bytetrack算法结合光流法做跟踪的python代码

ByteTrack是一种基于目标检测的目标跟踪算法，结合光流法可以进一步提高跟踪的准确性和鲁棒性。下面是一个简单的Python实现示例：

import numpy as np
import cv2

# 加载模型和配置文件
model = cv2.dnn_DetectionModel('models/ByteTrack.onnx')
model.setInputParams(size=(640, 640), scale=1/255, swapRB=True)

# 初始化光流法参数
lk_params = dict(winSize=(15, 15), maxLevel=2, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# 读取视频
cap = cv2.VideoCapture('video.mp4')

# 读取第一帧图像
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# 检测目标ROI区域
results = model.detect(gray, confThreshold=0.5)
if len(results) > 0:
    bbox = results[0][2]
    bbox = (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1])
    r, h, c, w = bbox
else:
    raise ValueError('No object detected in the first frame!')

# 初始化光流法追踪点
old_points = np.array([[c + w / 2, r + h / 2]], dtype=np.float32)

while True:
    # 读取当前帧图像
    ret, frame = cap.read()
    if not ret:
        break
    gray_next = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 计算光流向量
    new_points, status, error = cv2.calcOpticalFlowPyrLK(gray, gray_next, old_points, None, **lk_params)

    # 筛选出成功跟踪的点
    good_new = new_points[status == 1]
    good_old = old_points[status == 1]

    # 计算目标的位置和速度
    if len(good_new) > 0:
        bbox = (int(good_new[0][0] - w / 2), int(r), w, h)
        results = model.detect(frame, bbox, confThreshold=0.5)
        if len(results) > 0:
            bbox = results[0][2]
            bbox = (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1])
            r, h, c, w = bbox
            cv2.rectangle(frame, bbox, (0, 255, 0), 2)
        else:
            cv2.putText(frame, 'Lost', (c, r - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

    # 更新光流法追踪点
    old_points = good_new.reshape(-1, 1, 2)

    # 显示结果
    cv2.imshow("Frame", frame)
    if cv2.waitKey(1) == 27:
        break

# 释放资源
cap.release()
cv2.destroyAllWindows()

注意：这只是一个简单的示例，实际应用中，ByteTrack算法和光流法的参数需要进行更多的优化和改进，以提高目标跟踪的准确性和鲁棒性。