Python+OpenCV编写车辆计数器系统

import math
class EuclideanDistTracker:
    def __init__(self):
        # Storing the positions of center of the objects
        self.center_points = {}
        # Count of ID of boundng boxes
        # each time new object will be captured the id will be increassed by 1
        self.id_count = 0
    def update(self, objects_rect):
        objects_bbs_ids = []
        # Calculating the center of objects
        for rect in objects_rect:
            x, y, w, h = rect
            center_x = (x + x + w) // 2
            center_y = (y + y + h) // 2
            # Find if object is already detected or not
            same_object_detected = False
            for id, pt in self.center_points.items():
                dist = math.hypot(center_x - pt[0], center_y - pt[1])
                if dist < 25:
                    self.center_points[id] = (center_x, center_y)
                    print(self.center_points)
                    objects_bbs_ids.append([x, y, w, h, id])     
                    same_object_detected = True
                    break
           # Assign the ID to the detected object
           if same_object_detected is False:
               self.center_points[self.id_count] = (center_x, center_y)                      
               objects_bbs_ids.append([x, y, w, h, self.id_count])       
               self.id_count += 1
        # Cleaning the dictionary ids that are not used anymore
        new_center_points = {}
        for obj_bb_id in objects_bbs_ids:
            var,var,var,var, object_id = obj_bb_id
            center = self.center_points[object_id]
            new_center_points[object_id] = center
       # Updating the dictionary with IDs that is not used
       self.center_points = new_center_points.copy()
       return objects_bbs_ids

import cv2
import numpy as np
from tracker import EuclideanDistTracker
tracker = EuclideanDistTracker()
cap  = cv2.VideoCapture('highway.mp4')
ret, frame1 = cap.read()
ret, frame2 = cap.read()

while cap.isOpened():
    # ret, frame = cap.read()
    diff = cv2.absdiff(frame1, frame2)  
    # this method is used to find the difference bw two  frames
    gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0 )
    # here i would add the region of interest to count the single lane cars
    height, width = blur.shape
    print(height, width)
    # thresh_value = cv2.getTrackbarPos('thresh', 'trackbar')
    _, threshold = cv2.threshold(blur, 23, 255, cv2.THRESH_BINARY)
    dilated = cv2.dilate(threshold, (1,1), iterations=1)
    contours, _, = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    detections = []
    # DRAWING RECTANGLE BOX (Bounding Box)
    for contour in contours:
        (x,y,w,h) = cv2.boundingRect(contour)
        if cv2.contourArea(contour) <300:
            continue
        detections.append([x,y,w,h])
    boxes_ids = tracker.update(detections)
    for box_id in boxes_ids:
        x,y,w,h,id = box_id
        cv2.putText(frame1, str(id),(x,y-15),  cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,0,255), 2)
        cv2.rectangle(frame1, (x,y),(x+w, y+h), (0,255,0), 2)
        cv2.imshow('frame',frame1)
    frame1 = frame2
    ret, frame2 = cap.read()
    key = cv2.waitKey(30)
    if key == ord('q):
        break
cv2.destroyAllWindows()