一个简单的车辆计数程序，amazing！

import numpy as npfrom ultralytics import YOLOimport cv2import cvzoneimport mathfrom sort import *
cap = cv2.VideoCapture("cars.mp4")  # For Video
model = YOLO("yolov8n.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"              ]
mask = cv2.imread("mask.png")
# Trackingtracker = Sort(max_age=20, min_hits=3, iou_threshold=0.3)
limits = [400, 297, 673, 297]totalCount = []
while True:    success, img = cap.read()    imgRegion = cv2.bitwise_and(img, mask)
    imgGraphics = cv2.imread("graphics.png", cv2.IMREAD_UNCHANGED)
    img = cvzone.overlayPNG(img, imgGraphics, (0, 0))    results = model(imgRegion, 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 == "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)    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 limits[0] < cx < limits[2] and limits[1] - 15 < cy < limits[1] + 15:            if totalCount.count(id) == 0:                totalCount.append(id)                cv2.line(img, (limits[0], limits[1]), (limits[2], limits[3]), (0, 255, 0), 5)
    # cvzone.putTextRect(img, f' Count: {len(totalCount)}', (50, 50))
    cv2.putText(img, str(len(totalCount)), (255, 100), cv2.FONT_HERSHEY_PLAIN, 5, (50, 50, 255), 8)
    cv2.imshow("Image", img)
    # cv2.imshow("ImageRegion", imgRegion)    cv2.waitKey(1)