实战 | 基于YOLOv9和OpenCV实现车辆跟踪计数（步骤 + 源码）

pip install ultralytics

import math
class CustomTracker:    def __init__(self):        # Store the center positions of the objects        self.custom_center_points = {}        # Keep the count of the IDs        # each time a new object id detected, the count will increase by one        self.custom_id_count = 0
    def custom_update(self, custom_objects_rect):        # Objects boxes and ids        custom_objects_bbs_ids = []
        # Get center point of new object        for custom_rect in custom_objects_rect:            x, y, w, h = custom_rect            cx = (x + x + w) // 2            cy = (y + y + h) // 2
            # Find out if that object was detected already            same_object_detected = False            for custom_id, pt in self.custom_center_points.items():                dist = math.hypot(cx - pt[0], cy - pt[1])
                if dist < 35:                    self.custom_center_points[custom_id] = (cx, cy)                    custom_objects_bbs_ids.append([x, y, w, h, custom_id])                    same_object_detected = True                    break
            # New object is detected we assign the ID to that object            if same_object_detected is False:                self.custom_center_points[self.custom_id_count] = (cx, cy)                custom_objects_bbs_ids.append([x, y, w, h, self.custom_id_count])                self.custom_id_count += 1
        # Clean the dictionary by center points to remove IDS not used anymore        new_custom_center_points = {}        for custom_obj_bb_id in custom_objects_bbs_ids:            _, _, _, _, custom_object_id = custom_obj_bb_id            center = self.custom_center_points[custom_object_id]            new_custom_center_points[custom_object_id] = center
        # Update dictionary with IDs not used removed        self.custom_center_points = new_custom_center_points.copy()        return custom_objects_bbs_ids

# Import the Librariesimport cv2import pandas as pdfrom ultralytics import YOLOfrom tracker import *

model=YOLO('yolov9c.pt')

class_list = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', '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', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',              'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',              'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']

tracker=CustomTracker()count=0
cap = cv2.VideoCapture('traffictrim.mp4')
# Get video propertiesfps = int(cap.get(cv2.CAP_PROP_FPS))width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Create VideoWriter object to save the modified framesoutput_video_path = 'output_video.mp4'fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can use other codecs like 'XVID' based on your systemout = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))

# Looping over each frame and Performing the Detection
down = {}counter_down = set()while True:    ret, frame = cap.read()    if not ret:        break    count += 1
    results = model.predict(frame)
    a = results[0].boxes.data    a = a.detach().cpu().numpy()    px = pd.DataFrame(a).astype("float")    # print(px)
    list = []
    for index, row in px.iterrows():        #        print(row)        x1 = int(row[0])        y1 = int(row[1])        x2 = int(row[2])        y2 = int(row[3])        d = int(row[5])        c = class_list[d]        if 'car' in c:            list.append([x1, y1, x2, y2])
    bbox_id = tracker.custom_update(list)    # print(bbox_id)    for bbox in bbox_id:        x3, y3, x4, y4, id = bbox        cx = int(x3 + x4) // 2        cy = int(y3 + y4) // 2        # cv2.circle(frame,(cx,cy),4,(0,0,255),-1) #draw ceter points of bounding box        # cv2.rectangle(frame, (x3, y3), (x4, y4), (0, 255, 0), 2)  # Draw bounding box        # cv2.putText(frame,str(id),(cx,cy),cv2.FONT_HERSHEY_COMPLEX,0.8,(0,255,255),2)
        y = 308        offset = 7
        ''' condition for red line '''        if y < (cy + offset) and y > (cy - offset):            ''' this if condition is putting the id and the circle on the object when the center of the object touched the red line.'''
            down[id] = cy  # cy is current position. saving the ids of the cars which are touching the red line first.            # This will tell us the travelling direction of the car.            if id in down:                cv2.circle(frame, (cx, cy), 4, (0, 0, 255), -1)                #cv2.putText(frame, str(id), (cx, cy), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 255), 2)                counter_down.add(id)
                # # line    text_color = (255, 255, 255)  # white color for text    red_color = (0, 0, 255)  # (B, G, R)
    # print(down)    cv2.line(frame, (282, 308), (1004, 308), red_color, 3)  # starting cordinates and end of line cordinates    cv2.putText(frame, ('red line'), (280, 308), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1, cv2.LINE_AA)

    downwards = (len(counter_down))    cv2.putText(frame, ('Vehicle Counter - ') + str(downwards), (60, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5, red_color, 1,                cv2.LINE_AA)
    cv2.line(frame,(282,308),(1004,308),red_color,3)  #  starting cordinates and end of line cordinates    cv2.putText(frame,('red line'),(280,308),cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1, cv2.LINE_AA)        # This will write the Output Video to the location specified above    out.write(frame)