#!/usr/bin/env python import unittest import random import urllib2 import hashlib import numpy as np import cv2 import cv2.cv as cv class NewOpenCVTests(unittest.TestCase): def get_sample(self, filename, iscolor = cv.CV_LOAD_IMAGE_COLOR): if not filename in self.image_cache: filedata = urllib2.urlopen("https://raw.github.com/Itseez/opencv/2.4/" + filename).read() image = cv2.imdecode(np.fromstring(filedata, dtype=np.uint8), iscolor) self.assertFalse(image is None) self.image_cache[filename] = image return self.image_cache[filename] def setUp(self): self.image_cache = {} def hashimg(self, im): """ Compute a hash for an image, useful for image comparisons """ return hashlib.md5(im.tostring()).digest() # Tests to run first; check the handful of basic operations that the later tests rely on class Hackathon244Tests(NewOpenCVTests): def test_int_array(self): a = np.array([-1, 2, -3, 4, -5]) absa0 = np.abs(a) self.assert_(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(30, True) img = self.get_sample("samples/cpp/right02.jpg", 0) img = cv2.medianBlur(img, 3) imgc = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) keypoints = fd.detect(img) self.assert_(600 <= len(keypoints) <= 700) for kpt in keypoints: self.assertNotEqual(kpt.response, 0) def check_close_angles(self, a, b, angle_delta): self.assert_(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) if __name__ == '__main__': print "testing", cv2.__version__ random.seed(0) unittest.main()