mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 06:10:02 +08:00
111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
#!/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.
|
|
'''
|
|
|
|
# Python 2/3 compatibility
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
import cv2
|
|
|
|
#local modules
|
|
from tst_scene_render import TestSceneRender
|
|
from tests_common import NewOpenCVTests, intersectionRate, isPointInRect
|
|
|
|
lk_params = dict( winSize = (15, 15),
|
|
maxLevel = 2,
|
|
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
|
|
|
|
feature_params = dict( maxCorners = 500,
|
|
qualityLevel = 0.3,
|
|
minDistance = 7,
|
|
blockSize = 7 )
|
|
|
|
def getRectFromPoints(points):
|
|
|
|
distances = []
|
|
for point in points:
|
|
distances.append(cv2.norm(point, cv2.NORM_L2))
|
|
|
|
x0, y0 = points[np.argmin(distances)]
|
|
x1, y1 = points[np.argmax(distances)]
|
|
|
|
return np.array([x0, y0, x1, y1])
|
|
|
|
|
|
class lk_track_test(NewOpenCVTests):
|
|
|
|
track_len = 10
|
|
detect_interval = 5
|
|
tracks = []
|
|
frame_idx = 0
|
|
render = None
|
|
|
|
def test_lk_track(self):
|
|
|
|
self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'), self.get_sample('samples/data/box.png'))
|
|
self.runTracker()
|
|
|
|
def runTracker(self):
|
|
foregroundPointsNum = 0
|
|
|
|
while True:
|
|
frame = self.render.getNextFrame()
|
|
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
|
|
if len(self.tracks) > 0:
|
|
img0, img1 = self.prev_gray, frame_gray
|
|
p0 = np.float32([tr[-1][0] 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), self.frame_idx])
|
|
if len(tr) > self.track_len:
|
|
del tr[0]
|
|
new_tracks.append(tr)
|
|
self.tracks = new_tracks
|
|
|
|
if self.frame_idx % self.detect_interval == 0:
|
|
goodTracksCount = 0
|
|
for tr in self.tracks:
|
|
oldRect = self.render.getRectInTime(self.render.timeStep * tr[0][1])
|
|
newRect = self.render.getRectInTime(self.render.timeStep * tr[-1][1])
|
|
if isPointInRect(tr[0][0], oldRect) and isPointInRect(tr[-1][0], newRect):
|
|
goodTracksCount += 1
|
|
|
|
if self.frame_idx == self.detect_interval:
|
|
foregroundPointsNum = goodTracksCount
|
|
|
|
fgIndex = float(foregroundPointsNum) / (foregroundPointsNum + 1)
|
|
fgRate = float(goodTracksCount) / (len(self.tracks) + 1)
|
|
|
|
if self.frame_idx > 0:
|
|
self.assertGreater(fgIndex, 0.9)
|
|
self.assertGreater(fgRate, 0.2)
|
|
|
|
mask = np.zeros_like(frame_gray)
|
|
mask[:] = 255
|
|
for x, y in [np.int32(tr[-1][0]) 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]])
|
|
|
|
self.frame_idx += 1
|
|
self.prev_gray = frame_gray
|
|
|
|
if self.frame_idx > 300:
|
|
break |