视频目标检测与跟踪实现代码目标检测和跟踪是计算机视觉中的重要任务，可以用于识别视频中的目标对象并追踪其运动轨迹。
下面是一个实现视频目标检测和跟踪的示例代码。
1.导入所需的库```pythonimport cv2import numpy as np```2.加载目标检测模型和跟踪器```pythondef load_detection_model(:#加载目标检测模型model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) return modeldef load_tracking_model(:#加载跟踪器tracker = cv2.TrackerMOSSE_createreturn tracker#检测模型的配置文件和权重文件路径prototxt_path = 'detection_model.prototxt'model_path = 'detection_model.caffemodel'```3.定义目标检测函数```pythondef detect_objects(frame, detection_model):# 将帧图像转换为 blob 形式# 将 blob 输入到模型中进行目标检测detection_model.setInput(blob)detections = detection_model.forward#解析检测结果num_detections = detections.shape[2]for i in range(num_detections):confidence = detections[0, 0, i, 2]if confidence > 0.5:class_id = int(detections[0, 0, i, 1])x1 = int(detections[0, 0, i, 3] * frame.shape[1]) y1 = int(detections[0, 0, i, 4] * frame.shape[0])x2 = int(detections[0, 0, i, 5] * frame.shape[1])y2 = int(detections[0, 0, i, 6] * frame.shape[0])cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)label = f'Class: {class_id}, Confidence: {int(confidence * 100)}%'cv2.putText(frame, label, (x1, y1 - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2, cv2.LINE_AA) ```4.定义目标跟踪函数```pythondef track_objects(frame, bbox, tracker):#初始化跟踪器tracker.init(frame, bbox)while True:#读取下一帧图像success, frame = cap.readif not success:break#更新跟踪器success, bbox = tracker.update(frame)if success:#跟踪成功，绘制边界框x, y, w, h = [int(x) for x in bbox]cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) else:#跟踪失败，输出失败消息cv2.putText(frame, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,255),2)#显示图像cv2.imshow("Tracking", frame)if cv2.waitKey(1) == ord('q'):break```5.主函数```pythonif __name__ == '__main__':#打开摄像头或视频文件cap = cv2.VideoCapture(0)#加载目标检测模型和跟踪器detection_model = load_detection_model tracker = load_tracking_modelwhile True:#读取下一帧图像success, frame = cap.readif not success:break#目标检测detect_objects(frame, detection_model) #显示图像cv2.imshow("Object Detection", frame) if cv2.waitKey(1) == ord('t'):#选择一个ROI来跟踪bbox = cv2.selectROI(frame, False)cv2.destroyAllWindows#开始目标跟踪track_objects(frame, bbox, tracker) break#释放资源cap.releasecv2.destroyAllWindows```这是一个简单的视频目标检测和跟踪实现的示例代码。
