#!/usr/bin/env python from __future__ import print_function import unittest import random import time import math import sys import array import tarfile import hashlib import os import getopt import operator import functools import numpy as np import cv2 import argparse # Python 3 moved urlopen to urllib.requests try: from urllib.request import urlopen except ImportError: from urllib import urlopen from tests_common import NewOpenCVTests # Tests to run first; check the handful of basic operations that the later tests rely on basedir = os.path.abspath(os.path.dirname(__file__)) def load_tests(loader, tests, pattern): tests.addTests(loader.discover(basedir, pattern='test_*.py')) return tests class Hackathon244Tests(NewOpenCVTests): def test_int_array(self): a = np.array([-1, 2, -3, 4, -5]) absa0 = np.abs(a) self.assertTrue(cv2.norm(a, cv2.NORM_L1) == 15) absa1 = cv2.absdiff(a, 0) self.assertEqual(cv2.norm(absa1, absa0, cv2.NORM_INF), 0) def test_imencode(self): a = np.zeros((480, 640), dtype=np.uint8) flag, ajpg = cv2.imencode("img_q90.jpg", a, [cv2.IMWRITE_JPEG_QUALITY, 90]) self.assertEqual(flag, True) self.assertEqual(ajpg.dtype, np.uint8) self.assertGreater(ajpg.shape[0], 1) self.assertEqual(ajpg.shape[1], 1) def test_projectPoints(self): objpt = np.float64([[1,2,3]]) imgpt0, jac0 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), np.float64([])) imgpt1, jac1 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), None) self.assertEqual(imgpt0.shape, (objpt.shape[0], 1, 2)) self.assertEqual(imgpt1.shape, imgpt0.shape) self.assertEqual(jac0.shape, jac1.shape) self.assertEqual(jac0.shape[0], 2*objpt.shape[0]) def test_estimateAffine3D(self): pattern_size = (11, 8) pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32) pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2) pattern_points *= 10 (retval, out, inliers) = cv2.estimateAffine3D(pattern_points, pattern_points) self.assertEqual(retval, 1) if cv2.norm(out[2,:]) < 1e-3: out[2,2]=1 self.assertLess(cv2.norm(out, np.float64([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])), 1e-3) self.assertEqual(cv2.countNonZero(inliers), pattern_size[0]*pattern_size[1]) def test_fast(self): fd = cv2.FastFeatureDetector_create(30, True) img = self.get_sample("samples/data/right02.jpg", 0) img = cv2.medianBlur(img, 3) imgc = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) keypoints = fd.detect(img) self.assertTrue(600 <= len(keypoints) <= 700) for kpt in keypoints: self.assertNotEqual(kpt.response, 0) def check_close_angles(self, a, b, angle_delta): self.assertTrue(abs(a - b) <= angle_delta or abs(360 - abs(a - b)) <= angle_delta) def check_close_pairs(self, a, b, delta): self.assertLessEqual(abs(a[0] - b[0]), delta) self.assertLessEqual(abs(a[1] - b[1]), delta) def check_close_boxes(self, a, b, delta, angle_delta): self.check_close_pairs(a[0], b[0], delta) self.check_close_pairs(a[1], b[1], delta) self.check_close_angles(a[2], b[2], angle_delta) def test_geometry(self): npt = 100 np.random.seed(244) a = np.random.randn(npt,2).astype('float32')*50 + 150 img = np.zeros((300, 300, 3), dtype='uint8') be = cv2.fitEllipse(a) br = cv2.minAreaRect(a) mc, mr = cv2.minEnclosingCircle(a) be0 = ((150.2511749267578, 150.77322387695312), (158.024658203125, 197.57696533203125), 37.57804489135742) br0 = ((161.2974090576172, 154.41793823242188), (199.2301483154297, 207.7177734375), -9.164555549621582) mc0, mr0 = (160.41790771484375, 144.55152893066406), 136.713500977 self.check_close_boxes(be, be0, 5, 15) self.check_close_boxes(br, br0, 5, 15) self.check_close_pairs(mc, mc0, 5) self.assertLessEqual(abs(mr - mr0), 5) def test_inheritance(self): bm = cv2.StereoBM_create() bm.getPreFilterCap() # from StereoBM bm.getBlockSize() # from SteroMatcher boost = cv2.ml.Boost_create() boost.getBoostType() # from ml::Boost boost.getMaxDepth() # from ml::DTrees boost.isClassifier() # from ml::StatModel def test_umat_construct(self): data = np.random.random([512, 512]) # UMat constructors data_um = cv2.UMat(data) # from ndarray data_sub_um = cv2.UMat(data_um, [0, 256], [0, 256]) # from UMat data_dst_um = cv2.UMat(256, 256, cv2.CV_64F) # from size/type # simple test cv2.multiply(data_sub_um, 2., dst=data_dst_um) assert np.allclose(2. * data[:256, :256], data_dst_um.get()) def test_umat_matching(self): img1 = self.get_sample("samples/data/right01.jpg") img2 = self.get_sample("samples/data/right02.jpg") orb = cv2.ORB_create() img1, img2 = cv2.UMat(img1), cv2.UMat(img2) ps1, descs_umat1 = orb.detectAndCompute(img1, None) ps2, descs_umat2 = orb.detectAndCompute(img2, None) self.assertIsInstance(descs_umat1, cv2.UMat) self.assertIsInstance(descs_umat2, cv2.UMat) self.assertGreater(len(ps1), 0) self.assertGreater(len(ps2), 0) bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) res_umats = bf.match(descs_umat1, descs_umat2) res = bf.match(descs_umat1.get(), descs_umat2.get()) self.assertGreater(len(res), 0) self.assertEqual(len(res_umats), len(res)) def test_umat_optical_flow(self): img1 = self.get_sample("samples/data/right01.jpg", cv2.IMREAD_GRAYSCALE) img2 = self.get_sample("samples/data/right02.jpg", cv2.IMREAD_GRAYSCALE) # Note, that if you want to see performance boost by OCL implementation - you need enough data # For example you can increase maxCorners param to 10000 and increase img1 and img2 in such way: # img = np.hstack([np.vstack([img] * 6)] * 6) feature_params = dict(maxCorners=239, qualityLevel=0.3, minDistance=7, blockSize=7) p0 = cv2.goodFeaturesToTrack(img1, mask=None, **feature_params) p0_umat = cv2.goodFeaturesToTrack(cv2.UMat(img1), mask=None, **feature_params) self.assertEqual(p0_umat.get().shape, p0.shape) p0 = np.array(sorted(p0, key=lambda p: tuple(p[0]))) p0_umat = cv2.UMat(np.array(sorted(p0_umat.get(), key=lambda p: tuple(p[0])))) self.assertTrue(np.allclose(p0_umat.get(), p0)) p1_mask_err = cv2.calcOpticalFlowPyrLK(img1, img2, p0, None) p1_mask_err_umat0 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, img2, p0_umat, None)) p1_mask_err_umat1 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(cv2.UMat(img1), img2, p0_umat, None)) p1_mask_err_umat2 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, cv2.UMat(img2), p0_umat, None)) # # results of OCL optical flow differs from CPU implementation, so result can not be easily compared # for p1_mask_err_umat in [p1_mask_err_umat0, p1_mask_err_umat1, p1_mask_err_umat2]: # for data, data_umat in zip(p1_mask_err, p1_mask_err_umat): # self.assertTrue(np.allclose(data, data_umat)) if __name__ == '__main__': parser = argparse.ArgumentParser(description='run OpenCV python tests') parser.add_argument('--repo', help='use sample image files from local git repository (path to folder), ' 'if not set, samples will be downloaded from github.com') parser.add_argument('--data', help=' use data files from local folder (path to folder), ' 'if not set, data files will be downloaded from docs.opencv.org') args, other = parser.parse_known_args() print("Testing OpenCV", cv2.__version__) print("Local repo path:", args.repo) NewOpenCVTests.repoPath = args.repo try: NewOpenCVTests.extraTestDataPath = os.environ['OPENCV_TEST_DATA_PATH'] except KeyError: print('Missing opencv extra repository. Some of tests may fail.') random.seed(0) unit_argv = [sys.argv[0]] + other; unittest.main(argv=unit_argv)