Add new tests on python

modules/python/test/test.py

 #!/usr/bin/env python
 
+
 from __future__ import print_function
+
 import unittest
 import random
 import time
@@ -17,51 +19,24 @@ import numpy as np
 import cv2
 import argparse
 
+# local test modules
+from test_digits import digits_test
+from test_calibration import calibration_test
+from test_squares import squares_test
+from test_texture_flow import texture_flow_test
+from test_fitline import fitline_test
+from test_houghcircles import houghcircles_test
+from test_houghlines import houghlines_test
+from test_gaussian_mix import gaussian_mix_test
+from test_facedetect import facedetect_test
+
 # Python 3 moved urlopen to urllib.requests
 try:
     from urllib.request import urlopen
 except ImportError:
     from urllib import urlopen
 
-class NewOpenCVTests(unittest.TestCase):
-
-    # path to local repository folder containing 'samples' folder
-    repoPath = None
-    # github repository url
-    repoUrl = 'https://raw.github.com/Itseez/opencv/master'
-
-    def get_sample(self, filename, iscolor = cv2.IMREAD_COLOR):
-        if not filename in self.image_cache:
-            filedata = None
-            if NewOpenCVTests.repoPath is not None:
-                candidate = NewOpenCVTests.repoPath + '/' + filename
-                if os.path.isfile(candidate):
-                    with open(candidate, 'rb') as f:
-                        filedata = f.read()
-            if filedata is None:
-                filedata = urlopen(NewOpenCVTests.repoUrl + '/' + 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()
-
-    if sys.version_info[:2] == (2, 6):
-        def assertLess(self, a, b, msg=None):
-            if not a < b:
-                self.fail('%s not less than %s' % (repr(a), repr(b)))
-
-        def assertLessEqual(self, a, b, msg=None):
-            if not a <= b:
-                self.fail('%s not less than or equal to %s' % (repr(a), repr(b)))
-
-        def assertGreater(self, a, b, msg=None):
-            if not a > b:
-                self.fail('%s not greater than %s' % (repr(a), repr(b)))
+from tests_common import NewOpenCVTests
 
 # Tests to run first; check the handful of basic operations that the later tests rely on
 
@@ -167,4 +142,4 @@ if __name__ == '__main__':
     NewOpenCVTests.repoPath = args.repo
     random.seed(0)
     unit_argv = [sys.argv[0]] + other;
-    unittest.main(argv=unit_argv)
+    unittest.main(argv=unit_argv)
\ No newline at end of file

modules/python/test/test_calibration.py

+#!/usr/bin/env python
+
+'''
+camera calibration for distorted images with chess board samples
+reads distorted images, calculates the calibration and write undistorted images
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+
+from tests_common import NewOpenCVTests
+
+class calibration_test(NewOpenCVTests):
+
+    def test_calibration(self):
+
+        from glob import glob
+
+        img_mask = '../../../samples/data/left*.jpg'  # default
+        img_names = glob(img_mask)
+
+        square_size = 1.0
+        pattern_size = (9, 6)
+        pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
+        pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
+        pattern_points *= square_size
+
+        obj_points = []
+        img_points = []
+        h, w = 0, 0
+        img_names_undistort = []
+        for fn in img_names:
+            img = cv2.imread(fn, 0)
+            if img is None:
+                continue
+
+            h, w = img.shape[:2]
+            found, corners = cv2.findChessboardCorners(img, pattern_size)
+            if found:
+                term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
+                cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
+
+            if not found:
+                continue
+
+            img_points.append(corners.reshape(-1, 2))
+            obj_points.append(pattern_points)
+
+        # calculate camera distortion
+        rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), None, None, flags = 0)
+
+        eps = 0.01
+        normCamEps = 10.0
+        normDistEps = 0.01
+
+        cameraMatrixTest = [[ 532.80992189,    0.,          342.4952186 ],
+         [   0.,         532.93346422,  233.8879292 ],
+         [   0.,            0.,            1.        ]]
+
+        distCoeffsTest = [ -2.81325576e-01,   2.91130406e-02,
+           1.21234330e-03,  -1.40825372e-04, 1.54865844e-01]
+
+        self.assertLess(abs(rms - 0.196334638034), eps)
+        self.assertLess(cv2.norm(camera_matrix - cameraMatrixTest, cv2.NORM_L1), normCamEps)
+        self.assertLess(cv2.norm(dist_coefs - distCoeffsTest, cv2.NORM_L1), normDistEps)
\ No newline at end of file

modules/python/test/test_digits.py

+#!/usr/bin/env python
+
+'''
+SVM and KNearest digit recognition.
+
+Sample loads a dataset of handwritten digits from '../data/digits.png'.
+Then it trains a SVM and KNearest classifiers on it and evaluates
+their accuracy.
+
+Following preprocessing is applied to the dataset:
+ - Moment-based image deskew (see deskew())
+ - Digit images are split into 4 10x10 cells and 16-bin
+   histogram of oriented gradients is computed for each
+   cell
+ - Transform histograms to space with Hellinger metric (see [1] (RootSIFT))
+
+
+[1] R. Arandjelovic, A. Zisserman
+    "Three things everyone should know to improve object retrieval"
+    http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf
+
+'''
+
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+# built-in modules
+from multiprocessing.pool import ThreadPool
+
+import cv2
+
+import numpy as np
+from numpy.linalg import norm
+
+
+SZ = 20 # size of each digit is SZ x SZ
+CLASS_N = 10
+DIGITS_FN = '../../../samples/data/digits.png'
+
+def split2d(img, cell_size, flatten=True):
+    h, w = img.shape[:2]
+    sx, sy = cell_size
+    cells = [np.hsplit(row, w//sx) for row in np.vsplit(img, h//sy)]
+    cells = np.array(cells)
+    if flatten:
+        cells = cells.reshape(-1, sy, sx)
+    return cells
+
+def load_digits(fn):
+    digits_img = cv2.imread(fn, 0)
+    digits = split2d(digits_img, (SZ, SZ))
+    labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
+    return digits, labels
+
+def deskew(img):
+    m = cv2.moments(img)
+    if abs(m['mu02']) < 1e-2:
+        return img.copy()
+    skew = m['mu11']/m['mu02']
+    M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
+    img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
+    return img
+
+class StatModel(object):
+    def load(self, fn):
+        self.model.load(fn)  # Known bug: https://github.com/Itseez/opencv/issues/4969
+    def save(self, fn):
+        self.model.save(fn)
+
+class KNearest(StatModel):
+    def __init__(self, k = 3):
+        self.k = k
+        self.model = cv2.ml.KNearest_create()
+
+    def train(self, samples, responses):
+        self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
+
+    def predict(self, samples):
+        retval, results, neigh_resp, dists = self.model.findNearest(samples, self.k)
+        return results.ravel()
+
+class SVM(StatModel):
+    def __init__(self, C = 1, gamma = 0.5):
+        self.model = cv2.ml.SVM_create()
+        self.model.setGamma(gamma)
+        self.model.setC(C)
+        self.model.setKernel(cv2.ml.SVM_RBF)
+        self.model.setType(cv2.ml.SVM_C_SVC)
+
+    def train(self, samples, responses):
+        self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
+
+    def predict(self, samples):
+        return self.model.predict(samples)[1].ravel()
+
+
+def evaluate_model(model, digits, samples, labels):
+    resp = model.predict(samples)
+    err = (labels != resp).mean()
+
+    confusion = np.zeros((10, 10), np.int32)
+    for i, j in zip(labels, resp):
+        confusion[i, j] += 1
+
+    return err, confusion
+
+def preprocess_simple(digits):
+    return np.float32(digits).reshape(-1, SZ*SZ) / 255.0
+
+def preprocess_hog(digits):
+    samples = []
+    for img in digits:
+        gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
+        gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
+        mag, ang = cv2.cartToPolar(gx, gy)
+        bin_n = 16
+        bin = np.int32(bin_n*ang/(2*np.pi))
+        bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:]
+        mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
+        hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
+        hist = np.hstack(hists)
+
+        # transform to Hellinger kernel
+        eps = 1e-7
+        hist /= hist.sum() + eps
+        hist = np.sqrt(hist)
+        hist /= norm(hist) + eps
+
+        samples.append(hist)
+    return np.float32(samples)
+
+from tests_common import NewOpenCVTests
+
+class digits_test(NewOpenCVTests):
+
+    def test_digits(self):
+
+        digits, labels = load_digits(DIGITS_FN)
+
+        # shuffle digits
+        rand = np.random.RandomState(321)
+        shuffle = rand.permutation(len(digits))
+        digits, labels = digits[shuffle], labels[shuffle]
+
+        digits2 = list(map(deskew, digits))
+        samples = preprocess_hog(digits2)
+
+        train_n = int(0.9*len(samples))
+        digits_train, digits_test = np.split(digits2, [train_n])
+        samples_train, samples_test = np.split(samples, [train_n])
+        labels_train, labels_test = np.split(labels, [train_n])
+        errors = list()
+        confusionMatrixes = list()
+
+        model = KNearest(k=4)
+        model.train(samples_train, labels_train)
+        error, confusion = evaluate_model(model, digits_test, samples_test, labels_test)
+        errors.append(error)
+        confusionMatrixes.append(confusion)
+
+        model = SVM(C=2.67, gamma=5.383)
+        model.train(samples_train, labels_train)
+        error, confusion = evaluate_model(model, digits_test, samples_test, labels_test)
+        errors.append(error)
+        confusionMatrixes.append(confusion)
+
+        eps = 0.001
+        normEps = len(samples_test) * 0.02
+
+        confusionKNN = [[45,  0,  0,  0,  0,  0,  0,  0,  0,  0],
+         [ 0, 57,  0,  0,  0,  0,  0,  0,  0,  0],
+         [ 0,  0, 59,  1,  0,  0,  0,  0,  1,  0],
+         [ 0,  0,  0, 43,  0,  0,  0,  1,  0,  0],
+         [ 0,  0,  0,  0, 38,  0,  2,  0,  0,  0],
+         [ 0,  0,  0,  2,  0, 48,  0,  0,  1,  0],
+         [ 0,  1,  0,  0,  0,  0, 51,  0,  0,  0],
+         [ 0,  0,  1,  0,  0,  0,  0, 54,  0,  0],
+         [ 0,  0,  0,  0,  0,  1,  0,  0, 46,  0],
+         [ 1,  1,  0,  1,  1,  0,  0,  0,  2, 42]]
+
+        confusionSVM = [[45,  0,  0,  0,  0,  0,  0,  0,  0,  0],
+          [ 0, 57,  0,  0,  0,  0,  0,  0,  0,  0],
+          [ 0,  0, 59,  2,  0,  0,  0,  0,  0,  0],
+          [ 0,  0,  0, 43,  0,  0,  0,  1,  0,  0],
+          [ 0,  0,  0,  0, 40,  0,  0,  0,  0,  0],
+          [ 0,  0,  0,  1,  0, 50,  0,  0,  0,  0],
+          [ 0,  0,  0,  0,  1,  0,  51, 0,  0,  0],
+          [ 0,  0,  1,  0,  0,  0,  0,  54, 0,  0],
+          [ 0,  0,  0,  0,  0,  0,  0,  0, 47,  0],
+          [ 0,  1,  0,  1,  0,  0,  0,  0,  1, 45]]
+
+        self.assertLess(cv2.norm(confusionMatrixes[0] - confusionKNN, cv2.NORM_L1), normEps)
+        self.assertLess(cv2.norm(confusionMatrixes[1] - confusionSVM, cv2.NORM_L1), normEps)
+
+        self.assertLess(errors[0] - 0.034, eps)
+        self.assertLess(errors[1] - 0.018, eps)
\ No newline at end of file

modules/python/test/test_facedetect.py

+#!/usr/bin/env python
+
+'''
+face detection using haar cascades
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+def intersectionRate(s1, s2):
+
+    x1, y1, x2, y2 = s1
+    s1 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
+
+    x1, y1, x2, y2 = s2
+    s2 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
+
+    area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
+    return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
+
+def detect(img, cascade):
+    rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
+                                     flags=cv2.CASCADE_SCALE_IMAGE)
+    if len(rects) == 0:
+        return []
+    rects[:,2:] += rects[:,:2]
+    return rects
+
+from tests_common import NewOpenCVTests
+
+class facedetect_test(NewOpenCVTests):
+
+    def test_facedetect(self):
+        import sys, getopt
+
+        cascade_fn = "../../../data/haarcascades/haarcascade_frontalface_alt.xml"
+        nested_fn  = "../../../data/haarcascades/haarcascade_eye.xml"
+
+        cascade = cv2.CascadeClassifier(cascade_fn)
+        nested = cv2.CascadeClassifier(nested_fn)
+
+        dirPath = '../../../samples/data/'
+        samples = ['lena.jpg', 'kate.jpg']
+
+        faces = []
+        eyes = []
+
+        testFaces = [
+        #lena
+        [[218, 200, 389, 371],
+        [ 244, 240, 294, 290],
+        [ 309, 246, 352, 289]],
+
+        #kate
+        [[207,  89, 436, 318],
+        [245, 161, 294, 210],
+        [343, 139, 389, 185]]
+        ]
+
+        for sample in samples:
+
+            img = cv2.imread(dirPath + sample)
+            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            gray = cv2.GaussianBlur(gray, (3, 3), 1.1)
+
+            rects = detect(gray, cascade)
+            faces.append(rects)
+
+            if not nested.empty():
+                for x1, y1, x2, y2 in rects:
+                    roi = gray[y1:y2, x1:x2]
+                    subrects = detect(roi.copy(), nested)
+
+                    for rect in subrects:
+                        rect[0] += x1
+                        rect[2] += x1
+                        rect[1] += y1
+                        rect[3] += y1
+
+                    eyes.append(subrects)
+
+        faces_matches = 0
+        eyes_matches = 0
+
+        eps = 0.8
+
+        for i in range(len(faces)):
+            for j in range(len(testFaces)):
+                if intersectionRate(faces[i][0], testFaces[j][0]) > eps:
+                    faces_matches += 1
+                    #check eyes
+                    if len(eyes[i]) == 2:
+                        if intersectionRate(eyes[i][0], testFaces[j][1]) > eps and intersectionRate(eyes[i][1], testFaces[j][2]):
+                            eyes_matches += 1
+                        elif intersectionRate(eyes[i][1], testFaces[j][1]) > eps and intersectionRate(eyes[i][0], testFaces[j][2]):
+                            eyes_matches += 1
+
+        self.assertEqual(faces_matches, 2)
+        self.assertEqual(eyes_matches, 2)
\ No newline at end of file

modules/python/test/test_fitline.py

+#!/usr/bin/env python
+
+'''
+Robust line fitting.
+==================
+
+Example of using cv2.fitLine function for fitting line
+to points in presence of outliers.
+
+Switch through different M-estimator functions and see,
+how well the robust functions fit the line even
+in case of ~50% of outliers.
+
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+import sys
+PY3 = sys.version_info[0] == 3
+
+import numpy as np
+import cv2
+
+from tests_common import NewOpenCVTests
+
+w, h = 512, 256
+
+def toint(p):
+    return tuple(map(int, p))
+
+def sample_line(p1, p2, n, noise=0.0):
+    np.random.seed(10)
+    p1 = np.float32(p1)
+    t = np.random.rand(n,1)
+    return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
+
+dist_func_names = ['DIST_L2', 'DIST_L1', 'DIST_L12', 'DIST_FAIR', 'DIST_WELSCH', 'DIST_HUBER']
+
+class fitline_test(NewOpenCVTests):
+
+    def test_fitline(self):
+
+        noise = 5
+        n = 200
+        r = 5 / 100.0
+        outn = int(n*r)
+
+        p0, p1 = (90, 80), (w-90, h-80)
+        line_points = sample_line(p0, p1, n-outn, noise)
+        outliers = np.random.rand(outn, 2) * (w, h)
+        points = np.vstack([line_points, outliers])
+
+        lines = []
+
+        for name in dist_func_names:
+            func = getattr(cv2, name)
+            vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
+            line = [float(vx), float(vy), float(cx), float(cy)]
+            lines.append(line)
+
+        eps = 0.05
+
+        refVec =  (np.float32(p1) - p0) / cv2.norm(np.float32(p1) - p0)
+
+        for i in range(len(lines)):
+            self.assertLessEqual(cv2.norm(refVec - lines[i][0:2], cv2.NORM_L2), eps)
\ No newline at end of file

modules/python/test/test_gaussian_mix.py

+#!/usr/bin/env python
+
+# Python 2/3 compatibility
+from __future__ import print_function
+import sys
+PY3 = sys.version_info[0] == 3
+
+if PY3:
+    xrange = range
+
+import numpy as np
+from numpy import random
+import cv2
+
+def make_gaussians(cluster_n, img_size):
+    points = []
+    ref_distrs = []
+    for i in xrange(cluster_n):
+        mean = (0.1 + 0.8*random.rand(2)) * img_size
+        a = (random.rand(2, 2)-0.5)*img_size*0.1
+        cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
+        n = 100 + random.randint(900)
+        pts = random.multivariate_normal(mean, cov, n)
+        points.append( pts )
+        ref_distrs.append( (mean, cov) )
+    points = np.float32( np.vstack(points) )
+    return points, ref_distrs
+
+from tests_common import NewOpenCVTests
+
+class gaussian_mix_test(NewOpenCVTests):
+
+    def test_gaussian_mix(self):
+        
+        np.random.seed(10)
+        cluster_n = 5
+        img_size = 512
+
+        points, ref_distrs = make_gaussians(cluster_n, img_size)
+
+        em = cv2.ml.EM_create()
+        em.setClustersNumber(cluster_n)
+        em.setCovarianceMatrixType(cv2.ml.EM_COV_MAT_GENERIC)
+        em.trainEM(points)
+        means = em.getMeans()
+        covs = em.getCovs()  # Known bug: https://github.com/Itseez/opencv/pull/4232
+        found_distrs = zip(means, covs)
+
+        matches_count = 0
+
+        meanEps = 0.05
+        covEps = 0.1
+
+        for i in range(cluster_n):
+            for j in range(cluster_n):
+                if (cv2.norm(means[i] - ref_distrs[j][0], cv2.NORM_L2) / cv2.norm(ref_distrs[j][0], cv2.NORM_L2) < meanEps and 
+                    cv2.norm(covs[i] - ref_distrs[j][1], cv2.NORM_L2) / cv2.norm(ref_distrs[j][1], cv2.NORM_L2) < covEps):
+                    matches_count += 1
+
+        self.assertEqual(matches_count, cluster_n)
\ No newline at end of file

modules/python/test/test_houghcircles.py

+#!/usr/bin/python
+
+'''
+This example illustrates how to use cv2.HoughCircles() function.
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import cv2
+import numpy as np
+import sys
+
+from tests_common import NewOpenCVTests
+
+class houghcircles_test(NewOpenCVTests):
+
+    def test_houghcircles(self):
+
+        fn = "../../../samples/data/board.jpg"
+
+        src = cv2.imread(fn, 1)
+        img = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+        img = cv2.medianBlur(img, 5)
+
+        circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)[0]
+
+        testCircles = [[38, 181, 17.6],
+        [99.7, 166, 13.12],
+        [142.7, 160, 13.52],
+        [223.6, 110, 8.62],
+        [79.1, 206.7, 8.62],
+        [47.5, 351.6, 11.64],
+        [189.5, 354.4, 11.64],
+        [189.8, 298.9, 10.64],
+        [189.5, 252.4, 14.62],
+        [252.5, 393.4, 15.62],
+        [602.9, 467.5, 11.42],
+        [222, 210.4, 9.12],
+        [263.1, 216.7, 9.12],
+        [359.8, 222.6, 9.12],
+        [518.9, 120.9, 9.12],
+        [413.8, 113.4, 9.12],
+        [489, 127.2, 9.12],
+        [448.4, 121.3, 9.12],
+        [384.6, 128.9, 8.62]]
+
+        eps = 7
+        matches_counter = 0
+
+        for i in range(len(testCircles)):
+            for j in range(len(circles)):
+                if cv2.norm(testCircles[i] - circles[j], cv2.NORM_L2) < eps:
+                    matches_counter += 1
+
+        self.assertGreater(float(matches_counter) / len(testCircles), .5)
+        self.assertLess(float(len(circles) - matches_counter) / len(circles), .7)
\ No newline at end of file

modules/python/test/test_houghlines.py

+#!/usr/bin/python
+
+'''
+This example illustrates how to use Hough Transform to find lines
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import cv2
+import numpy as np
+import sys
+import math
+
+from tests_common import NewOpenCVTests
+
+def linesDiff(line1, line2):
+
+    norm1 = cv2.norm(line1 - line2, cv2.NORM_L2)
+    line3 = line1[2:4] + line1[0:2]
+    norm2 = cv2.norm(line3 - line2, cv2.NORM_L2)
+
+    return min(norm1, norm2)
+
+class houghlines_test(NewOpenCVTests):
+
+    def test_houghlines(self):
+
+        fn = "../../../samples/data/pic1.png"
+
+        src = cv2.imread(fn)
+        dst = cv2.Canny(src, 50, 200)
+
+        lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)[:,0,:]
+
+        eps = 5
+        testLines = [
+            #rect1
+             [ 232,  25, 43, 25],
+             [ 43, 129, 232, 129],
+             [ 43, 129,  43,  25],
+             [232, 129, 232,  25],
+            #rect2
+             [251,  86, 314, 183],
+             [252,  86, 323,  40],
+             [315, 183, 386, 137],
+             [324,  40, 386, 136],
+            #triangle
+             [245, 205, 377, 205],
+             [244, 206, 305, 278],
+             [306, 279, 377, 205],
+            #rect3
+             [153, 177, 196, 177],
+             [153, 277, 153, 179],
+             [153, 277, 196, 277],
+             [196, 177, 196, 277]]
+
+        matches_counter = 0
+
+        for i in range(len(testLines)):
+            for j in range(len(lines)):
+                if linesDiff(testLines[i], lines[j]) < eps:
+                    matches_counter += 1
+
+        self.assertGreater(float(matches_counter) / len(testLines), .7)
\ No newline at end of file

modules/python/test/test_squares.py

+#!/usr/bin/env python
+
+'''
+Simple "Square Detector" program.
+
+Loads several images sequentially and tries to find squares in each image.
+'''
+
+# Python 2/3 compatibility
+import sys
+PY3 = sys.version_info[0] == 3
+
+if PY3:
+    xrange = range
+
+import numpy as np
+import cv2
+
+
+def angle_cos(p0, p1, p2):
+    d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')
+    return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
+
+def find_squares(img):
+    img = cv2.GaussianBlur(img, (5, 5), 0)
+    squares = []
+    for gray in cv2.split(img):
+        for thrs in xrange(0, 255, 26):
+            if thrs == 0:
+                bin = cv2.Canny(gray, 0, 50, apertureSize=5)
+                bin = cv2.dilate(bin, None)
+            else:
+                retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
+            bin, contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+            for cnt in contours:
+                cnt_len = cv2.arcLength(cnt, True)
+                cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
+                if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):
+                    cnt = cnt.reshape(-1, 2)
+                    max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)])
+                    if max_cos < 0.1 and filterSquares(squares, cnt):
+                        squares.append(cnt)
+
+    return squares
+
+def intersectionRate(s1, s2):
+    area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
+    return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
+
+def filterSquares(squares, square):
+
+    for i in range(len(squares)):
+        if intersectionRate(squares[i], square) > 0.95:
+            return False
+
+    return True
+
+from tests_common import NewOpenCVTests
+
+class squares_test(NewOpenCVTests):
+
+    def test_squares(self):
+
+        img = cv2.imread('../../../samples/data/pic1.png')
+        squares = find_squares(img)
+
+        testSquares = [
+        [[43, 25],
+        [43, 129],
+        [232, 129],
+        [232, 25]],
+
+        [[252, 87],
+        [324, 40],
+        [387, 137],
+        [315, 184]],
+
+        [[154, 178],
+        [196, 180],
+        [198, 278],
+        [154, 278]],
+
+        [[0, 0],
+        [400, 0],
+        [400, 300],
+        [0, 300]]
+        ]
+
+        matches_counter = 0
+        for i in range(len(squares)):
+            for j in range(len(testSquares)):
+                if intersectionRate(squares[i], testSquares[j]) > 0.9:
+                    matches_counter += 1
+
+        self.assertGreater(matches_counter / len(testSquares), 0.9)
+        self.assertLess( (len(squares) - matches_counter) / len(squares), 0.2)
\ No newline at end of file

modules/python/test/test_texture_flow.py

+#!/usr/bin/env python
+
+'''
+Texture flow direction estimation.
+
+Sample shows how cv2.cornerEigenValsAndVecs function can be used
+to estimate image texture flow direction.
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+import sys
+
+from tests_common import NewOpenCVTests
+
+
+class texture_flow_test(NewOpenCVTests):
+
+    def test_texture_flow(self):
+
+        fn = '../../../samples/data/pic6.png'
+        img = cv2.imread(fn)
+
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        h, w = img.shape[:2]
+
+        eigen = cv2.cornerEigenValsAndVecs(gray, 15, 3)
+        eigen = eigen.reshape(h, w, 3, 2)  # [[e1, e2], v1, v2]
+        flow = eigen[:,:,2]
+
+        vis = img.copy()
+        vis[:] = (192 + np.uint32(vis)) / 2
+        d = 80
+        points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
+
+        textureVectors = []
+        
+        for x, y in np.int32(points):
+            textureVectors.append(np.int32(flow[y, x]*d))
+
+        eps = 0.05
+
+        testTextureVectors = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], 
+        [-38, 70], [-79, 3], [0, 0], [0, 0], [-39, 69], [-79, -1],
+        [0, 0], [0, 0], [0, -79], [17, -78], [-48, -63], [65, -46],
+        [-69, -39], [-48, -63], [-45, 66]]
+
+        for i in range(len(textureVectors)):
+            self.assertLessEqual(cv2.norm(textureVectors[i] - testTextureVectors[i], cv2.NORM_L2), eps)
\ No newline at end of file

modules/python/test/tests_common.py

+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import unittest
+import sys
+import hashlib
+import os
+import numpy as np
+import cv2
+
+# Python 3 moved urlopen to urllib.requests
+try:
+    from urllib.request import urlopen
+except ImportError:
+    from urllib import urlopen
+
+class NewOpenCVTests(unittest.TestCase):
+
+    # path to local repository folder containing 'samples' folder
+    repoPath = None
+    # github repository url
+    repoUrl = 'https://raw.github.com/Itseez/opencv/master'
+
+    def get_sample(self, filename, iscolor = cv2.IMREAD_COLOR):
+        if not filename in self.image_cache:
+            filedata = None
+            if NewOpenCVTests.repoPath is not None:
+                candidate = NewOpenCVTests.repoPath + '/' + filename
+                if os.path.isfile(candidate):
+                    with open(candidate, 'rb') as f:
+                        filedata = f.read()
+            if filedata is None:
+                filedata = urlopen(NewOpenCVTests.repoUrl + '/' + 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()
+
+    if sys.version_info[:2] == (2, 6):
+        def assertLess(self, a, b, msg=None):
+            if not a < b:
+                self.fail('%s not less than %s' % (repr(a), repr(b)))
+
+        def assertLessEqual(self, a, b, msg=None):
+            if not a <= b:
+                self.fail('%s not less than or equal to %s' % (repr(a), repr(b)))
+
+        def assertGreater(self, a, b, msg=None):
+            if not a > b:
+                self.fail('%s not greater than %s' % (repr(a), repr(b)))
\ No newline at end of file

samples/python/demo.py

@@ -87,8 +87,11 @@ class App:
         for fn in glob('*.py'):
             name = splitfn(fn)[1]
             if fn[0] != '_' and name not in exclude_list:
-                demos_lb.insert(tk.END, name)
                 self.samples[name] = fn
+
+        for name in sorted(self.samples):
+            demos_lb.insert(tk.END, name)
+
         demos_lb.bind('<<ListboxSelect>>', self.on_demo_select)
 
         self.cmd_entry = cmd_entry = tk.Entry(right)