十四.目标追踪

目标追踪

配置参数
ap = argparse.ArgumentParser() ap.addargument("-v", "--video", type=str, help="path to input video file") ap.addargument("-t", "--tracker", type=str, default="kcf", help="OpenCV object tracker type") args = vars(ap.parse_args())
opencv已经实现了的追踪算法
OPENCVOBJECTTRACKERS = { "csrt": cv2.TrackerCSRTcreate, "kcf": cv2.TrackerKCFcreate, "boosting": cv2.TrackerBoostingcreate, "mil": cv2.TrackerMILcreate, "tld": cv2.TrackerTLDcreate, "medianflow": cv2.TrackerMedianFlowcreate, "mosse": cv2.TrackerMOSSE_create }
实例化OpenCV's multi-object tracker
trackers = cv2.MultiTracker_create() vs = cv2.VideoCapture(args["video"])
视频流
while True: # 取当前帧 frame = vs.read() # (true, data) frame = frame[1] # 到头了就结束 if frame is None: break
# resize每一帧
(h, w) = frame.shape[:2]
width=600
r = width / float(w)
dim = (width, int(h * r))
frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)

# 追踪结果
(success, boxes) = trackers.update(frame)

# 绘制区域
for box in boxes:
    (x, y, w, h) = [int(v) for v in box]
    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

# 显示
cv2.imshow("Frame", frame)
key = cv2.waitKey(100) & 0xFF

if key == ord("s"):
    # 选择一个区域，按s
    box = cv2.selectROI("Frame", frame, fromCenter=False,
        showCrosshair=True)

    # 创建一个新的追踪器
    tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()
    trackers.add(tracker, frame, box)

# 退出
elif key == 27:
    break
vs.release() cv2.destroyAllWindows() ```