Add digital image processing tests

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

@@ -0,0 +1,62 @@
+"""
+PyTest's for Digital Image Processing
+"""
+
+import digital_image_processing.edge_detection.canny as canny
+import digital_image_processing.filters.gaussian_filter as gg
+import digital_image_processing.filters.median_filter as med
+import digital_image_processing.filters.sobel_filter as sob
+import digital_image_processing.filters.convolve as conv
+import digital_image_processing.change_contrast as cc
+from cv2 import imread, cvtColor, COLOR_BGR2GRAY
+from numpy import array, uint8
+from PIL import Image
+
+img = imread(r"digital_image_processing/image_data/lena.jpg")
+gray = cvtColor(img, COLOR_BGR2GRAY)
+
+# Test: change_contrast()
+def test_change_contrast():
+    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"
+        )
+
+
+# canny.gen_gaussian_kernel()
+def test_gen_gaussian_kernel():
+    resp = canny.gen_gaussian_kernel(9, sigma=1.4)
+    # Assert ambiguous array
+    assert resp.all()
+
+
+# canny.py
+def test_canny():
+    canny_img = imread("digital_image_processing/image_data/lena.jpg", 0)
+    # assert ambiguos array for all == True
+    assert canny_img.all()
+    canny_array = canny.canny(canny_img)
+    # assert canny array for at least one True
+    assert canny_array.any()
+
+
+# filters/gaussian_filter.py
+def test_gen_gaussian_kernel_filter():
+    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()