mirror of
https://github.com/opencv/opencv.git
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197 lines
7.9 KiB
Python
Executable File
197 lines
7.9 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import print_function
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import unittest
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import random
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import time
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import math
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import sys
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import array
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import tarfile
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import hashlib
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import os
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import getopt
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import operator
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import functools
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import numpy as np
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import cv2
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import argparse
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# Python 3 moved urlopen to urllib.requests
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try:
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from urllib.request import urlopen
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except ImportError:
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from urllib import urlopen
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from tests_common import NewOpenCVTests
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# Tests to run first; check the handful of basic operations that the later tests rely on
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basedir = os.path.abspath(os.path.dirname(__file__))
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def load_tests(loader, tests, pattern):
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tests.addTests(loader.discover(basedir, pattern='test_*.py'))
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return tests
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class Hackathon244Tests(NewOpenCVTests):
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def test_int_array(self):
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a = np.array([-1, 2, -3, 4, -5])
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absa0 = np.abs(a)
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self.assertTrue(cv2.norm(a, cv2.NORM_L1) == 15)
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absa1 = cv2.absdiff(a, 0)
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self.assertEqual(cv2.norm(absa1, absa0, cv2.NORM_INF), 0)
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def test_imencode(self):
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a = np.zeros((480, 640), dtype=np.uint8)
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flag, ajpg = cv2.imencode("img_q90.jpg", a, [cv2.IMWRITE_JPEG_QUALITY, 90])
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self.assertEqual(flag, True)
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self.assertEqual(ajpg.dtype, np.uint8)
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self.assertGreater(ajpg.shape[0], 1)
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self.assertEqual(ajpg.shape[1], 1)
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def test_projectPoints(self):
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objpt = np.float64([[1,2,3]])
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imgpt0, jac0 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), np.float64([]))
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imgpt1, jac1 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), None)
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self.assertEqual(imgpt0.shape, (objpt.shape[0], 1, 2))
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self.assertEqual(imgpt1.shape, imgpt0.shape)
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self.assertEqual(jac0.shape, jac1.shape)
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self.assertEqual(jac0.shape[0], 2*objpt.shape[0])
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def test_estimateAffine3D(self):
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pattern_size = (11, 8)
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pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
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pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
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pattern_points *= 10
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(retval, out, inliers) = cv2.estimateAffine3D(pattern_points, pattern_points)
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self.assertEqual(retval, 1)
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if cv2.norm(out[2,:]) < 1e-3:
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out[2,2]=1
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self.assertLess(cv2.norm(out, np.float64([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])), 1e-3)
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self.assertEqual(cv2.countNonZero(inliers), pattern_size[0]*pattern_size[1])
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def test_fast(self):
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fd = cv2.FastFeatureDetector_create(30, True)
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img = self.get_sample("samples/data/right02.jpg", 0)
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img = cv2.medianBlur(img, 3)
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imgc = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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keypoints = fd.detect(img)
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self.assertTrue(600 <= len(keypoints) <= 700)
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for kpt in keypoints:
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self.assertNotEqual(kpt.response, 0)
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def check_close_angles(self, a, b, angle_delta):
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self.assertTrue(abs(a - b) <= angle_delta or
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abs(360 - abs(a - b)) <= angle_delta)
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def check_close_pairs(self, a, b, delta):
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self.assertLessEqual(abs(a[0] - b[0]), delta)
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self.assertLessEqual(abs(a[1] - b[1]), delta)
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def check_close_boxes(self, a, b, delta, angle_delta):
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self.check_close_pairs(a[0], b[0], delta)
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self.check_close_pairs(a[1], b[1], delta)
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self.check_close_angles(a[2], b[2], angle_delta)
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def test_geometry(self):
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npt = 100
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np.random.seed(244)
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a = np.random.randn(npt,2).astype('float32')*50 + 150
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img = np.zeros((300, 300, 3), dtype='uint8')
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be = cv2.fitEllipse(a)
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br = cv2.minAreaRect(a)
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mc, mr = cv2.minEnclosingCircle(a)
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be0 = ((150.2511749267578, 150.77322387695312), (158.024658203125, 197.57696533203125), 37.57804489135742)
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br0 = ((161.2974090576172, 154.41793823242188), (199.2301483154297, 207.7177734375), -9.164555549621582)
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mc0, mr0 = (160.41790771484375, 144.55152893066406), 136.713500977
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self.check_close_boxes(be, be0, 5, 15)
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self.check_close_boxes(br, br0, 5, 15)
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self.check_close_pairs(mc, mc0, 5)
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self.assertLessEqual(abs(mr - mr0), 5)
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def test_inheritance(self):
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bm = cv2.StereoBM_create()
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bm.getPreFilterCap() # from StereoBM
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bm.getBlockSize() # from SteroMatcher
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boost = cv2.ml.Boost_create()
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boost.getBoostType() # from ml::Boost
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boost.getMaxDepth() # from ml::DTrees
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boost.isClassifier() # from ml::StatModel
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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 = cv2.ORB_create()
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img1, img2 = cv2.UMat(img1), cv2.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, cv2.UMat)
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self.assertIsInstance(descs_umat2, cv2.UMat)
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self.assertGreater(len(ps1), 0)
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self.assertGreater(len(ps2), 0)
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bf = cv2.BFMatcher(cv2.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", cv2.IMREAD_GRAYSCALE)
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img2 = self.get_sample("samples/data/right02.jpg", cv2.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 = cv2.goodFeaturesToTrack(img1, mask=None, **feature_params)
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p0_umat = cv2.goodFeaturesToTrack(cv2.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 = cv2.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 = cv2.calcOpticalFlowPyrLK(img1, img2, p0, None)
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p1_mask_err_umat0 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, img2, p0_umat, None))
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p1_mask_err_umat1 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(cv2.UMat(img1), img2, p0_umat, None))
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p1_mask_err_umat2 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, cv2.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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parser = argparse.ArgumentParser(description='run OpenCV python tests')
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parser.add_argument('--repo', help='use sample image files from local git repository (path to folder), '
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'if not set, samples will be downloaded from github.com')
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parser.add_argument('--data', help='<not used> use data files from local folder (path to folder), '
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'if not set, data files will be downloaded from docs.opencv.org')
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args, other = parser.parse_known_args()
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print("Testing OpenCV", cv2.__version__)
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print("Local repo path:", args.repo)
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NewOpenCVTests.repoPath = args.repo
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try:
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NewOpenCVTests.extraTestDataPath = os.environ['OPENCV_TEST_DATA_PATH']
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except KeyError:
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print('Missing opencv extra repository. Some of tests may fail.')
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random.seed(0)
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unit_argv = [sys.argv[0]] + other;
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unittest.main(argv=unit_argv)
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