Glydric

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

@@ -124,9 +124,9 @@ export class MachineLearningController {
   ) {
     const date = new Date();
 
-    console.log('Adding data to database');
+    // console.log('Adding data to database');
     await this.database.addData(new DataType(cameraId, date, null, file));
-    console.log('Sending intrusion detection notification');
+    // console.log('Sending intrusion detection notification');
     await this.telegramApi.sendIntrusionDetectionNotification(
       cameraId,
       date,

Backend/src/telegram/telegram.service.ts

@@ -7,7 +7,6 @@ import * as TelegramBot from 'node-telegram-bot-api';
 import * as process from 'process';
 import { DatabaseService } from '../database/database.service';
 import UserDTO from '../user.dto';
-import { JwtService } from '@nestjs/jwt';
 import * as bcrypt from 'bcrypt';
 
 @Injectable()

ImageRecognition/main.py

@@ -7,18 +7,11 @@
     initBufferSize(MAX)
 
     with concurrent.futures.ThreadPoolExecutor() as executor:
-        # args: (int, int) = [(i, i) for i in range(0, 8)]
-        args: (int, int) = [(i, i) for i in range(2, 4)]
+        args: (int, int) = [(i, i) for i in range(0, MAX)]
+        # args: (int, int) = [(i, i) for i in range(0, 1)]
 
         results = [executor.submit(detection, *arg) for arg in args]
         concurrent.futures.wait(results)
 
-
+# example
 # 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/src/ObjectDetectionYOLO.py

@@ -10,16 +10,15 @@
 model = YOLO("yolo-Wheights/yolov8n.pt")
 
 url = "https://localhost:8080"
-MSE_VALUE = 105.80
+MSE_VALUE = 105.57
 
 last_sent_image = []
+frame_rate = 3
 
 
 def post_request(camera_id: int, image):
     global last_sent_image
 
-    # image = cv2.resize(image, (0.5, 0.5))
-
     if last_sent_image[camera_id] is not None:
         # Resizing
         flat_last_sent_image = last_sent_image[camera_id].flatten()
@@ -69,51 +68,58 @@ def detection(camera_id: int, _: int):
 
     print(f"capturing {camera_id}")
 
-    # FIXME camera capture takes to much time to load the camera connection (first time)
-    cap = cv2.VideoCapture(f"rtsp://192.168.1.41:80/ch{camera_id}_0.264")
-    cap.set(cv2.CAP_PROP_FPS, 5)
+    # cap = cv2.VideoCapture(f"rtsp://172.20.14.1:80/ch{camera_id}_0.264")
+
+    while True:
+        cap = cv2.VideoCapture(f"rtsp://192.168.1.41:80/ch{camera_id}_0.264")
+
+        prev_time = 0
+
+        # TODO handle status
+        status = "offline"
 
-    # TODO handle status
-    status = "offline"
+        print(f"started {camera_id}")
 
-    print(f"started {camera_id}")
+        while cap.isOpened():
+            success, img = cap.read()
 
-    while cap.isOpened():
-        success, img = cap.read()
+            time_elapsed = time.time() - prev_time
 
-        if not success:
-            print(f"No frame {camera_id}")
-            continue
+            if time_elapsed <= 1.0 / frame_rate:
+                continue
 
-        print(f"Captured frame from {camera_id}")
+            prev_time = time.time()
 
-        # cv2.imshow(f"Camera {camera_id}", img)
-        # cv2.waitKey(0)
-        # cv2.destroyAllWindows()
+            if not success:
+                cap.release()
+                continue
+            print(f"Captured frame from {camera_id}")
 
-        status = "online"
-        results = model(img, stream=True, classes=0, conf=0.5, verbose=False)
-        foundPerson = False
-        # coordinates
-        for r in results:
-            boxes = r.boxes
-            for box in boxes:
-                # bounding box
-                x1, y1, x2, y2 = box.xyxy[0]
-                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-                cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
-                # confidence
-                confidence = math.ceil((box.conf[0] * 100)) / 100
-                print("Confidence --->", confidence)
+            status = "online"
+            results = model(img, stream=True, classes=0, conf=0.5, verbose=False)
+            foundPerson = False
+            # coordinates
+            for r in results:
+                boxes = r.boxes
+                for box in boxes:
+                    # bounding box
+                    x1, y1, x2, y2 = box.xyxy[0]
+                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                    cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
+                    # confidence
+                    confidence = math.ceil((box.conf[0] * 100)) / 100
+                    print(
+                        f"    Person detected in {camera_id} with Confidence ---> {confidence}"
+                    )
 
-                foundPerson = True
+                    foundPerson = True
 
-        if foundPerson:
-            _, image = cv2.imencode(".jpg", img)
+            if foundPerson:
+                _, image = cv2.imencode(".jpg", img)
 
-            post_request(camera_id, image)
+                post_request(camera_id, image)
 
-    cap.release()
+        cap.release()
 
 
 def initBufferSize(size: int):

docker-compose.yml

@@ -12,20 +12,20 @@ services:
     env_file:
       - .secrets.yml
 
-  openvidu:
-    image: openvidu/openvidu-dev:2.29.0 
-    restart: on-failure # always
-    ports:
-      - "4443:4443"
-    env_file:
-      - ./Backend/.env-openvidu
+  # openvidu:
+  #   image: openvidu/openvidu-dev:2.29.0 
+  #   restart: on-failure # always
+  #   ports:
+  #     - "4443:4443"
+  #   env_file:
+  #     - ./Backend/.env-openvidu
 
   backend:
     container_name: backend
     image: node:21-alpine
     depends_on:
      - mongo
-     - openvidu
+    #  - openvidu
     restart: on-failure # always
     ports:
       - "8080:8080"