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Open Source Computer Vision Library
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181 lines
6.4 KiB
181 lines
6.4 KiB
#!/usr/bin/env python |
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import cv2 as cv |
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import numpy as np |
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from tests_common import NewOpenCVTests |
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class stitching_test(NewOpenCVTests): |
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def test_simple(self): |
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img1 = self.get_sample('stitching/a1.png') |
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img2 = self.get_sample('stitching/a2.png') |
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stitcher = cv.Stitcher.create(cv.Stitcher_PANORAMA) |
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(_result, pano) = stitcher.stitch((img1, img2)) |
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#cv.imshow("pano", pano) |
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#cv.waitKey() |
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self.assertAlmostEqual(pano.shape[0], 685, delta=100, msg="rows: %r" % list(pano.shape)) |
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self.assertAlmostEqual(pano.shape[1], 1025, delta=100, msg="cols: %r" % list(pano.shape)) |
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class stitching_detail_test(NewOpenCVTests): |
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def test_simple(self): |
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img = self.get_sample('stitching/a1.png') |
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finder= cv.ORB.create() |
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imgFea = cv.detail.computeImageFeatures2(finder,img) |
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self.assertIsNotNone(imgFea) |
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# Added Test for PR #21180 |
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self.assertIsNotNone(imgFea.keypoints) |
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matcher = cv.detail_BestOf2NearestMatcher(False, 0.3) |
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self.assertIsNotNone(matcher) |
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matcher = cv.detail_AffineBestOf2NearestMatcher(False, False, 0.3) |
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self.assertIsNotNone(matcher) |
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matcher = cv.detail_BestOf2NearestRangeMatcher(2, False, 0.3) |
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self.assertIsNotNone(matcher) |
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estimator = cv.detail_AffineBasedEstimator() |
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self.assertIsNotNone(estimator) |
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estimator = cv.detail_HomographyBasedEstimator() |
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self.assertIsNotNone(estimator) |
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adjuster = cv.detail_BundleAdjusterReproj() |
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self.assertIsNotNone(adjuster) |
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adjuster = cv.detail_BundleAdjusterRay() |
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self.assertIsNotNone(adjuster) |
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adjuster = cv.detail_BundleAdjusterAffinePartial() |
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self.assertIsNotNone(adjuster) |
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adjuster = cv.detail_NoBundleAdjuster() |
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self.assertIsNotNone(adjuster) |
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compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_NO) |
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self.assertIsNotNone(compensator) |
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compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN) |
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self.assertIsNotNone(compensator) |
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compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN_BLOCKS) |
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self.assertIsNotNone(compensator) |
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seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO) |
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self.assertIsNotNone(seam_finder) |
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seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO) |
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self.assertIsNotNone(seam_finder) |
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seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM) |
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self.assertIsNotNone(seam_finder) |
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seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR") |
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self.assertIsNotNone(seam_finder) |
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seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD") |
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self.assertIsNotNone(seam_finder) |
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seam_finder = cv.detail_DpSeamFinder("COLOR") |
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self.assertIsNotNone(seam_finder) |
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seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD") |
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self.assertIsNotNone(seam_finder) |
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blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO) |
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self.assertIsNotNone(blender) |
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blender = cv.detail.Blender_createDefault(cv.detail.Blender_FEATHER) |
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self.assertIsNotNone(blender) |
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blender = cv.detail.Blender_createDefault(cv.detail.Blender_MULTI_BAND) |
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self.assertIsNotNone(blender) |
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timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_AS_IS); |
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self.assertIsNotNone(timelapser) |
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timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_CROP); |
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self.assertIsNotNone(timelapser) |
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class stitching_compose_panorama_test_no_args(NewOpenCVTests): |
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def test_simple(self): |
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img1 = self.get_sample('stitching/a1.png') |
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img2 = self.get_sample('stitching/a2.png') |
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stitcher = cv.Stitcher.create(cv.Stitcher_PANORAMA) |
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stitcher.estimateTransform((img1, img2)) |
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result, _ = stitcher.composePanorama() |
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assert result == 0 |
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class stitching_compose_panorama_args(NewOpenCVTests): |
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def test_simple(self): |
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img1 = self.get_sample('stitching/a1.png') |
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img2 = self.get_sample('stitching/a2.png') |
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stitcher = cv.Stitcher.create(cv.Stitcher_PANORAMA) |
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stitcher.estimateTransform((img1, img2)) |
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result, _ = stitcher.composePanorama((img1, img2)) |
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assert result == 0 |
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class stitching_matches_info_test(NewOpenCVTests): |
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def test_simple(self): |
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finder = cv.ORB.create() |
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img1 = self.get_sample('stitching/a1.png') |
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img2 = self.get_sample('stitching/a2.png') |
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img_feat1 = cv.detail.computeImageFeatures2(finder, img1) |
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img_feat2 = cv.detail.computeImageFeatures2(finder, img2) |
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matcher = cv.detail.BestOf2NearestMatcher_create() |
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matches_info = matcher.apply(img_feat1, img_feat2) |
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self.assertIsNotNone(matches_info.matches) |
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self.assertIsNotNone(matches_info.inliers_mask) |
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class stitching_range_matcher_test(NewOpenCVTests): |
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def test_simple(self): |
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images = [ |
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self.get_sample('stitching/a1.png'), |
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self.get_sample('stitching/a2.png'), |
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self.get_sample('stitching/a3.png') |
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] |
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orb = cv.ORB_create() |
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features = [cv.detail.computeImageFeatures2(orb, img) for img in images] |
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matcher = cv.detail_BestOf2NearestRangeMatcher(range_width=1) |
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matches = matcher.apply2(features) |
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# matches[1] is image 0 and image 1, should have non-zero confidence |
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self.assertNotEqual(matches[1].confidence, 0) |
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# matches[2] is image 0 and image 2, should have zero confidence due to range_width=1 |
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self.assertEqual(matches[2].confidence, 0) |
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class stitching_seam_finder_graph_cuts(NewOpenCVTests): |
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def test_simple(self): |
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images = [ |
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self.get_sample('stitching/a1.png'), |
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self.get_sample('stitching/a2.png'), |
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self.get_sample('stitching/a3.png') |
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] |
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images = [cv.resize(img, [100, 100]) for img in images] |
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finder = cv.detail_GraphCutSeamFinder('COST_COLOR_GRAD') |
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masks = [cv.UMat(255 * np.ones((img.shape[0], img.shape[1]), np.uint8)) for img in images] |
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images_f = [img.astype(np.float32) for img in images] |
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masks_warped = finder.find(images_f, [(0, 0), (75, 0), (150, 0)], masks) |
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self.assertIsNotNone(masks_warped) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap()
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