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objectdetection: refactor

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This commit is contained in:
Koushik Dutta
2021-12-10 14:03:59 -08:00
parent 24181ab217
commit 41c7912716
6 changed files with 214 additions and 151 deletions
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179
plugins/objectdetector/src/main.ts
View File

@@ -3,8 +3,8 @@ import sdk from '@scrypted/sdk';
import { SettingsMixinDeviceBase } from "../../../common/src/settings-mixin";
import { alertRecommendedPlugins } from '@scrypted/common/src/alert-recommended-plugins';
import { DenoisedDetectionEntry, denoiseDetections } from './denoise';
import { AutoenableMixinProvider} from "../../../common/src/autoenable-mixin-provider"
import { AutoenableMixinProvider } from "../../../common/src/autoenable-mixin-provider"
export interface DetectionInput {
jpegBuffer?: Buffer;
input: any;
@@ -12,7 +12,6 @@ export interface DetectionInput {
const { mediaManager, systemManager, log } = sdk;
const defaultMinConfidence = 0.7;
const defaultDetectionDuration = 60;
const defaultDetectionInterval = 60;
const defaultDetectionTimeout = 10;
@@ -23,7 +22,6 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
detectionListener: EventListenerRegister;
detections = new Map<string, DetectionInput>();
cameraDevice: ScryptedDevice & Camera & VideoCamera & MotionSensor;
minConfidence = parseFloat(this.storage.getItem('minConfidence')) || defaultMinConfidence;
detectionTimeout = parseInt(this.storage.getItem('detectionTimeout')) || defaultDetectionTimeout;
detectionDuration = parseInt(this.storage.getItem('detectionDuration')) || defaultDetectionDuration;
detectionInterval = parseInt(this.storage.getItem('detectionInterval')) || defaultDetectionInterval;
@@ -33,6 +31,7 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
detectionId: string;
running = false;
hasMotionType: boolean;
settings: Setting[];
constructor(mixinDevice: VideoCamera & Settings, mixinDeviceInterfaces: ScryptedInterface[], mixinDeviceState: { [key: string]: any }, public objectDetectionPlugin: ObjectDetectorMixin, public objectDetection: ObjectDetection & ScryptedDevice) {
super(mixinDevice, mixinDeviceState, {
@@ -49,8 +48,6 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
this.bindObjectDetection();
this.register();
this.resetDetectionTimeout();
this.snapshotDetection();
}
clearDetectionTimeout() {
@@ -66,18 +63,31 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
}, this.detectionInterval * 1000);
}
async snapshotDetection() {
if (this.hasMotionType === undefined) {
this.hasMotionType = false;
const models = await this.objectDetection.getInferenceModels();
for (const model of models) {
if (model.classes.includes('motion')) {
this.hasMotionType = true;
break;
}
}
async ensureSettings(): Promise<Setting[]> {
if (this.hasMotionType !== undefined)
return;
this.hasMotionType = false;
const model = await this.objectDetection.getDetectionModel();
this.hasMotionType = model.classes.includes('motion');
this.settings = model.settings;
}
async getCurrentSettings() {
await this.ensureSettings();
if (!this.settings)
return;
const ret: any = {};
for (const setting of this.settings) {
ret[setting.key] = this.storage.getItem(setting.key) || setting.value;
}
return ret;
}
async snapshotDetection() {
await this.ensureSettings();
if (this.hasMotionType) {
await this.startVideoDetection();
return;
@@ -86,7 +96,7 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
const picture = await this.cameraDevice.takePicture();
const detections = await this.objectDetection.detectObjects(picture, {
detectionId: this.detectionId,
minScore: this.minConfidence,
settings: await this.getCurrentSettings(),
});
this.objectsDetected(detections);
}
@@ -110,6 +120,8 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
this.running = eventData.running;
});
this.snapshotDetection();
}
async register() {
@@ -117,36 +129,36 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
if (!this.cameraDevice.motionDetected)
return;
await this.startVideoDetection();
await this.startVideoDetection();
});
}
async startVideoDetection() {
// prevent stream retrieval noise until notified that the detection is no logner running.
if (this.running)
return;
this.running = true;
// prevent stream retrieval noise until notified that the detection is no logner running.
if (this.running)
return;
this.running = true;
try {
let selectedStream: MediaStreamOptions;
try {
let selectedStream: MediaStreamOptions;
const streamingChannel = this.storage.getItem('streamingChannel');
if (streamingChannel) {
const msos = await this.cameraDevice.getVideoStreamOptions();
selectedStream = msos.find(mso => mso.name === streamingChannel);
}
const session = await this.objectDetection?.detectObjects(await this.cameraDevice.getVideoStream(selectedStream), {
detectionId: this.detectionId,
duration: this.getDetectionDuration(),
minScore: this.minConfidence,
});
this.running = session.running;
}
catch (e) {
this.running = false;
const streamingChannel = this.storage.getItem('streamingChannel');
if (streamingChannel) {
const msos = await this.cameraDevice.getVideoStreamOptions();
selectedStream = msos.find(mso => mso.name === streamingChannel);
}
const session = await this.objectDetection?.detectObjects(await this.cameraDevice.getVideoStream(selectedStream), {
detectionId: this.detectionId,
duration: this.getDetectionDuration(),
settings: await this.getCurrentSettings(),
});
this.running = session.running;
}
catch (e) {
this.running = false;
}
}
getDetectionDuration() {
@@ -185,7 +197,7 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
return;
}
const detections = detectionResult.detections.filter(d => d.score >= this.minConfidence);
const { detections } = detectionResult;
const found: DenoisedDetectionEntry<ObjectDetectionResult>[] = [];
denoiseDetections<ObjectDetectionResult>(this.currentDetections, detections.map(detection => ({
@@ -257,12 +269,7 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
}
async getObjectTypes(): Promise<ObjectDetectionTypes> {
const models = await this.objectDetection?.getInferenceModels();
return {
classes: models?.[0]?.classes || [],
faces: true,
people: models?.[0]?.people,
}
return this.objectDetection.getDetectionModel();
}
async getDetectionInput(detectionId: any): Promise<MediaObject> {
@@ -298,46 +305,47 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
});
}
settings.push(
{
title: 'Minimum Detection Confidence',
description: 'Higher values eliminate false positives and low quality recognition candidates.',
key: 'minConfidence',
type: 'number',
value: this.minConfidence.toString(),
},
{
title: 'Detection Duration',
description: 'The duration in seconds to analyze video when motion occurs.',
key: 'detectionDuration',
type: 'number',
value: this.detectionDuration.toString(),
},
{
title: 'Idle Detection Interval',
description: 'The interval in seconds to analyze snapshots when there is no motion.',
key: 'detectionInterval',
type: 'number',
value: this.detectionInterval.toString(),
},
{
title: 'Detection Timeout',
description: 'Timeout in seconds before removing an object that is no longer detected.',
key: 'detectionTimeout',
type: 'number',
value: this.detectionTimeout.toString(),
},
);
if (!this.hasMotionType) {
settings.push(
{
title: 'Detection Duration',
description: 'The duration in seconds to analyze video when motion occurs.',
key: 'detectionDuration',
type: 'number',
value: this.detectionDuration.toString(),
},
{
title: 'Idle Detection Interval',
description: 'The interval in seconds to analyze snapshots when there is no motion.',
key: 'detectionInterval',
type: 'number',
value: this.detectionInterval.toString(),
},
{
title: 'Detection Timeout',
description: 'Timeout in seconds before removing an object that is no longer detected.',
key: 'detectionTimeout',
type: 'number',
value: this.detectionTimeout.toString(),
},
)
}
if (this.settings) {
settings.push(...this.settings.map(setting =>
Object.assign({}, setting, {
value: this.storage.getItem(setting.key) || setting.value,
} as Setting))
);
}
return settings;
}
async putMixinSetting(key: string, value: string | number | boolean): Promise<void> {
const vs = value.toString();
this.storage.setItem(key, vs);
if (key === 'minConfidence') {
this.minConfidence = parseFloat(vs) || defaultMinConfidence;
}
else if (key === 'detectionDuration') {
if (key === 'detectionDuration') {
this.detectionDuration = parseInt(vs) || defaultDetectionDuration;
}
else if (key === 'detectionInterval') {
@@ -350,6 +358,13 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
else if (key === 'streamingChannel') {
this.bindObjectDetection();
}
else {
const settings = await this.getCurrentSettings();
if (settings && settings[key]) {
settings[key] = value;
}
this.bindObjectDetection();
}
}
release() {
@@ -365,7 +380,7 @@ class ObjectDetectionMixin extends SettingsMixinDeviceBase<VideoCamera & Camera
}
class ObjectDetectorMixin extends MixinDeviceBase<ObjectDetection> implements MixinProvider {
constructor(mixinDevice: ObjectDetection, mixinDeviceInterfaces: ScryptedInterface[], mixinDeviceState: DeviceState, mixinProviderNativeId: ScryptedNativeId) {
constructor(mixinDevice: ObjectDetection, mixinDeviceInterfaces: ScryptedInterface[], mixinDeviceState: DeviceState, mixinProviderNativeId: ScryptedNativeId) {
super(mixinDevice, mixinDeviceInterfaces, mixinDeviceState, mixinProviderNativeId);
// trigger mixin creation. todo: fix this to not be stupid hack.

70
plugins/opencv2/src/main.py
View File

@@ -1,15 +1,11 @@
from __future__ import annotations
import threading
from detect import DetectionSession, DetectPlugin
from typing import Any, List
from detect.safe_set_result import safe_set_result
import scrypted_sdk
import numpy as np
import io
import multiprocessing
import cv2
import imutils
from scrypted_sdk.types import ObjectDetectionModel, ObjectDetectionResult, ObjectsDetected
from scrypted_sdk.types import ObjectDetectionModel, ObjectDetectionResult, ObjectsDetected, Setting
def gst_to_opencv(sample):
buf = sample.get_buffer()
@@ -37,29 +33,65 @@ class OpenCVDetectionSession(DetectionSession):
self.frames_to_skip = 0
defaultThreshold = 25
defaultArea = 0
defaultArea = 2000
defaultInterval = 10
class OpenCVPlugin(DetectPlugin):
async def getInferenceModels(self) -> list[ObjectDetectionModel]:
ret: List[ObjectDetectionModel] = []
d = {
'id': 'opencv',
async def getDetectionModel(self) -> ObjectDetectionModel:
d: ObjectDetectionModel = {
'name': 'OpenCV',
'classes': ['motion'],
}
ret.append(d)
return ret
settings = [
{
'title': "Motion Area",
'description': "The area size required to trigger motion. Higher values (larger areas) are less sensitive.",
'value': defaultArea,
'key': 'area',
'placeholder': defaultArea,
'type': 'number',
},
{
'title': "Motion Threshold",
'description': "The threshold required to consider a pixel changed. Higher values (larger changes) are less sensitive.",
'value': defaultThreshold,
'key': 'threshold',
'placeholder': defaultThreshold,
'type': 'number',
},
{
'title': "Frame Analysis Interval",
'description': "The number of frames to wait between motion analysis.",
'value': defaultInterval,
'key': 'interval',
'placeholder': defaultInterval,
'type': 'number',
},
]
d['settings'] = settings
return d
def get_pixel_format(self):
return 'BGRA'
def detect(self, detection_session: OpenCVDetectionSession, frame, min_score: float, src_size, inference_box) -> ObjectsDetected:
def parse_settings(self, settings: Any):
area = defaultArea
threshold = defaultThreshold
interval = defaultInterval
if settings:
area = float(settings.get('area', area))
threshold = int(settings.get('threshold', threshold))
interval = float(settings.get('interval', interval))
return area, threshold, interval
def detect(self, detection_session: OpenCVDetectionSession, frame, settings: Any, src_size, inference_box) -> ObjectsDetected:
area, threshold, interval = self.parse_settings(settings)
if detection_session.frames_to_skip:
detection_session.frames_to_skip = detection_session.frames_to_skip - 1
return
else:
detection_session.frames_to_skip = 10
detection_session.frames_to_skip = interval
# todo: go from native yuv to gray. tested this with GRAY8 in the gstreamer
# pipeline but it failed...
@@ -73,7 +105,7 @@ class OpenCVPlugin(DetectPlugin):
frameDelta = cv2.absdiff(detection_session.previous_frame, curFrame)
detection_session.previous_frame = curFrame
_, thresh = cv2.threshold(frameDelta, defaultThreshold, 255, cv2.THRESH_BINARY)
_, thresh = cv2.threshold(frameDelta, threshold, 255, cv2.THRESH_BINARY)
dilated = cv2.dilate(thresh, None, iterations=2)
fcontours = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(fcontours)
@@ -85,7 +117,7 @@ class OpenCVPlugin(DetectPlugin):
for c in contours:
contour_area = cv2.contourArea(c)
if contour_area > defaultArea:
if contour_area > area:
x, y, w, h = cv2.boundingRect(c)
detection: ObjectDetectionResult = {}
@@ -122,9 +154,9 @@ class OpenCVPlugin(DetectPlugin):
detection_session.cap = None
return super().end_session(detection_session)
def run_detection_gstsample(self, detection_session: OpenCVDetectionSession, gst_sample, min_score: float, src_size, inference_box, scale)-> ObjectsDetected:
def run_detection_gstsample(self, detection_session: OpenCVDetectionSession, gst_sample, settings: Any, src_size, inference_box, scale)-> ObjectsDetected:
mat = gst_to_opencv(gst_sample)
return self.detect(detection_session, mat, min_score, src_size, inference_box)
return self.detect(detection_session, mat, settings, src_size, inference_box)
def create_detection_session(self):
return OpenCVDetectionSession()

21
plugins/tensorflow-lite/src/detect/__init__.py
View File

@@ -21,7 +21,7 @@ class DetectionSession:
timerHandle: TimerHandle
future: Future
loop: AbstractEventLoop
score_threshold: float
settings: Any
running: bool
thread: Any
@@ -89,7 +89,7 @@ class DetectPlugin(scrypted_sdk.ScryptedDeviceBase, ObjectDetection):
detection_result['timestamp'] = int(time.time() * 1000)
return detection_result
def run_detection_jpeg(self, detection_session: DetectionSession, image_bytes: bytes, min_score: float) -> ObjectsDetected:
def run_detection_jpeg(self, detection_session: DetectionSession, image_bytes: bytes, settings: Any) -> ObjectsDetected:
pass
def get_detection_input_size(self, src_size):
@@ -98,11 +98,11 @@ class DetectPlugin(scrypted_sdk.ScryptedDeviceBase, ObjectDetection):
def create_detection_session(self):
return DetectionSession()
def run_detection_gstsample(self, detection_session: DetectionSession, gst_sample, min_score: float, src_size, inference_box, scale)-> ObjectsDetected:
def run_detection_gstsample(self, detection_session: DetectionSession, gst_sample, settings: Any, src_size, inference_box, scale)-> ObjectsDetected:
pass
async def detectObjects(self, mediaObject: MediaObject, session: ObjectDetectionSession = None) -> ObjectsDetected:
score_threshold = None
settings = None
duration = None
detection_id = None
detection_session = None
@@ -110,7 +110,7 @@ class DetectPlugin(scrypted_sdk.ScryptedDeviceBase, ObjectDetection):
if session:
detection_id = session.get('detectionId', None)
duration = session.get('duration', None)
score_threshold = session.get('minScore', None)
settings = session.get('settings', None)
is_image = mediaObject and mediaObject.mimeType.startswith('image/')
@@ -132,8 +132,7 @@ class DetectPlugin(scrypted_sdk.ScryptedDeviceBase, ObjectDetection):
detection_session = self.create_detection_session()
detection_session.id = detection_id
detection_session.score_threshold = score_threshold or - \
float('inf')
detection_session.settings = settings
loop = asyncio.get_event_loop()
detection_session.loop = loop
self.detection_sessions[detection_id] = detection_session
@@ -147,15 +146,15 @@ class DetectPlugin(scrypted_sdk.ScryptedDeviceBase, ObjectDetection):
return self.create_detection_result_status(detection_id, False)
if is_image:
return self.run_detection_jpeg(detection_session, bytes(await scrypted_sdk.mediaManager.convertMediaObjectToBuffer(mediaObject, 'image/jpeg')), score_threshold)
return self.run_detection_jpeg(detection_session, bytes(await scrypted_sdk.mediaManager.convertMediaObjectToBuffer(mediaObject, 'image/jpeg')), settings)
new_session = not detection_session.running
if new_session:
detection_session.running = True
detection_session.setTimeout(duration / 1000)
if score_threshold != None:
detection_session.score_threshold = score_threshold
if settings != None:
detection_session.settings = settings
if not new_session:
print("existing session", detection_session.id)
@@ -191,7 +190,7 @@ class DetectPlugin(scrypted_sdk.ScryptedDeviceBase, ObjectDetection):
def user_callback(gst_sample, src_size, inference_box):
try:
detection_result = self.run_detection_gstsample(
detection_session, gst_sample, detection_session.score_threshold, src_size, inference_box, scale)
detection_session, gst_sample, detection_session.settings, src_size, inference_box, scale)
if detection_result:
self.detection_event(detection_session, detection_result)

77
plugins/tensorflow-lite/src/main.py
View File

@@ -1,35 +1,35 @@
from __future__ import annotations
from scrypted_sdk.types import ObjectDetectionModel, ObjectDetectionResult, ObjectsDetected, Setting
import asyncio
from detect.safe_set_result import safe_set_result
from third_party.sort import Sort
import multiprocessing
import io
import common
from PIL import Image
from pycoral.adapters import detect
from pycoral.adapters.common import input_size
from pycoral.utils.edgetpu import run_inference
from pycoral.utils.edgetpu import list_edge_tpus
from pycoral.utils.edgetpu import make_interpreter
import tflite_runtime.interpreter as tflite
import re
import numpy as np
import scrypted_sdk
from typing import Any, List
import matplotlib
from detect import DetectionSession, DetectPlugin
matplotlib.use('Agg')
from typing import List
import scrypted_sdk
import numpy as np
import re
import tflite_runtime.interpreter as tflite
from pycoral.utils.edgetpu import make_interpreter
from pycoral.utils.edgetpu import list_edge_tpus
from pycoral.utils.edgetpu import run_inference
from pycoral.adapters.common import input_size
from pycoral.adapters import detect
from PIL import Image
import common
import io
import multiprocessing
from third_party.sort import Sort
from detect.safe_set_result import safe_set_result
import asyncio
from scrypted_sdk.types import ObjectDetectionModel, ObjectDetectionResult, ObjectsDetected
class TrackerDetectionSession(DetectionSession):
def __init__(self) -> None:
super().__init__()
self.tracker = Sort()
def parse_label_contents(contents: str):
lines = contents.splitlines()
ret = {}
@@ -41,6 +41,8 @@ def parse_label_contents(contents: str):
ret[row_number] = content.strip()
return ret
defaultThreshold = .4
class CoralPlugin(DetectPlugin):
def __init__(self, nativeId: str | None = None):
super().__init__(nativeId=nativeId)
@@ -60,19 +62,25 @@ class CoralPlugin(DetectPlugin):
self.interpreter.allocate_tensors()
self.mutex = multiprocessing.Lock()
async def getInferenceModels(self) -> list[ObjectDetectionModel]:
ret: List[ObjectDetectionModel] = []
async def getDetectionModel(self) -> ObjectDetectionModel:
_, height, width, channels = self.interpreter.get_input_details()[
0]['shape']
d = {
'id': 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu',
d: ObjectDetectionModel = {
'name': 'Coco SSD',
'classes': list(self.labels.values()),
'inputShape': [int(width), int(height), int(channels)],
'inputSize': [int(width), int(height), int(channels)],
}
ret.append(d)
return ret
setting: Setting = {
'title': 'Minimum Detection Confidence',
'description': 'Higher values eliminate false positives and low quality recognition candidates.',
'key': 'score_threshold',
'type': 'number',
'value': defaultThreshold,
'placeholder': defaultThreshold,
}
d['settings'] = [setting]
return d
def create_detection_result(self, objs, size, tracker: Sort = None):
detections: List[ObjectDetectionResult] = []
@@ -129,7 +137,13 @@ class CoralPlugin(DetectPlugin):
return detection_result
def run_detection_jpeg(self, detection_session: TrackerDetectionSession, image_bytes: bytes, min_score: float) -> ObjectsDetected:
def parse_settings(self, settings: Any):
score_threshold = .4
if settings:
score_threshold = float(settings.get('score_threshold', score_threshold))
return score_threshold
def run_detection_jpeg(self, detection_session: TrackerDetectionSession, image_bytes: bytes, settings: Any) -> ObjectsDetected:
stream = io.BytesIO(image_bytes)
image = Image.open(stream)
@@ -140,22 +154,25 @@ class CoralPlugin(DetectPlugin):
if detection_session:
tracker = detection_session.tracker
score_threshold = self.parse_settings(settings)
with self.mutex:
self.interpreter.invoke()
objs = detect.get_objects(
self.interpreter, score_threshold=min_score or -float('inf'), image_scale=scale)
self.interpreter, score_threshold=score_threshold, image_scale=scale)
return self.create_detection_result(objs, image.size, tracker=tracker)
def get_detection_input_size(self, src_size):
return input_size(self.interpreter)
def run_detection_gstsample(self, detection_session: TrackerDetectionSession, gstsample, min_score: float, src_size, inference_box, scale)-> ObjectsDetected:
def run_detection_gstsample(self, detection_session: TrackerDetectionSession, gstsample, settings: Any, src_size, inference_box, scale) -> ObjectsDetected:
score_threshold = self.parse_settings(settings)
gst_buffer = gstsample.get_buffer()
with self.mutex:
run_inference(self.interpreter, gst_buffer)
objs = detect.get_objects(
self.interpreter, score_threshold=min_score, image_scale=scale)
self.interpreter, score_threshold=score_threshold, image_scale=scale)
return self.create_detection_result(objs, src_size, detection_session.tracker)

16
server/package-lock.json generated
View File

@@ -9,7 +9,7 @@
"version": "0.0.97",
"license": "ISC",
"dependencies": {
"@scrypted/sdk": "^0.0.128",
"@scrypted/sdk": "^0.0.129",
"adm-zip": "^0.5.3",
"axios": "^0.21.1",
"body-parser": "^1.19.0",
@@ -36,7 +36,7 @@
"ws": "^8.2.3"
},
"bin": {
"scrypted-server": "bin/scrypted-server"
"scrypted-serve": "bin/scrypted-serve"
},
"devDependencies": {
"@mapbox/node-pre-gyp": "^1.0.5",
@@ -1304,9 +1304,9 @@
}
},
"node_modules/@scrypted/sdk": {
"version": "0.0.128",
"resolved": "https://registry.npmjs.org/@scrypted/sdk/-/sdk-0.0.128.tgz",
"integrity": "sha512-JHgDb4H4zYrGiZH9qO8Oqwq3N5YmhH3KpzBoXPzpHIKNybF18Wve7brcVhqwQWNi+CcpJo5gonOE2MYEQBxvWg==",
"version": "0.0.129",
"resolved": "https://registry.npmjs.org/@scrypted/sdk/-/sdk-0.0.129.tgz",
"integrity": "sha512-km3/3QeDS4w+nLb2PVNOVLXrzzTBxMRcOTEiWFW5OVt8AZzXOEfS4BCEY6nj6OnVKiznGdXiwgbwY8le74hf8w==",
"dependencies": {
"@babel/plugin-proposal-class-properties": "^7.14.5",
"@babel/plugin-proposal-nullish-coalescing-operator": "^7.14.5",
@@ -7172,9 +7172,9 @@
}
},
"@scrypted/sdk": {
"version": "0.0.128",
"resolved": "https://registry.npmjs.org/@scrypted/sdk/-/sdk-0.0.128.tgz",
"integrity": "sha512-JHgDb4H4zYrGiZH9qO8Oqwq3N5YmhH3KpzBoXPzpHIKNybF18Wve7brcVhqwQWNi+CcpJo5gonOE2MYEQBxvWg==",
"version": "0.0.129",
"resolved": "https://registry.npmjs.org/@scrypted/sdk/-/sdk-0.0.129.tgz",
"integrity": "sha512-km3/3QeDS4w+nLb2PVNOVLXrzzTBxMRcOTEiWFW5OVt8AZzXOEfS4BCEY6nj6OnVKiznGdXiwgbwY8le74hf8w==",
"requires": {
"@babel/plugin-proposal-class-properties": "^7.14.5",
"@babel/plugin-proposal-nullish-coalescing-operator": "^7.14.5",

2
server/package.json
View File

@@ -3,7 +3,7 @@
"version": "0.0.97",
"description": "",
"dependencies": {
"@scrypted/sdk": "^0.0.128",
"@scrypted/sdk": "^0.0.129",
"adm-zip": "^0.5.3",
"axios": "^0.21.1",
"body-parser": "^1.19.0",