Fix loading images in python tests

modules/python/test/test.py

@@ -1,8 +1,6 @@
 #!/usr/bin/env python
 
-
 from __future__ import print_function
-
 import unittest
 import random
 import time

modules/python/test/test_calibration.py

@@ -19,9 +19,12 @@ class calibration_test(NewOpenCVTests):
     def test_calibration(self):
 
         from glob import glob
-
-        img_mask = '../../../samples/data/left*.jpg'  # default
-        img_names = glob(img_mask)
+        img_names = []
+        for i in range(1, 15):
+            if i < 10:
+                img_names.append('samples/data/left0{}.jpg'.format(str(i)))
+            else:
+                img_names.append('samples/data/left{}.jpg'.format(str(i)))
 
         square_size = 1.0
         pattern_size = (9, 6)
@@ -34,7 +37,7 @@ class calibration_test(NewOpenCVTests):
         h, w = 0, 0
         img_names_undistort = []
         for fn in img_names:
-            img = cv2.imread(fn, 0)
+            img = self.get_sample(fn, 0)
             if img is None:
                 continue
 

modules/python/test/test_digits.py

@@ -36,7 +36,7 @@ from numpy.linalg import norm
 
 SZ = 20 # size of each digit is SZ x SZ
 CLASS_N = 10
-DIGITS_FN = '../../../samples/data/digits.png'
+DIGITS_FN = 'samples/data/digits.png'
 
 def split2d(img, cell_size, flatten=True):
     h, w = img.shape[:2]
@@ -47,12 +47,6 @@ def split2d(img, cell_size, flatten=True):
         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:
@@ -134,9 +128,15 @@ from tests_common import NewOpenCVTests
 
 class digits_test(NewOpenCVTests):
 
+    def load_digits(self, fn):
+        digits_img = self.get_sample(fn, 0)
+        digits = split2d(digits_img, (SZ, SZ))
+        labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
+        return digits, labels
+
     def test_digits(self):
 
-        digits, labels = load_digits(DIGITS_FN)
+        digits, labels = self.load_digits(DIGITS_FN)
 
         # shuffle digits
         rand = np.random.RandomState(321)

modules/python/test/test_facedetect.py

@@ -36,13 +36,13 @@ 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_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
+        nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'
 
         cascade = cv2.CascadeClassifier(cascade_fn)
         nested = cv2.CascadeClassifier(nested_fn)
 
-        dirPath = '../../../samples/data/'
+        dirPath = 'samples/data/'
         samples = ['lena.jpg', 'kate.jpg']
 
         faces = []
@@ -62,7 +62,7 @@ class facedetect_test(NewOpenCVTests):
 
         for sample in samples:
 
-            img = cv2.imread(dirPath + sample)
+            img = self.get_sample(dirPath + sample)
             gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
             gray = cv2.GaussianBlur(gray, (3, 3), 1.1)
 

modules/python/test/test_gaussian_mix.py

@@ -31,7 +31,7 @@ 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
@@ -53,7 +53,7 @@ class gaussian_mix_test(NewOpenCVTests):
 
         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 
+                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
 

modules/python/test/test_houghcircles.py

@@ -17,9 +17,9 @@ class houghcircles_test(NewOpenCVTests):
 
     def test_houghcircles(self):
 
-        fn = "../../../samples/data/board.jpg"
+        fn = "samples/data/board.jpg"
 
-        src = cv2.imread(fn, 1)
+        src = self.get_sample(fn, 1)
         img = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
         img = cv2.medianBlur(img, 5)
 

modules/python/test/test_houghlines.py

@@ -26,9 +26,9 @@ class houghlines_test(NewOpenCVTests):
 
     def test_houghlines(self):
 
-        fn = "../../../samples/data/pic1.png"
+        fn = "/samples/data/pic1.png"
 
-        src = cv2.imread(fn)
+        src = self.get_sample(fn)
         dst = cv2.Canny(src, 50, 200)
 
         lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)[:,0,:]

modules/python/test/test_squares.py

@@ -61,7 +61,7 @@ class squares_test(NewOpenCVTests):
 
     def test_squares(self):
 
-        img = cv2.imread('../../../samples/data/pic1.png')
+        img = self.get_sample('samples/data/pic1.png')
         squares = find_squares(img)
 
         testSquares = [

modules/python/test/test_texture_flow.py

@@ -21,8 +21,7 @@ class texture_flow_test(NewOpenCVTests):
 
     def test_texture_flow(self):
 
-        fn = '../../../samples/data/pic6.png'
-        img = cv2.imread(fn)
+        img = self.get_sample('samples/data/pic6.png')
 
         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
         h, w = img.shape[:2]
@@ -43,7 +42,7 @@ class texture_flow_test(NewOpenCVTests):
 
         eps = 0.05
 
-        testTextureVectors = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], 
+        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]]