Open Source Computer Vision Library https://opencv.org/
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#!/usr/bin/env python
import unittest
import random
import urllib
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 = urllib.urlopen("https://raw.github.com/Itseez/opencv/2.4/" + 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()