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96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
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#!/usr/bin/env python
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'''
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Lucas-Kanade homography tracker test
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===============================
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Uses goodFeaturesToTrack for track initialization and back-tracking for match verification
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between frames. Finds homography between reference and current views.
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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, isPointInRect
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lk_params = dict( winSize = (19, 19),
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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 = 1000,
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qualityLevel = 0.01,
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minDistance = 8,
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blockSize = 19 )
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def checkedTrace(img0, img1, p0, back_threshold = 1.0):
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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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status = d < back_threshold
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return p1, status
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class lk_homography_test(NewOpenCVTests):
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render = None
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framesCounter = 0
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frame = frame0 = None
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p0 = None
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p1 = None
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gray0 = gray1 = None
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numFeaturesInRectOnStart = 0
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def test_lk_homography(self):
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self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'),
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self.get_sample('samples/data/box.png'), noise = 0.1, speed = 1.0)
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frame = self.render.getNextFrame()
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frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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self.frame0 = frame.copy()
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self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
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isForegroundHomographyFound = False
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if self.p0 is not None:
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self.p1 = self.p0
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self.gray0 = frame_gray
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self.gray1 = frame_gray
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currRect = self.render.getCurrentRect()
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for (x,y) in self.p0[:,0]:
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if isPointInRect((x,y), currRect):
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self.numFeaturesInRectOnStart += 1
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while self.framesCounter < 200:
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self.framesCounter += 1
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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 self.p0 is not None:
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p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
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self.p1 = p2[trace_status].copy()
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self.p0 = self.p0[trace_status].copy()
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self.gray1 = frame_gray
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if len(self.p0) < 4:
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self.p0 = None
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continue
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H, status = cv2.findHomography(self.p0, self.p1, cv2.RANSAC, 5.0)
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goodPointsInRect = 0
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goodPointsOutsideRect = 0
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for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
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if good:
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if isPointInRect((x1,y1), self.render.getCurrentRect()):
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goodPointsInRect += 1
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else: goodPointsOutsideRect += 1
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if goodPointsOutsideRect < goodPointsInRect:
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isForegroundHomographyFound = True
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self.assertGreater(float(goodPointsInRect) / (self.numFeaturesInRectOnStart + 1), 0.6)
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else:
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p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
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self.assertEqual(isForegroundHomographyFound, True)
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