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