12 KLT算法

1 去除多余模块的

#-*- coding:utf-8 -*-

'''
Lucas-Kanade tracker
====================

Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
for track initialization and back-tracking for match verification
between frames.

Lucas Kanade稀疏光流Demo。使用goodfeaturestotrack
用于轨迹初始化和跟踪跟踪的匹配验证
帧间。

Usage
-----
lk_track.py [<video_source>]


Keys
----
ESC - exit
'''

# Python 2/3 compatibility


import numpy as np
import cv2


lk_params = dict( winSize  = (15, 15),
                  maxLevel = 3,
                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

feature_params = dict( maxCorners = 800,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )


class App:
    def __init__(self, video_src):
        self.track_len = 10          #跟踪轨迹长度10
        self.detect_interval = 5
        self.tracks = []             #储存跟踪点
        self.cam = cv2.VideoCapture(video_src)
        self.frame_idx = 0

    def run(self):
        while True:
            ret, frame = self.cam.read()
            frame_gray = cv2.cvtColor(frame, cv2.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 = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
                p0r, st, err = cv2.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)
                    cv2.circle(vis, (x, y), 2, (0, 255, 0), -1)
                self.tracks = new_tracks
                cv2.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]:
                    cv2.circle(mask, (x, y), 5, 0, -1)
                p = cv2.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
            cv2.imshow('lk_track', vis)


            ch = cv2.waitKey(1)
            if ch == 27:
                break

def main():
    video_src = 'traffic.flv'
    App(video_src).run()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

2。还原成普通函数

#-*- coding:utf-8 -*-
import numpy as np
import cv2

#ShiTomasi 角检测的参数
lk_params = dict( winSize  = (15, 15),
                  maxLevel = 3,
                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# l-k 光流参数
feature_params = dict( maxCorners = 800,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )

track_len = 10  # 跟踪轨迹长度10
detect_interval = 5
tracks = []  # 储存跟踪点
frame_idx = 0

# 1,定义一个对象，存储读取的视频
video_src = 'traffic.flv'
cam = cv2.VideoCapture(video_src)

while True:
    ret, frame = cam.read()
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    vis = frame.copy()

    if len(tracks) > 0:
        img0, img1 = prev_gray, frame_gray
        p0 = np.float32([tr[-1] for tr in tracks]).reshape(-1, 1, 2)
        p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
        p0r, st, err = cv2.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(tracks, p1.reshape(-1, 2), good):
            if not good_flag:
                continue
            tr.append((x, y))
            if len(tr) > track_len:
                del tr[0]
            new_tracks.append(tr)
            cv2.circle(vis, (x, y), 2, (0, 255, 0), -1)
        tracks = new_tracks
        cv2.polylines(vis, [np.int32(tr) for tr in tracks], False, (0, 255, 0))
        # draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks))

    if frame_idx % detect_interval == 0:
        mask = np.zeros_like(frame_gray)
        mask[:] = 255
        for x, y in [np.int32(tr[-1]) for tr in tracks]:
            cv2.circle(mask, (x, y), 5, 0, -1)
        p = cv2.goodFeaturesToTrack(frame_gray, mask=mask, **feature_params)
        if p is not None:
            for x, y in np.float32(p).reshape(-1, 2):
                tracks.append([(x, y)])

    frame_idx += 1
    prev_gray = frame_gray
    cv2.imshow('lk_track', vis)

    ch = cv2.waitKey(1)
    if ch == 27:
        break


cv2.destroyAllWindows()
cam.release()

3。效果图

 

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