python code for object tracking with RCNN
时间: 2023-02-06 20:10:58 浏览: 52
在 Python 中使用 RCNN 进行目标跟踪的代码可能会像这样:
```
import cv2
# 读入视频文件
video = cv2.VideoCapture("video.mp4")
# 创建并加载 RCNN 模型
model = cv2.dnn_DetectionModel("model.pb", "model.pbtxt")
model.setInputSize(320, 320)
model.setInputScale(1.0 / 127.5)
model.setInputMean((127.5, 127.5, 127.5))
model.setInputSwapRB(True)
# 循环处理每一帧
while True:
# 读入视频帧
success, frame = video.read()
if not success:
break
# 使用 RCNN 模型检测目标
detections = model.detect([frame], confThreshold=0.5, nmsThreshold=0.4)[0]
# 遍历检测到的目标
for i in range(detections.shape[2]):
# 获取目标的置信度、边界框坐标、类别标签
confidence = detections[0, 0, i, 2]
x1, y1, x2, y2 = detections[0, 0, i, 3:7] * [frame.shape[1], frame.shape[0], frame.shape[1], frame.shape[0]]
label = int(detections[0, 0, i, 1])
# 在视频帧上绘制边界框
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
# 在视频帧上添加类别标签和置信度
cv2.putText(frame, f"{label}: {confidence:.2f}", (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
# 显示视频帧
cv2.imshow("Frame", frame)
# 按下 'q' 键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放视频文件
video.release()
# 销毁窗口
cv2.destroyAllWindows()
```
上面的代