真的，无论多少次我还是会感叹，高翔的《SLAM十四讲》YYDS。把复杂的问题解释的很清楚。

基本原理-光流定义

以下截图皆来自《SLAM十四讲》

 

 

 

光流的OpenCV简单实现

#!/usr/bin/env python'''
Lucas-Kanade tracker
====================Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
for track initialization and back-tracking for match verification
between frames.Usage
-----
lk_track.py [<video_source>]Keys
----
ESC - 退出
'''# Python 2/3 compatibility
from __future__ import print_functionimport numpy as np
import cv2 as cvimport video
from common import anorm2, draw_strlk_params = dict( winSize  = (15, 15),maxLevel = 2,criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))  # 光流跟踪参数设置feature_params = dict( maxCorners = 500,qualityLevel = 0.3,minDistance = 7,blockSize = 7 )  # 角点检测参数设置class App:                              # 进入主程序def __init__(self, video_src):      # 初始化self.track_len = 10self.detect_interval = 5self.tracks = []self.cam = video.create_capture(video_src)self.frame_idx = 0def run(self):while True:_ret, frame = self.cam.read()                       # 打开相机frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)  # 灰度图像vis = frame.copy()if len(self.tracks) > 0:img0, img1 = self.prev_gray, frame_grayp0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2)p1, _st, _err = cv.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)  # 计算光流p1p0r, _st, _err = cv.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)  # 计算光流p0d = abs(p0-p0r).reshape(-1, 2).max(-1)  # 计算距离good = d < 1new_tracks = []for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):if not good_flag:continuetr.append((x, y))if len(tr) > self.track_len:del tr[0]new_tracks.append(tr)cv.circle(vis, (int(x), int(y)), 2, (0, 255, 0), -1)  # 标记光流点self.tracks = new_trackscv.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0))  # 画出运行线条，可视化的更棒draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks))if self.frame_idx % self.detect_interval == 0:mask = np.zeros_like(frame_gray)mask[:] = 255for x, y in [np.int32(tr[-1]) for tr in self.tracks]:cv.circle(mask, (x, y), 5, 0, -1)p = cv.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)  # 使用 goodFeaturesToTrack 进行轨道初始化和回溯以进行帧之间的匹配验证。if p is not None:for x, y in np.float32(p).reshape(-1, 2):self.tracks.append([(x, y)])self.frame_idx += 1self.prev_gray = frame_graycv.imshow('lk_track', vis)ch = cv.waitKey(1)if ch == 27:breakdef main():import systry:video_src = sys.argv[1]except:video_src = 0App(video_src).run()print('Done')if __name__ == '__main__':print(__doc__)main()cv.destroyAllWindows()