mirror of https://github.com/opencv/opencv.git
Merge pull request #9548 from alalek:python_tests
Maksim Shabunin
8 years ago
commit
791a11f949
30 changed files with 373 additions and 261 deletions
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#!/usr/bin/env python |
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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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from tests_common import NewOpenCVTests |
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class Hackathon244Tests(NewOpenCVTests): |
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def test_int_array(self): |
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a = np.array([-1, 2, -3, 4, -5]) |
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absa0 = np.abs(a) |
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self.assertTrue(cv2.norm(a, cv2.NORM_L1) == 15) |
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absa1 = cv2.absdiff(a, 0) |
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self.assertEqual(cv2.norm(absa1, absa0, cv2.NORM_INF), 0) |
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def test_imencode(self): |
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a = np.zeros((480, 640), dtype=np.uint8) |
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flag, ajpg = cv2.imencode("img_q90.jpg", a, [cv2.IMWRITE_JPEG_QUALITY, 90]) |
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self.assertEqual(flag, True) |
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self.assertEqual(ajpg.dtype, np.uint8) |
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self.assertGreater(ajpg.shape[0], 1) |
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self.assertEqual(ajpg.shape[1], 1) |
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def test_projectPoints(self): |
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objpt = np.float64([[1,2,3]]) |
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imgpt0, jac0 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), np.float64([])) |
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imgpt1, jac1 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), None) |
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self.assertEqual(imgpt0.shape, (objpt.shape[0], 1, 2)) |
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self.assertEqual(imgpt1.shape, imgpt0.shape) |
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self.assertEqual(jac0.shape, jac1.shape) |
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self.assertEqual(jac0.shape[0], 2*objpt.shape[0]) |
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def test_estimateAffine3D(self): |
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pattern_size = (11, 8) |
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pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32) |
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pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2) |
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pattern_points *= 10 |
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(retval, out, inliers) = cv2.estimateAffine3D(pattern_points, pattern_points) |
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self.assertEqual(retval, 1) |
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if cv2.norm(out[2,:]) < 1e-3: |
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out[2,2]=1 |
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self.assertLess(cv2.norm(out, np.float64([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])), 1e-3) |
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self.assertEqual(cv2.countNonZero(inliers), pattern_size[0]*pattern_size[1]) |
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def test_fast(self): |
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fd = cv2.FastFeatureDetector_create(30, True) |
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img = self.get_sample("samples/data/right02.jpg", 0) |
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img = cv2.medianBlur(img, 3) |
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keypoints = fd.detect(img) |
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self.assertTrue(600 <= len(keypoints) <= 700) |
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for kpt in keypoints: |
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self.assertNotEqual(kpt.response, 0) |
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def check_close_angles(self, a, b, angle_delta): |
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self.assertTrue(abs(a - b) <= angle_delta or |
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abs(360 - abs(a - b)) <= angle_delta) |
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def check_close_pairs(self, a, b, delta): |
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self.assertLessEqual(abs(a[0] - b[0]), delta) |
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self.assertLessEqual(abs(a[1] - b[1]), delta) |
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def check_close_boxes(self, a, b, delta, angle_delta): |
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self.check_close_pairs(a[0], b[0], delta) |
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self.check_close_pairs(a[1], b[1], delta) |
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self.check_close_angles(a[2], b[2], angle_delta) |
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def test_geometry(self): |
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npt = 100 |
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np.random.seed(244) |
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a = np.random.randn(npt,2).astype('float32')*50 + 150 |
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be = cv2.fitEllipse(a) |
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br = cv2.minAreaRect(a) |
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mc, mr = cv2.minEnclosingCircle(a) |
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be0 = ((150.2511749267578, 150.77322387695312), (158.024658203125, 197.57696533203125), 37.57804489135742) |
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br0 = ((161.2974090576172, 154.41793823242188), (199.2301483154297, 207.7177734375), -9.164555549621582) |
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mc0, mr0 = (160.41790771484375, 144.55152893066406), 136.713500977 |
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self.check_close_boxes(be, be0, 5, 15) |
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self.check_close_boxes(br, br0, 5, 15) |
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self.check_close_pairs(mc, mc0, 5) |
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self.assertLessEqual(abs(mr - mr0), 5) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap() |
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#!/usr/bin/env python |
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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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from tests_common import NewOpenCVTests |
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class Bindings(NewOpenCVTests): |
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def test_inheritance(self): |
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bm = cv2.StereoBM_create() |
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bm.getPreFilterCap() # from StereoBM |
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bm.getBlockSize() # from SteroMatcher |
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boost = cv2.ml.Boost_create() |
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boost.getBoostType() # from ml::Boost |
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boost.getMaxDepth() # from ml::DTrees |
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boost.isClassifier() # from ml::StatModel |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap() |
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#!/usr/bin/env python |
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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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from tests_common import NewOpenCVTests |
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class UMat(NewOpenCVTests): |
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def test_umat_construct(self): |
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data = np.random.random([512, 512]) |
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# UMat constructors |
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data_um = cv2.UMat(data) # from ndarray |
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data_sub_um = cv2.UMat(data_um, [128, 256], [128, 256]) # from UMat |
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data_dst_um = cv2.UMat(128, 128, cv2.CV_64F) # from size/type |
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# test continuous and submatrix flags |
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assert data_um.isContinuous() and not data_um.isSubmatrix() |
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assert not data_sub_um.isContinuous() and data_sub_um.isSubmatrix() |
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# test operation on submatrix |
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cv2.multiply(data_sub_um, 2., dst=data_dst_um) |
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assert np.allclose(2. * data[128:256, 128:256], data_dst_um.get()) |
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def test_umat_handle(self): |
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a_um = cv2.UMat(256, 256, cv2.CV_32F) |
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_ctx_handle = cv2.UMat.context() # obtain context handle |
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_queue_handle = cv2.UMat.queue() # obtain queue handle |
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_a_handle = a_um.handle(cv2.ACCESS_READ) # obtain buffer handle |
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_offset = a_um.offset # obtain buffer offset |
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def test_umat_matching(self): |
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img1 = self.get_sample("samples/data/right01.jpg") |
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img2 = self.get_sample("samples/data/right02.jpg") |
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orb = cv2.ORB_create() |
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img1, img2 = cv2.UMat(img1), cv2.UMat(img2) |
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ps1, descs_umat1 = orb.detectAndCompute(img1, None) |
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ps2, descs_umat2 = orb.detectAndCompute(img2, None) |
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self.assertIsInstance(descs_umat1, cv2.UMat) |
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self.assertIsInstance(descs_umat2, cv2.UMat) |
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self.assertGreater(len(ps1), 0) |
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self.assertGreater(len(ps2), 0) |
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bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) |
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res_umats = bf.match(descs_umat1, descs_umat2) |
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res = bf.match(descs_umat1.get(), descs_umat2.get()) |
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self.assertGreater(len(res), 0) |
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self.assertEqual(len(res_umats), len(res)) |
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def test_umat_optical_flow(self): |
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img1 = self.get_sample("samples/data/right01.jpg", cv2.IMREAD_GRAYSCALE) |
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img2 = self.get_sample("samples/data/right02.jpg", cv2.IMREAD_GRAYSCALE) |
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# Note, that if you want to see performance boost by OCL implementation - you need enough data |
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# For example you can increase maxCorners param to 10000 and increase img1 and img2 in such way: |
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# img = np.hstack([np.vstack([img] * 6)] * 6) |
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feature_params = dict(maxCorners=239, |
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qualityLevel=0.3, |
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minDistance=7, |
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blockSize=7) |
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p0 = cv2.goodFeaturesToTrack(img1, mask=None, **feature_params) |
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p0_umat = cv2.goodFeaturesToTrack(cv2.UMat(img1), mask=None, **feature_params) |
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self.assertEqual(p0_umat.get().shape, p0.shape) |
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p0 = np.array(sorted(p0, key=lambda p: tuple(p[0]))) |
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p0_umat = cv2.UMat(np.array(sorted(p0_umat.get(), key=lambda p: tuple(p[0])))) |
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self.assertTrue(np.allclose(p0_umat.get(), p0)) |
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_p1_mask_err = cv2.calcOpticalFlowPyrLK(img1, img2, p0, None) |
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_p1_mask_err_umat0 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, img2, p0_umat, None)) |
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_p1_mask_err_umat1 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(cv2.UMat(img1), img2, p0_umat, None)) |
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_p1_mask_err_umat2 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, cv2.UMat(img2), p0_umat, None)) |
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# # results of OCL optical flow differs from CPU implementation, so result can not be easily compared |
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# for p1_mask_err_umat in [p1_mask_err_umat0, p1_mask_err_umat1, p1_mask_err_umat2]: |
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# for data, data_umat in zip(p1_mask_err, p1_mask_err_umat): |
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# self.assertTrue(np.allclose(data, data_umat)) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap() |
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