一个简单的车辆目标检测和跟踪示例

一个简单的车辆目标检测和跟踪示例

引用

CSDN

1.

https://blog.csdn.net/weixin_38739735/article/details/140789355

本项目旨在通过结合使用YOLOv8m（一种中等复杂度的YOLO变体）、OpenCV（一个开源的计算机视觉库）和SORT算法，实现对视频中特定区域内通过的车辆数量的准确计算。这一过程不仅确保了目标检测的准确性，也提高了整个系统的效率。

项目简介

本项目将使用YOLOv8m（中等版本）、OpenCV和SORT进行目标检测，以确保准确性和效率，来计算通过我们视频中特定区域的车辆数量。

1. 选择一个视频

2. 创建掩膜

为了专注于桥下的车辆，我们将利用画布创建一个掩膜。掩膜是一个二值图像，仅包含黑色（0）和白色（255）两种像素值。在RGB色彩空间中，这对应于：

• 白色（255, 255, 255）表示感兴趣的区域，算法将在这些区域进行处理。
• 黑色（0, 0, 0）表示要忽略或排除在处理之外的区域。

通过按位操作将掩膜与视频结合，我们实现以下结果：

3. 定义一个区域

我们将在视频中定义两个区域：一个用于计算向下行驶的车辆数量，另一个用于计算向上行驶的车辆数量。当在指定区域内识别到车辆时，我们将改变该区域的颜色为绿色，表示检测到车辆。

4. 构建布局

让我们使用cvzone构建计数器的布局。

5. 代码

import cv2
import numpy as np
from ultralytics import YOLO
import cvzone
from sort import sort

class_names = [
  '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'
]

class_names_goal = ['car']
model = YOLO('yolov8m.pt')
tracker = sort.Sort(max_age=20) 
mask = cv2.imread('mask.png') 
video = cv2.VideoCapture('traffic.mp4')

width = 1280
height = 720
line_left_road_x1 = 256
line_left_road_x2 = 500
line_left_road_y = 472
line_right_road_x1 = 672
line_right_road_x2 = 904
line_right_road_y = 472
vehicle_left_road_id_count = []
vehicle_right_road_id_count = []

while True:
  success, frame = video.read()
  if not success:
    break
  frame = cv2.resize(frame, (width, height))
  image_region = cv2.bitwise_and(frame, mask) 
  results = model(image_region, stream=True) 
  detections = []
  
  cv2.line(frame, (line_left_road_x1, line_left_road_y) ,(line_left_road_x2, line_left_road_y), (0, 0, 255))
  cv2.line(frame, (line_right_road_x1, line_right_road_y) ,(line_right_road_x2, line_right_road_y), (0, 0, 255))
  
  for result in results:
    for box in result.boxes:
      class_name = class_names[int(box.cls[0])]
      if not class_name in class_names_goal:
        continue
      confidence = round(float(box.conf[0]) * 100, 2)
      if confidence < 30:
        continue
      x1, y1, x2, y2 = box.xyxy[0]
      x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
      detections.append([x1, y1, x2, y2, float(box.conf[0])])
    
    tracked_objects = tracker.update(np.array(detections))
    for obj in tracked_objects:
      x1, y1, x2, y2, obj_id = [int(i) for i in obj]
      confidence_pos_x1 = max(0, x1)
      confidence_pos_y1 = max(36, y1)
      cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 255), 2)
      cvzone.putTextRect(frame, f'ID: {obj_id}', (confidence_pos_x1, confidence_pos_y1), 1, 1)
      center_x = (x1 + x2) // 2
      center_y = (y1 + y2) // 2
      
      if  line_left_road_y - 10 < center_y < line_left_road_y + 10 and line_left_road_x1 < center_x < line_left_road_x2:
        if not obj_id in vehicle_left_road_id_count:
          vehicle_left_road_id_count.append(obj_id)
          cv2.line(frame, (line_left_road_x1, line_left_road_y) ,(line_left_road_x2, line_left_road_y), (0, 255, 0), 2)
      
      if  line_right_road_y - 10 < center_y < line_right_road_y + 10 and line_right_road_x1 < center_x < line_right_road_x2:
        if not obj_id in vehicle_right_road_id_count:
          vehicle_right_road_id_count.append(obj_id)
          cv2.line(frame, (line_right_road_x1, line_right_road_y) ,(line_right_road_x2, line_right_road_y), (0, 255, 0), 2)
    
    cvzone.putTextRect(frame, f'Car Left Road Count: {len(vehicle_left_road_id_count)}', (50, 50), 2, 2, offset=20, border=2, colorR=(140, 57, 31), colorB=(140, 57, 31))
    cvzone.putTextRect(frame, f'Car Right Road Count: {len(vehicle_right_road_id_count)}', (width - 460, 50), 2, 2, offset=20, border=2, colorR=(140, 57, 31), colorB=(140, 57, 31))
    
    cv2.imshow('Image', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
      break

video.release()
cv2.destroyAllWindows()  

6. 结果

源码：https://github.com/VladeMelo/collaborative-filtering