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| import numpy as np | |
| from ultralytics import YOLO | |
| import cv2 | |
| import cvzone | |
| import math | |
| totalFare=0 | |
| pricePerKm=1.5 | |
| def distanceCalculations(i): | |
| return 3 | |
| pass | |
| def fareCalculations(): | |
| global totalFare | |
| print(inBetweenDepaturePoints,inBetweenOnBoardingPoints) | |
| for i in inBetweenDepaturePoints: | |
| fare=distanceCalculations(i)*pricePerKm | |
| print(fare) | |
| totalFare+=fare | |
| for i in inBetweenOnBoardingPoints: | |
| fare=distanceCalculations(i)*pricePerKm/2 | |
| print(fare) | |
| totalFare+=fare | |
| pass | |
| 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':'','long':''} | |
| stationTo='Delhi' | |
| reachedDestination=False | |
| inBetweenDepaturePoints=[{}] | |
| inBetweenOnBoardingPoints=[{}] | |
| countPeople=0 | |
| 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 | |
| yelloLine = [270, 0, 270, 600] | |
| RedLine = [173, 0, 173, 600] | |
| totalCountUp = [] | |
| while True: | |
| print("starting loop",inBetweenOnBoardingPoints) | |
| success, img = cap.read() | |
| # imgRegion=cv2.bitwise_and(img,mask) | |
| results = model(img, stream=True) | |
| detections = np.empty((0, 5)) | |
| for r in results: | |
| countPeople=0 | |
| 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)) | |
| countPeople+=1 | |
| cv2.putText(img, str(countPeople), (110, 245), cv2.FONT_HERSHEY_PLAIN, 5, (50, 50, 230), 7) | |
| # print('count is ', entry_count) | |
| # print(dict) | |
| print('count is ', countPeople) | |
| print(dict,"dict") | |
| cv2.imshow("Image", img) | |
| cv2.waitKey(1) | |