Digital image processing doctests

Maths/ZellersCongruence.py

@@ -0,0 +1,45 @@
+import argparse
+"""Zellers Congruence Birthday Algorithm
+    Find out what day of the week you were born on, or any other date. 
+"""
+
+def zeller(date_input):
+
+    weekdays = {
+        '0': 'Sunday',
+        '1': 'Monday',
+        '2': 'Tuesday',
+        '3': 'Wednesday',
+        '4': 'Thursday',
+        '5': 'Friday',
+        '6': 'Saturday'
+    }
+
+    m = int(date_input[0] + date_input[1])
+    d = int(date_input[3] + date_input[4])
+    y = int(date_input[6] + date_input[7] + date_input[8] + date_input[9])
+
+    if m <= 2:
+        y = y - 1
+        m = m + 12
+    c = int(str(y)[:2])
+    k = int(str(y)[2:])
+
+    t = int(2.6*m - 5.39)
+    u = int(c / 4)
+    v = int(k / 4)
+    x = d + k
+    z = t + u + v + x
+    w = z - (2 * c)
+
+    f = round(w%7)
+
+    for day in weekdays:
+        if f == int(day):
+            print("The date " + date_input + ", is a " + weekdays[day] + ".")
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Find out what day of the week you were born on, or any other date. Accepts birthday as a string in mm-dd-yyyy or mm/dd/yyyy format.')
+    parser.add_argument('date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)')
+    args = parser.parse_args()
+    zeller(args.date_input)

digital_image_processing/change_contrast.py

@@ -28,6 +28,8 @@ def contrast(c: int) -> float:
 
 
 if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
     # Load image
     with Image.open("image_data/lena.jpg") as img:
         # Change contrast to 170

digital_image_processing/edge_detection/canny.py

@@ -5,7 +5,6 @@
 
 PI = 180
 
-
 def gen_gaussian_kernel(k_size, sigma):
     center = k_size // 2
     x, y = np.mgrid[0 - center:k_size - center, 0 - center:k_size - center]

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 +1,67 @@
+"""
+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():
+    # 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():
+    # get ambiguous array
+    resp = canny.gen_gaussian_kernel(9, sigma=1.4)
+    # Assert ambiguous array
+    assert resp.all()
+
+# Test: canny()
+def test_canny():
+    # read image in gray
+    canny_img = imread('digital_image_processing/image_data/lena.jpg', 0)
+    # assert ambiguos array for all == True
+    assert canny_img.all()
+    # Get canny array
+    canny_array = canny.canny(canny_img)
+    # assert canny array for at least one True
+    assert canny_array.any()
+
+# Test: filters/gaussian_filter.py
+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()