Added morphological operations, fixes: TheAlgorithms#5197

Author: Spnetic-5

digital_image_processing/morphological_operations/dilation_operation.py

@@ -0,0 +1,59 @@
+import numpy as np
+from PIL import Image
+
+
+def rgb2gray(rgb):
+    """
+    Return gray image from rgb image
+    """
+    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
+    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
+    return gray
+
+
+def gray2binary(gray):
+    """
+    Return binary image from gray image
+    """
+    return (127 < gray) & (gray <= 255)
+
+
+def dilation(image, kernel):
+    """
+    Return dilated image
+    """
+    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()
+            if summation > 0:
+                output[y, x] = 1
+            else:
+                output[y, x] = 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"))
+    # convert it into binary image
+    binary = gray2binary(rgb2gray(image))
+    # Apply dilation operation
+    output = dilation(binary, structuring_element)
+    # Save the output image
+    pil_img = Image.fromarray(output).convert("RGB")
+    pil_img.save("result_dilation.png")

digital_image_processing/morphological_operations/erosion_operation.py

@@ -0,0 +1,58 @@
+import numpy as np
+from PIL import Image
+
+
+def rgb2gray(rgb):
+    """
+    Return gray image from rgb image
+    """
+    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
+    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
+    return gray
+
+
+def gray2binary(gray):
+    """
+    Return binary image from gray image
+    """
+    return (127 < gray) & (gray <= 255)
+
+
+def erosion(image, kernel):
+    """
+    Return eroded image
+    """
+    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()
+            if summation == 5:
+                output[y, x] = 1
+            else:
+                output[y, x] = 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"))
+    # convert it into binary image
+    binary = gray2binary(rgb2gray(image))
+    # Apply erosion operation
+    output = erosion(binary, structuring_element)
+    # Save the output image
+    pil_img = Image.fromarray(output).convert("RGB")
+    pil_img.save("result_erosion.png")