async def getSettings(self) -> list[Setting]:
        model = self.storage.getItem("model") or "Default"
+        deviceIds = self.storage.getItem("deviceIds") or '["0"]'
+        deviceIds = json.loads(deviceIds)
+
        return [
            {
                "key": "model",
@@ -102,9 +190,26 @@ class ONNXPlugin(
                "choices": availableModels,
                "value": model,
            },
+            {
+                "key": "deviceIds",
+                "title": "Device IDs",
+                "description": "Optional: Assign multiple CUDA Device IDs to use for detection.",
+                "choices": deviceIds,
+                "combobox": True,
+                "multiple": True,
+                "value": deviceIds,
+            },
+            {
+                "key": "execution_device",
+                "title": "Execution Device",
+                "readonly": True,
+                "value": onnxruntime.get_device(),
+            }
        ]

    async def putSetting(self, key: str, value: SettingValue):
+        if (key == 'deviceIds'):
+            value = json.dumps(value)
        self.storage.setItem(key, value)
        await self.onDeviceEvent(scrypted_sdk.ScryptedInterface.Settings.value, None)
        self.requestRestart()
@@ -117,25 +222,32 @@ class ONNXPlugin(
        return [self.model_dim, self.model_dim]

    async def detect_once(self, input: Image.Image, settings: Any, src_size, cvss):
-        def predict(input_tensor):
-            output_tensors = self.compiled_model.run(None, { self.input_name: input_tensor })
-            objs = yolo.parse_yolov9(output_tensors[0][0])
-            return objs
+        def prepare(): 
+            im = np.array(input)
+            im = np.expand_dims(input, axis=0)
+            im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
+            im = im.astype(np.float32) / 255.0
+            im = np.ascontiguousarray(im)  # contiguous
+            return im

-        im = np.array(input)
-        im = np.stack([input])
-        im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
-        im = im.astype(np.float32) / 255.0
-        im = np.ascontiguousarray(im)  # contiguous
-        input_tensor = im
+        def predict(input_tensor):
+            compiled_model = self.compiled_models[threading.current_thread().name]
+            output_tensors = compiled_model.run(None, { self.input_name: input_tensor })
+            if self.scrypted_yolov10:
+                return yolo.parse_yolov10(output_tensors[0][0])
+            if self.scrypted_yolo_nas:
+                return yolo.parse_yolo_nas([output_tensors[1], output_tensors[0]])
+            return yolo.parse_yolov9(output_tensors[0][0])

        try:
+            input_tensor = await asyncio.get_event_loop().run_in_executor(
+                self.prepareExecutor, lambda: prepare()
+            )
            objs = await asyncio.get_event_loop().run_in_executor(
-                predictExecutor, lambda: predict(input_tensor)
+                self.executor, lambda: predict(input_tensor)
            )

        except:
-            import traceback

            traceback.print_exc()
            raise
--- a/plugins/onnx/src/ort/face_recognition.py
+++ b/plugins/onnx/src/ort/face_recognition.py
@@ -0,0 +1,112 @@
+from __future__ import annotations
+
+import asyncio
+import concurrent.futures
+import platform
+import sys
+import threading
+
+import numpy as np
+import onnxruntime
+from PIL import Image
+
+from predict.face_recognize import FaceRecognizeDetection
+
+
+class ONNXFaceRecognition(FaceRecognizeDetection):
+    def __init__(self, plugin, nativeId: str | None = None):
+        self.plugin = plugin
+
+        super().__init__(nativeId=nativeId)
+
+    def downloadModel(self, model: str):
+        onnxmodel = "best" if "scrypted" in model else model
+        model_version = "v1"
+        onnxfile = self.downloadFile(
+            f"https://raw.githubusercontent.com/koush/onnx-models/main/{model}/{onnxmodel}.onnx",
+            f"{model_version}/{model}/{onnxmodel}.onnx",
+        )
+        print(onnxfile)
+
+        compiled_models_array = []
+        compiled_models = {}
+        deviceIds = self.plugin.deviceIds
+
+        for deviceId in deviceIds:
+            sess_options = onnxruntime.SessionOptions()
+
+            providers: list[str] = []
+            if sys.platform == "darwin":
+                providers.append("CoreMLExecutionProvider")
+
+            if "linux" in sys.platform and platform.machine() == "x86_64":
+                deviceId = int(deviceId)
+                providers.append(("CUDAExecutionProvider", {"device_id": deviceId}))
+
+            providers.append("CPUExecutionProvider")
+
+            compiled_model = onnxruntime.InferenceSession(
+                onnxfile, sess_options=sess_options, providers=providers
+            )
+            compiled_models_array.append(compiled_model)
+
+            input = compiled_model.get_inputs()[0]
+            input_name = input.name
+
+        def executor_initializer():
+            thread_name = threading.current_thread().name
+            interpreter = compiled_models_array.pop()
+            compiled_models[thread_name] = interpreter
+            print("Runtime initialized on thread {}".format(thread_name))
+
+        executor = concurrent.futures.ThreadPoolExecutor(
+            initializer=executor_initializer,
+            max_workers=len(compiled_models_array),
+            thread_name_prefix="face",
+        )
+
+        prepareExecutor = concurrent.futures.ThreadPoolExecutor(
+            max_workers=len(compiled_models_array),
+            thread_name_prefix="face-prepare",
+        )
+
+        return compiled_models, input_name, prepareExecutor, executor
+
+    async def predictDetectModel(self, input: Image.Image):
+        compiled_models, input_name, prepareExecutor, executor = self.detectModel
+
+        def prepare():
+            im = np.array(input)
+            im = np.expand_dims(input, axis=0)
+            im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
+            im = im.astype(np.float32) / 255.0
+            im = np.ascontiguousarray(im)  # contiguous
+            return im
+
+        def predict(input_tensor):
+            compiled_model = compiled_models[threading.current_thread().name]
+            output_tensors = compiled_model.run(None, {input_name: input_tensor})
+            return output_tensors
+
+        input_tensor = await asyncio.get_event_loop().run_in_executor(
+            prepareExecutor, lambda: prepare()
+        )
+        objs = await asyncio.get_event_loop().run_in_executor(
+            executor, lambda: predict(input_tensor)
+        )
+
+        return objs[0][0]
+
+    async def predictFaceModel(self, input: np.ndarray):
+        compiled_models, input_name, prepareExecutor, executor = self.faceModel
+
+        def predict():
+            compiled_model = compiled_models[threading.current_thread().name]
+            output_tensors = compiled_model.run(None, {input_name: input})
+            return output_tensors
+
+        objs = await asyncio.get_event_loop().run_in_executor(
+            executor, lambda: predict()
+        )
+
+        return objs[0]
--- a/plugins/onnx/src/ort/text_recognition.py
+++ b/plugins/onnx/src/ort/text_recognition.py
@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+import asyncio
+import concurrent.futures
+import platform
+import sys
+import threading
+
+import numpy as np
+import onnxruntime
+from PIL import Image
+
+from predict.text_recognize import TextRecognition
+
+
+class ONNXTextRecognition(TextRecognition):
+    def __init__(self, plugin, nativeId: str | None = None):
+        self.plugin = plugin
+
+        super().__init__(nativeId=nativeId)
+
+    def downloadModel(self, model: str):
+        onnxmodel = model
+        model_version = "v3"
+        onnxfile = self.downloadFile(
+            f"https://raw.githubusercontent.com/koush/onnx-models/main/{model}/{onnxmodel}.onnx",
+            f"{model_version}/{model}/{onnxmodel}.onnx",
+        )
+        print(onnxfile)
+
+        compiled_models_array = []
+        compiled_models = {}
+        deviceIds = self.plugin.deviceIds
+
+        for deviceId in deviceIds:
+            sess_options = onnxruntime.SessionOptions()
+
+            providers: list[str] = []
+            if sys.platform == "darwin":
+                providers.append("CoreMLExecutionProvider")
+
+            if "linux" in sys.platform and platform.machine() == "x86_64":
+                deviceId = int(deviceId)
+                providers.append(("CUDAExecutionProvider", {"device_id": deviceId}))
+
+            providers.append("CPUExecutionProvider")
+
+            compiled_model = onnxruntime.InferenceSession(
+                onnxfile, sess_options=sess_options, providers=providers
+            )
+            compiled_models_array.append(compiled_model)
+
+            input = compiled_model.get_inputs()[0]
+            input_name = input.name
+
+        def executor_initializer():
+            thread_name = threading.current_thread().name
+            interpreter = compiled_models_array.pop()
+            compiled_models[thread_name] = interpreter
+            print("Runtime initialized on thread {}".format(thread_name))
+
+        executor = concurrent.futures.ThreadPoolExecutor(
+            initializer=executor_initializer,
+            max_workers=len(compiled_models_array),
+            thread_name_prefix="text",
+        )
+
+        prepareExecutor = concurrent.futures.ThreadPoolExecutor(
+            max_workers=len(compiled_models_array),
+            thread_name_prefix="text-prepare",
+        )
+
+        return compiled_models, input_name, prepareExecutor, executor
+
+    async def predictDetectModel(self, input: Image.Image):
+        compiled_models, input_name, prepareExecutor, executor = self.detectModel
+
+        def predict():
+            compiled_model = compiled_models[threading.current_thread().name]
+            output_tensors = compiled_model.run(None, {input_name: input})
+            return output_tensors
+
+        objs = await asyncio.get_event_loop().run_in_executor(
+            executor, lambda: predict()
+        )
+
+        return objs[0]
+
+    async def predictTextModel(self, input: np.ndarray):
+        input = input.astype(np.float32)
+        compiled_models, input_name, prepareExecutor, executor = self.textModel
+
+        def predict():
+            compiled_model = compiled_models[threading.current_thread().name]
+            output_tensors = compiled_model.run(None, {input_name: input})
+            return output_tensors
+
+        objs = await asyncio.get_event_loop().run_in_executor(
+            executor, lambda: predict()
+        )
+
+        return objs[0]
--- a/plugins/onnx/src/requirements.optional.txt
+++ b/plugins/onnx/src/requirements.optional.txt
@@ -0,0 +1 @@
+opencv-python
--- a/plugins/onnx/src/requirements.txt
+++ b/plugins/onnx/src/requirements.txt
@@ -4,6 +4,7 @@
 onnxruntime-gpu; 'linux' in sys_platform and platform_machine == 'x86_64'
 # cpu and coreml execution provider
 onnxruntime; 'linux' not in sys_platform or platform_machine != 'x86_64'
+# nightly?
 # ort-nightly-gpu==1.17.3.dev20240409002

 # pillow-simd is available on x64 linux
--- a/plugins/openvino/.vscode/settings.json
+++ b/plugins/openvino/.vscode/settings.json
@@ -4,6 +4,10 @@
    // "scrypted.debugHost": "koushik-ubuntu",
    // "scrypted.serverRoot": "/server",

+    // proxmox installation
+    // "scrypted.debugHost": "scrypted-server",
+    // "scrypted.serverRoot": "/root/.scrypted",
+
    // pi local installation
    // "scrypted.debugHost": "192.168.2.119",
    // "scrypted.serverRoot": "/home/pi/.scrypted",
--- a/plugins/openvino/package-lock.json
+++ b/plugins/openvino/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/openvino",
-   "version": "0.1.80",
+   "version": "0.1.89",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/openvino",
-         "version": "0.1.80",
+         "version": "0.1.89",
         "devDependencies": {
            "@scrypted/sdk": "file:../../sdk"
         }
--- a/plugins/openvino/package.json
+++ b/plugins/openvino/package.json
@@ -42,5 +42,5 @@
   "devDependencies": {
      "@scrypted/sdk": "file:../../sdk"
   },
-   "version": "0.1.80"
+   "version": "0.1.89"
 }
--- a/plugins/openvino/src/common/yolo.py
+++ b/plugins/openvino/src/common/yolo.py
@@ -6,8 +6,54 @@ from predict.rectangle import Rectangle

 defaultThreshold = .2

-def parse_yolov9(results, threshold = defaultThreshold, scale = None, confidence_scale  = None):
+def parse_yolov10(results, threshold = defaultThreshold, scale = None, confidence_scale  = None):
+    objs: list[Prediction] = []
+    keep = np.argwhere(results[4:] > threshold)
+    for indices in keep:
+        class_id = indices[0]
+        index = indices[1]
+        confidence = results[class_id + 4, index].astype(float)
+        l = results[0][index].astype(float)
+        t = results[1][index].astype(float)
+        r = results[2][index].astype(float)
+        b = results[3][index].astype(float)
+        if scale:
+            l = scale(l)
+            t = scale(t)
+            r = scale(r)
+            b = scale(b)
+        if confidence_scale:
+            confidence = confidence_scale(confidence)
+        obj = Prediction(
+            int(class_id),
+            confidence,
+            Rectangle(
+                l,
+                t,
+                r,
+                b,
+            ),
+        )
+        objs.append(obj)
+
+    return objs
+
+def parse_yolo_nas(predictions):
    objs = []
+    for pred_scores, pred_bboxes in zip(*predictions):
+        i, j = np.nonzero(pred_scores > .5)
+        pred_bboxes = pred_bboxes[i]
+        pred_cls_conf = pred_scores[i, j]
+        pred_cls_label = j[:]
+        for box, conf, label in zip(pred_bboxes, pred_cls_conf, pred_cls_label):
+            obj = Prediction(
+                int(label), conf.astype(float), Rectangle(box[0].astype(float), box[1].astype(float), box[2].astype(float), box[3].astype(float))
+            )
+            objs.append(obj)
+    return objs
+
+def parse_yolov9(results, threshold = defaultThreshold, scale = None, confidence_scale  = None):
+    objs: list[Prediction] = []
    keep = np.argwhere(results[4:] > threshold)
    for indices in keep:
        class_id = indices[0]
--- a/plugins/openvino/src/ov/init.py
+++ b/plugins/openvino/src/ov/init.py
@@ -1,8 +1,10 @@
 from __future__ import annotations

 import asyncio
+import concurrent.futures
 import json
 import re
+import traceback
 from typing import Any, Tuple

 import numpy as np
@@ -11,30 +13,30 @@ import scrypted_sdk
 from PIL import Image
 from scrypted_sdk.other import SettingValue
 from scrypted_sdk.types import Setting
-import concurrent.futures

 import common.yolo as yolo
 from predict import Prediction, PredictPlugin
 from predict.rectangle import Rectangle

 from .face_recognition import OpenVINOFaceRecognition
+
 try:
    from .text_recognition import OpenVINOTextRecognition
 except:
    OpenVINOTextRecognition = None

 predictExecutor = concurrent.futures.ThreadPoolExecutor(1, "OpenVINO-Predict")
+prepareExecutor = concurrent.futures.ThreadPoolExecutor(1, "OpenVINO-Prepare")

 availableModels = [
    "Default",
+    "scrypted_yolov10m_320",
+    "scrypted_yolov10n_320",
+    "scrypted_yolo_nas_s_320",
    "scrypted_yolov6n_320",
-    "scrypted_yolov6n",
    "scrypted_yolov6s_320",
-    "scrypted_yolov6s",
    "scrypted_yolov9c_320",
-    "scrypted_yolov9c",
    "scrypted_yolov8n_320",
-    "scrypted_yolov8n",
    "ssd_mobilenet_v1_coco",
    "ssdlite_mobilenet_v2",
    "yolo-v3-tiny-tf",
@@ -121,10 +123,10 @@ class OpenVINOPlugin(
            if using_mode == "AUTO":
                if "GPU" in available_devices:
                    using_mode = "GPU"
-            if using_mode == "GPU":
-                precision = "FP16"
-            else:
-                precision = "FP32"
+
+            # FP16 is smaller and the default export. no tangible performance difference.
+            # https://docs.openvino.ai/2023.3/openvino_docs_OV_Converter_UG_Conversion_Options.html
+            precision = "FP16"

        self.precision = precision

@@ -134,6 +136,8 @@ class OpenVINOPlugin(
                self.storage.setItem("model", "Default")
            model = "scrypted_yolov8n_320"
        self.yolo = "yolo" in model
+        self.scrypted_yolov10 = "scrypted_yolov10" in model
+        self.scrypted_yolo_nas = "scrypted_yolo_nas" in model
        self.scrypted_yolo = "scrypted_yolo" in model
        self.scrypted_model = "scrypted" in model
        self.sigmoid = model == "yolo-v4-tiny-tf"
@@ -151,7 +155,12 @@ class OpenVINOPlugin(
            f"https://raw.githubusercontent.com/koush/openvino-models/main/{model}/{precision}/{ovmodel}.bin",
            f"{model_version}/{model}/{precision}/{ovmodel}.bin",
        )
-        if self.scrypted_model:
+        if self.scrypted_yolo_nas:
+            labelsFile = self.downloadFile(
+                "https://raw.githubusercontent.com/koush/openvino-models/main/scrypted_nas_labels.txt",
+                "scrypted_nas_labels.txt",
+            )
+        elif self.scrypted_model:
            labelsFile = self.downloadFile(
                "https://raw.githubusercontent.com/koush/openvino-models/main/scrypted_labels.txt",
                "scrypted_labels.txt",
@@ -194,6 +203,8 @@ class OpenVINOPlugin(
        labels_contents = open(labelsFile, "r").read()
        self.labels = parse_label_contents(labels_contents)

+        self.faceDevice = None
+        self.textDevice = None
        asyncio.ensure_future(self.prepareRecognitionModels(), loop=self.loop)

    async def getSettings(self) -> list[Setting]:
@@ -262,8 +273,11 @@ class OpenVINOPlugin(
            objs = []

            if self.scrypted_yolo:
-                objs = yolo.parse_yolov9(output_tensors[0][0])
-                return objs
+                if self.scrypted_yolov10:
+                    return yolo.parse_yolov10(output_tensors[0][0])
+                if self.scrypted_yolo_nas:
+                    return yolo.parse_yolo_nas([output_tensors[1], output_tensors[0]])
+                return yolo.parse_yolov9(output_tensors[0][0])

            if self.yolo:
                # index 2 will always either be 13 or 26
@@ -314,30 +328,34 @@ class OpenVINOPlugin(

            return objs

-        # the input_tensor can be created with the shared_memory=True parameter,
-        # but that seems to cause issues on some platforms.
-        if self.scrypted_yolo:
-            im = np.stack([input])
-            im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
-            im = im.astype(np.float32) / 255.0
-            im = np.ascontiguousarray(im)  # contiguous
-            im = ov.Tensor(array=im)
-            input_tensor = im
-        elif self.yolo:
-            input_tensor = ov.Tensor(
-                array=np.expand_dims(np.array(input), axis=0).astype(np.float32)
-            )
-        else:
-            input_tensor = ov.Tensor(array=np.expand_dims(np.array(input), axis=0))
+
+        def prepare(): 
+            # the input_tensor can be created with the shared_memory=True parameter,
+            # but that seems to cause issues on some platforms.
+            if self.scrypted_yolo:
+                im = np.array(input)
+                im = np.expand_dims(input, axis=0)
+                im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
+                im = im.astype(np.float32) / 255.0
+                im = np.ascontiguousarray(im)  # contiguous
+                input_tensor = ov.Tensor(array=im)
+            elif self.yolo:
+                input_tensor = ov.Tensor(
+                    array=np.expand_dims(np.array(input), axis=0).astype(np.float32)
+                )
+            else:
+                input_tensor = ov.Tensor(array=np.expand_dims(np.array(input), axis=0))
+            return input_tensor

        try:
+            input_tensor = await asyncio.get_event_loop().run_in_executor(
+                prepareExecutor, lambda: prepare()
+            )
            objs = await asyncio.get_event_loop().run_in_executor(
                predictExecutor, lambda: predict(input_tensor)
            )

        except:
-            import traceback
-
            traceback.print_exc()
            raise

@@ -379,7 +397,9 @@ class OpenVINOPlugin(

    async def getDevice(self, nativeId: str) -> Any:
        if nativeId == "facerecognition":
-            return OpenVINOFaceRecognition(self, nativeId)
+            self.faceDevice = self.faceDevice or OpenVINOFaceRecognition(self, nativeId)
+            return self.faceDevice
        elif nativeId == "textrecognition":
-            return OpenVINOTextRecognition(self, nativeId)
+            self.textDevice = self.textDevice or OpenVINOTextRecognition(self, nativeId)
+            return self.textDevice
        raise Exception("unknown device")
--- a/plugins/openvino/src/ov/async_infer.py
+++ b/plugins/openvino/src/ov/async_infer.py
@@ -0,0 +1,7 @@
+import concurrent.futures
+
+
+def create_executors(name: str):
+    prepare = concurrent.futures.ThreadPoolExecutor(1, "OpenVINO-{f}Prepare")
+    predict = concurrent.futures.ThreadPoolExecutor(1, "OpenVINO-{f}}Predict")
+    return prepare, predict
--- a/plugins/openvino/src/ov/face_recognition.py
+++ b/plugins/openvino/src/ov/face_recognition.py
@@ -1,23 +1,19 @@
 from __future__ import annotations

-import concurrent.futures
-import openvino.runtime as ov
+import asyncio

 import numpy as np
+import openvino.runtime as ov
+from PIL import Image

+from ov import async_infer
 from predict.face_recognize import FaceRecognizeDetection

+faceDetectPrepare, faceDetectPredict = async_infer.create_executors("FaceDetect")
+faceRecognizePrepare, faceRecognizePredict = async_infer.create_executors(
+    "FaceRecognize"
+)

-def euclidean_distance(arr1, arr2):
-    return np.linalg.norm(arr1 - arr2)
-
-
-def cosine_similarity(vector_a, vector_b):
-    dot_product = np.dot(vector_a, vector_b)
-    norm_a = np.linalg.norm(vector_a)
-    norm_b = np.linalg.norm(vector_b)
-    similarity = dot_product / (norm_a * norm_b)
-    return similarity

 class OpenVINOFaceRecognition(FaceRecognizeDetection):
    def __init__(self, plugin, nativeId: str | None = None):
@@ -40,32 +36,35 @@ class OpenVINOFaceRecognition(FaceRecognizeDetection):
        print(xmlFile, binFile)
        return self.plugin.core.compile_model(xmlFile, self.plugin.mode)

-    def predictDetectModel(self, input):
-        infer_request = self.detectModel.create_infer_request()
-        im = np.stack([input])
-        im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
-        im = im.astype(np.float32) / 255.0
-        im = np.ascontiguousarray(im)  # contiguous
-        im = ov.Tensor(array=im)
-        input_tensor = im
-        infer_request.set_input_tensor(input_tensor)
-        infer_request.start_async()
-        infer_request.wait()
-        return infer_request.output_tensors[0].data[0]
+    async def predictDetectModel(self, input: Image.Image):
+        def predict():
+            im = np.expand_dims(input, axis=0)
+            im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
+            im = im.astype(np.float32) / 255.0
+            im = np.ascontiguousarray(im)  # contiguous

-    def predictFaceModel(self, input):
-        im = ov.Tensor(array=input)
-        infer_request = self.faceModel.create_infer_request()
-        infer_request.set_input_tensor(im)
-        infer_request.start_async()
-        infer_request.wait()
-        return infer_request.output_tensors[0].data[0]
+            infer_request = self.detectModel.create_infer_request()
+            tensor = ov.Tensor(array=im)
+            infer_request.set_input_tensor(tensor)
+            output_tensors = infer_request.infer()
+            ret = output_tensors[0][0]
+            return ret

-    def predictTextModel(self, input):
-        input = input.astype(np.float32)
-        im = ov.Tensor(array=input)
-        infer_request = self.textModel.create_infer_request()
-        infer_request.set_input_tensor(im)
-        infer_request.start_async()
-        infer_request.wait()
-        return infer_request.output_tensors[0].data
+        ret = await asyncio.get_event_loop().run_in_executor(
+            faceDetectPredict, lambda: predict()
+        )
+        return ret
+
+    async def predictFaceModel(self, input: np.ndarray):
+        def predict():
+            im = ov.Tensor(array=input)
+            infer_request = self.faceModel.create_infer_request()
+            infer_request.set_input_tensor(im)
+            output_tensors = infer_request.infer()
+            ret = output_tensors[0]
+            return ret
+
+        ret = await asyncio.get_event_loop().run_in_executor(
+            faceRecognizePredict, lambda: predict()
+        )
+        return ret
--- a/plugins/openvino/src/ov/text_recognition.py
+++ b/plugins/openvino/src/ov/text_recognition.py
@@ -1,10 +1,18 @@
 from __future__ import annotations

-import openvino.runtime as ov
-import numpy as np
+import asyncio

+import numpy as np
+import openvino.runtime as ov
+
+from ov import async_infer
 from predict.text_recognize import TextRecognition

+textDetectPrepare, textDetectPredict = async_infer.create_executors("TextDetect")
+textRecognizePrepare, textRecognizePredict = async_infer.create_executors(
+    "TextRecognize"
+)
+

 class OpenVINOTextRecognition(TextRecognition):
    def __init__(self, plugin, nativeId: str | None = None):
@@ -27,20 +35,31 @@ class OpenVINOTextRecognition(TextRecognition):
        print(xmlFile, binFile)
        return self.plugin.core.compile_model(xmlFile, self.plugin.mode)

-    def predictDetectModel(self, input):
-        infer_request = self.detectModel.create_infer_request()
-        im = ov.Tensor(array=input)
-        input_tensor = im
-        infer_request.set_input_tensor(input_tensor)
-        infer_request.start_async()
-        infer_request.wait()
-        return infer_request.output_tensors[0].data
+    async def predictDetectModel(self, input: np.ndarray):
+        def predict():
+            infer_request = self.detectModel.create_infer_request()
+            im = ov.Tensor(array=input)
+            input_tensor = im
+            infer_request.set_input_tensor(input_tensor)
+            output_tensors = infer_request.infer()
+            ret = output_tensors[0]
+            return ret

-    def predictTextModel(self, input):
-        input = input.astype(np.float32)
-        im = ov.Tensor(array=input)
-        infer_request = self.textModel.create_infer_request()
-        infer_request.set_input_tensor(im)
-        infer_request.start_async()
-        infer_request.wait()
-        return infer_request.output_tensors[0].data
+        ret = await asyncio.get_event_loop().run_in_executor(
+            textDetectPredict, lambda: predict()
+        )
+        return ret
+
+    async def predictTextModel(self, input: np.ndarray):
+        def predict():
+            im = ov.Tensor(array=input.astype(np.float32))
+            infer_request = self.textModel.create_infer_request()
+            infer_request.set_input_tensor(im)
+            output_tensors = infer_request.infer()
+            ret = output_tensors[0]
+            return ret
+
+        ret = await asyncio.get_event_loop().run_in_executor(
+            textDetectPredict, lambda: predict()
+        )
+        return ret
--- a/plugins/openvino/src/requirements.txt
+++ b/plugins/openvino/src/requirements.txt
@@ -1,5 +1,4 @@
-# 2024-04-23 - modify timestamp to force pip reinstall
-openvino==2024.0.0
+openvino==2024.1.0

 # pillow-simd is available on x64 linux
 # pillow-simd confirmed not building with arm64 linux or apple silicon
--- a/plugins/prebuffer-mixin/package-lock.json
+++ b/plugins/prebuffer-mixin/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/prebuffer-mixin",
-   "version": "0.10.18",
+   "version": "0.10.23",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/prebuffer-mixin",
-         "version": "0.10.18",
+         "version": "0.10.23",
         "license": "Apache-2.0",
         "dependencies": {
            "@scrypted/common": "file:../../common",
--- a/plugins/prebuffer-mixin/package.json
+++ b/plugins/prebuffer-mixin/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/prebuffer-mixin",
-   "version": "0.10.18",
+   "version": "0.10.23",
   "description": "Video Stream Rebroadcast, Prebuffer, and Management Plugin for Scrypted.",
   "author": "Scrypted",
   "license": "Apache-2.0",
--- a/plugins/prebuffer-mixin/src/ffmpeg-rebroadcast.ts
+++ b/plugins/prebuffer-mixin/src/ffmpeg-rebroadcast.ts
@@ -1,15 +1,13 @@
+import { cloneDeep } from '@scrypted/common/src/clone-deep';
+import { Deferred } from "@scrypted/common/src/deferred";
+import { listenZeroSingleClient } from '@scrypted/common/src/listen-cluster';
+import { ffmpegLogInitialOutput, safeKillFFmpeg, safePrintFFmpegArguments } from '@scrypted/common/src/media-helpers';
+import { createRtspParser } from "@scrypted/common/src/rtsp-server";
+import { parseSdp } from "@scrypted/common/src/sdp-utils";
+import { StreamChunk, StreamParser } from '@scrypted/common/src/stream-parser';
 import sdk, { FFmpegInput, RequestMediaStreamOptions, ResponseMediaStreamOptions } from "@scrypted/sdk";
 import child_process, { ChildProcess, StdioOptions } from 'child_process';
 import { EventEmitter } from 'events';
-import { Server } from 'net';
-import { Duplex } from 'stream';
-import { cloneDeep } from './clone-deep';
-import { Deferred } from "./deferred";
-import { listenZeroSingleClient } from './listen-cluster';
-import { ffmpegLogInitialOutput, safeKillFFmpeg, safePrintFFmpegArguments } from './media-helpers';
-import { createRtspParser } from "./rtsp-server";
-import { parseSdp } from "./sdp-utils";
-import { StreamChunk, StreamParser } from './stream-parser';

 const { mediaManager } = sdk;

@@ -339,64 +337,3 @@ export async function startParserSession<T extends string>(ffmpegInput: FFmpegIn
        }
    };
 }
-
-export interface Rebroadcaster {
-    server: Server;
-    port: number;
-    url: string;
-    clients: number;
-}
-
-export interface RebroadcastSessionCleanup {
-    (): void;
-}
-
-export interface RebroadcasterConnection {
-    writeData: (data: StreamChunk) => number;
-    destroy: () => void;
-}
-
-export interface RebroadcasterOptions {
-    connect?: (connection: RebroadcasterConnection) => RebroadcastSessionCleanup | undefined;
-    console?: Console;
-    idle?: {
-        timeout: number,
-        callback: () => void,
-    },
-}
-
-export function handleRebroadcasterClient(socket: Duplex, options?: RebroadcasterOptions) {
-    const firstWriteData = (data: StreamChunk) => {
-        if (data.startStream) {
-            socket.write(data.startStream)
-        }
-        connection.writeData = writeData;
-        return writeData(data);
-    }
-    const writeData = (data: StreamChunk) => {
-        for (const chunk of data.chunks) {
-            socket.write(chunk);
-        }
-
-        return socket.writableLength;
-    };
-
-    const destroy = () => {
-        const cb = cleanupCallback;
-        cleanupCallback = undefined;
-        socket.destroy();
-        cb?.();
-    }
-
-    const connection: RebroadcasterConnection = {
-        writeData: firstWriteData,
-        destroy,
-    };
-
-    let cleanupCallback = options?.connect(connection);
-
-    socket.once('close', () => {
-        destroy();
-    });
-    socket.on('error', e => options?.console?.log('client stream ended'));
-}
--- a/plugins/prebuffer-mixin/src/main.ts
+++ b/plugins/prebuffer-mixin/src/main.ts
@@ -1,8 +1,6 @@
-import path from 'path'
 import { AutoenableMixinProvider } from '@scrypted/common/src/autoenable-mixin-provider';
 import { getDebugModeH264EncoderArgs, getH264EncoderArgs } from '@scrypted/common/src/ffmpeg-hardware-acceleration';
 import { addVideoFilterArguments } from '@scrypted/common/src/ffmpeg-helpers';
-import { ParserOptions, ParserSession, handleRebroadcasterClient, startParserSession } from '@scrypted/common/src/ffmpeg-rebroadcast';
 import { ListenZeroSingleClientTimeoutError, closeQuiet, listenZeroSingleClient } from '@scrypted/common/src/listen-cluster';
 import { readLength } from '@scrypted/common/src/read-stream';
 import { H264_NAL_TYPE_FU_B, H264_NAL_TYPE_IDR, H264_NAL_TYPE_MTAP16, H264_NAL_TYPE_MTAP32, H264_NAL_TYPE_RESERVED0, H264_NAL_TYPE_RESERVED30, H264_NAL_TYPE_RESERVED31, H264_NAL_TYPE_SEI, H264_NAL_TYPE_STAP_B, RtspServer, RtspTrack, createRtspParser, findH264NaluType, getNaluTypes, listenSingleRtspClient } from '@scrypted/common/src/rtsp-server';
@@ -10,14 +8,16 @@ import { addTrackControls, parseSdp } from '@scrypted/common/src/sdp-utils';
 import { SettingsMixinDeviceBase, SettingsMixinDeviceOptions } from "@scrypted/common/src/settings-mixin";
 import { sleep } from '@scrypted/common/src/sleep';
 import { StreamChunk, StreamParser } from '@scrypted/common/src/stream-parser';
-import sdk, { BufferConverter, ChargeState, DeviceProvider, DeviceState, EventListenerRegister, FFmpegInput, H264Info, MediaObject, MediaStreamDestination, MediaStreamOptions, MixinProvider, RequestMediaStreamOptions, ResponseMediaStreamOptions, ScryptedDevice, ScryptedDeviceType, ScryptedInterface, ScryptedMimeTypes, Setting, SettingValue, Settings, VideoCamera, VideoCameraConfiguration, WritableDeviceState } from '@scrypted/sdk';
+import sdk, { BufferConverter, ChargeState, DeviceProvider, EventListenerRegister, FFmpegInput, H264Info, MediaObject, MediaStreamDestination, MediaStreamOptions, MixinProvider, RequestMediaStreamOptions, ResponseMediaStreamOptions, ScryptedDevice, ScryptedDeviceType, ScryptedInterface, ScryptedMimeTypes, Setting, SettingValue, Settings, VideoCamera, VideoCameraConfiguration, WritableDeviceState } from '@scrypted/sdk';
 import { StorageSettings } from '@scrypted/sdk/storage-settings';
 import crypto from 'crypto';
 import { once } from 'events';
 import net, { AddressInfo } from 'net';
+import path from 'path';
 import semver from 'semver';
 import { Duplex } from 'stream';
 import { Worker } from 'worker_threads';
+import { ParserOptions, ParserSession, startParserSession } from './ffmpeg-rebroadcast';
 import { FileRtspServer } from './file-rtsp-server';
 import { getUrlLocalAdresses } from './local-addresses';
 import { REBROADCAST_MIXIN_INTERFACE_TOKEN } from './rebroadcast-mixin-token';
@@ -41,13 +41,6 @@ interface PrebufferStreamChunk extends StreamChunk {
  time?: number;
 }

-type Prebuffers<T extends string> = {
-  [key in T]: PrebufferStreamChunk[];
-}
-
-type PrebufferParsers = 'rtsp';
-const PrebufferParserValues: PrebufferParsers[] = ['rtsp'];
-
 function hasOddities(h264Info: H264Info) {
  const h264Oddities = h264Info.fuab
    || h264Info.mtap16
@@ -60,13 +53,13 @@ function hasOddities(h264Info: H264Info) {
  return h264Oddities;
 }

+type PrebufferParsers = 'rtsp';
+
 class PrebufferSession {

  parserSessionPromise: Promise<ParserSession<PrebufferParsers>>;
  parserSession: ParserSession<PrebufferParsers>;
-  prebuffers: Prebuffers<PrebufferParsers> = {
-    rtsp: [],
-  };
+  rtspPrebuffer: PrebufferStreamChunk[] = []
  parsers: { [container: string]: StreamParser };
  sdp: Promise<string>;
  usingScryptedParser = false;
@@ -148,10 +141,10 @@ class PrebufferSession {

  getDetectedIdrInterval() {
    const durations: number[] = [];
-    if (this.prebuffers.rtsp.length) {
+    if (this.rtspPrebuffer.length) {
      let last: number;

-      for (const chunk of this.prebuffers.rtsp) {
+      for (const chunk of this.rtspPrebuffer) {
        if (findH264NaluType(chunk, H264_NAL_TYPE_IDR)) {
          if (last)
            durations.push(chunk.time - last);
@@ -176,9 +169,7 @@ class PrebufferSession {
  }

  clearPrebuffers() {
-    for (const prebuffer of PrebufferParserValues) {
-      this.prebuffers[prebuffer] = [];
-    }
+    this.rtspPrebuffer = [];
  }

  release() {
@@ -251,7 +242,7 @@ class PrebufferSession {

    let total = 0;
    let start = 0;
-    for (const prebuffer of this.prebuffers.rtsp) {
+    for (const prebuffer of this.rtspPrebuffer) {
      start = start || prebuffer.time;
      for (const chunk of prebuffer.chunks) {
        total += chunk.byteLength;
@@ -685,11 +676,10 @@ class PrebufferSession {
      session.killed.finally(() => clearTimeout(refreshTimeout));
    }

-    for (const container of PrebufferParserValues) {
      let shifts = 0;
-      let prebufferContainer: PrebufferStreamChunk[] = this.prebuffers[container];
+      let prebufferContainer: PrebufferStreamChunk[] = this.rtspPrebuffer;

-      session.on(container, (chunk: PrebufferStreamChunk) => {
+      session.on('rtsp', (chunk: PrebufferStreamChunk) => {
        const now = Date.now();

        chunk.time = now;
@@ -702,11 +692,10 @@ class PrebufferSession {

        if (shifts > 100000) {
          prebufferContainer = prebufferContainer.slice();
-          this.prebuffers[container] = prebufferContainer;
+          this.rtspPrebuffer = prebufferContainer;
          shifts = 0;
        }
      });
-    }

    session.start();
    return session;
@@ -783,19 +772,24 @@ class PrebufferSession {
  async handleRebroadcasterClient(options: {
    findSyncFrame: boolean,
    isActiveClient: boolean,
-    container: PrebufferParsers,
    session: ParserSession<PrebufferParsers>,
    socketPromise: Promise<Duplex>,
    requestedPrebuffer: number,
    filter?: (chunk: StreamChunk, prebuffer: boolean) => StreamChunk,
  }) {
-    const { isActiveClient, container, session, socketPromise, requestedPrebuffer } = options;
+    const { isActiveClient, session, socketPromise, requestedPrebuffer } = options;
    this.console.log('sending prebuffer', requestedPrebuffer);

    let socket: Duplex;

    try {
      socket = await socketPromise;
+
+      if (!session.isActive) {
+        // session may be killed while waiting for socket.
+        socket.destroy();
+        throw new Error('session terminated before socket connected');
+      }
    }
    catch (e) {
      // in case the client never connects, do an inactivity check.
@@ -820,70 +814,81 @@ class PrebufferSession {
      this.inactivityCheck(session, isActiveClient);
    });

-    handleRebroadcasterClient(socket, {
-      // console: this.console,
-      connect: (connection) => {
-        const now = Date.now();
-
-        const safeWriteData = (chunk: StreamChunk, prebuffer?: boolean) => {
-          if (options.filter) {
-            chunk = options.filter(chunk, prebuffer);
-            if (!chunk)
-              return;
-          }
-          const buffered = connection.writeData(chunk);
-          if (buffered > 100000000) {
-            this.console.log('more than 100MB has been buffered, did downstream die? killing connection.', this.streamName);
-            cleanup();
-          }
-        }
-
-        const cleanup = () => {
-          session.removeListener(container, safeWriteData);
-          session.removeListener('killed', cleanup);
-          connection.destroy();
-        }
-
-        session.on(container, safeWriteData);
-        session.once('killed', cleanup);
-
-        const prebufferContainer: PrebufferStreamChunk[] = this.prebuffers[container];
-        // if the requested container or the source container is not rtsp, use an exact seek.
-        // this works better when the requested container is mp4, and rtsp is the source.
-        // if starting on a sync frame, ffmpeg will skip the first segment while initializing
-        // on live sources like rtsp. the buffer before the sync frame stream will be enough
-        // for ffmpeg to analyze and start up in time for the sync frame.
-        // may be worth considering playing with a few other things to avoid this:
-        // mpeg-ts as a container (would need to write a muxer)
-        // specifying the buffer before the sync frame with probesize.
-        // If h264 oddities are detected, assume ffmpeg will be used.
-        if (container !== 'rtsp' || !options.findSyncFrame || this.getLastH264Oddities()) {
-          for (const chunk of prebufferContainer) {
-            if (chunk.time < now - requestedPrebuffer)
-              continue;
-
-            safeWriteData(chunk, true);
-          }
-        }
-        else {
-          const parser = this.parsers[container];
-          const filtered = prebufferContainer.filter(pb => pb.time >= now - requestedPrebuffer);
-          let availablePrebuffers = parser.findSyncFrame(filtered);
-          if (!availablePrebuffers) {
-            this.console.warn('Unable to find sync frame in rtsp prebuffer.');
-            availablePrebuffers = [];
-          }
-          else {
-            this.console.log('Found sync frame in rtsp prebuffer.');
-          }
-          for (const prebuffer of availablePrebuffers) {
-            safeWriteData(prebuffer, true);
-          }
-        }
-
-        return cleanup;
+    let writeData = (data: StreamChunk): number => {
+      if (data.startStream) {
+        socket.write(data.startStream)
      }
-    })
+
+      const writeDataWithoutStartStream = (data: StreamChunk) => {
+        for (const chunk of data.chunks) {
+          socket.write(chunk);
+        }
+
+        return socket.writableLength;
+      };
+
+      writeData = writeDataWithoutStartStream;
+      return writeDataWithoutStartStream(data);
+    }
+
+    const safeWriteData = (chunk: StreamChunk, prebuffer?: boolean) => {
+      if (options.filter) {
+        chunk = options.filter(chunk, prebuffer);
+        if (!chunk)
+          return;
+      }
+      const buffered = writeData(chunk);
+      if (buffered > 100000000) {
+        this.console.log('more than 100MB has been buffered, did downstream die? killing connection.', this.streamName);
+        cleanup();
+      }
+    }
+
+    const cleanup = () => {
+      socket.destroy();
+      session.removeListener('rtsp', safeWriteData);
+      session.removeListener('killed', cleanup);
+    };
+
+    session.on('rtsp', safeWriteData);
+    session.once('killed', cleanup);
+
+    socket.once('close', () => {
+      cleanup();
+    });
+
+    // socket.on('error', e => this.console.log('client stream ended'));
+
+
+    const now = Date.now();
+    const prebufferContainer: PrebufferStreamChunk[] = this.rtspPrebuffer;
+    // if starting on a sync frame, ffmpeg will skip the first segment while initializing
+    // on live sources like rtsp. the buffer before the sync frame stream will be enough
+    // for ffmpeg to analyze and start up in time for the sync frame.
+    // If h264 oddities are detected, assume ffmpeg will be used.
+    if (!options.findSyncFrame || this.getLastH264Oddities()) {
+      for (const chunk of prebufferContainer) {
+        if (chunk.time < now - requestedPrebuffer)
+          continue;
+
+        safeWriteData(chunk, true);
+      }
+    }
+    else {
+      const parser = this.parsers['rtsp'];
+      const filtered = prebufferContainer.filter(pb => pb.time >= now - requestedPrebuffer);
+      let availablePrebuffers = parser.findSyncFrame(filtered);
+      if (!availablePrebuffers) {
+        this.console.warn('Unable to find sync frame in rtsp prebuffer.');
+        availablePrebuffers = [];
+      }
+      else {
+        this.console.log('Found sync frame in rtsp prebuffer.');
+      }
+      for (const prebuffer of availablePrebuffers) {
+        safeWriteData(prebuffer, true);
+      }
+    }
  }

  async getVideoStream(findSyncFrame: boolean, options?: RequestMediaStreamOptions) {
@@ -1010,8 +1015,6 @@ class PrebufferSession {
      urls = await getUrlLocalAdresses(this.console, url);
    }

-    const container = 'rtsp';
-
    mediaStreamOptions.sdp = sdp;

    const isActiveClient = options?.refresh !== false;
@@ -1019,7 +1022,6 @@ class PrebufferSession {
    this.handleRebroadcasterClient({
      findSyncFrame,
      isActiveClient,
-      container,
      requestedPrebuffer,
      socketPromise,
      session,
@@ -1045,7 +1047,7 @@ class PrebufferSession {

    const now = Date.now();
    let available = 0;
-    const prebufferContainer: PrebufferStreamChunk[] = this.prebuffers[container];
+    const prebufferContainer: PrebufferStreamChunk[] = this.rtspPrebuffer;
    for (const prebuffer of prebufferContainer) {
      if (prebuffer.time < now - requestedPrebuffer)
        continue;
@@ -1066,11 +1068,11 @@ class PrebufferSession {
    const ffmpegInput: FFmpegInput = {
      url,
      urls,
-      container,
+      container: 'rtsp',
      inputArguments: [
        ...inputArguments,
-        ...(this.parsers[container].inputArguments || []),
-        '-f', this.parsers[container].container,
+        ...(this.parsers['rtsp'].inputArguments || []),
+        '-f', this.parsers['rtsp'].container,
        '-i', url,
      ],
      mediaStreamOptions,
@@ -1165,7 +1167,6 @@ class PrebufferMixin extends SettingsMixinDeviceBase<VideoCamera> implements Vid
        prebufferSession.handleRebroadcasterClient({
          findSyncFrame: true,
          isActiveClient: true,
-          container: 'rtsp',
          session,
          socketPromise: Promise.resolve(client),
          requestedPrebuffer,
--- a/plugins/prebuffer-mixin/src/rfc4571.ts
+++ b/plugins/prebuffer-mixin/src/rfc4571.ts
@@ -1,14 +1,13 @@
 import { cloneDeep } from "@scrypted/common/src/clone-deep";
-import { ParserOptions, ParserSession, setupActivityTimer } from "@scrypted/common/src/ffmpeg-rebroadcast";
 import { read16BELengthLoop } from "@scrypted/common/src/read-stream";
-import { findH264NaluType, H264_NAL_TYPE_SPS, RTSP_FRAME_MAGIC } from "@scrypted/common/src/rtsp-server";
+import { H264_NAL_TYPE_SPS, RTSP_FRAME_MAGIC, findH264NaluType } from "@scrypted/common/src/rtsp-server";
 import { parseSdp } from "@scrypted/common/src/sdp-utils";
-import { sleep } from "@scrypted/common/src/sleep";
 import { StreamChunk } from "@scrypted/common/src/stream-parser";
 import { MediaStreamOptions, ResponseMediaStreamOptions } from "@scrypted/sdk";
 import { parse as spsParse } from "h264-sps-parser";
 import net from 'net';
 import { EventEmitter, Readable } from "stream";
+import { ParserSession, setupActivityTimer } from "./ffmpeg-rebroadcast";
 import { getSpsResolution } from "./sps-resolution";

 export function negotiateMediaStream(sdp: string, mediaStreamOptions: MediaStreamOptions, inputVideoCodec: string, inputAudioCodec: string, requestMediaStream: MediaStreamOptions) {
--- a/plugins/prebuffer-mixin/src/rtsp-session.ts
+++ b/plugins/prebuffer-mixin/src/rtsp-session.ts
@@ -1,12 +1,12 @@
-import { ParserSession, setupActivityTimer } from "@scrypted/common/src/ffmpeg-rebroadcast";
-import { closeQuiet, createBindZero } from "@scrypted/common/src/listen-cluster";
-import { findH264NaluType, H264_NAL_TYPE_SPS, parseSemicolonDelimited, RtspClient, RtspClientUdpSetupOptions, RTSP_FRAME_MAGIC } from "@scrypted/common/src/rtsp-server";
+import { closeQuiet } from "@scrypted/common/src/listen-cluster";
+import { H264_NAL_TYPE_SPS, RTSP_FRAME_MAGIC, RtspClient, RtspClientUdpSetupOptions, findH264NaluType, parseSemicolonDelimited } from "@scrypted/common/src/rtsp-server";
 import { parseSdp } from "@scrypted/common/src/sdp-utils";
 import { StreamChunk } from "@scrypted/common/src/stream-parser";
 import { ResponseMediaStreamOptions } from "@scrypted/sdk";
 import dgram from 'dgram';
 import { parse as spsParse } from "h264-sps-parser";
 import { EventEmitter } from "stream";
+import { ParserSession, setupActivityTimer } from "./ffmpeg-rebroadcast";
 import { negotiateMediaStream } from "./rfc4571";
 import { getSpsResolution } from "./sps-resolution";

--- a/plugins/ring/package-lock.json
+++ b/plugins/ring/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/ring",
-   "version": "0.0.137",
+   "version": "0.0.138",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/ring",
-         "version": "0.0.137",
+         "version": "0.0.138",
         "dependencies": {
            "@koush/ring-client-api": "file:../../external/ring-client-api",
            "@scrypted/common": "file:../../common",
--- a/plugins/ring/package.json
+++ b/plugins/ring/package.json
@@ -44,5 +44,5 @@
      "got": "11.8.6",
      "socket.io-client": "^2.5.0"
   },
-   "version": "0.0.137"
+   "version": "0.0.138"
 }
--- a/plugins/rknn/package-lock.json
+++ b/plugins/rknn/package-lock.json
@@ -1,19 +1,19 @@
 {
   "name": "@scrypted/rknn",
-   "version": "0.0.4",
+   "version": "0.1.1",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/rknn",
-         "version": "0.0.4",
+         "version": "0.1.1",
         "devDependencies": {
            "@scrypted/sdk": "file:../../sdk"
         }
      },
      "../../sdk": {
         "name": "@scrypted/sdk",
-         "version": "0.3.29",
+         "version": "0.3.31",
         "dev": true,
         "license": "ISC",
         "dependencies": {
--- a/plugins/rknn/package.json
+++ b/plugins/rknn/package.json
@@ -39,11 +39,12 @@
      "type": "API",
      "interfaces": [
         "ObjectDetection",
-         "ObjectDetectionPreview"
+         "ObjectDetectionPreview",
+         "DeviceProvider"
      ]
   },
   "devDependencies": {
      "@scrypted/sdk": "file:../../sdk"
   },
-   "version": "0.0.4"
+   "version": "0.1.1"
 }
--- a/plugins/rknn/src/det_utils/init.py
+++ b/plugins/rknn/src/det_utils/init.py
--- a/plugins/rknn/src/det_utils/db_postprocess.py
+++ b/plugins/rknn/src/det_utils/db_postprocess.py
@@ -0,0 +1,269 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refered from:
+https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import cv2
+# import paddle
+from shapely.geometry import Polygon
+import pyclipper
+
+
+class DBPostProcess(object):
+    """
+    The post process for Differentiable Binarization (DB).
+    """
+
+    def __init__(self,
+                 thresh=0.3,
+                 box_thresh=0.7,
+                 max_candidates=1000,
+                 unclip_ratio=2.0,
+                 use_dilation=False,
+                 score_mode="fast",
+                 **kwargs):
+        self.thresh = thresh
+        self.box_thresh = box_thresh
+        self.max_candidates = max_candidates
+        self.unclip_ratio = unclip_ratio
+        self.min_size = 3
+        self.score_mode = score_mode
+        assert score_mode in [
+            "slow", "fast"
+        ], "Score mode must be in [slow, fast] but got: {}".format(score_mode)
+
+        self.dilation_kernel = None if not use_dilation else np.array(
+            [[1, 1], [1, 1]])
+
+    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+        '''
+        _bitmap: single map with shape (1, H, W),
+                whose values are binarized as {0, 1}
+        '''
+
+        bitmap = _bitmap
+        height, width = bitmap.shape
+
+        outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
+                                cv2.CHAIN_APPROX_SIMPLE)
+        if len(outs) == 3:
+            img, contours, _ = outs[0], outs[1], outs[2]
+        elif len(outs) == 2:
+            contours, _ = outs[0], outs[1]
+
+        num_contours = min(len(contours), self.max_candidates)
+
+        boxes = []
+        scores = []
+        for index in range(num_contours):
+            contour = contours[index]
+            points, sside = self.get_mini_boxes(contour)
+            if sside < self.min_size:
+                continue
+            points = np.array(points)
+            if self.score_mode == "fast":
+                score = self.box_score_fast(pred, points.reshape(-1, 2))
+            else:
+                score = self.box_score_slow(pred, contour)
+            if self.box_thresh > score:
+                continue
+
+            box = self.unclip(points).reshape(-1, 1, 2)
+            box, sside = self.get_mini_boxes(box)
+            if sside < self.min_size + 2:
+                continue
+            box = np.array(box)
+
+            box[:, 0] = np.clip(
+                np.round(box[:, 0] / width * dest_width), 0, dest_width)
+            box[:, 1] = np.clip(
+                np.round(box[:, 1] / height * dest_height), 0, dest_height)
+            boxes.append(box.astype(np.int16))
+            scores.append(score)
+        return np.array(boxes, dtype=np.int16), scores
+
+    def unclip(self, box):
+        unclip_ratio = self.unclip_ratio
+        poly = Polygon(box)
+        distance = poly.area * unclip_ratio / poly.length
+        offset = pyclipper.PyclipperOffset()
+        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+        expanded = np.array(offset.Execute(distance))
+        return expanded
+
+    def get_mini_boxes(self, contour):
+        bounding_box = cv2.minAreaRect(contour)
+        points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+        index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+        if points[1][1] > points[0][1]:
+            index_1 = 0
+            index_4 = 1
+        else:
+            index_1 = 1
+            index_4 = 0
+        if points[3][1] > points[2][1]:
+            index_2 = 2
+            index_3 = 3
+        else:
+            index_2 = 3
+            index_3 = 2
+
+        box = [
+            points[index_1], points[index_2], points[index_3], points[index_4]
+        ]
+        return box, min(bounding_box[1])
+
+    def box_score_fast(self, bitmap, _box):
+        '''
+        box_score_fast: use bbox mean score as the mean score
+        '''
+        h, w = bitmap.shape[:2]
+        box = _box.copy()
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+        box[:, 0] = box[:, 0] - xmin
+        box[:, 1] = box[:, 1] - ymin
+        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
+    def box_score_slow(self, bitmap, contour):
+        '''
+        box_score_slow: use polyon mean score as the mean score
+        '''
+        h, w = bitmap.shape[:2]
+        contour = contour.copy()
+        contour = np.reshape(contour, (-1, 2))
+
+        xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
+        xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
+        ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
+        ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+
+        contour[:, 0] = contour[:, 0] - xmin
+        contour[:, 1] = contour[:, 1] - ymin
+
+        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
+    def __call__(self, outs_dict, shape_list):
+        pred = outs_dict['maps']
+        # if isinstance(pred, paddle.Tensor):
+        #     pred = pred.numpy()
+        pred = pred[:, 0, :, :]
+        segmentation = pred > self.thresh
+
+        boxes_batch = []
+        for batch_index in range(pred.shape[0]):
+            src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
+            if self.dilation_kernel is not None:
+                mask = cv2.dilate(
+                    np.array(segmentation[batch_index]).astype(np.uint8),
+                    self.dilation_kernel)
+            else:
+                mask = segmentation[batch_index]
+            boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
+                                                   src_w, src_h)
+
+            boxes_batch.append({'points': boxes})
+        return boxes_batch
+
+
+class DistillationDBPostProcess(object):
+    def __init__(self,
+                 model_name=["student"],
+                 key=None,
+                 thresh=0.3,
+                 box_thresh=0.6,
+                 max_candidates=1000,
+                 unclip_ratio=1.5,
+                 use_dilation=False,
+                 score_mode="fast",
+                 **kwargs):
+        self.model_name = model_name
+        self.key = key
+        self.post_process = DBPostProcess(
+            thresh=thresh,
+            box_thresh=box_thresh,
+            max_candidates=max_candidates,
+            unclip_ratio=unclip_ratio,
+            use_dilation=use_dilation,
+            score_mode=score_mode)
+
+    def __call__(self, predicts, shape_list):
+        results = {}
+        for k in self.model_name:
+            results[k] = self.post_process(predicts[k], shape_list=shape_list)
+        return results
+
+
+class DetPostProcess(object):
+    def __init__(self) -> None:
+        pass
+
+    def order_points_clockwise(self, pts):
+        """
+        reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+        # sort the points based on their x-coordinates
+        """
+        xSorted = pts[np.argsort(pts[:, 0]), :]
+
+        # grab the left-most and right-most points from the sorted
+        # x-roodinate points
+        leftMost = xSorted[:2, :]
+        rightMost = xSorted[2:, :]
+
+        # now, sort the left-most coordinates according to their
+        # y-coordinates so we can grab the top-left and bottom-left
+        # points, respectively
+        leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+        (tl, bl) = leftMost
+
+        rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+        (tr, br) = rightMost
+
+        rect = np.array([tl, tr, br, bl], dtype="float32")
+        return rect
+
+    def clip_det_res(self, points, img_height, img_width):
+        for pno in range(points.shape[0]):
+            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+        return points
+
+    def filter_tag_det_res(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.order_points_clockwise(box)
+            box = self.clip_det_res(box, img_height, img_width)
+            rect_width = int(np.linalg.norm(box[0] - box[1]))
+            rect_height = int(np.linalg.norm(box[0] - box[3]))
+            if rect_width <= 3 or rect_height <= 3:
+                continue
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
--- a/plugins/rknn/src/det_utils/operators.py
+++ b/plugins/rknn/src/det_utils/operators.py
@@ -0,0 +1,373 @@
+"""
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import sys
+import six
+import cv2
+import numpy as np
+
+
+class DecodeImage(object):
+    """ decode image """
+
+    def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+        self.img_mode = img_mode
+        self.channel_first = channel_first
+
+    def __call__(self, data):
+        img = data['image']
+        if six.PY2:
+            assert type(img) is str and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        else:
+            assert type(img) is bytes and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        img = np.frombuffer(img, dtype='uint8')
+        img = cv2.imdecode(img, 1)
+        if img is None:
+            return None
+        if self.img_mode == 'GRAY':
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        elif self.img_mode == 'RGB':
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
+            img = img[:, :, ::-1]
+
+        if self.channel_first:
+            img = img.transpose((2, 0, 1))
+
+        data['image'] = img
+        return data
+
+
+class NRTRDecodeImage(object):
+    """ decode image """
+
+    def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+        self.img_mode = img_mode
+        self.channel_first = channel_first
+
+    def __call__(self, data):
+        img = data['image']
+        if six.PY2:
+            assert type(img) is str and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        else:
+            assert type(img) is bytes and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        img = np.frombuffer(img, dtype='uint8')
+
+        img = cv2.imdecode(img, 1)
+
+        if img is None:
+            return None
+        if self.img_mode == 'GRAY':
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        elif self.img_mode == 'RGB':
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
+            img = img[:, :, ::-1]
+        img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        if self.channel_first:
+            img = img.transpose((2, 0, 1))
+        data['image'] = img
+        return data
+
+class NormalizeImage(object):
+    """ normalize image such as substract mean, divide std
+    """
+
+    def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
+        if isinstance(scale, str):
+            scale = eval(scale)
+        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+        mean = mean if mean is not None else [0.485, 0.456, 0.406]
+        std = std if std is not None else [0.229, 0.224, 0.225]
+
+        shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+        self.mean = np.array(mean).reshape(shape).astype('float32')
+        self.std = np.array(std).reshape(shape).astype('float32')
+
+    def __call__(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+
+        assert isinstance(img,
+                          np.ndarray), "invalid input 'img' in NormalizeImage"
+        data['image'] = (
+                                img.astype('float32') * self.scale - self.mean) / self.std
+        return data
+
+
+class ToCHWImage(object):
+    """ convert hwc image to chw image
+    """
+
+    def __init__(self, **kwargs):
+        pass
+
+    def __call__(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+        data['image'] = img.transpose((2, 0, 1))
+        return data
+
+
+class KeepKeys(object):
+    def __init__(self, keep_keys, **kwargs):
+        self.keep_keys = keep_keys
+
+    def __call__(self, data):
+        data_list = []
+        for key in self.keep_keys:
+            data_list.append(data[key])
+        return data_list
+
+
+class DetResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(DetResizeForTest, self).__init__()
+        self.square_input = True
+        self.resize_type = 0
+        if 'image_shape' in kwargs:
+            self.image_shape = kwargs['image_shape']
+            self.resize_type = 1
+        elif 'limit_side_len' in kwargs:
+            self.limit_side_len = kwargs['limit_side_len']
+            self.limit_type = kwargs.get('limit_type', 'min')
+        elif 'resize_long' in kwargs:
+            self.resize_type = 2
+            self.resize_long = kwargs.get('resize_long', 960)
+        else:
+            self.limit_side_len = 736
+            self.limit_type = 'min'
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+
+        if self.resize_type == 0:
+            # img, shape = self.resize_image_type0(img)
+            img, [ratio_h, ratio_w] = self.resize_image_type0(img)
+        elif self.resize_type == 2:
+            img, [ratio_h, ratio_w] = self.resize_image_type2(img)
+        else:
+            # img, shape = self.resize_image_type1(img)
+            img, [ratio_h, ratio_w] = self.resize_image_type1(img)
+
+
+
+        data['image'] = img
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        if len(data['shape'].shape) == 1:
+            data['shape'] = np.expand_dims(data['shape'], axis=0)
+        return data
+
+    def resize_image_type1(self, img):
+        resize_h, resize_w = self.image_shape
+        ori_h, ori_w = img.shape[:2]  # (h, w, c)
+        ratio_h = float(resize_h) / ori_h
+        ratio_w = float(resize_w) / ori_w
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        # return img, np.array([ori_h, ori_w])
+        return img, [ratio_h, ratio_w]
+
+    def resize_image_type0(self, img):
+        """
+        resize image to a size multiple of 32 which is required by the network
+        args:
+            img(array): array with shape [h, w, c]
+        return(tuple):
+            img, (ratio_h, ratio_w)
+        """
+        limit_side_len = self.limit_side_len
+        h, w, c = img.shape
+
+        # limit the max side
+        if self.limit_type == 'max':
+            if max(h, w) > limit_side_len:
+                if h > w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        elif self.limit_type == 'min':
+            if min(h, w) < limit_side_len:
+                if h < w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        elif self.limit_type == 'resize_long':
+            ratio = float(limit_side_len) / max(h,w)
+        else:
+            raise Exception('not support limit type, image ')
+        resize_h = int(h * ratio)
+        resize_w = int(w * ratio)
+
+        resize_h = max(int(round(resize_h / 32) * 32), 32)
+        resize_w = max(int(round(resize_w / 32) * 32), 32)
+
+        try:
+            if int(resize_w) <= 0 or int(resize_h) <= 0:
+                return None, (None, None)
+            img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        except:
+            print(img.shape, resize_w, resize_h)
+            sys.exit(0)
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return img, [ratio_h, ratio_w]
+
+    def resize_image_type2(self, img):
+        h, w, _ = img.shape
+
+        resize_w = w
+        resize_h = h
+
+        if resize_h > resize_w:
+            ratio = float(self.resize_long) / resize_h
+        else:
+            ratio = float(self.resize_long) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return img, [ratio_h, ratio_w]
+
+
+class E2EResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(E2EResizeForTest, self).__init__()
+        self.max_side_len = kwargs['max_side_len']
+        self.valid_set = kwargs['valid_set']
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+        if self.valid_set == 'totaltext':
+            im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
+                img, max_side_len=self.max_side_len)
+        else:
+            im_resized, (ratio_h, ratio_w) = self.resize_image(
+                img, max_side_len=self.max_side_len)
+        data['image'] = im_resized
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        return data
+
+    def resize_image_for_totaltext(self, im, max_side_len=512):
+
+        h, w, _ = im.shape
+        resize_w = w
+        resize_h = h
+        ratio = 1.25
+        if h * ratio > max_side_len:
+            ratio = float(max_side_len) / resize_h
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return im, (ratio_h, ratio_w)
+
+    def resize_image(self, im, max_side_len=512):
+        """
+        resize image to a size multiple of max_stride which is required by the network
+        :param im: the resized image
+        :param max_side_len: limit of max image size to avoid out of memory in gpu
+        :return: the resized image and the resize ratio
+        """
+        h, w, _ = im.shape
+
+        resize_w = w
+        resize_h = h
+
+        # Fix the longer side
+        if resize_h > resize_w:
+            ratio = float(max_side_len) / resize_h
+        else:
+            ratio = float(max_side_len) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return im, (ratio_h, ratio_w)
+
+
+
+class Pad_to_max_len(object):
+    def __init__(self, **kwargs):
+        super(Pad_to_max_len, self).__init__()
+        self.max_h = kwargs['max_h']
+        self.max_w = kwargs['max_w']
+
+    def __call__(self, data):
+        img = data['image']
+        if img.shape[-1] == 3:
+            # hwc
+            if img.shape[0]!= self.max_h:
+                # TODO support
+                # assert False, "not support"
+                pad_h = self.max_h - img.shape[0]
+                pad_w = self.max_w - img.shape[1]
+                img = np.pad(img, ((0, pad_h), (0, pad_w), (0, 0)), 'constant', constant_values=0)
+            if img.shape[1] < self.max_w:
+                pad_w = self.max_w - img.shape[1]
+                img = np.pad(img, ((0, 0), (0, pad_w), (0, 0)), 'constant', constant_values=0)
+
+        elif img.shape[0] == 3:
+            # chw
+            img = img.transpose((1, 2, 0))
+            if img.shape[1]!= self.max_h:
+                # TODO support
+                assert False, "not support"
+            if img.shape[0] < self.max_w:
+                pad_w = self.max_w - img.shape[0]
+                img = np.pad(img, ((0, 0), (0, 0), (0, pad_w)), 'constant', constant_values=0)
+
+        else:
+            assert False, "not support"
+
+        data['image'] = img
+
+        return data
--- a/plugins/rknn/src/rec_utils/init.py
+++ b/plugins/rknn/src/rec_utils/init.py
--- a/plugins/rknn/src/rec_utils/operators.py
+++ b/plugins/rknn/src/rec_utils/operators.py
@@ -0,0 +1,376 @@
+"""
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import sys
+import six
+import cv2
+import numpy as np
+
+
+class DecodeImage(object):
+    """ decode image """
+
+    def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+        self.img_mode = img_mode
+        self.channel_first = channel_first
+
+    def __call__(self, data):
+        img = data['image']
+        if six.PY2:
+            assert type(img) is str and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        else:
+            assert type(img) is bytes and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        img = np.frombuffer(img, dtype='uint8')
+        img = cv2.imdecode(img, 1)
+        if img is None:
+            return None
+        if self.img_mode == 'GRAY':
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        elif self.img_mode == 'RGB':
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
+            img = img[:, :, ::-1]
+
+        if self.channel_first:
+            img = img.transpose((2, 0, 1))
+
+        data['image'] = img
+        return data
+
+
+class NRTRDecodeImage(object):
+    """ decode image """
+
+    def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+        self.img_mode = img_mode
+        self.channel_first = channel_first
+
+    def __call__(self, data):
+        img = data['image']
+        if six.PY2:
+            assert type(img) is str and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        else:
+            assert type(img) is bytes and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        img = np.frombuffer(img, dtype='uint8')
+
+        img = cv2.imdecode(img, 1)
+
+        if img is None:
+            return None
+        if self.img_mode == 'GRAY':
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        elif self.img_mode == 'RGB':
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
+            img = img[:, :, ::-1]
+        img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        if self.channel_first:
+            img = img.transpose((2, 0, 1))
+        data['image'] = img
+        return data
+
+class NormalizeImage(object):
+    """ normalize image such as substract mean, divide std
+    """
+
+    def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
+        if isinstance(scale, str):
+            scale = eval(scale)
+        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+        mean = mean if mean is not None else [0.485, 0.456, 0.406]
+        std = std if std is not None else [0.229, 0.224, 0.225]
+
+        shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+        self.mean = np.array(mean).reshape(shape).astype('float32')
+        self.std = np.array(std).reshape(shape).astype('float32')
+
+    def __call__(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+
+        assert isinstance(img, np.ndarray), "invalid input 'img' in NormalizeImage"
+
+        i = img.astype('float32')
+        i = i * self.scale
+        i = i - self.mean
+        i = i / self.std
+        data['image'] = i
+        return data
+
+
+class ToCHWImage(object):
+    """ convert hwc image to chw image
+    """
+
+    def __init__(self, **kwargs):
+        pass
+
+    def __call__(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+        data['image'] = img.transpose((2, 0, 1))
+        return data
+
+
+class KeepKeys(object):
+    def __init__(self, keep_keys, **kwargs):
+        self.keep_keys = keep_keys
+
+    def __call__(self, data):
+        data_list = []
+        for key in self.keep_keys:
+            data_list.append(data[key])
+        return data_list
+
+
+class DetResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(DetResizeForTest, self).__init__()
+        self.square_input = True
+        self.resize_type = 0
+        if 'image_shape' in kwargs:
+            self.image_shape = kwargs['image_shape']
+            self.resize_type = 1
+        elif 'limit_side_len' in kwargs:
+            self.limit_side_len = kwargs['limit_side_len']
+            self.limit_type = kwargs.get('limit_type', 'min')
+        elif 'resize_long' in kwargs:
+            self.resize_type = 2
+            self.resize_long = kwargs.get('resize_long', 960)
+        else:
+            self.limit_side_len = 736
+            self.limit_type = 'min'
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+
+        if self.resize_type == 0:
+            # img, shape = self.resize_image_type0(img)
+            img, [ratio_h, ratio_w] = self.resize_image_type0(img)
+        elif self.resize_type == 2:
+            img, [ratio_h, ratio_w] = self.resize_image_type2(img)
+        else:
+            # img, shape = self.resize_image_type1(img)
+            img, [ratio_h, ratio_w] = self.resize_image_type1(img)
+
+
+
+        data['image'] = img
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        if len(data['shape'].shape) == 1:
+            data['shape'] = np.expand_dims(data['shape'], axis=0)
+        return data
+
+    def resize_image_type1(self, img):
+        resize_h, resize_w = self.image_shape
+        ori_h, ori_w = img.shape[:2]  # (h, w, c)
+        ratio_h = float(resize_h) / ori_h
+        ratio_w = float(resize_w) / ori_w
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        # return img, np.array([ori_h, ori_w])
+        return img, [ratio_h, ratio_w]
+
+    def resize_image_type0(self, img):
+        """
+        resize image to a size multiple of 32 which is required by the network
+        args:
+            img(array): array with shape [h, w, c]
+        return(tuple):
+            img, (ratio_h, ratio_w)
+        """
+        limit_side_len = self.limit_side_len
+        h, w, c = img.shape
+
+        # limit the max side
+        if self.limit_type == 'max':
+            if max(h, w) > limit_side_len:
+                if h > w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        elif self.limit_type == 'min':
+            if min(h, w) < limit_side_len:
+                if h < w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        elif self.limit_type == 'resize_long':
+            ratio = float(limit_side_len) / max(h,w)
+        else:
+            raise Exception('not support limit type, image ')
+        resize_h = int(h * ratio)
+        resize_w = int(w * ratio)
+
+        resize_h = max(int(round(resize_h / 32) * 32), 32)
+        resize_w = max(int(round(resize_w / 32) * 32), 32)
+
+        try:
+            if int(resize_w) <= 0 or int(resize_h) <= 0:
+                return None, (None, None)
+            img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        except:
+            print(img.shape, resize_w, resize_h)
+            sys.exit(0)
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return img, [ratio_h, ratio_w]
+
+    def resize_image_type2(self, img):
+        h, w, _ = img.shape
+
+        resize_w = w
+        resize_h = h
+
+        if resize_h > resize_w:
+            ratio = float(self.resize_long) / resize_h
+        else:
+            ratio = float(self.resize_long) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return img, [ratio_h, ratio_w]
+
+
+class E2EResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(E2EResizeForTest, self).__init__()
+        self.max_side_len = kwargs['max_side_len']
+        self.valid_set = kwargs['valid_set']
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+        if self.valid_set == 'totaltext':
+            im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
+                img, max_side_len=self.max_side_len)
+        else:
+            im_resized, (ratio_h, ratio_w) = self.resize_image(
+                img, max_side_len=self.max_side_len)
+        data['image'] = im_resized
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        return data
+
+    def resize_image_for_totaltext(self, im, max_side_len=512):
+
+        h, w, _ = im.shape
+        resize_w = w
+        resize_h = h
+        ratio = 1.25
+        if h * ratio > max_side_len:
+            ratio = float(max_side_len) / resize_h
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return im, (ratio_h, ratio_w)
+
+    def resize_image(self, im, max_side_len=512):
+        """
+        resize image to a size multiple of max_stride which is required by the network
+        :param im: the resized image
+        :param max_side_len: limit of max image size to avoid out of memory in gpu
+        :return: the resized image and the resize ratio
+        """
+        h, w, _ = im.shape
+
+        resize_w = w
+        resize_h = h
+
+        # Fix the longer side
+        if resize_h > resize_w:
+            ratio = float(max_side_len) / resize_h
+        else:
+            ratio = float(max_side_len) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return im, (ratio_h, ratio_w)
+
+
+
+class Pad_to_max_len(object):
+    def __init__(self, **kwargs):
+        super(Pad_to_max_len, self).__init__()
+        self.max_h = kwargs['max_h']
+        self.max_w = kwargs['max_w']
+
+    def __call__(self, data):
+        img = data['image']
+        if img.shape[-1] == 3:
+            # hwc
+            if img.shape[0]!= self.max_h:
+                # TODO support
+                # assert False, "not support"
+                pad_h = self.max_h - img.shape[0]
+                pad_w = self.max_w - img.shape[1]
+                img = np.pad(img, ((0, pad_h), (0, pad_w), (0, 0)), 'constant', constant_values=0)
+            if img.shape[1] < self.max_w:
+                pad_w = self.max_w - img.shape[1]
+                img = np.pad(img, ((0, 0), (0, pad_w), (0, 0)), 'constant', constant_values=0)
+
+        elif img.shape[0] == 3:
+            # chw
+            img = img.transpose((1, 2, 0))
+            if img.shape[1]!= self.max_h:
+                # TODO support
+                assert False, "not support"
+            if img.shape[0] < self.max_w:
+                pad_w = self.max_w - img.shape[0]
+                img = np.pad(img, ((0, 0), (0, 0), (0, pad_w)), 'constant', constant_values=0)
+
+        else:
+            assert False, "not support"
+
+        data['image'] = img
+
+        return data
--- a/plugins/rknn/src/rec_utils/rec_postprocess.py
+++ b/plugins/rknn/src/rec_utils/rec_postprocess.py
@@ -0,0 +1,814 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+# import paddle
+# from paddle.nn import functional as F
+import re
+
+
+class BaseRecLabelDecode(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False):
+        self.beg_str = "sos"
+        self.end_str = "eos"
+
+        self.character_str = []
+        if character_dict_path is None:
+            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+            dict_character = list(self.character_str)
+        else:
+            with open(character_dict_path, "rb") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    line = line.decode('utf-8').strip("\n").strip("\r\n")
+                    self.character_str.append(line)
+            if use_space_char:
+                self.character_str.append(" ")
+            dict_character = list(self.character_str)
+
+        dict_character = self.add_special_char(dict_character)
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.character = dict_character
+
+        if 'arabic' in character_dict_path:
+            self.reverse = True
+        else:
+            self.reverse = False
+
+    def pred_reverse(self, pred):
+        pred_re = []
+        c_current = ''
+        for c in pred:
+            if not bool(re.search('[a-zA-Z0-9 :*./%+-]', c)):
+                if c_current != '':
+                    pred_re.append(c_current)
+                pred_re.append(c)
+                c_current = ''
+            else:
+                c_current += c
+        if c_current != '':
+            pred_re.append(c_current)
+
+        return ''.join(pred_re[::-1])
+
+    def add_special_char(self, dict_character):
+        return dict_character
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        ignored_tokens = self.get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            selection = np.ones(len(text_index[batch_idx]), dtype=bool)
+            if is_remove_duplicate:
+                selection[1:] = text_index[batch_idx][1:] != text_index[
+                    batch_idx][:-1]
+            for ignored_token in ignored_tokens:
+                selection &= text_index[batch_idx] != ignored_token
+
+            char_list = [
+                self.character[text_id]
+                for text_id in text_index[batch_idx][selection]
+            ]
+            if text_prob is not None:
+                conf_list = text_prob[batch_idx][selection]
+            else:
+                conf_list = [1] * len(selection)
+            if len(conf_list) == 0:
+                conf_list = [0]
+
+            text = ''.join(char_list)
+
+            if self.reverse:  # for arabic rec
+                text = self.pred_reverse(text)
+
+            result_list.append((text, np.mean(conf_list).tolist()))
+        return result_list
+
+    def get_ignored_tokens(self):
+        return [0]  # for ctc blank
+
+
+class CTCLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(CTCLabelDecode, self).__init__(character_dict_path,
+                                             use_space_char)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        if isinstance(preds, tuple) or isinstance(preds, list):
+            preds = preds[-1]
+        # if isinstance(preds, paddle.Tensor):
+        #     preds = preds.numpy()
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
+        if label is None:
+            return text
+        label = self.decode(label)
+        return text, label
+
+    def add_special_char(self, dict_character):
+        dict_character = ['blank'] + dict_character
+        return dict_character
+
+
+class DistillationCTCLabelDecode(CTCLabelDecode):
+    """
+    Convert 
+    Convert between text-label and text-index
+    """
+
+    def __init__(self,
+                 character_dict_path=None,
+                 use_space_char=False,
+                 model_name=["student"],
+                 key=None,
+                 multi_head=False,
+                 **kwargs):
+        super(DistillationCTCLabelDecode, self).__init__(character_dict_path,
+                                                         use_space_char)
+        if not isinstance(model_name, list):
+            model_name = [model_name]
+        self.model_name = model_name
+
+        self.key = key
+        self.multi_head = multi_head
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        output = dict()
+        for name in self.model_name:
+            pred = preds[name]
+            if self.key is not None:
+                pred = pred[self.key]
+            if self.multi_head and isinstance(pred, dict):
+                pred = pred['ctc']
+            output[name] = super().__call__(pred, label=label, *args, **kwargs)
+        return output
+
+
+class AttnLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(AttnLabelDecode, self).__init__(character_dict_path,
+                                              use_space_char)
+
+    def add_special_char(self, dict_character):
+        self.beg_str = "sos"
+        self.end_str = "eos"
+        dict_character = dict_character
+        dict_character = [self.beg_str] + dict_character + [self.end_str]
+        return dict_character
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        ignored_tokens = self.get_ignored_tokens()
+        [beg_idx, end_idx] = self.get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] in ignored_tokens:
+                    continue
+                if int(text_index[batch_idx][idx]) == int(end_idx):
+                    break
+                if is_remove_duplicate:
+                    # only for predict
+                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
+                            batch_idx][idx]:
+                        continue
+                char_list.append(self.character[int(text_index[batch_idx][
+                    idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            result_list.append((text, np.mean(conf_list).tolist()))
+        return result_list
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        """
+        text = self.decode(text)
+        if label is None:
+            return text
+        else:
+            label = self.decode(label, is_remove_duplicate=False)
+            return text, label
+        """
+        # if isinstance(preds, paddle.Tensor):
+        #     preds = preds.numpy()
+
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+        if label is None:
+            return text
+        label = self.decode(label, is_remove_duplicate=False)
+        return text, label
+
+    def get_ignored_tokens(self):
+        beg_idx = self.get_beg_end_flag_idx("beg")
+        end_idx = self.get_beg_end_flag_idx("end")
+        return [beg_idx, end_idx]
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if beg_or_end == "beg":
+            idx = np.array(self.dict[self.beg_str])
+        elif beg_or_end == "end":
+            idx = np.array(self.dict[self.end_str])
+        else:
+            assert False, "unsupport type %s in get_beg_end_flag_idx" \
+                          % beg_or_end
+        return idx
+
+
+class SEEDLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(SEEDLabelDecode, self).__init__(character_dict_path,
+                                              use_space_char)
+
+    def add_special_char(self, dict_character):
+        self.padding_str = "padding"
+        self.end_str = "eos"
+        self.unknown = "unknown"
+        dict_character = dict_character + [
+            self.end_str, self.padding_str, self.unknown
+        ]
+        return dict_character
+
+    def get_ignored_tokens(self):
+        end_idx = self.get_beg_end_flag_idx("eos")
+        return [end_idx]
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if beg_or_end == "sos":
+            idx = np.array(self.dict[self.beg_str])
+        elif beg_or_end == "eos":
+            idx = np.array(self.dict[self.end_str])
+        else:
+            assert False, "unsupport type %s in get_beg_end_flag_idx" % beg_or_end
+        return idx
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        [end_idx] = self.get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if int(text_index[batch_idx][idx]) == int(end_idx):
+                    break
+                if is_remove_duplicate:
+                    # only for predict
+                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
+                            batch_idx][idx]:
+                        continue
+                char_list.append(self.character[int(text_index[batch_idx][
+                    idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            result_list.append((text, np.mean(conf_list).tolist()))
+        return result_list
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        """
+        text = self.decode(text)
+        if label is None:
+            return text
+        else:
+            label = self.decode(label, is_remove_duplicate=False)
+            return text, label
+        """
+        preds_idx = preds["rec_pred"]
+        # if isinstance(preds_idx, paddle.Tensor):
+        #     preds_idx = preds_idx.numpy()
+        if "rec_pred_scores" in preds:
+            preds_idx = preds["rec_pred"]
+            preds_prob = preds["rec_pred_scores"]
+        else:
+            preds_idx = preds["rec_pred"].argmax(axis=2)
+            preds_prob = preds["rec_pred"].max(axis=2)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+        if label is None:
+            return text
+        label = self.decode(label, is_remove_duplicate=False)
+        return text, label
+
+
+class SRNLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(SRNLabelDecode, self).__init__(character_dict_path,
+                                             use_space_char)
+        self.max_text_length = kwargs.get('max_text_length', 25)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        pred = preds['predict']
+        char_num = len(self.character_str) + 2
+        # if isinstance(pred, paddle.Tensor):
+        #     pred = pred.numpy()
+        pred = np.reshape(pred, [-1, char_num])
+
+        preds_idx = np.argmax(pred, axis=1)
+        preds_prob = np.max(pred, axis=1)
+
+        preds_idx = np.reshape(preds_idx, [-1, self.max_text_length])
+
+        preds_prob = np.reshape(preds_prob, [-1, self.max_text_length])
+
+        text = self.decode(preds_idx, preds_prob)
+
+        if label is None:
+            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+            return text
+        label = self.decode(label)
+        return text, label
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        ignored_tokens = self.get_ignored_tokens()
+        batch_size = len(text_index)
+
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] in ignored_tokens:
+                    continue
+                if is_remove_duplicate:
+                    # only for predict
+                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
+                            batch_idx][idx]:
+                        continue
+                char_list.append(self.character[int(text_index[batch_idx][
+                    idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+
+            text = ''.join(char_list)
+            result_list.append((text, np.mean(conf_list).tolist()))
+        return result_list
+
+    def add_special_char(self, dict_character):
+        dict_character = dict_character + [self.beg_str, self.end_str]
+        return dict_character
+
+    def get_ignored_tokens(self):
+        beg_idx = self.get_beg_end_flag_idx("beg")
+        end_idx = self.get_beg_end_flag_idx("end")
+        return [beg_idx, end_idx]
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if beg_or_end == "beg":
+            idx = np.array(self.dict[self.beg_str])
+        elif beg_or_end == "end":
+            idx = np.array(self.dict[self.end_str])
+        else:
+            assert False, "unsupport type %s in get_beg_end_flag_idx" \
+                          % beg_or_end
+        return idx
+
+
+class SARLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(SARLabelDecode, self).__init__(character_dict_path,
+                                             use_space_char)
+
+        self.rm_symbol = kwargs.get('rm_symbol', False)
+
+    def add_special_char(self, dict_character):
+        beg_end_str = "<BOS/EOS>"
+        unknown_str = "<UKN>"
+        padding_str = "<PAD>"
+        dict_character = dict_character + [unknown_str]
+        self.unknown_idx = len(dict_character) - 1
+        dict_character = dict_character + [beg_end_str]
+        self.start_idx = len(dict_character) - 1
+        self.end_idx = len(dict_character) - 1
+        dict_character = dict_character + [padding_str]
+        self.padding_idx = len(dict_character) - 1
+        return dict_character
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        ignored_tokens = self.get_ignored_tokens()
+
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] in ignored_tokens:
+                    continue
+                if int(text_index[batch_idx][idx]) == int(self.end_idx):
+                    if text_prob is None and idx == 0:
+                        continue
+                    else:
+                        break
+                if is_remove_duplicate:
+                    # only for predict
+                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
+                            batch_idx][idx]:
+                        continue
+                char_list.append(self.character[int(text_index[batch_idx][
+                    idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            if self.rm_symbol:
+                comp = re.compile('[^A-Z^a-z^0-9^\u4e00-\u9fa5]')
+                text = text.lower()
+                text = comp.sub('', text)
+            result_list.append((text, np.mean(conf_list).tolist()))
+        return result_list
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        # if isinstance(preds, paddle.Tensor):
+        #     preds = preds.numpy()
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+
+        if label is None:
+            return text
+        label = self.decode(label, is_remove_duplicate=False)
+        return text, label
+
+    def get_ignored_tokens(self):
+        return [self.padding_idx]
+
+
+class DistillationSARLabelDecode(SARLabelDecode):
+    """
+    Convert 
+    Convert between text-label and text-index
+    """
+
+    def __init__(self,
+                 character_dict_path=None,
+                 use_space_char=False,
+                 model_name=["student"],
+                 key=None,
+                 multi_head=False,
+                 **kwargs):
+        super(DistillationSARLabelDecode, self).__init__(character_dict_path,
+                                                         use_space_char)
+        if not isinstance(model_name, list):
+            model_name = [model_name]
+        self.model_name = model_name
+
+        self.key = key
+        self.multi_head = multi_head
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        output = dict()
+        for name in self.model_name:
+            pred = preds[name]
+            if self.key is not None:
+                pred = pred[self.key]
+            if self.multi_head and isinstance(pred, dict):
+                pred = pred['sar']
+            output[name] = super().__call__(pred, label=label, *args, **kwargs)
+        return output
+
+
+class PRENLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(PRENLabelDecode, self).__init__(character_dict_path,
+                                              use_space_char)
+
+    def add_special_char(self, dict_character):
+        padding_str = '<PAD>'  # 0 
+        end_str = '<EOS>'  # 1
+        unknown_str = '<UNK>'  # 2
+
+        dict_character = [padding_str, end_str, unknown_str] + dict_character
+        self.padding_idx = 0
+        self.end_idx = 1
+        self.unknown_idx = 2
+
+        return dict_character
+
+    def decode(self, text_index, text_prob=None):
+        """ convert text-index into text-label. """
+        result_list = []
+        batch_size = len(text_index)
+
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] == self.end_idx:
+                    break
+                if text_index[batch_idx][idx] in \
+                    [self.padding_idx, self.unknown_idx]:
+                    continue
+                char_list.append(self.character[int(text_index[batch_idx][
+                    idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+
+            text = ''.join(char_list)
+            if len(text) > 0:
+                result_list.append((text, np.mean(conf_list).tolist()))
+            else:
+                # here confidence of empty recog result is 1
+                result_list.append(('', 1))
+        return result_list
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        preds = preds.numpy()
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.decode(preds_idx, preds_prob)
+        if label is None:
+            return text
+        label = self.decode(label)
+        return text, label
+
+
+class NRTRLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
+        super(NRTRLabelDecode, self).__init__(character_dict_path,
+                                              use_space_char)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+
+        if len(preds) == 2:
+            preds_id = preds[0]
+            preds_prob = preds[1]
+            # if isinstance(preds_id, paddle.Tensor):
+            #     preds_id = preds_id.numpy()
+            # if isinstance(preds_prob, paddle.Tensor):
+            #     preds_prob = preds_prob.numpy()
+            if preds_id[0][0] == 2:
+                preds_idx = preds_id[:, 1:]
+                preds_prob = preds_prob[:, 1:]
+            else:
+                preds_idx = preds_id
+            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+            if label is None:
+                return text
+            label = self.decode(label[:, 1:])
+        else:
+            # if isinstance(preds, paddle.Tensor):
+            #     preds = preds.numpy()
+            preds_idx = preds.argmax(axis=2)
+            preds_prob = preds.max(axis=2)
+            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+            if label is None:
+                return text
+            label = self.decode(label[:, 1:])
+        return text, label
+
+    def add_special_char(self, dict_character):
+        dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
+        return dict_character
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                try:
+                    char_idx = self.character[int(text_index[batch_idx][idx])]
+                except:
+                    continue
+                if char_idx == '</s>':  # end
+                    break
+                char_list.append(char_idx)
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            result_list.append((text.lower(), np.mean(conf_list).tolist()))
+        return result_list
+
+
+class ViTSTRLabelDecode(NRTRLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(ViTSTRLabelDecode, self).__init__(character_dict_path,
+                                                use_space_char)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        # if isinstance(preds, paddle.Tensor):
+        #     preds = preds[:, 1:].numpy()
+        # else:
+        #     preds = preds[:, 1:]
+        preds = preds[:, 1:].numpy()
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+        if label is None:
+            return text
+        label = self.decode(label[:, 1:])
+        return text, label
+
+    def add_special_char(self, dict_character):
+        dict_character = ['<s>', '</s>'] + dict_character
+        return dict_character
+
+
+class ABINetLabelDecode(NRTRLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(ABINetLabelDecode, self).__init__(character_dict_path,
+                                                use_space_char)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        if isinstance(preds, dict):
+            preds = preds['align'][-1].numpy()
+        # elif isinstance(preds, paddle.Tensor):
+        #     preds = preds.numpy()
+        # else:
+        #     preds = preds
+        preds = preds.numpy()
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+        if label is None:
+            return text
+        label = self.decode(label)
+        return text, label
+
+    def add_special_char(self, dict_character):
+        dict_character = ['</s>'] + dict_character
+        return dict_character
+
+
+class SPINLabelDecode(AttnLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(SPINLabelDecode, self).__init__(character_dict_path,
+                                              use_space_char)
+
+    def add_special_char(self, dict_character):
+        self.beg_str = "sos"
+        self.end_str = "eos"
+        dict_character = dict_character
+        dict_character = [self.beg_str] + [self.end_str] + dict_character
+        return dict_character
+
+
+# class VLLabelDecode(BaseRecLabelDecode):
+#     """ Convert between text-label and text-index """
+
+#     def __init__(self, character_dict_path=None, use_space_char=False,
+#                  **kwargs):
+#         super(VLLabelDecode, self).__init__(character_dict_path, use_space_char)
+#         self.max_text_length = kwargs.get('max_text_length', 25)
+#         self.nclass = len(self.character) + 1
+#         self.character = self.character[10:] + self.character[
+#             1:10] + [self.character[0]]
+
+#     def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+#         """ convert text-index into text-label. """
+#         result_list = []
+#         ignored_tokens = self.get_ignored_tokens()
+#         batch_size = len(text_index)
+#         for batch_idx in range(batch_size):
+#             selection = np.ones(len(text_index[batch_idx]), dtype=bool)
+#             if is_remove_duplicate:
+#                 selection[1:] = text_index[batch_idx][1:] != text_index[
+#                     batch_idx][:-1]
+#             for ignored_token in ignored_tokens:
+#                 selection &= text_index[batch_idx] != ignored_token
+
+#             char_list = [
+#                 self.character[text_id - 1]
+#                 for text_id in text_index[batch_idx][selection]
+#             ]
+#             if text_prob is not None:
+#                 conf_list = text_prob[batch_idx][selection]
+#             else:
+#                 conf_list = [1] * len(selection)
+#             if len(conf_list) == 0:
+#                 conf_list = [0]
+
+#             text = ''.join(char_list)
+#             result_list.append((text, np.mean(conf_list).tolist()))
+#         return result_list
+
+#     def __call__(self, preds, label=None, length=None, *args, **kwargs):
+#         if len(preds) == 2:  # eval mode
+#             text_pre, x = preds
+#             b = text_pre.shape[1]
+#             lenText = self.max_text_length
+#             nsteps = self.max_text_length
+
+#             if not isinstance(text_pre, paddle.Tensor):
+#                 text_pre = paddle.to_tensor(text_pre, dtype='float32')
+
+#             out_res = paddle.zeros(
+#                 shape=[lenText, b, self.nclass], dtype=x.dtype)
+#             out_length = paddle.zeros(shape=[b], dtype=x.dtype)
+#             now_step = 0
+#             for _ in range(nsteps):
+#                 if 0 in out_length and now_step < nsteps:
+#                     tmp_result = text_pre[now_step, :, :]
+#                     out_res[now_step] = tmp_result
+#                     tmp_result = tmp_result.topk(1)[1].squeeze(axis=1)
+#                     for j in range(b):
+#                         if out_length[j] == 0 and tmp_result[j] == 0:
+#                             out_length[j] = now_step + 1
+#                     now_step += 1
+#             for j in range(0, b):
+#                 if int(out_length[j]) == 0:
+#                     out_length[j] = nsteps
+#             start = 0
+#             output = paddle.zeros(
+#                 shape=[int(out_length.sum()), self.nclass], dtype=x.dtype)
+#             for i in range(0, b):
+#                 cur_length = int(out_length[i])
+#                 output[start:start + cur_length] = out_res[0:cur_length, i, :]
+#                 start += cur_length
+#             net_out = output
+#             length = out_length
+
+#         else:  # train mode
+#             net_out = preds[0]
+#             length = length
+#             net_out = paddle.concat([t[:l] for t, l in zip(net_out, length)])
+#         text = []
+#         if not isinstance(net_out, paddle.Tensor):
+#             net_out = paddle.to_tensor(net_out, dtype='float32')
+#         net_out = F.softmax(net_out, axis=1)
+#         for i in range(0, length.shape[0]):
+#             preds_idx = net_out[int(length[:i].sum()):int(length[:i].sum(
+#             ) + length[i])].topk(1)[1][:, 0].tolist()
+#             preds_text = ''.join([
+#                 self.character[idx - 1]
+#                 if idx > 0 and idx <= len(self.character) else ''
+#                 for idx in preds_idx
+#             ])
+#             preds_prob = net_out[int(length[:i].sum()):int(length[:i].sum(
+#             ) + length[i])].topk(1)[0][:, 0]
+#             preds_prob = paddle.exp(
+#                 paddle.log(preds_prob).sum() / (preds_prob.shape[0] + 1e-6))
+#             text.append((preds_text, preds_prob.numpy()[0]))
+#         if label is None:
+#             return text
+#         label = self.decode(label)
+#         return text, label
+
--- a/plugins/rknn/src/requirements.txt
+++ b/plugins/rknn/src/requirements.txt
@@ -1,2 +1,6 @@
 https://github.com/airockchip/rknn-toolkit2/raw/v2.0.0-beta0/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.0.0b0-cp310-cp310-linux_aarch64.whl
-pillow==10.3.0
+pillow==10.3.0
+six==1.16.0
+shapely== 2.0.4
+pyclipper==1.3.0.post5
+opencv-python-headless==4.9.0.80
--- a/plugins/rknn/src/rknn/plugin.py
+++ b/plugins/rknn/src/rknn/plugin.py
@@ -2,8 +2,8 @@ import asyncio
 import concurrent.futures
 import os
 import platform
-import queue
 import threading
+import traceback
 from typing import Any, Coroutine, List, Tuple
 import urllib.request

@@ -14,9 +14,14 @@ from rknnlite.api import RKNNLite
 from predict import PredictPlugin, Prediction
 from predict.rectangle import Rectangle

+import scrypted_sdk
+from scrypted_sdk import DeviceProvider, ScryptedDeviceType, ScryptedInterface
+
 # for Rockchip-optimized models, the postprocessing is slightly different from the original models
 from .optimized.yolo import post_process, IMG_SIZE, CLASSES

+from .text_recognition import TEXT_RECOGNITION_NATIVE_ID, TextRecognition
+

 rknn_verbose = False
 lib_download = 'https://github.com/airockchip/rknn-toolkit2/raw/v2.0.0-beta0/rknpu2/runtime/Linux/librknn_api/aarch64/librknnrt.so'
@@ -53,13 +58,16 @@ def ensure_compatibility_and_get_cpu():
        raise


-class RKNNPlugin(PredictPlugin):
+class RKNNPlugin(PredictPlugin, DeviceProvider):
    labels = {i: CLASSES[i] for i in range(len(CLASSES))}
    rknn_runtimes: dict
+    executor: concurrent.futures.ThreadPoolExecutor
+    text_recognition: TextRecognition = None
+    cpu: str

    def __init__(self, nativeId=None):
        super().__init__(nativeId)
-        cpu = ensure_compatibility_and_get_cpu()
+        self.cpu = ensure_compatibility_and_get_cpu()
        model = 'yolov6n'

        self.rknn_runtimes = {}
@@ -72,7 +80,7 @@ class RKNNPlugin(PredictPlugin):
            else:
                raise RuntimeError('librknnrt.so not found. Please download it from {} and place it at {}'.format(lib_download, lib_path))

-        model_download = model_download_tmpl.format(model, cpu)
+        model_download = model_download_tmpl.format(model, self.cpu)
        model_file = os.path.basename(model_download)
        model_path = self.downloadFile(model_download, model_file)
        print('Using model {}'.format(model_path))
@@ -101,7 +109,33 @@ class RKNNPlugin(PredictPlugin):
            self.rknn_runtimes[thread_name] = rknn
            print('RKNNLite runtime initialized on thread {}'.format(thread_name))

-        self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=3, initializer=executor_initializer)
+        self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=3, thread_name_prefix=type(self).__name__, initializer=executor_initializer)
+
+        asyncio.create_task(self.discoverRecognitionModels())
+
+    async def discoverRecognitionModels(self) -> None:
+        devices = [
+            {
+                "nativeId": TEXT_RECOGNITION_NATIVE_ID,
+                "name": "Rockchip NPU Text Recognition",
+                "type": ScryptedDeviceType.API.value,
+                "interfaces": [
+                    ScryptedInterface.ObjectDetection.value,
+                ],
+            }
+        ]
+        await scrypted_sdk.deviceManager.onDevicesChanged({
+            "devices": devices,
+        })
+
+    async def getDevice(self, nativeId: str) -> TextRecognition:
+        try:
+            if nativeId == TEXT_RECOGNITION_NATIVE_ID:
+                self.text_recognition = self.text_recognition or TextRecognition(nativeId, self.cpu)
+                return self.text_recognition
+        except:
+            traceback.print_exc()
+            raise

    def get_input_details(self) -> Tuple[int]:
        return (IMG_SIZE[0], IMG_SIZE[1], 3)
--- a/plugins/rknn/src/rknn/text_recognition.py
+++ b/plugins/rknn/src/rknn/text_recognition.py
@@ -0,0 +1,264 @@
+import asyncio
+import concurrent.futures
+import math
+import os
+import threading
+import traceback
+from typing import Any, Callable, List
+
+import numpy as np
+from PIL import Image, ImageOps
+from rknnlite.api import RKNNLite
+
+from common.text import skew_image, crop_text, calculate_y_change
+from predict import Prediction
+from predict.rectangle import Rectangle
+from predict.text_recognize import TextRecognition
+import scrypted_sdk
+from scrypted_sdk.types import ObjectsDetected, ObjectDetectionResult
+import det_utils.operators
+import det_utils.db_postprocess
+import rec_utils.operators
+import rec_utils.rec_postprocess
+
+
+TEXT_RECOGNITION_NATIVE_ID = "textrecognition"
+DET_IMG_SIZE = (480, 480)
+
+RKNN_DET_PREPROCESS_CONFIG = [
+    {
+        'DetResizeForTest': {
+            'image_shape': DET_IMG_SIZE
+        }
+    },
+    {
+        'NormalizeImage': {
+            'std': [1., 1., 1.],
+            'mean': [0., 0., 0.],
+            'scale': '1.',
+            'order': 'hwc'
+        }
+    }
+]
+
+RKNN_DET_POSTPROCESS_CONFIG = {
+    'DBPostProcess': {
+        'thresh': 0.3,
+        'box_thresh': 0.6,
+        'max_candidates': 1000,
+        'unclip_ratio': 1.5,
+        'use_dilation': False,
+        'score_mode': 'fast',
+    }
+}
+
+RKNN_REC_PREPROCESS_CONFIG = [
+    {
+        'NormalizeImage': {
+            'std': [1, 1, 1],
+            'mean': [0, 0, 0],
+            'scale': '1./255.',
+            'order': 'hwc'
+        }
+    }
+]
+
+RKNN_REC_POSTPROCESS_CONFIG = {
+    'CTCLabelDecode':{
+        "character_dict_path": None, # will be replaced by RKNNDetection.__init__()
+        "use_space_char": True
+    }
+}
+
+rknn_verbose = False
+model_download_tmpl = 'https://github.com/bjia56/scrypted-rknn/raw/main/models/{}_{}.rknn'
+chardict_link = 'https://github.com/bjia56/scrypted-rknn/raw/main/models/ppocr_keys_v1.txt'
+
+
+class RKNNText:
+    model_path: str
+    rknn_runtimes: dict
+    executor: concurrent.futures.ThreadPoolExecutor
+    preprocess_funcs: List[Callable]
+    postprocess_func: Callable
+    print: Callable
+
+    def __init__(self, model_path, print) -> None:
+        self.model_path = model_path
+        self.rknn_runtimes = {}
+        self.print = print
+
+        if not self.model_path:
+            raise ValueError('model_path is not set')
+
+        def executor_initializer():
+            thread_name = threading.current_thread().name
+            rknn = RKNNLite(verbose=rknn_verbose)
+            ret = rknn.load_rknn(self.model_path)
+            if ret != 0:
+                raise RuntimeError('Failed to load model: {}'.format(ret))
+
+            ret = rknn.init_runtime()
+            if ret != 0:
+                raise RuntimeError('Failed to init runtime: {}'.format(ret))
+
+            self.rknn_runtimes[thread_name] = rknn
+            self.print('RKNNLite runtime initialized on thread {}'.format(thread_name))
+
+        self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=3, thread_name_prefix=type(self).__name__, initializer=executor_initializer)
+
+    def detect(self, img):
+        def do_detect(img):
+            model_input = img
+            for p in self.preprocess_funcs:
+                model_input = p(model_input)
+
+            rknn = self.rknn_runtimes[threading.current_thread().name]
+            output = rknn.inference(inputs=[np.expand_dims(model_input['image'], axis=0)])
+
+            return self.postprocess_func(output, model_input['shape'], model_input['image'].shape)
+
+        future = self.executor.submit(do_detect, {'image': img, 'shape': img.shape})
+        return future
+
+
+class RKNNDetection(RKNNText):
+    db_preprocess = None
+    det_postprocess = None
+
+    def __init__(self, model_path, print):
+        super().__init__(model_path, print)
+
+        self.preprocess_funcs = []
+        for item in RKNN_DET_PREPROCESS_CONFIG:
+            for key in item:
+                pclass = getattr(det_utils.operators, key)
+                p = pclass(**item[key])
+                self.preprocess_funcs.append(p)
+
+        self.db_postprocess = det_utils.db_postprocess.DBPostProcess(**RKNN_DET_POSTPROCESS_CONFIG['DBPostProcess'])
+        self.det_postprocess = det_utils.db_postprocess.DetPostProcess()
+
+        def postprocess(output, model_shape, img_shape):
+            preds = {'maps': output[0].astype(np.float32)}
+            result = self.db_postprocess(preds, model_shape)
+            return self.det_postprocess.filter_tag_det_res(result[0]['points'], img_shape)
+        self.postprocess_func = postprocess
+
+
+class RKNNRecognition(RKNNText):
+    ctc_postprocess = None
+
+    def __init__(self, model_path, print):
+        super().__init__(model_path, print)
+
+        self.preprocess_funcs = []
+        for item in RKNN_REC_PREPROCESS_CONFIG:
+            for key in item:
+                pclass = getattr(rec_utils.operators, key)
+                p = pclass(**item[key])
+                self.preprocess_funcs.append(p)
+
+        self.ctc_postprocess = rec_utils.rec_postprocess.CTCLabelDecode(**RKNN_REC_POSTPROCESS_CONFIG['CTCLabelDecode'])
+
+        def postprocess(output, model_shape, img_shape):
+            preds = output[0].astype(np.float32)
+            output = self.ctc_postprocess(preds)
+            return output
+        self.postprocess_func = postprocess
+
+
+async def prepare_text_result(d: ObjectDetectionResult, image: scrypted_sdk.Image, skew_angle: float):
+    textImage = await crop_text(d, image)
+
+    skew_height_change = calculate_y_change(d["boundingBox"][3], skew_angle)
+    skew_height_change = math.floor(skew_height_change)
+    textImage = skew_image(textImage, skew_angle)
+    # crop skew_height_change from top
+    if skew_height_change > 0:
+        textImage = textImage.crop((0, 0, textImage.width, textImage.height - skew_height_change))
+    elif skew_height_change < 0:
+        textImage = textImage.crop((0, -skew_height_change, textImage.width, textImage.height))
+
+    new_height = 48
+    new_width = int(textImage.width * new_height / textImage.height)
+    textImage = textImage.resize((new_width, new_height), resample=Image.LANCZOS).convert("L")
+
+    new_width = 320
+    # calculate padding dimensions
+    padding = (0, 0, new_width - textImage.width, 0)
+    # todo: clamp entire edge rather than just center
+    edge_color = textImage.getpixel((textImage.width - 1, textImage.height // 2))
+    # pad image
+    textImage = ImageOps.expand(textImage, padding, fill=edge_color)
+    # pil to numpy
+    image_array = np.array(textImage)
+    image_array = image_array.reshape(textImage.height, textImage.width, 1)
+    image_tensor = image_array#.transpose((2, 0, 1)) / 255
+
+    # test normalize contrast
+    # image_tensor = (image_tensor - np.min(image_tensor)) / (np.max(image_tensor) - np.min(image_tensor))
+
+    image_tensor = (image_tensor - 0.5) / 0.5
+
+    return image_tensor
+
+
+class TextRecognition(TextRecognition):
+    detection: RKNNDetection
+    recognition: RKNNRecognition
+
+    def __init__(self, nativeId=None, cpu=""):
+        super().__init__(nativeId)
+
+        model_download = model_download_tmpl.format("ppocrv4_det", cpu)
+        model_file = os.path.basename(model_download)
+        det_model_path = self.downloadFile(model_download, model_file)
+
+        model_download = model_download_tmpl.format("ppocrv4_rec", cpu)
+        model_file = os.path.basename(model_download)
+        rec_model_path = self.downloadFile(model_download, model_file)
+
+        chardict_file = os.path.basename(chardict_link)
+        chardict_path = self.downloadFile(chardict_link, chardict_file)
+        RKNN_REC_POSTPROCESS_CONFIG['CTCLabelDecode']['character_dict_path'] = chardict_path
+
+        self.detection = RKNNDetection(det_model_path, lambda *args, **kwargs: self.print(*args, **kwargs))
+        self.recognition = RKNNRecognition(rec_model_path, lambda *args, **kwargs: self.print(*args, **kwargs))
+        self.inputheight = DET_IMG_SIZE[0]
+        self.inputwidth = DET_IMG_SIZE[1]
+
+    async def detect_once(self, input: Image, settings: Any, src_size, cvss) -> ObjectsDetected:
+        detections = await asyncio.wrap_future(
+            self.detection.detect(np.array(input)), loop=asyncio.get_event_loop()
+        )
+
+        #self.print(detections)
+
+        predictions: List[Prediction] = []
+        for box in detections:
+            #self.print(box)
+            tl, tr, br, bl = box
+            l = min(tl[0], bl[0])
+            t = min(tl[1], tr[1])
+            r = max(tr[0], br[0])
+            b = max(bl[1], br[1])
+
+            pred = Prediction(0, 1, Rectangle(l, t, r, b))
+            predictions.append(pred)
+
+        return self.create_detection_result(predictions, src_size, cvss)
+
+    async def setLabel(
+        self, d: ObjectDetectionResult, image: scrypted_sdk.Image, skew_angle: float
+    ):
+        try:
+            image_tensor = await prepare_text_result(d, image, skew_angle)
+            preds = await asyncio.wrap_future(
+                self.recognition.detect(image_tensor), loop=asyncio.get_event_loop()
+            )
+            #self.print("preds", preds)
+            d["label"] = preds[0][0]
+        except Exception as e:
+            traceback.print_exc()
+            pass
--- a/plugins/sip/package-lock.json
+++ b/plugins/sip/package-lock.json
--- a/plugins/sip/package.json
+++ b/plugins/sip/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/sip",
-   "version": "0.0.9",
+   "version": "0.0.10",
   "scripts": {
      "scrypted-setup-project": "scrypted-setup-project",
      "prescrypted-setup-project": "scrypted-package-json",
@@ -32,17 +32,16 @@
      ]
   },
   "dependencies": {
-      "@homebridge/camera-utils": "^2.0.4",
      "@slyoldfox/sip": "^0.0.6-1",
+      "pick-port": "^1.0.0",
+      "rxjs": "^7.8.1",
      "sdp": "^3.0.3",
-      "stun": "^2.1.0",
-      "uuid": "^8.3.2"
+      "stun": "^2.1.0"
   },
   "devDependencies": {
      "@scrypted/common": "file:../../common",
      "@scrypted/sdk": "file:../../sdk",
      "@types/node": "^16.9.6",
-      "@types/uuid": "^8.3.4",
      "cross-env": "^7.0.3"
   }
 }
--- a/plugins/sip/src/port-utils.ts
+++ b/plugins/sip/src/port-utils.ts
@@ -0,0 +1,62 @@
+import { Socket } from 'dgram'
+import { AddressInfo } from 'net'
+import { pickPort } from 'pick-port'
+
+// Need to reserve ports in sequence because ffmpeg uses the next port up by default.  If it's taken, ffmpeg will error
+export async function reservePorts({
+  count = 1,
+  type = 'udp',
+  attemptNumber = 0,
+}: {
+  count?: number
+  type?: 'udp' | 'tcp'
+  attemptNumber?: number
+} = {}): Promise<number[]> {
+  if (attemptNumber > 100) {
+    throw new Error('Failed to reserve ports after 100 tries')
+  }
+
+  const pickPortOptions = {
+      type,
+      reserveTimeout: 15, // 15 seconds is max setup time for HomeKit streams, so the port should be in use by then
+    },
+    port = await pickPort(pickPortOptions),
+    ports = [port],
+    tryAgain = () => {
+      return reservePorts({
+        count,
+        type,
+        attemptNumber: attemptNumber + 1,
+      })
+    }
+
+  for (let i = 1; i < count; i++) {
+    try {
+      const targetConsecutivePort = port + i,
+        openPort = await pickPort({
+          ...pickPortOptions,
+          minPort: targetConsecutivePort,
+          maxPort: targetConsecutivePort,
+        })
+
+      ports.push(openPort)
+    } catch (_) {
+      // can't reserve next port, bail and get another set
+      return tryAgain()
+    }
+  }
+
+  return ports
+}
+
+export function bindToPort(socket: Socket) {
+  return new Promise<number>((resolve, reject) => {
+    socket.on('error', reject)
+
+    // 0 means select a random open port
+    socket.bind(0, () => {
+      const { port } = socket.address() as AddressInfo
+      resolve(port)
+    })
+  })
+}
--- a/plugins/sip/src/rtp-utils.ts
+++ b/plugins/sip/src/rtp-utils.ts
@@ -1,10 +1,14 @@
 // by @dgrief from @homebridge/camera-utils
-import { SrtpOptions } from '@homebridge/camera-utils'
 import dgram from 'dgram'
 const stun = require('stun')

 const stunMagicCookie = 0x2112a442 // https://tools.ietf.org/html/rfc5389#section-6

+export interface SrtpOptions {
+  srtpKey: Buffer
+  srtpSalt: Buffer
+}
+
 export interface RtpStreamOptions extends SrtpOptions {
  port: number
  rtcpPort: number
--- a/plugins/sip/src/sip-call-session.ts
+++ b/plugins/sip/src/sip-call-session.ts
@@ -1,4 +1,4 @@
-import { reservePorts } from '@homebridge/camera-utils';
+import { reservePorts } from './port-utils';
 import { createBindUdp, createBindZero } from '@scrypted/common/src/listen-cluster';
 import dgram from 'dgram';
 import { ReplaySubject, timer } from 'rxjs';
--- a/plugins/sip/src/sip-manager.ts
+++ b/plugins/sip/src/sip-manager.ts
@@ -2,7 +2,7 @@ import { noop, Subject } from 'rxjs'
 import { randomInteger, randomString } from './util'
 import { RtpDescription, RtpOptions, RtpStreamDescription } from './rtp-utils'
 import { decodeSrtpOptions } from '../../ring/src/srtp-utils'
-import { stringify, stringifyUri } from '@slyoldfox/sip'
+import { stringify } from '@slyoldfox/sip'
 import { timeoutPromise } from '@scrypted/common/src/promise-utils';
 import sdp from 'sdp'

@@ -181,6 +181,9 @@ export class SipManager {
        // },
        ws: false,
        logger: {
+          error: function(e) {
+            if( sipOptions.debugSip ) console.error(e)
+          },
          recv:  function(m, remote) {
            if( (m.status == '200' || m.method === 'INVITE' ) && m.headers && m.headers.cseq && m.headers.cseq.method === 'INVITE' && m.headers.contact && m.headers.contact[0] ) {
              // ACK for INVITE and BYE must use the registrar contact uri
@@ -447,10 +450,6 @@ export class SipManager {

      return parseRtpDescription(this.console, incomingCallRequest)
    } else {
-      if( this.sipOptions.to.toLocaleLowerCase().indexOf('c300x') >= 0 ) {
-        // Needed for bt_answering_machine (bticino specific)
-        audio.unshift('a=DEVADDR:20')
-      }
      let inviteResponse = await this.request({
        method: 'INVITE',
        headers: {
--- a/plugins/sip/src/util.ts
+++ b/plugins/sip/src/util.ts
@@ -1,13 +1,7 @@
-import { v4 as generateRandomUuid, v5 as generateUuidFromNamespace } from 'uuid'
-
-const uuidNamespace = 'e53ffdc0-e91d-4ce1-bec2-df939d94739d'
+const crypto = require('crypto');

 export function generateUuid(seed?: string) {
-  if (seed) {
-    return generateUuidFromNamespace(seed, uuidNamespace)
-  }
-
-  return generateRandomUuid()
+  return crypto.randomUUID();
 }

 export function randomInteger() {
--- a/plugins/snapshot/package-lock.json
+++ b/plugins/snapshot/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/snapshot",
-   "version": "0.2.50",
+   "version": "0.2.52",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/snapshot",
-         "version": "0.2.50",
+         "version": "0.2.52",
         "dependencies": {
            "@types/node": "^20.10.6",
            "sharp": "^0.33.1",
--- a/plugins/snapshot/package.json
+++ b/plugins/snapshot/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/snapshot",
-   "version": "0.2.50",
+   "version": "0.2.52",
   "description": "Snapshot Plugin for Scrypted",
   "scripts": {
      "scrypted-setup-project": "scrypted-setup-project",
--- a/plugins/snapshot/src/main.ts
+++ b/plugins/snapshot/src/main.ts
@@ -271,10 +271,6 @@ class SnapshotMixin extends SettingsMixinDeviceBase<Camera> implements Camera {
    }

    async takePictureRaw(options?: RequestPictureOptions): Promise<Buffer> {
-        let rawPicturePromise: Promise<{
-            picture: Buffer;
-            pictureTime: number;
-        }>;
        const eventSnapshot = options?.reason === 'event';
        const periodicSnapshot = options?.reason === 'periodic';

@@ -282,50 +278,61 @@ class SnapshotMixin extends SettingsMixinDeviceBase<Camera> implements Camera {
        if (this.currentPictureTime < Date.now() - 1 * 60 * 60 * 1000)
            this.currentPicture = undefined;

-        const allowedSnapshotStaleness = eventSnapshot ? 0 : periodicSnapshot ? 20000 : 10000;
-
-        let needRefresh = true;
-        if (this.currentPicture && this.currentPictureTime > Date.now() - allowedSnapshotStaleness) {
-            this.debugConsole?.log('Using cached snapshot for', options?.reason);
-            rawPicturePromise = Promise.resolve({
-                picture: this.currentPicture,
-                pictureTime: this.currentPictureTime,
-            });
-            needRefresh = this.currentPictureTime < Date.now() - allowedSnapshotStaleness / 2;
-        }
-
-        if (needRefresh) {
-            const debounced = this.snapshotDebouncer({
-                id: options?.id,
-                reason: options?.reason,
-            }, eventSnapshot ? 0 : 10000, async () => {
-                const snapshotTimer = Date.now();
-                let picture = await this.takePictureInternal();
-                picture = await this.cropAndScale(picture);
-                this.clearCachedPictures();
-                const pictureTime = Date.now();
-                this.currentPicture = picture;
-                this.currentPictureTime = pictureTime;
-                this.lastAvailablePicture = picture;
-                this.debugConsole?.debug(`Periodic snapshot took ${(this.currentPictureTime - snapshotTimer) / 1000} seconds to retrieve.`)
-                return {
-                    picture,
-                    pictureTime,
-                };
-            });
-            debounced.catch(() => { });
-
-            rawPicturePromise ||= debounced;
-        }
+        // always grab/debounce a snapshot
+        // event snapshot are special and should immediately expire.
+        // other snapshots may be debounced for 4s.
+        const debounced = this.snapshotDebouncer({
+            id: options?.id,
+            type: 'source',
+            event: options?.reason === 'event',
+        }, eventSnapshot ? 0 : 4000, async () => {
+            const snapshotTimer = Date.now();
+            let picture = await this.takePictureInternal();
+            picture = await this.cropAndScale(picture);
+            this.clearCachedPictures();
+            const pictureTime = Date.now();
+            this.currentPicture = picture;
+            this.currentPictureTime = pictureTime;
+            this.lastAvailablePicture = picture;
+            this.debugConsole?.debug(`Periodic snapshot took ${(this.currentPictureTime - snapshotTimer) / 1000} seconds to retrieve.`)
+            return {
+                picture,
+                pictureTime,
+            };
+        });
+        debounced.catch(() => { });

        // prevent this from expiring
        let availablePicture = this.currentPicture;
        let availablePictureTime = this.currentPictureTime;

-        let rawPicture: Awaited<typeof rawPicturePromise>;
+        let rawPicture: Awaited<typeof debounced>;
        try {
-            const pictureTimeout = options?.timeout || (periodicSnapshot && availablePicture ? 1000 : 10000) || 10000;
-            rawPicture = await timeoutPromise(pictureTimeout, rawPicturePromise);
+            let pictureTimeout = options?.timeout;
+            if (!pictureTimeout) {
+                // determine a fetch timeout based on the reason and staleness
+                const allowedSnapshotStaleness = eventSnapshot ? 0 : periodicSnapshot ? 20000 : 10000;
+                if (!availablePicture) {
+                    // none available so wait a while
+                    pictureTimeout = 10000;
+                }
+                else {
+                    if (availablePictureTime > Date.now() - 3000) {
+                        // very recent, don't wait for too long
+                        pictureTimeout = 1000;
+                    }
+                    else if (availablePictureTime > Date.now() - allowedSnapshotStaleness) {
+                        // fairly recent so give it little time to get a fresh one
+                        // idr interval is typically 4000 for reference
+                        pictureTimeout = 3000;
+                    }
+                    else {
+                        // stale so wait a while
+                        pictureTimeout = 10000;
+                    }
+                }
+            }
+            rawPicture = await timeoutPromise(pictureTimeout, debounced);
        }
        catch (e) {
            // a best effort was made to get a recent snapshot from cache or from a camera request,
@@ -336,7 +343,11 @@ class SnapshotMixin extends SettingsMixinDeviceBase<Camera> implements Camera {
            if (eventSnapshot)
                throw e;

-            availablePicture = this.currentPicture || availablePicture;
+            if (this.currentPicture) {
+                // use the current picture if it is still available as it may be newer.
+                availablePicture = this.currentPicture;
+                availablePictureTime = this.currentPictureTime;
+            }

            if (!availablePicture)
                return this.createErrorImage(e);
@@ -358,8 +369,8 @@ class SnapshotMixin extends SettingsMixinDeviceBase<Camera> implements Camera {

        try {
            const key = {
+                type: 'resize',
                pictureTime: rawPicture.pictureTime,
-                reason: options?.reason,
                needSoftwareResize: true,
                picture: options.picture,
            };
--- a/plugins/tapo/package-lock.json
+++ b/plugins/tapo/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/tapo",
-   "version": "0.0.13",
+   "version": "0.0.16",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/tapo",
-         "version": "0.0.13",
+         "version": "0.0.16",
         "dependencies": {
            "@scrypted/common": "file:../../common",
            "@scrypted/sdk": "file:../../sdk",
--- a/plugins/tapo/package.json
+++ b/plugins/tapo/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/tapo",
-   "version": "0.0.13",
+   "version": "0.0.16",
   "description": "Tapo Camera Plugin for Scrypted",
   "scripts": {
      "scrypted-setup-project": "scrypted-setup-project",
--- a/plugins/tensorflow-lite/package-lock.json
+++ b/plugins/tensorflow-lite/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/tensorflow-lite",
-   "version": "0.1.59",
+   "version": "0.1.60",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/tensorflow-lite",
-         "version": "0.1.59",
+         "version": "0.1.60",
         "devDependencies": {
            "@scrypted/sdk": "file:../../sdk"
         }
--- a/plugins/tensorflow-lite/package.json
+++ b/plugins/tensorflow-lite/package.json
@@ -53,5 +53,5 @@
   "devDependencies": {
      "@scrypted/sdk": "file:../../sdk"
   },
-   "version": "0.1.59"
+   "version": "0.1.60"
 }
--- a/plugins/tensorflow-lite/src/detect/init.py
+++ b/plugins/tensorflow-lite/src/detect/init.py
@@ -73,7 +73,6 @@ class DetectPlugin(scrypted_sdk.ScryptedDeviceBase, ObjectDetection):
        if mediaObject.mimeType == ScryptedMimeTypes.Image.value:
            image = await scrypted_sdk.sdk.connectRPCObject(mediaObject)
        else:
-            print('non image provided')
            image = await scrypted_sdk.mediaManager.convertMediaObjectToBuffer(mediaObject, ScryptedMimeTypes.Image.value)

        return await self.run_detection_image(image, session)
--- a/plugins/tensorflow-lite/src/predict/face_recognize.py
+++ b/plugins/tensorflow-lite/src/predict/face_recognize.py
@@ -1,51 +1,27 @@
 from __future__ import annotations

 import asyncio
-from asyncio import Future
 import base64
-import concurrent.futures
-import os
-from typing import Any, Tuple, List
+import traceback
+from asyncio import Future
+from typing import Any, List, Tuple

 import numpy as np
-# import Quartz
 import scrypted_sdk
-# from Foundation import NSData, NSMakeSize
 from PIL import Image
-from scrypted_sdk import (
-    Setting,
-    SettingValue,
-    ObjectDetectionSession,
-    ObjectsDetected,
-    ObjectDetectionResult,
-)
-import traceback
+from scrypted_sdk import (ObjectDetectionResult, ObjectDetectionSession,
+                          ObjectsDetected)

-# import Vision
-from predict import PredictPlugin
 from common import yolo
-from common.text import prepare_text_result, process_text_result
+from predict import PredictPlugin

-def euclidean_distance(arr1, arr2):
-    return np.linalg.norm(arr1 - arr2)
-
-
-def cosine_similarity(vector_a, vector_b):
-    dot_product = np.dot(vector_a, vector_b)
-    norm_a = np.linalg.norm(vector_a)
-    norm_b = np.linalg.norm(vector_b)
-    similarity = dot_product / (norm_a * norm_b)
-    return similarity
-
-
-predictExecutor = concurrent.futures.ThreadPoolExecutor(1, "Recognize")

 class FaceRecognizeDetection(PredictPlugin):
    def __init__(self, nativeId: str | None = None):
        super().__init__(nativeId=nativeId)

-        self.inputheight = 640
-        self.inputwidth = 640
+        self.inputheight = 320
+        self.inputwidth = 320

        self.labels = {
            0: "face",
@@ -55,21 +31,12 @@ class FaceRecognizeDetection(PredictPlugin):
        self.loop = asyncio.get_event_loop()
        self.minThreshold = 0.7

-        self.detectModel = self.downloadModel("scrypted_yolov9c_flt")
-        self.textModel = self.downloadModel("vgg_english_g2")
+        self.detectModel = self.downloadModel("scrypted_yolov8n_flt_320")
        self.faceModel = self.downloadModel("inception_resnet_v1")

    def downloadModel(self, model: str):
        pass

-    async def getSettings(self) -> list[Setting]:
-        pass
-
-    async def putSetting(self, key: str, value: SettingValue):
-        self.storage.setItem(key, value)
-        await self.onDeviceEvent(scrypted_sdk.ScryptedInterface.Settings.value, None)
-        await scrypted_sdk.deviceManager.requestRestart()
-
    # width, height, channels
    def get_input_details(self) -> Tuple[int, int, int]:
        return (self.inputwidth, self.inputheight, 3)
@@ -81,9 +48,7 @@ class FaceRecognizeDetection(PredictPlugin):
        return "rgb"

    async def detect_once(self, input: Image.Image, settings: Any, src_size, cvss):
-        results = await asyncio.get_event_loop().run_in_executor(
-            predictExecutor, lambda: self.predictDetectModel(input)
-        )
+        results = await self.predictDetectModel(input)
        objs = yolo.parse_yolov9(results)
        ret = self.create_detection_result(objs, src_size, cvss)
        return ret
@@ -112,10 +77,7 @@ class FaceRecognizeDetection(PredictPlugin):
            processed_tensor = (image_tensor - 127.5) / 128.0
            processed_tensor = np.expand_dims(processed_tensor, axis=0)

-            output = await asyncio.get_event_loop().run_in_executor(
-                predictExecutor,
-                lambda: self.predictFaceModel(processed_tensor)
-            )
+            output = await self.predictFaceModel(processed_tensor)

            b = output.tobytes()
            embedding = base64.b64encode(b).decode("utf-8")
@@ -124,30 +86,13 @@ class FaceRecognizeDetection(PredictPlugin):

            traceback.print_exc()
            pass
-    
-    def predictTextModel(self, input):
+
+    async def predictDetectModel(self, input: Image.Image):
        pass

-    def predictDetectModel(self, input):
+    async def predictFaceModel(self, prepareTensor):
        pass

-    def predictFaceModel(self, input):
-        pass
-
-    async def setLabel(self, d: ObjectDetectionResult, image: scrypted_sdk.Image):
-        try:
-
-            image_tensor = await prepare_text_result(d, image)
-            preds = await asyncio.get_event_loop().run_in_executor(
-                predictExecutor,
-                lambda: self.predictTextModel(image_tensor),
-            )
-            d['label'] = process_text_result(preds)
-
-        except Exception as e:
-            traceback.print_exc()
-            pass
-
    async def run_detection_image(
        self, image: scrypted_sdk.Image, detection_session: ObjectDetectionSession
    ) -> ObjectsDetected:
@@ -206,31 +151,8 @@ class FaceRecognizeDetection(PredictPlugin):
        for d in ret["detections"]:
            if d["className"] == "face":
                futures.append(asyncio.ensure_future(self.setEmbedding(d, image)))
-            # elif d["className"] == "plate":
-            #     futures.append(asyncio.ensure_future(self.setLabel(d, image)))
-            # elif d['className'] == 'text':
-            #     futures.append(asyncio.ensure_future(self.setLabel(d, image)))

        if len(futures):
            await asyncio.wait(futures)

-        last = None
-        for d in ret['detections']:
-            if d["className"] != "face":
-                continue
-            check = d.get("embedding")
-            if check is None:
-                continue
-            # decode base64 string check
-            embedding = base64.b64decode(check)
-            embedding = np.frombuffer(embedding, dtype=np.float32)
-            if last is None:
-                last = embedding
-                continue
-            # convert to numpy float32 arrays
-            similarity = cosine_similarity(last, embedding)
-            print('similarity', similarity)
-            last = embedding
-
        return ret
-
--- a/plugins/tensorflow-lite/src/predict/text_recognize.py
+++ b/plugins/tensorflow-lite/src/predict/text_recognize.py
@@ -41,10 +41,10 @@ class TextRecognition(PredictPlugin):
    def downloadModel(self, model: str):
        pass

-    def predictDetectModel(self, input):
+    async def predictDetectModel(self, input: np.ndarray):
        pass

-    def predictTextModel(self, input):
+    async def predictTextModel(self, input: np.ndarray):
        pass

    async def detect_once(
@@ -56,9 +56,7 @@ class TextRecognition(PredictPlugin):
        # add extra dimension to tensor
        image_tensor = np.expand_dims(image_tensor, axis=0)

-        y = await asyncio.get_event_loop().run_in_executor(
-            predictExecutor, lambda: self.predictDetectModel(image_tensor)
-        )
+        y = await self.predictDetectModel(image_tensor)

        estimate_num_chars = False
        ratio_h = ratio_w = 1
@@ -156,12 +154,8 @@ class TextRecognition(PredictPlugin):
        self, d: ObjectDetectionResult, image: scrypted_sdk.Image, skew_angle: float
    ):
        try:
-
            image_tensor = await prepare_text_result(d, image, skew_angle)
-            preds = await asyncio.get_event_loop().run_in_executor(
-                predictExecutor,
-                lambda: self.predictTextModel(image_tensor),
-            )
+            preds = await self.predictTextModel(image_tensor)
            d["label"] = process_text_result(preds)

        except Exception as e:
--- a/plugins/tensorflow-lite/src/tflite/init.py
+++ b/plugins/tensorflow-lite/src/tflite/init.py
@@ -1,6 +1,7 @@
 from __future__ import annotations

 import json
+import threading

 from PIL import Image
 from pycoral.adapters import detect
@@ -121,7 +122,8 @@ class TensorFlowLitePlugin(
            labels_contents = open(labelsFile, "r").read()
            self.labels = parse_label_contents(labels_contents)

-        self.interpreters = queue.Queue()
+        self.interpreters = {}
+        available_interpreters = []
        self.interpreter_count = 0

        def downloadModel():
@@ -145,7 +147,7 @@ class TensorFlowLitePlugin(
                            "shape"
                        ]
                        self.input_details = int(width), int(height), int(channels)
-                        self.interpreters.put(interpreter)
+                        available_interpreters.append(interpreter)
                        self.interpreter_count = self.interpreter_count + 1
                        print("added tpu %s" % (edge_tpu))
                    except Exception as e:
@@ -165,12 +167,20 @@ class TensorFlowLitePlugin(
            interpreter.allocate_tensors()
            _, height, width, channels = interpreter.get_input_details()[0]["shape"]
            self.input_details = int(width), int(height), int(channels)
-            self.interpreters.put(interpreter)
+            available_interpreters.append(interpreter)
            self.interpreter_count = self.interpreter_count + 1

        print(modelFile, labelsFile)

+        def executor_initializer():
+            thread_name = threading.current_thread().name
+            interpreter = available_interpreters.pop()
+            self.interpreters[thread_name] = interpreter
+            print('Interpreter initialized on thread {}'.format(thread_name))
+
+
        self.executor = concurrent.futures.ThreadPoolExecutor(
+            initializer=executor_initializer,
            max_workers=self.interpreter_count,
            thread_name_prefix="tflite",
        )
@@ -208,53 +218,50 @@ class TensorFlowLitePlugin(

    async def detect_once(self, input: Image.Image, settings: Any, src_size, cvss):
        def predict():
-            interpreter = self.interpreters.get()
-            try:
-                if self.yolo:
-                    tensor_index = input_details(interpreter, "index")
+            interpreter = self.interpreters[threading.current_thread().name]
+            if self.yolo:
+                tensor_index = input_details(interpreter, "index")

-                    im = np.stack([input])
-                    i = interpreter.get_input_details()[0]
-                    if i["dtype"] == np.int8:
-                        scale, zero_point = i["quantization"]
-                        if scale == 0.003986024297773838 and zero_point == -128:
-                            # fast path for quantization 1/255 = 0.003986024297773838
-                            im = im.view(np.int8)
-                            im -= 128
-                        else:
-                            im = im.astype(np.float32) / (255.0 * scale)
-                            im = (im + zero_point).astype(np.int8)  # de-scale
+                im = np.stack([input])
+                i = interpreter.get_input_details()[0]
+                if i["dtype"] == np.int8:
+                    scale, zero_point = i["quantization"]
+                    if scale == 0.003986024297773838 and zero_point == -128:
+                        # fast path for quantization 1/255 = 0.003986024297773838
+                        im = im.view(np.int8)
+                        im -= 128
                    else:
-                        # this code path is unused.
-                        im = im.astype(np.float32) / 255.0
-                    interpreter.set_tensor(tensor_index, im)
-                    interpreter.invoke()
-                    output_details = interpreter.get_output_details()
-                    output = output_details[0]
-                    x = interpreter.get_tensor(output["index"])
-                    input_scale = self.get_input_details()[0]
-                    if x.dtype == np.int8:
-                        scale, zero_point = output["quantization"]
-                        threshold = yolo.defaultThreshold / scale + zero_point
-                        combined_scale = scale * input_scale
-                        objs = yolo.parse_yolov9(
-                            x[0],
-                            threshold,
-                            scale=lambda v: (v - zero_point) * combined_scale,
-                            confidence_scale=lambda v: (v - zero_point) * scale,
-                        )
-                    else:
-                        # this code path is unused.
-                        objs = yolo.parse_yolov9(x[0], scale=lambda v: v * input_scale)
+                        im = im.astype(np.float32) / (255.0 * scale)
+                        im = (im + zero_point).astype(np.int8)  # de-scale
                else:
-                    tflite_common.set_input(interpreter, input)
-                    interpreter.invoke()
-                    objs = detect.get_objects(
-                        interpreter, score_threshold=0.2, image_scale=(1, 1)
+                    # this code path is unused.
+                    im = im.astype(np.float32) / 255.0
+                interpreter.set_tensor(tensor_index, im)
+                interpreter.invoke()
+                output_details = interpreter.get_output_details()
+                output = output_details[0]
+                x = interpreter.get_tensor(output["index"])
+                input_scale = self.get_input_details()[0]
+                if x.dtype == np.int8:
+                    scale, zero_point = output["quantization"]
+                    threshold = yolo.defaultThreshold / scale + zero_point
+                    combined_scale = scale * input_scale
+                    objs = yolo.parse_yolov9(
+                        x[0],
+                        threshold,
+                        scale=lambda v: (v - zero_point) * combined_scale,
+                        confidence_scale=lambda v: (v - zero_point) * scale,
                    )
-                return objs
-            finally:
-                self.interpreters.put(interpreter)
+                else:
+                    # this code path is unused.
+                    objs = yolo.parse_yolov9(x[0], scale=lambda v: v * input_scale)
+            else:
+                tflite_common.set_input(interpreter, input)
+                interpreter.invoke()
+                objs = detect.get_objects(
+                    interpreter, score_threshold=0.2, image_scale=(1, 1)
+                )
+            return objs

        objs = await asyncio.get_event_loop().run_in_executor(self.executor, predict)

--- a/plugins/unifi-protect/package-lock.json
+++ b/plugins/unifi-protect/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/unifi-protect",
-   "version": "0.0.146",
+   "version": "0.0.149",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/unifi-protect",
-         "version": "0.0.146",
+         "version": "0.0.149",
         "license": "Apache",
         "dependencies": {
            "@koush/unifi-protect": "file:../../external/unifi-protect",
@@ -27,12 +27,12 @@
         "dependencies": {
            "@scrypted/sdk": "file:../sdk",
            "@scrypted/server": "file:../server",
-            "http-auth-utils": "^3.0.2",
-            "node-fetch-commonjs": "^3.1.1",
-            "typescript": "^4.4.3"
+            "http-auth-utils": "^5.0.1",
+            "typescript": "^5.3.3"
         },
         "devDependencies": {
-            "@types/node": "^16.9.0"
+            "@types/node": "^20.11.0",
+            "ts-node": "^10.9.2"
         }
      },
      "../../external/unifi-protect": {
@@ -61,12 +61,12 @@
      },
      "../../sdk": {
         "name": "@scrypted/sdk",
-         "version": "0.2.103",
+         "version": "0.3.31",
         "license": "ISC",
         "dependencies": {
            "@babel/preset-typescript": "^7.18.6",
            "adm-zip": "^0.4.13",
-            "axios": "^0.21.4",
+            "axios": "^1.6.5",
            "babel-loader": "^9.1.0",
            "babel-plugin-const-enum": "^1.1.0",
            "esbuild": "^0.15.9",
@@ -260,10 +260,10 @@
         "requires": {
            "@scrypted/sdk": "file:../sdk",
            "@scrypted/server": "file:../server",
-            "@types/node": "^16.9.0",
-            "http-auth-utils": "^3.0.2",
-            "node-fetch-commonjs": "^3.1.1",
-            "typescript": "^4.4.3"
+            "@types/node": "^20.11.0",
+            "http-auth-utils": "^5.0.1",
+            "ts-node": "^10.9.2",
+            "typescript": "^5.3.3"
         }
      },
      "@scrypted/sdk": {
@@ -273,7 +273,7 @@
            "@types/node": "^18.11.18",
            "@types/stringify-object": "^4.0.0",
            "adm-zip": "^0.4.13",
-            "axios": "^0.21.4",
+            "axios": "^1.6.5",
            "babel-loader": "^9.1.0",
            "babel-plugin-const-enum": "^1.1.0",
            "esbuild": "^0.15.9",
--- a/plugins/unifi-protect/package.json
+++ b/plugins/unifi-protect/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/unifi-protect",
-   "version": "0.0.146",
+   "version": "0.0.149",
   "description": "Unifi Protect Plugin for Scrypted",
   "author": "Scrypted",
   "license": "Apache",
--- a/plugins/unifi-protect/src/main.ts
+++ b/plugins/unifi-protect/src/main.ts
@@ -157,10 +157,11 @@ export class UnifiProtect extends ScryptedDeviceBase implements Settings, Device
                const payload = updatePacket.payload as ProtectNvrUpdatePayloadEventAdd;
                if (!payload.camera)
                    return;
-                const unifiCamera = this.cameras.get(payload.camera);
+                const nativeId = this.getNativeId({ id: payload.camera }, false);
+                const unifiCamera = this.cameras.get(nativeId);

                if (!unifiCamera) {
-                    this.console.log('unknown device event, sync needed?', payload.camera);
+                    this.console.log('unknown device event, sync needed?', payload);
                    return;
                }

@@ -195,7 +196,7 @@ export class UnifiProtect extends ScryptedDeviceBase implements Settings, Device
                //     id: '661d86bf03e69c03e408d62a',
                //     modelKey: 'event'
                // }
-                  
+
                if (payload.type === 'smartDetectZone' || payload.type === 'smartDetectLine') {
                    unifiCamera.resetDetectionTimeout();

@@ -602,7 +603,7 @@ export class UnifiProtect extends ScryptedDeviceBase implements Settings, Device
        return this.storageSettings.values.idMaps.nativeId?.[nativeId] || nativeId;
    }

-    getNativeId(device: any, update: boolean) {
+    getNativeId(device: { id?: string, mac?: string; anonymousDeviceId?: string }, update: boolean) {
        const { id, mac, anonymousDeviceId } = device;
        const idMaps = this.storageSettings.values.idMaps;

--- a/plugins/webrtc/package-lock.json
+++ b/plugins/webrtc/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "@scrypted/webrtc",
-   "version": "0.2.23",
+   "version": "0.2.24",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
      "": {
         "name": "@scrypted/webrtc",
-         "version": "0.2.23",
+         "version": "0.2.24",
         "dependencies": {
            "@scrypted/common": "file:../../common",
            "@scrypted/sdk": "file:../../sdk",
--- a/plugins/webrtc/package.json
+++ b/plugins/webrtc/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@scrypted/webrtc",
-   "version": "0.2.23",
+   "version": "0.2.24",
   "scripts": {
      "scrypted-setup-project": "scrypted-setup-project",
      "prescrypted-setup-project": "scrypted-package-json",
--- a/plugins/webrtc/src/rtp-forwarders.ts
+++ b/plugins/webrtc/src/rtp-forwarders.ts
@@ -68,7 +68,7 @@ async function setupRtspClient(console: Console, rtspClient: RtspClient, channel
                path: section.control,
                onRtp: (rtspHeader, rtp) => deliver(rtp),
            });
-            console.log('rtsp/udp', section.codec, result);
+            // console.log('rtsp/udp', section.codec, result);
            return false;
        }
    }
@@ -82,7 +82,7 @@ async function setupRtspClient(console: Console, rtspClient: RtspClient, channel
        path: section.control,
        onRtp: (rtspHeader, rtp) => deliver(rtp),
    });
-    console.log('rtsp/tcp', section.codec);
+    // console.log('rtsp/tcp', section.codec);
    return true;
 }

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Koushik Dutta	f9f597ef01	server: guard entire plugin load block	2024-06-01 13:07:55 -07:00
Koushik Dutta	2e07788c0c	server: log plugin load failure	2024-06-01 13:05:56 -07:00
Koushik Dutta	9c0fbc1cb6	common: listenZeroSingleClient configurable timeout	2024-06-01 09:44:51 -07:00
Koushik Dutta	239d49899d	unifi-protect: fix id remapping	2024-06-01 09:19:32 -07:00
Koushik Dutta	2d3589b5a3	unifi-protect: fix id remapping	2024-06-01 08:49:37 -07:00
Koushik Dutta	96ec465a38	unifi: more logging	2024-06-01 08:07:24 -07:00
Koushik Dutta	5bb6b87c7d	predict: yolov10m	2024-05-31 15:17:24 -07:00
Koushik Dutta	fcfedccaf8	postrelease	2024-05-31 14:01:24 -07:00
Koushik Dutta	98373833fd	postrelease	2024-05-31 13:38:43 -07:00
Brett Jia	03588be125	rknn: use correct nativeId for text recognition (#1492 )	2024-05-31 13:24:18 -07:00
Koushik Dutta	cdd81daec5	Merge branch 'main' of github.com:koush/scrypted	2024-05-31 10:49:12 -07:00
Koushik Dutta	d64f90c0c8	predict: republish with smaller plate/face models. fix openvino thread bugs	2024-05-31 10:49:08 -07:00
Brett Jia	ec31dee36e	onnx: fix text recognition thread names (#1491 )	2024-05-31 09:56:18 -07:00
Brett Jia	11f2e88590	rknn: add text recognition (#1490 ) * rknn: add text recognition * disable verbose	2024-05-31 09:56:09 -07:00
Koushik Dutta	bf51ddb2d5	server: checks to ensure plugin restart doesnt ignore zombie states	2024-05-31 08:26:20 -07:00
Koushik Dutta	26000f1828	predict: yolov10	2024-05-30 09:55:28 -07:00
Koushik Dutta	f65485af97	Merge remote-tracking branch 'origin/main' into rebroadcast	2024-05-30 09:37:02 -07:00
Koushik Dutta	72c5690d05	rebroadcast: beta	2024-05-30 09:29:48 -07:00
Koushik Dutta	e076d61122	rebroadcast: fixup reverts	2024-05-30 09:29:14 -07:00
Koushik Dutta	7071808514	Revert "rebroadcast: parser perf refactor" This reverts commit `f677cf7393`.	2024-05-30 09:27:27 -07:00
Koushik Dutta	1e2fd46cd3	Revert "rebroadcast: more parser refactor" This reverts commit `5432b5b917`.	2024-05-30 09:24:53 -07:00
Koushik Dutta	e3cdd4326f	videoanalysis: label scores	2024-05-30 09:21:07 -07:00
Koushik Dutta	227f932ad8	coreml: yolov10	2024-05-30 09:20:53 -07:00
Koushik Dutta	67cec188ce	docker: fix partition detection	2024-05-30 07:49:38 -07:00
Koushik Dutta	1ee276185e	sdk: label score	2024-05-28 21:59:59 -07:00
Brett Jia	42ed855b05	actions: replace local install test with setup-scrypted action (#1488 ) * actions: replace local install test with setup-scrypted action * update * extract server version from package.json * use package-lock.json	2024-05-28 12:59:13 -07:00
Jonathan Yip	93da4eed30	docker: Add security_opt to allow container to talk to host avahi daemon (#1487 )	2024-05-28 09:21:31 -07:00
Long Zheng	a72a596578	homekit: Homekit camera close recording tweaks (#1486 ) * Change throw to log Throw will not work since the `handleFragmentsRequests` async generator is already closed/finished by HAP * Move isOpen check HAP still requests fragment after closing the recording stream. Skip processing it. * Change catch message * Add another !isOpen in case race condition with await	2024-05-27 10:12:00 -07:00
Brett Jia	72663dd68c	installer: allow specifying exact server version to install (#1485 ) * Update install-scrypted-dependencies-mac.sh * Update install-scrypted-dependencies-linux.sh * Update install-scrypted-dependencies-win.ps1 * Update install-scrypted-dependencies-win.ps1 * Update install-scrypted-dependencies-win.ps1 * Update install-scrypted-dependencies-win.ps1	2024-05-26 12:51:02 -07:00
Koushik Dutta	108d57dbdd	Merge remote-tracking branch 'origin/main' into rebroadcast	2024-05-26 09:06:54 -07:00
Brett Jia	bc71fd8515	server: print python interpreter path (#1484 )	2024-05-25 22:29:46 -07:00
Koushik Dutta	a51070767b	homekit: change default advertiser back to ciao due to issues. use identifying material	2024-05-25 19:26:21 -07:00
Koushik Dutta	269cc4dbc9	rebroadcast: beta	2024-05-24 22:43:18 -07:00
Koushik Dutta	684961fa4b	openvino: types	2024-05-24 22:43:11 -07:00
Koushik Dutta	4f60b7e379	sdk: update	2024-05-24 22:42:48 -07:00
Koushik Dutta	5d72061151	ha: publish	2024-05-21 09:19:43 -07:00
Brett Jia	f2c940c1d3	server: add SCRYPTED_COMPATIBILITY_FILE (#1479 )	2024-05-19 13:38:57 -07:00
Koushik Dutta	7e817b0b30	rebroadcast: further removal of legacy code	2024-05-19 11:22:10 -07:00
Brett Jia	75bb15d3b7	Revert "server: make fetching network interfaces optional (#1474 )" (#1478 ) This reverts commit `0160502da8`.	2024-05-17 17:39:24 -07:00
Koushik Dutta	ba1a1eff67	onnx: report device in use	2024-05-17 09:08:07 -07:00
Koushik Dutta	5432b5b917	rebroadcast: more parser refactor	2024-05-16 22:33:23 -07:00
Koushik Dutta	f677cf7393	rebroadcast: parser perf refactor	2024-05-15 14:17:06 -07:00
Koushik Dutta	bdf9278131	rebroadcast: initial pass and removing legacy parsers	2024-05-15 10:03:26 -07:00
Koushik Dutta	0ae93a9c3f	cli: publish	2024-05-15 09:24:18 -07:00
Long Zheng	72422cdd8b	windows: Fix Windows server install with installDir containing space (#1471 ) * Fix server install with installDir containing space * Revert "Fix server install with installDir containing space" This reverts commit `b99ccd3c3d`. * Alternate fix by wrapping each runCommand arg in a quote for Windows	2024-05-15 09:23:05 -07:00
Koushik Dutta	390d1b3329	onnx: add windows cuda support	2024-05-14 15:18:17 -07:00
Koushik Dutta	024e99766a	amcrest: fix legacy boundary https://github.com/koush/scrypted/issues/1475	2024-05-14 15:06:21 -07:00
Brett Jia	0160502da8	server: make fetching network interfaces optional (#1474 )	2024-05-14 13:40:12 -07:00
Koushik Dutta	f0d65982de	postrelease	2024-05-13 19:31:55 -07:00
Koushik Dutta	1445933bd4	postbeta	2024-05-13 17:40:33 -07:00
Koushik Dutta	508f31c254	core: update intel opencl in lxc	2024-05-13 17:36:33 -07:00
Koushik Dutta	fd1aa10a2a	postbeta	2024-05-13 17:08:32 -07:00
Koushik Dutta	fceed68d75	postbeta	2024-05-13 15:49:05 -07:00
Koushik Dutta	955e780c64	docker: fix missing intel dep	2024-05-13 13:22:37 -07:00
Koushik Dutta	452fe20e8f	docker/lxc: update intel graphics install script	2024-05-13 12:44:36 -07:00
Koushik Dutta	9083e16cdb	postbeta	2024-05-13 10:22:09 -07:00
Koushik Dutta	840a278e5d	server: add methods to manage plugin engine.io connections	2024-05-13 10:21:43 -07:00
Koushik Dutta	6d036dbd60	server: fix python runtime worker setup	2024-05-13 10:21:29 -07:00
Koushik Dutta	d5ba6f34d6	onnx: cleanup	2024-05-13 10:00:53 -07:00
Koushik Dutta	0321846c22	storage-settings/videoanalysis: fix default value of 0	2024-05-12 21:43:55 -07:00
Koushik Dutta	714747fcee	videoanalysis: fix bug	2024-05-12 21:20:37 -07:00
Koushik Dutta	e3906da3c4	videoanalysis: new option to reset smart motion sensor only when motion stops	2024-05-12 21:17:41 -07:00
Koushik Dutta	820ef70033	predict plugins: refactor recog, add onnx, fix spurious model leaks	2024-05-12 21:00:48 -07:00
Koushik Dutta	0c95f5c052	tapo: fix 2 way audio on some models	2024-05-11 19:11:58 -07:00
Koushik Dutta	4cfd7c4362	tapo: fix 2 way audio on some models	2024-05-11 19:11:42 -07:00
Koushik Dutta	1e8126dec8	common: header casing preservation	2024-05-11 19:11:30 -07:00
Koushik Dutta	d3fbc58736	openvino: fix yolo nas labels	2024-05-11 12:05:02 -07:00
Koushik Dutta	46113744b3	dev: fix setup script	2024-05-11 10:07:18 -07:00
Koushik Dutta	3947624ae0	openvino: rollback to stable openvino	2024-05-11 09:07:32 -07:00
Koushik Dutta	4ac5ded012	openvino/onnx: publish yolo nas	2024-05-10 20:47:41 -07:00
Koushik Dutta	aadfacf50a	Merge branch 'main' of github.com:koush/scrypted	2024-05-10 19:46:01 -07:00
Koushik Dutta	bb1e0ac82b	coreml: yolo-nas	2024-05-10 19:45:56 -07:00
Koushik Dutta	23a15a1533	docker: mount changes may need systemctl daemon-reload	2024-05-09 12:59:36 -07:00
Koushik Dutta	01dd480c01	Merge branch 'main' of github.com:koush/scrypted	2024-05-08 20:33:17 -07:00
Koushik Dutta	364cae3273	openvino: update pypi	2024-05-08 20:33:12 -07:00
Koushik Dutta	8a986ab707	windows: -y on choco install	2024-05-08 07:56:31 -07:00
Koushik Dutta	ca96959de8	openvino/onnx: move prep into separate threads	2024-05-07 20:42:06 -07:00
Koushik Dutta	2f0ae9ef50	snapshot: fix weird npm cache bug	2024-05-07 20:26:59 -07:00
Koushik Dutta	8b84bac2c2	snapshot/homekit: fix stale snapshots	2024-05-07 20:22:45 -07:00
Koushik Dutta	976ed7f1a5	onnx: multiple gpu support	2024-05-07 11:26:56 -07:00
Koushik Dutta	b4e6821da8	tensorflow-lite: use dict vs queue for perf	2024-05-07 10:52:06 -07:00
Koushik Dutta	540b990a08	ha: Update config.yaml	2024-05-06 18:26:18 -07:00
Koushik Dutta	ce75b072da	various: publish	2024-05-06 16:31:37 -07:00
Greg Thornton	5bca9b7156	homekit: fix late 2way setup (#1461 ) * homekit: fix late 2way setup * homekit: use 2-way state rather than playing/intitializing	2024-05-06 16:28:38 -07:00
Greg Thornton	ae4914346b	webrtc: don't store audio codec before intercom start (#1462 )	2024-05-06 14:08:47 -07:00
slyoldfox	b593209558	Fix bticino intercom, clean up some dependencies (#1463 ) * Allow setting the DEVADDR option * Fix intercom by using startRtpForwarderProcess() Clean up some dependencies * Allow logging errors in sip-manager Cleanup bloated and barely used dependencies	2024-05-06 13:07:30 -07:00
Koushik Dutta	9df399708f	postrelease	2024-05-04 13:07:02 -07:00