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#!/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
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