add pytests to digital_image_processing

Author: mrvnmchm

digital_image_processing/filters/median_filter.py

@@ -15,7 +15,7 @@ def median_filter(gray_img, mask=3):
     # set image borders
     bd = int(mask / 2)
     # copy image size
-    median_img = zeros_like(gray)
+    median_img = zeros_like(gray_img)
     for i in range(bd, gray_img.shape[0] - bd):
         for j in range(bd, gray_img.shape[1] - bd):
             # get mask according with mask

digital_image_processing/test_digital_image_processing.py

@@ -1,45 +1,67 @@
 """
 PyTest's for Digital Image Processing
 """
+
+# add change_contrast
+from PIL import Image
+import digital_image_processing.change_contrast as cc
+# add canny
+import digital_image_processing.edge_detection.canny as canny
+import cv2
+# add gen_gaussian
+from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
+from numpy import array, pi, mgrid, exp, square, zeros, ravel, dot, uint8
+import digital_image_processing.filters.gaussian_filter as gg
+# add convolve
+import digital_image_processing.filters.convolve as conv
+# add median
+import digital_image_processing.filters.median_filter as med
+# add sobel_filter
+import digital_image_processing.filters.sobel_filter as sob
+# add img
+img = imread(r'digital_image_processing/image_data/lena.jpg')
+# add gray
+gray = cvtColor(img, COLOR_BGR2GRAY)
+
 # Test: change_contrast()
 def test_change_contrast():
-    from PIL import Image
-    import digital_image_processing.change_contrast as cc
     # Image object
     with Image.open("digital_image_processing/image_data/lena.jpg") as img:
         # Work around assertion for response
         assert str(cc.change_contrast(img, 110)).startswith('<PIL.Image.Image image mode=RGB size=512x512 at')
 
 # Test: canny.gen_gaussian_kernel()
 def test_gen_gaussian_kernel():
-    # import canny
-    import digital_image_processing.edge_detection.canny as cc
     # get ambiguous array
-    resp = cc.gen_gaussian_kernel(9, sigma=1.4)
+    resp = canny.gen_gaussian_kernel(9, sigma=1.4)
     # Assert ambiguous array
     assert resp.all()
 
 # Test: canny()
 def test_canny():
-    # import reqs
-    import cv2
-    import digital_image_processing.edge_detection.canny as cc
     # read image in gray
-    img = cv2.imread('digital_image_processing/image_data/lena.jpg', 0)
+    canny_img = imread('digital_image_processing/image_data/lena.jpg', 0)
     # assert ambiguos array for all == True
-    assert img.all()
+    assert canny_img.all()
     # Get canny array
-    canny_array = cc.canny(img)
+    canny_array = canny.canny(canny_img)
     # assert canny array for at least one True
     assert canny_array.any()
 
-"""
 # Test: filters/gaussian_filter.py
-def gen_gaussian_kernel_filter():
-    # imports
-    from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
-    from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8
-    # import gaussian filter file
-    import digital_image_processing.filters.gen_gaussian_kernel as gg 
-    
-"""
+def test_gen_gaussian_kernel_filter():
+    # Filter 5x5
+    assert gg.gaussian_filter(gray, 5, sigma=0.9).all()
+
+def test_convolve_filter():
+    # laplace diagonals
+    Laplace = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
+    res = conv.img_convolve(gray, Laplace).astype(uint8) 
+    assert res.any()
+
+def test_median_filter():
+    assert med.median_filter(gray, 3).any()
+
+def test_sobel_filter():
+    grad, theta = sob.sobel_filter(gray)
+    assert grad.any() and theta.any()