Python动态目标追踪完整代码

下面是一个完整的动态目标跟踪项目，使用OpenCV的CSRT跟踪器实现，包含以下功能：

• 视频文件或摄像头输入

• 目标选择界面

• 跟踪结果显示

• 跟踪失败处理

• 性能评估指标

项目结构

target_tracking/
├── main.py # 主程序
├── requirements.txt # 依赖库
├── utils/
│ ├── init.py
│ ├── evaluation.py # 评估指标计算
│ └── visualization.py # 可视化工具
└── README.md # 项目说明

代码实现

main.py

【python】
import cv2
import time
import argparse
from utils.evaluation import TrackingResult
from utils.visualization import draw_tracking_results

class TargetTracker:
def init(self, tracker_type=‘csrt’):
“”“初始化跟踪器”""
self.tracker_types = {
‘csrt’: cv2.TrackerCSRT_create,
‘kcf’: cv2.TrackerKCF_create,
‘mosse’: cv2.TrackerMOSSE_create,
‘medianflow’: cv2.TrackerMedianFlow_create
}

    if tracker_type not in self.tracker_types:
        raise ValueError(f"不支持的跟踪器类型: {tracker_type}")
        
    self.tracker = self.tracker_types[tracker_type]()
    self.tracking_results = []
    self.frame_count = 0
    self.total_time = 0

def initialize(self, frame, bbox):
    """初始化跟踪器"""
    self.tracker.init(frame, bbox)
    self.tracking_results = []
    self.frame_count = 0
    self.total_time = 0

def update(self, frame):
    """更新跟踪器并返回结果"""
    start_time = time.time()
    success, bbox = self.tracker.update(frame)
    elapsed_time = time.time() - start_time
    
    self.frame_count += 1
    self.total_time += elapsed_time
    
    result = {
        'frame': self.frame_count,
        'success': success,
        'bbox': bbox if success else None,
        'time': elapsed_time
    }
    self.tracking_results.append(result)
    
    return success, bbox

def get_stats(self):
    """获取跟踪统计信息"""
    if self.frame_count == 0:
        return {}
        
    avg_fps = self.frame_count / self.total_time
    return {
        'frame_count': self.frame_count,
        'total_time': self.total_time,
        'avg_fps': avg_fps
    }

def main():
parser = argparse.ArgumentParser(description=‘动态目标跟踪系统’)
parser.add_argument(’–input’, type=str, default=‘0’,
help=‘输入视频路径或摄像头索引 (默认: 0)’)
parser.add_argument(’–tracker’, type=str, default=‘csrt’,
choices=[‘csrt’, ‘kcf’, ‘mosse’, ‘medianflow’],
help=‘跟踪器类型 (默认: csrt)’)
parser.add_argument(’–output’, type=str, default=None,
help=‘输出视频路径’)
args = parser.parse_args()

# 初始化视频捕获
cap = cv2.VideoCapture(args.input)
if not cap.isOpened():
    print(f"无法打开视频源: {args.input}")
    return

# 初始化视频写入器
output_video = None
if args.output:
    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)
    output_video = cv2.VideoWriter(args.output, 
                                 cv2.VideoWriter_fourcc(*'XVID'), 
                                 fps, 
                                 (frame_width, frame_height))

# 初始化跟踪器
tracker = TargetTracker(args.tracker)

# 读取第一帧
ret, frame = cap.read()
if not ret:
    print("无法读取视频")
    return

# 选择ROI
print("请用鼠标选择要跟踪的目标区域，然后按Enter确认")
bbox = cv2.selectROI("目标选择", frame, False)
cv2.destroyWindow("目标选择")

# 初始化跟踪器
tracker.initialize(frame, bbox)

# 跟踪循环
while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    # 更新跟踪器
    success, bbox = tracker.update(frame)
    
    # 绘制结果
    frame_with_results = draw_tracking_results(frame, bbox, success, tracker.get_stats())
    
    # 显示结果
    cv2.imshow("目标跟踪", frame_with_results)
    
    # 写入输出视频
    if args.output:
        output_video.write(frame_with_results)
    
    # 按q退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放资源
cap.release()
if args.output:
    output_video.release()
cv2.destroyAllWindows()

# 打印统计信息
stats = tracker.get_stats()
print("\n跟踪统计信息:")
print(f"总帧数: {stats.get('frame_count', 0)}")
print(f"总耗时: {stats.get('total_time', 0):.2f}秒")
print(f"平均FPS: {stats.get('avg_fps', 0):.2f}")

if name == “main”:
main()

utils/evaluation.py

【python】
class TrackingResult:
“”“跟踪结果评估类”""
def init(self):
self.results = []

def add_result(self, frame, success, bbox, time):
    """添加跟踪结果"""
    self.results.append({
        'frame': frame,
        'success': success,
        'bbox': bbox,
        'time': time
    })

def get_success_rate(self):
    """计算跟踪成功率"""
    if not self.results:
        return 0.0
    
    success_count = sum(1 for r in self.results if r['success'])
    return success_count / len(self.results)

def get_average_speed(self):
    """计算平均处理速度"""
    if not self.results:
        return 0.0
    
    total_time = sum(r['time'] for r in self.results)
    return total_time / len(self.results)

def get_stats(self):
    """获取所有统计信息"""
    return {
        'success_rate': self.get_success_rate(),
        'avg_speed': self.get_average_speed(),
        'total_frames': len(self.results)
    }

utils/visualization.py

【python】
import cv2
import numpy as np

def draw_tracking_results(frame, bbox, success, stats):
“”“绘制跟踪结果”""
if success and bbox is not None:
x, y, w, h = [int(v) for v in bbox]
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    # 绘制中心点
    center = (x + w // 2, y + h // 2)
    cv2.circle(frame, center, 3, (255, 0, 0), -1)

# 显示状态信息
status_text = "跟踪中" if success else "跟踪失败"
fps_text = f"FPS: {stats.get('avg_fps', 0):.1f}" if stats else ""

cv2.putText(frame, status_text, (20, 4))