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Open Source Computer Vision Library
https://opencv.org/
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92 lines
4.1 KiB
92 lines
4.1 KiB
#!/usr/bin/env python |
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from __future__ import print_function |
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import xml.etree.ElementTree as ET |
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from glob import glob |
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from pprint import PrettyPrinter as PP |
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LONG_TESTS_DEBUG_VALGRIND = [ |
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('calib3d', 'Calib3d_InitUndistortRectifyMap.accuracy', 2017.22), |
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('features2d', 'Features2d_Feature2d.no_crash', 1235.68), |
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('ml', 'ML_RTrees.regression', 1423.47), |
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('optflow', 'DenseOpticalFlow_DeepFlow.ReferenceAccuracy', 1360.95), |
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('optflow', 'DenseOpticalFlow_DeepFlow_perf.perf/0', 1881.59), |
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('optflow', 'DenseOpticalFlow_DeepFlow_perf.perf/1', 5608.75), |
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('optflow', 'DenseOpticalFlow_GlobalPatchColliderDCT.ReferenceAccuracy', 5433.84), |
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('optflow', 'DenseOpticalFlow_GlobalPatchColliderWHT.ReferenceAccuracy', 5232.73), |
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('optflow', 'DenseOpticalFlow_SimpleFlow.ReferenceAccuracy', 1542.1), |
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('photo', 'Photo_Denoising.speed', 1484.87), |
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('photo', 'Photo_DenoisingColoredMulti.regression', 2447.11), |
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('rgbd', 'Rgbd_Normals.compute', 1156.32), |
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('shape', 'Hauss.regression', 2625.72), |
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('shape', 'ShapeEMD_SCD.regression', 61913.7), |
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('shape', 'Shape_SCD.regression', 3311.46), |
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('tracking', 'AUKF.br_mean_squared_error', 10764.6), |
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('tracking', 'UKF.br_mean_squared_error', 5228.27), |
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('xfeatures2d', 'Features2d_RotationInvariance_Descriptor_BoostDesc_LBGM.regression', 1124.51), |
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('xfeatures2d', 'Features2d_RotationInvariance_Descriptor_VGG120.regression', 2198.1), |
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('xfeatures2d', 'Features2d_RotationInvariance_Descriptor_VGG48.regression', 1958.52), |
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('xfeatures2d', 'Features2d_RotationInvariance_Descriptor_VGG64.regression', 2113.12), |
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('xfeatures2d', 'Features2d_RotationInvariance_Descriptor_VGG80.regression', 2167.16), |
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('xfeatures2d', 'Features2d_ScaleInvariance_Descriptor_BoostDesc_LBGM.regression', 1511.39), |
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('xfeatures2d', 'Features2d_ScaleInvariance_Descriptor_VGG120.regression', 1222.07), |
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('xfeatures2d', 'Features2d_ScaleInvariance_Descriptor_VGG48.regression', 1059.14), |
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('xfeatures2d', 'Features2d_ScaleInvariance_Descriptor_VGG64.regression', 1163.41), |
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('xfeatures2d', 'Features2d_ScaleInvariance_Descriptor_VGG80.regression', 1179.06), |
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('ximgproc', 'L0SmoothTest.SplatSurfaceAccuracy', 6382.26), |
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('ximgproc', 'L0SmoothTest_perf.perf/17', 2052.16), |
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('ximgproc', 'RollingGuidanceFilterTest_perf.perf/59', 2760.29), |
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('ximgproc', 'TypicalSet1/RollingGuidanceFilterTest.MultiThreadReproducibility/5', 1086.33), |
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('ximgproc', 'TypicalSet1/RollingGuidanceFilterTest.MultiThreadReproducibility/7', 1405.05), |
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('ximgproc', 'TypicalSet1/RollingGuidanceFilterTest.SplatSurfaceAccuracy/5', 1253.07), |
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('ximgproc', 'TypicalSet1/RollingGuidanceFilterTest.SplatSurfaceAccuracy/7', 1599.98), |
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] |
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def longTestFilter(data): |
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res = ['*', '-'] |
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for _, v, _ in data: |
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res.append(v) |
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return '--gtest_filter={}'.format(':'.join(res)) |
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# Parse one xml file, filter out tests which took less than 'timeLimit' seconds |
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# Returns tuple: ( <module_name>, [ (<module_name>, <test_name>, <test_time>), ... ] ) |
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def parseOneFile(filename, timeLimit): |
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tree = ET.parse(filename) |
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root = tree.getroot() |
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def guess(s, delims): |
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for delim in delims: |
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tmp = s.partition(delim) |
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if len(tmp[1]) != 0: |
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return tmp[0] |
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return None |
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module = guess(filename, ['_posix_', '_nt_', '__']) or root.get('cv_module_name') |
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if not module: |
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return (None, None) |
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res = [] |
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for elem in root.findall('.//testcase'): |
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key = '{}.{}'.format(elem.get('classname'), elem.get('name')) |
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val = elem.get('time') |
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if float(val) >= timeLimit: |
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res.append((module, key, float(val))) |
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return (module, res) |
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# Parse all xml files in current folder and combine results into one list |
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# Print result to the stdout |
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if __name__ == '__main__': |
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LIMIT = 1000 |
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res = [] |
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xmls = glob('*.xml') |
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for xml in xmls: |
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print('Parsing file', xml, '...') |
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module, testinfo = parseOneFile(xml, LIMIT) |
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if not module: |
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print('SKIP') |
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continue |
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res.extend(testinfo) |
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print('========= RESULTS =========') |
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PP(indent=4, width=100).pprint(sorted(res))
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