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Add new tests on python

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This commit is contained in:
Vladislav Sovrasov 2016-01-28 15:43:08 +03:00
parent bc6ed1467b
commit ab4d375349
13 changed files with 839 additions and 41 deletions
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53
modules/python/test/test.py Normal file → Executable file
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#!/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

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modules/python/test/test_calibration.py Normal file
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#!/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)

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modules/python/test/test_digits.py Normal file
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#!/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)

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modules/python/test/test_facedetect.py Normal file
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#!/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)

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modules/python/test/test_fitline.py Normal file
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#!/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)

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modules/python/test/test_gaussian_mix.py Normal file
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#!/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)

57
modules/python/test/test_houghcircles.py Normal file
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#!/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)

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modules/python/test/test_houghlines.py Normal file
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#!/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)

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modules/python/test/test_squares.py Normal file
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#!/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)

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modules/python/test/test_texture_flow.py Normal file
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@ -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)

56
modules/python/test/tests_common.py Normal file
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#!/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)))

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5
samples/python/demo.py
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@ -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)