Upload cv2CCTV.py

cv2CCTV.py

+import numpy as np
+from ultralytics import YOLO
+import cv2
+import cvzone
+import math
+from sort import *
+import time
+import torch
+#from PIL import Image
+import shutil
+import os
+from detecto import core, utils
+# from detecto.visualize import show_labeled_image, plot_prediction_grid
+from torchvision import transforms
+import matplotlib.pyplot as plt
+import pyrebase
+
+firebaseConfig = {
+  "apiKey": "AIzaSyAcltJ26-MoRUlsyAiYdztrlxtuBtjUKko",
+  "authDomain": "cctv-584b2.firebaseapp.com",
+  "databaseURL": "https://cctv-584b2-default-rtdb.firebaseio.com",
+  "projectId": "cctv-584b2",
+  "storageBucket": "cctv-584b2.appspot.com",
+  "messagingSenderId": "972965540260",
+  "appId": "1:972965540260:web:0e8bf3e4465d7bd55fd131",
+  "measurementId": "G-GN9MTT80BZ",
+  "serviceAccount":"Firebase_Service_Account_Keys.json"
+}
+
+firebase = pyrebase.initialize_app(firebaseConfig)
+storage = firebase.storage()
+
+model = YOLO("Yolo-Weights/yolov8l.pt")
+
+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"
+              ]
+
+tracker = Sort(max_age=20, min_hits=3, iou_threshold=0.3)
+
+limits = [0, 200, 1344, 241]
+limits1 = [0, 400, 1344, 540]
+
+totalCount = []
+uniqIds = []
+linepassIds = []
+timeDuration = []
+fulldata = []
+
+mask = cv2.imread("mask.png")
+
+def calculate_speed(time_seconds):
+    # Convert distance to kilometers and time to hours
+    distance_km = 20 * 0.001
+    time_hours = time_seconds / 3600
+    
+    # Calculate speed in km/h
+    speed_kmph = distance_km / time_hours
+    
+    return speed_kmph
+
+model_number = torch.hub.load('ultralytics_yolov5_master', 'custom', path='best.pt',force_reload=True,source='local')
+model_ocr = core.Model.load('./model_weights.pth', ['0','1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G','H','I','J','K','L','M',
+                    'N','O','P','Q','R','S','T','U','V','Z','X','Y','W'])
+
+def number_plate(img):
+    # img = cv2.imread(image_path)
+
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img_rgb = cv2.cvtColor(gray, cv2.COLOR_BGR2RGB)
+
+    # original = img.copy()
+    # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # xp = [0, 64, 112, 128, 144, 192, 255] # setting reference values
+    # fp = [0, 16, 64, 128, 192, 240, 255] # setting values to be taken for reference values
+    # x = np.arange(256)
+    # table = np.interp(x, xp, fp).astype('uint8') # creating lookup table
+    # img1 = cv2.LUT(gray, table) # changing values based on lookup table
+
+    results = model_number(img_rgb)
+    # results
+
+    class_names = model_number.module.names if hasattr(model_number, 'module') else model_number.names
+    jk = 0
+    crp_img_num = ""
+    for det in results.pred[0]:
+        box = det[:4].cpu().numpy()
+        score = det[4].cpu().numpy()
+        labelx = int(det[5].cpu().numpy())
+        
+        x1, y1, x2, y2 = box.astype(int)
+        
+        # Draw bounding box
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        crop_img_number = img[y1:y2, x1:x2]
+        crp_img_num = f'num_{jk}.jpg'
+        cv2.imwrite(crp_img_num, crop_img_number)
+        jk = jk+1
+        predictions = model_ocr.predict(crop_img_number)
+        labels, boxes, scores = predictions
+
+        thresh=0.5
+
+        filtered_indices=np.where(scores>thresh)
+        filtered_scores=scores[filtered_indices]
+        filtered_boxes=boxes[filtered_indices]
+        num_list = filtered_indices[0].tolist()
+
+        print(labels)
+
+        filtered_boxes_list = filtered_boxes.numpy()
+        filtered_labels = [labels[i] for i in num_list]
+
+        combined_data = []
+        for box, label in zip(filtered_boxes, filtered_labels):
+            combined_data.append(np.concatenate((box, [label])))
+
+        combined_data = np.array(combined_data)
+        print(combined_data) 
+
+        try:
+            # Convert the array to numeric, excluding the last column with non-numeric values
+            numeric_data = combined_data[:, :-1].astype(np.float64)
+
+            # Sort the rows based on the values in the first column
+            sorted_indices = np.argsort(numeric_data[:, 0])
+            sorted_data = combined_data[sorted_indices]
+
+            print(sorted_data)
+        
+            number_plate_str = ""
+            for i in sorted_data:
+                number_plate_str =number_plate_str+i[4]
+        except:
+            number_plate_str = ""
+
+        print(number_plate_str)
+
+        # Count the letters
+        letter_count = sum(1 for char in number_plate_str if char.isalpha())
+
+        # Remove the first letter if it's not 'A'
+        if letter_count >= 3 and number_plate_str[0].isalpha() and number_plate_str[0] != 'A':
+            number_plate_str = number_plate_str[1:]
+
+        print(number_plate_str)               
+               
+        # Put label text
+        label_text = f'{class_names[labelx]}: {score:.2f} : {number_plate_str}'        
+        cv2.putText(img, label_text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+        print(crp_img_num)
+        print("crp_img_num ggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggg")
+        print(number_plate_str)
+        return number_plate_str,crp_img_num
+# Get the starting time
+start_time = time.time()
+
+
+def read_vid(vid):
+    cap = cv2.VideoCapture(vid)
+    i = 0
+    while True:
+        success, img = cap.read()
+        if not success:  # Break loop when video capture is finished
+            break
+        current_time = time.time()
+        img = cv2.resize(img, None, fx=0.5, fy=0.5)
+        imgRegion = cv2.bitwise_and(img, mask)
+
+        results = model(imgRegion, stream=True)
+
+        detections = np.empty((0, 5))
+
+        for r in results:
+            boxes = r.boxes
+            for box in boxes:
+                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)
+                w, h = x2 - x1, y2 - y1
+                conf = math.ceil((box.conf[0] * 100)) / 100
+                cls = int(box.cls[0])
+                currentClass = classNames[cls]
+                if currentClass == "car" or currentClass == "truck" or currentClass == "bus" or currentClass == "motorbike" and conf > 0.3:
+                    # cvzone.putTextRect(img, f'{currentClass} {conf}', (max(0, x1), max(35, y1)),scale=0.6, thickness=1, offset=3)
+                    # cvzone.cornerRect(img, (x1, y1, w, h), l=9, rt=5)
+                    currentArray = np.array([x1, y1, x2, y2, conf])
+                    detections = np.vstack((detections, currentArray))
+
+        resultsTracker = tracker.update(detections)
+        cv2.line(img, (limits[0], limits[1]), (limits[2], limits[3]), (0, 0, 255), 5)
+        cv2.line(img, (limits1[0], limits1[1]), (limits1[2], limits1[3]), (0, 0, 255), 5)
+        for result in resultsTracker:
+            x1, y1, x2, y2, id = result
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            w, h = x2 - x1, y2 - y1
+            # cvzone.cornerRect(img, (x1, y1, w, h), l=9, rt=2, colorR=(255, 0, 255))
+            cvzone.putTextRect(img, f' {int(id)}', (max(0, x1), max(35, y1)),
+                            scale=2, thickness=3, offset=10)    
+            cx, cy = x1 + w // 2, y1 + h // 2
+            # cv2.circle(img, (cx, cy), 5, (255, 0, 255), cv2.FILLED)
+
+            if limits[0] < cx < limits[2] and limits[1] - 5 < cy < limits[1] + 25:
+                if totalCount.count(id) == 0:
+                    totalCount.append(id)
+                    cv2.line(img, (limits[0], limits[1]), (limits[2], limits[3]), (0, 255, 0), 5)
+                    foundAT = time.time()
+                    uniqData = {'id': id, 'foundAT': int(foundAT) }
+                    uniqIds.append(uniqData)
+
+            if limits1[0] < cx < limits1[2] and limits1[1] - 5 < cy < limits1[1] + 25:
+                    cv2.line(img, (limits1[0], limits1[1]), (limits1[2], limits1[3]), (0, 255, 0), 5)
+                    PassAT = time.time()
+                    uniqLineTime = {'id': id, 'passAT': int(PassAT) }
+                    linepassIds.append(uniqLineTime)
+                    for item in uniqIds:
+                        if item['id'] == uniqLineTime['id']:
+                            foundAT_value = item['foundAT']
+                            time1 = PassAT - foundAT_value
+                            speed = calculate_speed(round(time1, 2))
+                            if speed > 30:
+                                uid = item['id']
+                                crop_img = img[y1:y2, x1:x2]
+                                try:
+                                    number_plate_num,img_num =number_plate(crop_img)
+                                    storage.child("CCTV_IMG").child(img_num).put(img_num)
+                                    img_num_url =  storage.child("CCTV_IMG").child(img_num).get_url(None)
+                                except:
+                                    number_plate_num =number_plate(crop_img)
+                                    img_num_url = None
+                                img_file_name = f'crop_{uid}.jpg'
+                                cv2.imwrite(img_file_name, crop_img)
+                                storage.child("CCTV_IMG").child(img_file_name).put(img_file_name)
+                                # storage.child("CCTV_IMG").child(img_num).put(img_num)
+                                # os.remove(file.filename)
+                                img_url = storage.child("CCTV_IMG").child(img_file_name).get_url(None)
+                                # img_num_url =  storage.child("CCTV_IMG").child(img_num).get_url(None)
+                                i = i+1
+                                f_data = {'item_id':item['id'],'pass_id':uniqLineTime['id'],'foundAT': int(foundAT_value),'passAT': int(PassAT),'timeduration':round(time1, 2),'speed':speed,'number_plate_num':number_plate_num,'img':img_url,'number_img':img_num_url}
+                                fulldata.append(f_data)        
+    
+        # cv2.putText(img,str(len(totalCount)),(255,100),cv2.FONT_HERSHEY_PLAIN,5,(50,50,255),8)
+        #uncomment from here
+        # cv2.imshow("Image", img)
+        # if cv2.waitKey(1) & 0xFF == ord('q'):
+        #     break
+        elapsed_time = current_time - start_time
+        if elapsed_time >= 300:
+            break
+    cap.release()
+    return fulldata
+       
+cv2.destroyAllWindows()   
+# return fulldata
+        # if uniqIds:
+        #     cv2.putText(img,str(uniqIds[-1]),(100,600),cv2.FONT_HERSHEY_PLAIN,3,(50,50,255),2)
+        # if linepassIds:
+        #     cv2.putText(img,str(linepassIds[-1]),(100,670),cv2.FONT_HERSHEY_PLAIN,3,(50,50,255),2)
+        # if fulldata:
+        #     cv2.putText(img,str(fulldata[-1]['speed']),(100,720),cv2.FONT_HERSHEY_PLAIN,2,(50,50,255),2)
+        # cv2.imshow("Image", img)
+        # if cv2.waitKey(1) & 0xFF == ord('q'):
+        #     break
+    
+