zinating
diff --git a/‎digital_image_processing/morphological_operations/dilation_operation.py‎
Lines changed: 74 additions & 0 deletions b/‎digital_image_processing/morphological_operations/dilation_operation.py‎
Lines changed: 74 additions & 0 deletions
diff --git a/‎digital_image_processing/morphological_operations/erosion_operation.py‎
Lines changed: 74 additions & 0 deletions b/‎digital_image_processing/morphological_operations/erosion_operation.py‎
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+import numpy as np
+from PIL import Image
+
+
+def rgb2gray(rgb: np.array) -> np.array:
+    """
+    Return gray image from rgb image
+    >>> rgb2gray(np.array([[[127, 255, 0]]]))
+    array([[187.6453]])
+    >>> rgb2gray(np.array([[[0, 0, 0]]]))
+    array([[0.]])
+    >>> rgb2gray(np.array([[[2, 4, 1]]]))
+    array([[3.0598]])
+    >>> rgb2gray(np.array([[[26, 255, 14], [5, 147, 20], [1, 200, 0]]]))
+    array([[159.0524,  90.0635, 117.6989]])
+    """
+    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
+    return 0.2989 * r + 0.5870 * g + 0.1140 * b
+
+
+def gray2binary(gray: np.array) -> np.array:
+    """
+    Return binary image from gray image
+    >>> gray2binary(np.array([[127, 255, 0]]))
+    array([[False,  True, False]])
+    >>> gray2binary(np.array([[0]]))
+    array([[False]])
+    >>> gray2binary(np.array([[26.2409, 4.9315, 1.4729]]))
+    array([[False, False, False]])
+    >>> gray2binary(np.array([[26, 255, 14], [5, 147, 20], [1, 200, 0]]))
+    array([[False,  True, False],
+           [False,  True, False],
+           [False,  True, False]])
+    """
+    return (127 < gray) & (gray <= 255)
+
+
+def dilation(image: np.array, kernel: np.array) -> np.array:
+    """
+    Return dilated image
+    >>> dilation(np.array([[True, False, True]]), np.array([[0, 1, 0]]))
+    array([[False, False, False]])
+    >>> dilation(np.array([[False, False, True]]), np.array([[1, 0, 1]]))
+    array([[False, False, False]])
+    """
+    output = np.zeros_like(image)
+    image_padded = np.zeros(
+        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)
+    )
+
+    # Copy image to padded image
+    image_padded[kernel.shape[0] - 2 : -1 :, kernel.shape[1] - 2 : -1 :] = image
+
+    # Iterate over image & apply kernel
+    for x in range(image.shape[1]):
+        for y in range(image.shape[0]):
+            summation = (
+                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
+            ).sum()
+            output[y, x] = int(summation > 0)
+    return output
+
+
+# kernel to be applied
+structuring_element = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
+
+
+if __name__ == "__main__":
+    # read original image
+    image = np.array(Image.open(r"..\image_data\lena.jpg"))
+    output = dilation(gray2binary(rgb2gray(image)), structuring_element)
+    # Save the output image
+    pil_img = Image.fromarray(output).convert("RGB")
+    pil_img.save("result_dilation.png")
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+import numpy as np
+from PIL import Image
+
+
+def rgb2gray(rgb: np.array) -> np.array:
+    """
+    Return gray image from rgb image
+    >>> rgb2gray(np.array([[[127, 255, 0]]]))
+    array([[187.6453]])
+    >>> rgb2gray(np.array([[[0, 0, 0]]]))
+    array([[0.]])
+    >>> rgb2gray(np.array([[[2, 4, 1]]]))
+    array([[3.0598]])
+    >>> rgb2gray(np.array([[[26, 255, 14], [5, 147, 20], [1, 200, 0]]]))
+    array([[159.0524,  90.0635, 117.6989]])
+    """
+    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
+    return 0.2989 * r + 0.5870 * g + 0.1140 * b
+
+
+def gray2binary(gray: np.array) -> np.array:
+    """
+    Return binary image from gray image
+    >>> gray2binary(np.array([[127, 255, 0]]))
+    array([[False,  True, False]])
+    >>> gray2binary(np.array([[0]]))
+    array([[False]])
+    >>> gray2binary(np.array([[26.2409, 4.9315, 1.4729]]))
+    array([[False, False, False]])
+    >>> gray2binary(np.array([[26, 255, 14], [5, 147, 20], [1, 200, 0]]))
+    array([[False,  True, False],
+           [False,  True, False],
+           [False,  True, False]])
+    """
+    return (127 < gray) & (gray <= 255)
+
+
+def erosion(image: np.array, kernel: np.array) -> np.array:
+    """
+    Return eroded image
+    >>> erosion(np.array([[True, True, False]]), np.array([[0, 1, 0]]))
+    array([[False, False, False]])
+    >>> erosion(np.array([[True, False, False]]), np.array([[1, 1, 0]]))
+    array([[False, False, False]])
+    """
+    output = np.zeros_like(image)
+    image_padded = np.zeros(
+        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)
+    )
+
+    # Copy image to padded image
+    image_padded[kernel.shape[0] - 2 : -1 :, kernel.shape[1] - 2 : -1 :] = image
+
+    # Iterate over image & apply kernel
+    for x in range(image.shape[1]):
+        for y in range(image.shape[0]):
+            summation = (
+                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
+            ).sum()
+            output[y, x] = int(summation == 5)
+    return output
+
+
+# kernel to be applied
+structuring_element = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
+
+if __name__ == "__main__":
+    # read original image
+    image = np.array(Image.open(r"..\image_data\lena.jpg"))
+    # Apply erosion operation to a binary image
+    output = erosion(gray2binary(rgb2gray(image)), structuring_element)
+    # Save the output image
+    pil_img = Image.fromarray(output).convert("RGB")
+    pil_img.save("result_erosion.png")