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a213cea5f5
* updating DIRECTORY.md
* Fix mypy errors in dilation_operation.py
* Rename functions to use snake case
* updating DIRECTORY.md
* updating DIRECTORY.md
* Replace raw file string with pathlib Path
* Update digital_image_processing/morphological_operations/dilation_operation.py
Co-authored-by: Christian Clauss <cclauss@me.com>
---------
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
from pathlib import Path
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import numpy as np
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from PIL import Image
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def rgb_to_gray(rgb: np.ndarray) -> np.ndarray:
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"""
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Return gray image from rgb image
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>>> rgb_to_gray(np.array([[[127, 255, 0]]]))
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array([[187.6453]])
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>>> rgb_to_gray(np.array([[[0, 0, 0]]]))
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array([[0.]])
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>>> rgb_to_gray(np.array([[[2, 4, 1]]]))
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array([[3.0598]])
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>>> rgb_to_gray(np.array([[[26, 255, 14], [5, 147, 20], [1, 200, 0]]]))
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array([[159.0524, 90.0635, 117.6989]])
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"""
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r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
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return 0.2989 * r + 0.5870 * g + 0.1140 * b
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def gray_to_binary(gray: np.ndarray) -> np.ndarray:
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"""
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Return binary image from gray image
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>>> gray_to_binary(np.array([[127, 255, 0]]))
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array([[False, True, False]])
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>>> gray_to_binary(np.array([[0]]))
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array([[False]])
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>>> gray_to_binary(np.array([[26.2409, 4.9315, 1.4729]]))
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array([[False, False, False]])
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>>> gray_to_binary(np.array([[26, 255, 14], [5, 147, 20], [1, 200, 0]]))
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array([[False, True, False],
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[False, True, False],
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[False, True, False]])
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"""
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return (gray > 127) & (gray <= 255)
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def dilation(image: np.ndarray, kernel: np.ndarray) -> np.ndarray:
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"""
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Return dilated image
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>>> dilation(np.array([[True, False, True]]), np.array([[0, 1, 0]]))
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array([[False, False, False]])
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>>> dilation(np.array([[False, False, True]]), np.array([[1, 0, 1]]))
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array([[False, False, False]])
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"""
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output = np.zeros_like(image)
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image_padded = np.zeros(
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(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)
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)
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# Copy image to padded image
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image_padded[kernel.shape[0] - 2 : -1 :, kernel.shape[1] - 2 : -1 :] = image
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# Iterate over image & apply kernel
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for x in range(image.shape[1]):
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for y in range(image.shape[0]):
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summation = (
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kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
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).sum()
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output[y, x] = int(summation > 0)
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return output
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if __name__ == "__main__":
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# read original image
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lena_path = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
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lena = np.array(Image.open(lena_path))
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# kernel to be applied
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structuring_element = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
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output = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
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# Save the output image
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pil_img = Image.fromarray(output).convert("RGB")
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pil_img.save("result_dilation.png")
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