#!/usr/bin/env python import unittest import random import time import math import sys import array import urllib import tarfile import hashlib import os import getopt import operator import functools 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 = urllib.urlopen("https://raw.github.com/Itseez/opencv/master/" + filename).read() self.image_cache[filename] = cv2.imdecode(np.fromstring(filedata, dtype=np.uint8), iscolor) 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()