Merge branch 'main' of https://github.com/unicam-complex-system/camer…

…a-security-system-monorepo into stream-integration

ImageRecognition/src/ObjectDetectionYOLO.py

@@ -0,0 +1,165 @@
+from ultralytics import YOLO
+import cv2
+import math 
+import threading
+import requests
+import numpy as np
+from skimage.metrics import structural_similarity as ssim
+from sklearn.metrics import mean_squared_error
+
+#model
+model = YOLO("yolo-Wheights/yolov8n.pt")
+#class of YOLO
+classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
+              "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
+              "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
+              "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
+              "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
+              "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
+              "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
+              "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
+              "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
+              "teddy bear", "hair drier", "toothbrush"
+              ]
+
+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'
+
+
+last_sent_image = None
+
+def post_request(image):
+    global last_sent_image
+    apiURL = f"https://api.telegram.org/bot{TOKEN}/sendPhoto?chat_id={channel_id}"
+
+    if last_sent_image is None:
+        last_sent_image = image
+        try:
+            response = requests.post(apiURL, files={'photo':  image})
+            print("Status code is: ", response.status_code)      
+        except requests.exceptions.RequestException as e:
+            print("There was an exception that occurred while handling your request.", e)
+    else:  
+
+        # Resizing
+        flat_last_sent_image = last_sent_image.flatten()
+        flat_image = image.flatten()
+        min_len = min(len(flat_last_sent_image), len(flat_image))
+        r_last_sent_image = cv2.resize(flat_last_sent_image.reshape(1, -1), (min_len, 1))
+        r_image = cv2.resize(flat_image.reshape(1, -1), (min_len, 1))
+
+        #mean squared error
+        mse = mean_squared_error(r_last_sent_image, r_image)
+        print("MEAN SQUARED ERROR is: ", mse)
+        if mse > 105.85:
+            try:
+                response = requests.post(apiURL, files={'photo':  image})
+                print("Status code is: ", response.status_code)
+
+                if response.status_code == 200:
+                    last_sent_image = image      
+            except requests.exceptions.RequestException as e:
+                print("There was an exception that occurred while handling your request.", e)
+
+
+
+
+#filename = url of cam
+#file_index = index of the file that can be assigned to each thread. cam1 has file_index as 1, cam2 has file_index as 2...
+def detection(filename, file_index):
+    cap = cv2.VideoCapture(filename)  # Read the video file
+    global last_sent_image
+
+    while cap.isOpened():
+
+       success, img = cap.read()
+       # Exit the loop if no more frames in either video
+       if not success:
+           break
+
+       results = model(img, stream = True, classes = 0, conf=0.5) 
+       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) # convert to int values
+            # put box in cam
+            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
+            # confidence
+            confidence = math.ceil((box.conf[0]*100))/100
+            print("Confidence --->",confidence)
+            # class name
+            cls = int(box.cls[0])
+            print("Class name -->", classNames[cls])
+
+            foundPerson = True
+
+       if(foundPerson):
+           _, img_encoded = cv2.imencode('.jpg', img)
+           post_request(img_encoded)
+
+
+    cap.release()
+
+
+
+#initialize thread
+detect_thread1 = threading.Thread(target=detection,
+                                   args=(cam1, 1),
+                                   daemon=True)
+
+detect_thread2 = threading.Thread(target=detection,
+                                   args=(cam2, 2),
+                                   daemon=True)
+
+detect_thread3 = threading.Thread(target=detection,
+                                   args=(cam3, 3),
+                                   daemon=True)
+
+detect_thread4 = threading.Thread(target=detection,
+                                   args=(cam4, 4),
+                                   daemon=True)
+
+detect_thread5 = threading.Thread(target=detection,
+                                   args=(cam5, 5),
+                                   daemon=True)
+
+detect_thread6 = threading.Thread(target=detection,
+                                   args=(cam6, 6),
+                                   daemon=True)
+
+detect_thread7 = threading.Thread(target=detection,
+                                   args=(cam7, 7),
+                                   daemon=True)
+
+detect_thread8 = threading.Thread(target=detection,
+                                   args=(cam8, 8),
+                                   daemon=True)
+
+#start thread
+detect_thread1.start()
+detect_thread2.start()
+detect_thread3.start()
+detect_thread4.start()
+detect_thread5.start()
+detect_thread6.start()
+detect_thread7.start()
+detect_thread8.start()
+
+detect_thread1.join()
+detect_thread2.join()
+detect_thread3.join()
+detect_thread4.join()
+detect_thread5.join()
+detect_thread6.join()
+detect_thread7.join()
+detect_thread8.join()