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| import numpy as np | |
| from ultralytics import YOLO | |
| import cv2 | |
| import cvzone | |
| import math | |
| from sort import * | |
| import datetime | |
| import geocoder | |
| #i want to take co ordinates in lat long of this device | |
| # def get_coordinates(): | |
| # g = geocoder.ip('me') | |
| # if g.latlng: | |
| # return {'lat': str(g.latlng[0]), 'long': str(g.latlng[1])} | |
| # else: | |
| # return None | |
| # currentCoordinates = get_coordinates() | |
| # if currentCoordinates: | |
| # print(currentCoordinates) | |
| # else: | |
| # print("Unable to get device coordinates.") | |
| #calculate distance b/w two coordinates | |
| def distance_calculations(stationFromCoordinates, stationToCoordinates): | |
| stationFromCoordinates = {'lat': ' 28.98', 'long': '77.7064'} | |
| stationToCoordinates = {'lat': '28.66', 'long': '77.22'} | |
| lat1 = float(stationFromCoordinates['lat']) | |
| lon1 = float(stationFromCoordinates['long']) | |
| lat2 = float(stationToCoordinates['lat']) | |
| lon2 = float(stationToCoordinates['long']) | |
| R = 6371 | |
| dlat = math.radians(lat2 - lat1) | |
| dlon = math.radians(lon2 - lon1) | |
| a = math.sin(dlat / 2) * math.sin(dlat / 2) + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dlon / 2) * math.sin(dlon / 2) | |
| c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a)) | |
| distance = R * c | |
| return distance | |
| print(distance_calculations({'lat': ' 28.98', 'long': '77.7'}, {'lat': '28.66', 'long': '77.22'})) | |
| totalFare=0 | |
| pricePerKm=1.5 | |
| def distanceCalculations(inBetweenDepaturePoints,inBetweenOnBoardingPoints): | |
| print(inBetweenDepaturePoints,inBetweenOnBoardingPoints ,'inbetween points') | |
| return 3 | |
| def fareCalculations(): | |
| global totalFare | |
| print(dict) | |
| index=0 | |
| for i in dict.values(): | |
| print(len(i)) | |
| inBetweenDepaturePoints=[] | |
| inBetweenOnBoardingPoints=[] | |
| if(len(i)>3): | |
| print(True) | |
| #index of the dict | |
| print(index,"index") | |
| print(i[0],i[1],i[2],"values of the dict","key of the dict") | |
| if i[0]==True and i[1]==False: | |
| inBetweenDepaturePoints.append(i[2]) | |
| if i[0]==False and i[1]==True: | |
| inBetweenOnBoardingPoints.append(i[2]) | |
| fare=distanceCalculations(inBetweenDepaturePoints,inBetweenOnBoardingPoints)*pricePerKm | |
| print(fare,'fare') | |
| totalFare+=fare | |
| index+=1 | |
| print(totalFare) | |
| cap = cv2.VideoCapture('./TrialFootage.mp4') | |
| model = YOLO("./Yolo-Weights/yolov8n.pt") | |
| stationFrom='Meerut' | |
| stationFromCoordinates={"lat":"12.12.54.4","long":"44.36.09"} # Meerut | |
| stationToCoordinates={'lat':'54.45.56',"long":'45.45.45'} | |
| currentCoordinates={'lat': '28.98', 'long': '77.7064'} | |
| stationTo='Delhi' | |
| reachedDestination=False | |
| ListPeople = [] | |
| dict = {} | |
| width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| print(f"Video Resolution: {width}x{height}") | |
| 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" | |
| ] | |
| # Tracking | |
| tracker = Sort(max_age=20, min_hits=3, iou_threshold=0.3) | |
| yelloLine = [270, 0, 270, 600] | |
| RedLine = [173, 0, 173, 600] | |
| totalCountUp = [] | |
| #mask=cv2.imread('mask.jpg') | |
| entry_count = 0 | |
| while True: | |
| success, img = cap.read() | |
| # imgRegion=cv2.bitwise_and(img,mask) | |
| results = model(img, stream=True) | |
| detections = np.empty((0, 5)) | |
| 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) | |
| w, h = x2 - x1, y2 - y1 | |
| # Confidence | |
| conf = math.ceil((box.conf[0] * 100)) / 100 | |
| # Class Name | |
| cls = int(box.cls[0]) | |
| currentClass = classNames[cls] | |
| if currentClass == "person" 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, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5) | |
| cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5) | |
| for result in resultsTracker: | |
| x1, y1, x2, y2, id = result | |
| x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) | |
| print(result) | |
| 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 yelloLine[0] - 20 < cx < yelloLine[2] + 20: | |
| if totalCountUp.count(id) == 0: | |
| totalCountUp.append(id) | |
| dict[id] = [False] | |
| cv2.line(img, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5) | |
| elif totalCountUp.count(id) == 1: | |
| if (dict[id].count(False) < 1): | |
| dict[id].append(False) | |
| cv2.line(img, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5) | |
| if RedLine[0] - 20 < cx < RedLine[2] + 30: | |
| if totalCountUp.count(id) == 0: | |
| totalCountUp.append(id) | |
| dict[id] = [True] | |
| dict[id].append(currentCoordinates) | |
| #adding timestamp | |
| current_time = datetime.datetime.now() | |
| dict[id].append(current_time) | |
| cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5) | |
| elif totalCountUp.count(id) == 1: | |
| if (dict[id].count(True) < 1): | |
| dict[id].append(True) | |
| dict[id].append(currentCoordinates) | |
| #adding timestamp | |
| current_time = datetime.datetime.now() | |
| dict[id].append(current_time) | |
| cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5) | |
| print(totalCountUp) | |
| entry_count=0 | |
| for i in dict.values(): | |
| if (len(i) >= 2): | |
| if i[0] == True and i[1] == False: | |
| print('in true/False') | |
| if entry_count > 0: | |
| entry_count -= 1 | |
| if i[0] == False and i[1] == True: | |
| print('in /False/True') | |
| entry_count += 1 | |
| # print('count is ', entry_count) | |
| # print(dict) | |
| cv2.putText(img, str(entry_count), (110, 245), cv2.FONT_HERSHEY_PLAIN, 5, (50, 50, 230), 7) | |
| print('count is ', entry_count) | |
| print(dict) | |
| cv2.imshow("Image", img) | |
| cv2.waitKey(1) | |
| print(entry_count) | |
| # if currentCoordinates==stationToCoordinates: | |
| # reachedDestination=True | |
| # fareCalculations() | |
| # dict=[] | |
| # break | |
| if entry_count>2: | |
| print('reached destination') | |
| fareCalculations() | |
| break | |