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Open Source Computer Vision Library
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111 lines
3.8 KiB
111 lines
3.8 KiB
#!/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 |