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#!/usr/bin/env python
'''
Lucas-Kanade homography tracker
===============================
Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
for track initialization and back-tracking for match verification
between frames. Finds homography between reference and current views.
Usage
-----
lk_homography.py [<video_source>]
Keys
----
ESC - exit
SPACE - start tracking
r - toggle RANSAC
'''
import numpy as np
import cv2
import video
from common import draw_str
lk_params = dict( winSize = (19, 19),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
feature_params = dict( maxCorners = 1000,
qualityLevel = 0.01,
minDistance = 8,
blockSize = 19 )
def checkedTrace(img0, img1, p0, back_threshold = 1.0):
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)
status = d < back_threshold
return p1, status
green = (0, 255, 0)
red = (0, 0, 255)
class App:
def __init__(self, video_src):
self.cam = video.create_capture(video_src)
self.p0 = None
self.use_ransac = True
def run(self):
while True:
ret, frame = self.cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if self.p0 is not None:
p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
self.p1 = p2[trace_status].copy()
self.p0 = self.p0[trace_status].copy()
self.gray1 = frame_gray
if len(self.p0) < 4:
self.p0 = None
continue
H, status = cv2.findHomography(self.p0, self.p1, (0, cv2.RANSAC)[self.use_ransac], 10.0)
h, w = frame.shape[:2]
overlay = cv2.warpPerspective(self.frame0, H, (w, h))
vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
if good:
cv2.line(vis, (x0, y0), (x1, y1), (0, 128, 0))
cv2.circle(vis, (x1, y1), 2, (red, green)[good], -1)
draw_str(vis, (20, 20), 'track count: %d' % len(self.p1))
if self.use_ransac:
draw_str(vis, (20, 40), 'RANSAC')
else:
p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
if p is not None:
for x, y in p[:,0]:
cv2.circle(vis, (x, y), 2, green, -1)
draw_str(vis, (20, 20), 'feature count: %d' % len(p))
cv2.imshow('lk_homography', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
if ch == ord(' '):
self.frame0 = frame.copy()
self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
if self.p0 is not None:
self.p1 = self.p0
self.gray0 = frame_gray
self.gray1 = frame_gray
if ch == ord('r'):
self.use_ransac = not self.use_ransac
def main():
import sys
try: video_src = sys.argv[1]
except: video_src = 0
print __doc__
App(video_src).run()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
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