Question

我按照opencv教程中的步骤进行操作：

http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.html

它在这里对我不起作用。

任何人都可以帮我弄清楚为什么我不能得到那样的输出

import numpy as np
import cv2

cap = cv2.VideoCapture('Shorttest.mp4')

# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )

# Parameters for lucas kanade optical flow
lk_params = dict( winSize  = (15,15),
                  maxLevel = 2,
                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# Create some random colors
color = np.random.randint(0,255,(100,3))

# Take first frame and find corners in it
ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)

while(1):
    ret,frame = cap.read()
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # calculate optical flow
    p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)

    # Select good points
    good_new = p1[st==1]
    good_old = p0[st==1]

    # draw the tracks
    for i,(new,old) in enumerate(zip(good_new,good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
        frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)
    img = cv2.add(frame,mask)

    cv2.imshow('frame',img)
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

    # Now update the previous frame and previous points
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1,1,2)

cv2.destroyAllWindows()
cap.release()

Answer 1

您显示所需结果的图像是通过opencv示例中的示例生成的，您可以在路径中找到它：samples / python2 / lk_track.py

#!/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 - exit
'''

# Python 2/3 compatibility
from __future__ import print_function

import numpy as np
import cv2 as cv
import video
from common import anorm2, draw_str
from time import clock

lk_params = dict( winSize  = (15, 15),
                  maxLevel = 2,
                  criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))

feature_params = dict( maxCorners = 500,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )

class App:
    def __init__(self, video_src):
        self.track_len = 10
        self.detect_interval = 5
        self.tracks = []
        self.cam = video.create_capture(video_src)
        self.frame_idx = 0

    def run(self):
        while True:
            _ret, frame = self.cam.read()
            frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
            vis = frame.copy()

            if len(self.tracks) > 0:
                img0, img1 = self.prev_gray, frame_gray
                p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2)
                p1, _st, _err = cv.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
                p0r, _st, _err = cv.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
                d = abs(p0-p0r).reshape(-1, 2).max(-1)
                good = d < 1
                new_tracks = []
                for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
                    if not good_flag:
                        continue
                    tr.append((x, y))
                    if len(tr) > self.track_len:
                        del tr[0]
                    new_tracks.append(tr)
                    cv.circle(vis, (x, y), 2, (0, 255, 0), -1)
                self.tracks = new_tracks
                cv.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0))
                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[:] = 255
                for 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)
                if p is not None:
                    for x, y in np.float32(p).reshape(-1, 2):
                        self.tracks.append([(x, y)])


            self.frame_idx += 1
            self.prev_gray = frame_gray
            cv.imshow('lk_track', vis)

            ch = cv.waitKey(1)
            if ch == 27:
                break

def main():
    import sys
    try:
        video_src = sys.argv[1]
    except:
        video_src = 0

    print(__doc__)
    App(video_src).run()
    cv.destroyAllWindows()

if __name__ == '__main__':
    main()

Answer 2

光流估计可能导致效果不佳的原因有很多。考虑到您的具体问题，我首先关注的是：

你是否有太大的位移。这可能是因为帧速率低，或者物品快速移动或靠近相机。按像素/帧测量位移。对于每个金字塔等级，您只能希望用LK检测几个像素/帧。
你有运动模糊吗？不要与大位移相混淆。对于现实世界中的大型运动，物品沿运动方向变得模糊。
阴影和反射。更大类问题的一部分：未通过＆＃34;亮度恒定约束的数据＆＃34; （一个点必须在帧与帧之间显示相同，尽管它的位置不同）。查看您的数据并确保自己的位置实际上看起来一样。如果使用稀疏流，则可以检查特征检测模块在每个帧上的执行情况。相同的点应该倾向于作为跟踪的好特征弹出。
你有多个动作吗？例如，这是汽车相互移动的时候。也不好。

有一些方法可以解决所有这些问题，但需要为自己的工程项目做好准备。

Lucas-Kanade在OpenCV中的光流不适合我

2 个答案: