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Open Source Computer Vision Library
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86 lines
3.7 KiB
86 lines
3.7 KiB
#!/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 as cv |
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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 = cv.UMat(data) # from ndarray |
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data_sub_um = cv.UMat(data_um, (128, 256), (128, 256)) # from UMat |
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data_dst_um = cv.UMat(128, 128, cv.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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cv.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 = cv.UMat(256, 256, cv.CV_32F) |
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_ctx_handle = cv.UMat.context() # obtain context handle |
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_queue_handle = cv.UMat.queue() # obtain queue handle |
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_a_handle = a_um.handle(cv.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 = cv.ORB_create() |
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img1, img2 = cv.UMat(img1), cv.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, cv.UMat) |
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self.assertIsInstance(descs_umat2, cv.UMat) |
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self.assertGreater(len(ps1), 0) |
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self.assertGreater(len(ps2), 0) |
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bf = cv.BFMatcher(cv.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", cv.IMREAD_GRAYSCALE) |
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img2 = self.get_sample("samples/data/right02.jpg", cv.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 = cv.goodFeaturesToTrack(img1, mask=None, **feature_params) |
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p0_umat = cv.goodFeaturesToTrack(cv.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 = cv.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 = cv.calcOpticalFlowPyrLK(img1, img2, p0, None) |
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_p1_mask_err_umat0 = map(cv.UMat.get, cv.calcOpticalFlowPyrLK(img1, img2, p0_umat, None)) |
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_p1_mask_err_umat1 = map(cv.UMat.get, cv.calcOpticalFlowPyrLK(cv.UMat(img1), img2, p0_umat, None)) |
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_p1_mask_err_umat2 = map(cv.UMat.get, cv.calcOpticalFlowPyrLK(img1, cv.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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