important parallelization improvements and fixes

.gitignore

@@ -112,8 +112,9 @@ dist
 # TernJS port file
 .tern-port
 
-# Stores VSCode versions used for testing VSCode extensions
+# VSCode files
 .vscode-test
+.vscode
 
 # yarn v2
 .yarn/cache

Backend/.env-openvidu

@@ -1,5 +1,3 @@
 OPENVIDU_SECRET=MY_SECRET
 OPENVIDU_WEBHOOK=true
 OPENVIDU_WEBHOOK_ENDPOINT=https://localhost:8080/media-server
-
-

Backend/src/app/machineLearning/machineLearning.controller.ts

@@ -114,7 +114,7 @@ export class MachineLearningController {
       new ParseFilePipeBuilder()
         .addFileTypeValidator({ fileType: 'image/jpeg' })
         .addMaxSizeValidator({
-          maxSize: 100000, // 100Kb
+          maxSize: 50000000, // 100Kb
         })
         .build({
           errorHttpStatusCode: HttpStatus.UNPROCESSABLE_ENTITY,

Backend/src/cameraStream/cameraStream.gateway.spec.ts

@@ -2,7 +2,6 @@ import { Test, TestingModule } from '@nestjs/testing';
 import { CameraStreamGateway } from './cameraStream.gateway';
 import { JwtModule } from '@nestjs/jwt';
 
-import { OpenVidu } from 'openvidu-node-client';
 import { CSSOpenVidu } from './open-vidu.service';
 import { DatabaseService } from '../database/database.service';
 

Backend/src/database/database.service.spec.ts

@@ -21,7 +21,7 @@ describe('DatabaseService', () => {
   });
 
   it('should get aggregated data', async () => {
-    const aggregateData = await databaseService.aggregateCamera();
+    const aggregateData = await databaseService.aggregateCamera('all');
 
     expect(aggregateData).not.toBeNull();
   });

Backend/src/database/database.service.ts

@@ -59,11 +59,29 @@ export class DatabaseService {
     );
   }
 
+  async initDBNvr() {
+    const data = {
+      name: 'NVR',
+    };
+    const filter = {
+      name: 'NVR',
+      ip: process.env.NVR_IP_ADDRESS,
+      channels: [0, 1, 2, 3, 4, 5, 6, 7],
+    };
+
+    const size = await this.DB.collection('General').countDocuments(filter);
+
+    if (size == 0) {
+      await this.DB.collection('General').insertOne(data);
+    }
+  }
+
   constructor() {
     const client = new MongoClient(url);
 
     this.DB = client.db('csd');
     this.initDBUser();
+    this.initDBNvr();
   }
 
   async addData(data: DataType<number>) {
@@ -101,9 +119,23 @@ export class DatabaseService {
     else return res.toArray();
   }
 
-  aggregateCamera(filter?: FiltersAvailable): Promise<Document[]> {
+  aggregateCamera(filter: FiltersAvailable): Promise<Document[]> {
     return this.DB.collection('cameras')
-      .aggregate()
+      .aggregate([
+        {
+          $addFields: {
+            intrusionDetection: {
+              $cond: {
+                if: {
+                  $ifNull: ['$intrusionDetection', false],
+                },
+                then: true,
+                else: false,
+              },
+            },
+          },
+        },
+      ])
       .match(this.getFilter(filter))
       .group({
         _id: '$cameraId',
@@ -149,7 +181,7 @@ export class DatabaseService {
     });
   }
 
-  private getFilter(filter?: FiltersAvailable) {
+  private getFilter(filter: FiltersAvailable) {
     switch (filter) {
       case 'intrusionDetection':
         return {
@@ -206,54 +238,13 @@ export class DatabaseService {
   // TODO TESTME
   // Returns NVR info such as IP address and available channels
   async getNVRData(): Promise<Document> {
-    const array = await this.getOtherwiseInsert(
-      'General',
-      {
-        name: 'NVR',
-      },
-      {
-        name: 'NVR',
-        ip: process.env.NVR_IP_ADDRESS,
-        channels: [0, 1, 2, 3, 4, 5, 6, 7],
-      },
-    );
+    const array = await this.getRawDataArray('General', {
+      name: 'NVR',
+    });
+
     return {
       ip: array[0].ip,
       channels: array[0].channels,
     };
-
-    // try {
-    //   const array = await this.getRawDataArray('General', {
-    //     name: 'NVR',
-    //   });
-    //   return array[0];
-    // } catch (e) {
-    //   if (e instanceof NotFoundException) {
-    //     const data = {
-    //       name: 'NVR',
-    //       ip: process.env.NVR_IP_ADDRESS,
-    //       channels: [0, 1, 2, 3, 4, 5, 6, 7],
-    //     };
-    //
-    //     await this.DB.collection(`General`).insertOne(data);
-    //     return data;
-    //   } else {
-    //     console.error(e);
-    //   }
-    // }
-  }
-
-  async getOtherwiseInsert(
-    name: string,
-    filter: Filter<Document>,
-    data: Document,
-  ): Promise<WithId<Document>[]> {
-    const size = await this.DB.collection(name).countDocuments(filter);
-
-    if (size == 0) {
-      await this.DB.collection(name).insertOne(data);
-    }
-
-    return await this.getRawDataArray(name, filter);
   }
 }

ImageRecognition/README.md

@@ -0,0 +1,24 @@
+## Technology
+<p align="center">
+  <a href="https://www.ultralytics.com" target="blank"><img src="https://assets-global.website-files.com/646dd1f1a3703e451ba81ecc/64777c3e071ec953437e6950_logo.svg"  alt="Ultralytics Yolo" /></a>
+</p>
+
+Ultralytics YOLO for building accurate and efficient object detection models.
+
+## Running the app
+First, open the terminal and move to the ImageRecognition directiory.
+Run send_frame.py for sending camera frames:
+
+```bash
+python send_frame.py
+```
+
+To start the object detection function move to the src directory and run ObjectDetectionYOLO.py:
+
+```bash
+python ObjectDetectionYOLO.py
+```
+
+## Learn More
+To learn more about YOLO architecture, take a look at the following resource:
+- [YOLO Documentation](https://docs.ultralytics.com).

ImageRecognition/main.py

@@ -0,0 +1,19 @@
+import concurrent.futures
+from src.ObjectDetectionYOLO import detection
+
+if __name__ == "__main__":
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        args: (int, int) = [(i, i) for i in range(0, 8)]
+
+        results = [executor.submit(detection, *arg) for arg in args]
+        concurrent.futures.wait(results)
+
+
+cam1 = "rtsp://192.168.1.41:80/ch0_0.264"
+cam2 = "rtsp://192.168.1.41:80/ch1_0.264"
+cam3 = "rtsp://192.168.1.41:80/ch2_0.264"
+cam4 = "rtsp://192.168.1.41:80/ch3_0.264"
+cam5 = "rtsp://192.168.1.41:80/ch4_0.264"
+cam6 = "rtsp://192.168.1.41:80/ch5_0.264"
+cam7 = "rtsp://192.168.1.41:80/ch6_0.264"
+cam8 = "rtsp://192.168.1.41:80/ch7_0.264"

ImageRecognition/requirements.txt

@@ -1,4 +1,6 @@
 opencv-python~=4.8.1.78
 numpy~=1.26.1
 ping3~=4.0.4
-ffmpeg-python~=0.2.0
+ultralytics>=8.1.5
+scikit-learn~=1.3.2
+torch>=1.8

ImageRecognition/send_frame.py

@@ -1,6 +1,5 @@
 import cv2
 import socketio
-import base64
 import threading
 
 #cameras data
@@ -40,7 +39,7 @@ def send_frames(camera_id, capture, interval=0.1):
 for camera_id, camera_url in cameras.items():
     capture = cv2.VideoCapture(camera_url)
     if not capture.isOpened():
-        print(f"Errore nell'apertura della telecamera {camera_id}")
+        print(f"Error stream with camera {camera_id}")
         continue
 
     threading.Thread(target=send_frames, args=(camera_id, capture)).start()