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