diff --git a/modules/python/test/test.py b/modules/python/test/test.py old mode 100644 new mode 100755 index 093979abaf..074b6edad8 --- a/modules/python/test/test.py +++ b/modules/python/test/test.py @@ -1,6 +1,8 @@ #!/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 diff --git a/modules/python/test/test_calibration.py b/modules/python/test/test_calibration.py new file mode 100644 index 0000000000..af8d0fcea0 --- /dev/null +++ b/modules/python/test/test_calibration.py @@ -0,0 +1,69 @@ +#!/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 diff --git a/modules/python/test/test_digits.py b/modules/python/test/test_digits.py new file mode 100644 index 0000000000..9d1d2557c9 --- /dev/null +++ b/modules/python/test/test_digits.py @@ -0,0 +1,197 @@ +#!/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 diff --git a/modules/python/test/test_facedetect.py b/modules/python/test/test_facedetect.py new file mode 100644 index 0000000000..7fe64e2072 --- /dev/null +++ b/modules/python/test/test_facedetect.py @@ -0,0 +1,102 @@ +#!/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 diff --git a/modules/python/test/test_fitline.py b/modules/python/test/test_fitline.py new file mode 100644 index 0000000000..7de9573385 --- /dev/null +++ b/modules/python/test/test_fitline.py @@ -0,0 +1,66 @@ +#!/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 diff --git a/modules/python/test/test_gaussian_mix.py b/modules/python/test/test_gaussian_mix.py new file mode 100644 index 0000000000..58802d4c14 --- /dev/null +++ b/modules/python/test/test_gaussian_mix.py @@ -0,0 +1,60 @@ +#!/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 diff --git a/modules/python/test/test_houghcircles.py b/modules/python/test/test_houghcircles.py new file mode 100644 index 0000000000..dc4284a41f --- /dev/null +++ b/modules/python/test/test_houghcircles.py @@ -0,0 +1,57 @@ +#!/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 diff --git a/modules/python/test/test_houghlines.py b/modules/python/test/test_houghlines.py new file mode 100644 index 0000000000..b779129796 --- /dev/null +++ b/modules/python/test/test_houghlines.py @@ -0,0 +1,65 @@ +#!/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 diff --git a/modules/python/test/test_squares.py b/modules/python/test/test_squares.py new file mode 100644 index 0000000000..937b526b08 --- /dev/null +++ b/modules/python/test/test_squares.py @@ -0,0 +1,96 @@ +#!/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 diff --git a/modules/python/test/test_texture_flow.py b/modules/python/test/test_texture_flow.py new file mode 100644 index 0000000000..46d680a7f4 --- /dev/null +++ b/modules/python/test/test_texture_flow.py @@ -0,0 +1,52 @@ +#!/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 diff --git a/modules/python/test/tests_common.py b/modules/python/test/tests_common.py new file mode 100644 index 0000000000..6ab26050b3 --- /dev/null +++ b/modules/python/test/tests_common.py @@ -0,0 +1,56 @@ +#!/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 diff --git a/samples/data/kate.jpg b/samples/data/kate.jpg new file mode 100644 index 0000000000..40ab433bfd Binary files /dev/null and b/samples/data/kate.jpg differ diff --git a/samples/python/demo.py b/samples/python/demo.py index 864953a316..81c6a8590b 100755 --- a/samples/python/demo.py +++ b/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('<>', self.on_demo_select) self.cmd_entry = cmd_entry = tk.Entry(right)