使用视频追踪算法研究物体运动轨迹

以APMCM19年A题为例，该题目要求我们使用所给100多张图片，确定图片中二氧化硅的质心运动轨迹，即下图中高亮部分（这样的图有100多张）

首先我们可以将这100多张图片合成一个视频，即

然后使用cv包搭建视频追踪环境

import cv2
import sys

(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')

track_points = []

if __name__ == '__main__':

    # 建立跟踪器，选择跟踪器的类型

    tracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']
    tracker_type = tracker_types[7]

    if tracker_type == 'BOOSTING':
        tracker = cv2.TrackerBoosting_create()
    if tracker_type == 'MIL':
        tracker = cv2.TrackerMIL_create()
    if tracker_type == 'KCF':
        tracker = cv2.TrackerKCF_create()
    if tracker_type == 'TLD':
        tracker = cv2.TrackerTLD_create()
    if tracker_type == 'MEDIANFLOW':
        tracker = cv2.TrackerMedianFlow_create()
    if tracker_type == 'GOTURN':
        tracker = cv2.TrackerGOTURN_create()
    if tracker_type == 'MOSSE':
        tracker = cv2.TrackerMOSSE_create()
    if tracker_type == "CSRT":
        tracker = cv2.TrackerCSRT_create()

    # 读取视频
    video = cv2.VideoCapture("data.avi")

    # 打开错误时退出
    if not video.isOpened():
        print("Could not open video")
        sys.exit()

    print("start tracking......")
    # 读取视频的第一帧
    ok, frame = video.read()
    if not ok:
        print('Cannot read video file')
        sys.exit()

    print("initialize bbox......")
    # 定义初始边界框
    bbox = (0,0,1792,1231)

    # Uncomment the line below to select a different bounding box
    # 选择不同的边界框
    bbox = cv2.selectROI(frame, False)

    # Initialize tracker with first frame and bounding box
    # 使用视频的第一帧和边界框初始化跟踪器
    ok = tracker.init(frame, bbox)

    while True:
        # Read a new frame
        ok, frame = video.read()
        if not ok:
            break

        # Start timer 记录开始时间
        timer = cv2.getTickCount()

        # Update tracker 更新检测器
        ok, bbox = tracker.update(frame)

        # Calculate Frames per second (FPS) 计算FPS
        fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)

        # Draw bounding box 绘制边界框
        if ok:
            # Tracking success 跟踪成功
            print(bbox)
            p1 = (int(bbox[0]), int(bbox[1]))
            p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
            track_points.append([int(bbox[0]) + int(bbox[2])/2, int(bbox[1]) + int(bbox[3])/2])
            print("position of central points: {}, {}".format(int(bbox[0]) + int(bbox[2])/2, int(bbox[1]) + int(bbox[3])/2))
            cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
        else:  # 跟踪失败
            # Tracking failure
            cv2.putText(frame, "Tracking failure detected", (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)

        # Display tracker type on frame
        # 显示跟踪器的类别
        cv2.putText(frame, tracker_type + " Tracker", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2);

        # Display FPS on frame 显示FPS
        cv2.putText(frame, "FPS : " + str(int(fps)), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2);

        # Display result 显示跟踪结果
        cv2.imshow("Tracking", frame)

        # Exit if ESC pressed 按取消键退出
        k = cv2.waitKey(1) & 0xff
        if k == 27: break

    with open('track.txt', 'w') as f:
        for i, each in enumerate(track_points):
            f.write(str(i)+' '+str(each[0])+' '+str(each[1])+'\n')
    f.close()

效果为

效果还是不错的。