111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
#!/usr/bin/env python
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'''
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Lucas-Kanade tracker
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====================
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Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
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for track initialization and back-tracking for match verification
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between frames.
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2
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#local modules
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from tst_scene_render import TestSceneRender
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from tests_common import NewOpenCVTests, intersectionRate, isPointInRect
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lk_params = dict( winSize = (15, 15),
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maxLevel = 2,
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criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
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feature_params = dict( maxCorners = 500,
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qualityLevel = 0.3,
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minDistance = 7,
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blockSize = 7 )
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def getRectFromPoints(points):
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distances = []
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for point in points:
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distances.append(cv2.norm(point, cv2.NORM_L2))
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x0, y0 = points[np.argmin(distances)]
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x1, y1 = points[np.argmax(distances)]
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return np.array([x0, y0, x1, y1])
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class lk_track_test(NewOpenCVTests):
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track_len = 10
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detect_interval = 5
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tracks = []
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frame_idx = 0
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render = None
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def test_lk_track(self):
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self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'), self.get_sample('samples/data/box.png'))
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self.runTracker()
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def runTracker(self):
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foregroundPointsNum = 0
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while True:
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frame = self.render.getNextFrame()
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frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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if len(self.tracks) > 0:
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img0, img1 = self.prev_gray, frame_gray
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p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2)
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p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
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p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
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d = abs(p0-p0r).reshape(-1, 2).max(-1)
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good = d < 1
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new_tracks = []
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for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
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if not good_flag:
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continue
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tr.append([(x, y), self.frame_idx])
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if len(tr) > self.track_len:
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del tr[0]
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new_tracks.append(tr)
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self.tracks = new_tracks
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if self.frame_idx % self.detect_interval == 0:
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goodTracksCount = 0
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for tr in self.tracks:
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oldRect = self.render.getRectInTime(self.render.timeStep * tr[0][1])
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newRect = self.render.getRectInTime(self.render.timeStep * tr[-1][1])
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if isPointInRect(tr[0][0], oldRect) and isPointInRect(tr[-1][0], newRect):
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goodTracksCount += 1
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if self.frame_idx == self.detect_interval:
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foregroundPointsNum = goodTracksCount
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fgIndex = float(foregroundPointsNum) / (foregroundPointsNum + 1)
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fgRate = float(goodTracksCount) / (len(self.tracks) + 1)
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if self.frame_idx > 0:
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self.assertGreater(fgIndex, 0.9)
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self.assertGreater(fgRate, 0.2)
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mask = np.zeros_like(frame_gray)
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mask[:] = 255
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for x, y in [np.int32(tr[-1][0]) for tr in self.tracks]:
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cv2.circle(mask, (x, y), 5, 0, -1)
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p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
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if p is not None:
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for x, y in np.float32(p).reshape(-1, 2):
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self.tracks.append([[(x, y), self.frame_idx]])
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self.frame_idx += 1
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self.prev_gray = frame_gray
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if self.frame_idx > 300:
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break |