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bc8d494617
Parallelize implementation of HDR MergeMertens. * Parallelize MergeMertens. * Added performance tests for HDR. * Ran clang-format. * Optimizations. * Fix data path for Windows. * Remove compiiation warning on Windows. * Remove clang-format for existing file. * Addressing reviewer comments. * Ensure correct summation order. * Add test for determinism. * Move result pyramid into sync struct. * Reuse sync for first loop as well. * Use OpenCV's threading primitives. * Remove cout.
128 lines
5.1 KiB
Python
128 lines
5.1 KiB
Python
#!/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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import os
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from tests_common import NewOpenCVTests
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def load_exposure_seq(path):
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images = []
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times = []
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with open(os.path.join(path, 'list.txt'), 'r') as list_file:
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for line in list_file.readlines():
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name, time = line.split()
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images.append(cv.imread(os.path.join(path, name)))
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times.append(1. / float(time))
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return images, times
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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 = list(map(lambda umat: umat.get(), cv.calcOpticalFlowPyrLK(img1, img2, p0_umat, None)))
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_p1_mask_err_umat1 = list(map(lambda umat: umat.get(), cv.calcOpticalFlowPyrLK(cv.UMat(img1), img2, p0_umat, None)))
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_p1_mask_err_umat2 = list(map(lambda umat: umat.get(), cv.calcOpticalFlowPyrLK(img1, cv.UMat(img2), p0_umat, None)))
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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.assertEqual(data.shape, data_umat.shape)
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self.assertEqual(data.dtype, data_umat.dtype)
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for _p1_mask_err_umat in [_p1_mask_err_umat1, _p1_mask_err_umat2]:
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for data_umat0, data_umat in zip(_p1_mask_err_umat0[:2], _p1_mask_err_umat[:2]):
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self.assertTrue(np.allclose(data_umat0, data_umat))
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def test_umat_merge_mertens(self):
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if self.extraTestDataPath is None:
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self.fail('Test data is not available')
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test_data_path = os.path.join(self.extraTestDataPath, 'cv', 'hdr')
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images, _ = load_exposure_seq(os.path.join(test_data_path, 'exposures'))
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# As we want to test mat vs. umat here, we temporarily set only one worker-thread to achieve
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# deterministic summations inside mertens' parallelized process.
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num_threads = cv.getNumThreads()
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cv.setNumThreads(1)
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merge = cv.createMergeMertens()
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mat_result = merge.process(images)
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umat_images = [cv.UMat(img) for img in images]
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umat_result = merge.process(umat_images)
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cv.setNumThreads(num_threads)
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self.assertTrue(np.allclose(umat_result.get(), mat_result))
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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