added laplacian_filter file

digital_image_processing/filters/laplacian_filter.py

@@ -0,0 +1,81 @@
+# @Author : ojas-wani
+# @File : laplacian_filter.py
+# @Date : 10/04/2023
+
+import numpy as np
+from cv2 import (
+ BORDER_DEFAULT,
+ COLOR_BGR2GRAY,
+ CV_64F,
+ cvtColor,
+ filter2D,
+ imread,
+ imshow,
+ waitKey,
+)
+
+from digital_image_processing.filters.gaussian_filter import gaussian_filter
+
+
+def my_laplacian(src: np.ndarray, ksize: int) -> np.ndarray:
+ """
+ :param src: the source image, which should be a grayscale or color image.
+ :param ksize: the size of the kernel used to compute the Laplacian filter,
+ which can be 1, 3, 5, or 7.
+
+ >>> my_laplacian(src=np.array([]), ksize=0)
+ Traceback (most recent call last):
+ ...
+ ValueError: ksize must be in (1, 3, 5, 7)
+ """
+ kernels = {
+ 1: np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+ 3: np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]),
+ 5: np.array(
+ [
+ [0, 0, -1, 0, 0],
+ [0, -1, -2, -1, 0],
+ [-1, -2, 16, -2, -1],
+ [0, -1, -2, -1, 0],
+ [0, 0, -1, 0, 0],
+ ]
+ ),
+ 7: np.array(
+ [
+ [0, 0, 0, -1, 0, 0, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, -2, -7, -10, -7, -2, 0],
+ [-1, -3, -10, 68, -10, -3, -1],
+ [0, -2, -7, -10, -7, -2, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, 0, 0, -1, 0, 0, 0],
+ ]
+ ),
+ }
+ if ksize not in kernels:
+ msg = f"ksize must be in {tuple(kernels)}"
+ raise ValueError(msg)
+
+ # Apply the Laplacian kernel using convolution
+ return filter2D(
+ src, CV_64F, kernels[ksize], 0, borderType=BORDER_DEFAULT, anchor=(0, 0)
+ )
+
+
+if __name__ == "__main__":
+ # read original image
+ img = imread(r"../image_data/lena.jpg")
+
+ # turn image in gray scale value
+ gray = cvtColor(img, COLOR_BGR2GRAY)
+
+ # Applying gaussian filter
+ blur_image = gaussian_filter(gray, 3, sigma=1)
+
+ # Apply multiple Kernel to detect edges
+ laplacian_image = my_laplacian(ksize=3, src=blur_image)
+
+ imshow("Original image", img)
+ imshow("Detected edges using laplacian filter", laplacian_image)
+
+ waitKey(0)

digital_image_processing/filters/laplacian_filter.py

@@ -0,0 +1,81 @@
+# @Author : ojas-wani
+# @File : laplacian_filter.py
+# @Date : 10/04/2023
+
+import numpy as np
+from cv2 import (
+ BORDER_DEFAULT,
+ COLOR_BGR2GRAY,
+ CV_64F,
+ cvtColor,
+ filter2D,
+ imread,
+ imshow,
+ waitKey,
+)
+
+from digital_image_processing.filters.gaussian_filter import gaussian_filter
+
+
+def my_laplacian(src: np.ndarray, ksize: int) -> np.ndarray:
+ """
+ :param src: the source image, which should be a grayscale or color image.
+ :param ksize: the size of the kernel used to compute the Laplacian filter,
+ which can be 1, 3, 5, or 7.
+
+ >>> my_laplacian(src=np.array([]), ksize=0)
+ Traceback (most recent call last):
+ ...
+ ValueError: ksize must be in (1, 3, 5, 7)
+ """
+ kernels = {
+ 1: np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+ 3: np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]),
+ 5: np.array(
+ [
+ [0, 0, -1, 0, 0],
+ [0, -1, -2, -1, 0],
+ [-1, -2, 16, -2, -1],
+ [0, -1, -2, -1, 0],
+ [0, 0, -1, 0, 0],
+ ]
+ ),
+ 7: np.array(
+ [
+ [0, 0, 0, -1, 0, 0, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, -2, -7, -10, -7, -2, 0],
+ [-1, -3, -10, 68, -10, -3, -1],
+ [0, -2, -7, -10, -7, -2, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, 0, 0, -1, 0, 0, 0],
+ ]
+ ),
+ }
+ if ksize not in kernels:
+ msg = f"ksize must be in {tuple(kernels)}"
+ raise ValueError(msg)
+
+ # Apply the Laplacian kernel using convolution
+ return filter2D(
+ src, CV_64F, kernels[ksize], 0, borderType=BORDER_DEFAULT, anchor=(0, 0)
+ )
+
+
+if __name__ == "__main__":
+ # read original image
+ img = imread(r"../image_data/lena.jpg")
+
+ # turn image in gray scale value
+ gray = cvtColor(img, COLOR_BGR2GRAY)
+
+ # Applying gaussian filter
+ blur_image = gaussian_filter(gray, 3, sigma=1)
+
+ # Apply multiple Kernel to detect edges
+ laplacian_image = my_laplacian(ksize=3, src=blur_image)
+
+ imshow("Original image", img)
+ imshow("Detected edges using laplacian filter", laplacian_image)
+
+ waitKey(0)

digital_image_processing/filters/laplacian_filter.py

@@ -0,0 +1,81 @@
+# @Author : ojas-wani
+# @File : laplacian_filter.py
+# @Date : 10/04/2023
+
+import numpy as np
+from cv2 import (
+ BORDER_DEFAULT,
+ COLOR_BGR2GRAY,
+ CV_64F,
+ cvtColor,
+ filter2D,
+ imread,
+ imshow,
+ waitKey,
+)
+
+from digital_image_processing.filters.gaussian_filter import gaussian_filter
+
+
+def my_laplacian(src: np.ndarray, ksize: int) -> np.ndarray:
+ """
+ :param src: the source image, which should be a grayscale or color image.
+ :param ksize: the size of the kernel used to compute the Laplacian filter,
+ which can be 1, 3, 5, or 7.
+
+ >>> my_laplacian(src=np.array([]), ksize=0)
+ Traceback (most recent call last):
+ ...
+ ValueError: ksize must be in (1, 3, 5, 7)
+ """
+ kernels = {
+ 1: np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+ 3: np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]),
+ 5: np.array(
+ [
+ [0, 0, -1, 0, 0],
+ [0, -1, -2, -1, 0],
+ [-1, -2, 16, -2, -1],
+ [0, -1, -2, -1, 0],
+ [0, 0, -1, 0, 0],
+ ]
+ ),
+ 7: np.array(
+ [
+ [0, 0, 0, -1, 0, 0, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, -2, -7, -10, -7, -2, 0],
+ [-1, -3, -10, 68, -10, -3, -1],
+ [0, -2, -7, -10, -7, -2, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, 0, 0, -1, 0, 0, 0],
+ ]
+ ),
+ }
+ if ksize not in kernels:
+ msg = f"ksize must be in {tuple(kernels)}"
+ raise ValueError(msg)
+
+ # Apply the Laplacian kernel using convolution
+ return filter2D(
+ src, CV_64F, kernels[ksize], 0, borderType=BORDER_DEFAULT, anchor=(0, 0)
+ )
+
+
+if __name__ == "__main__":
+ # read original image
+ img = imread(r"../image_data/lena.jpg")
+
+ # turn image in gray scale value
+ gray = cvtColor(img, COLOR_BGR2GRAY)
+
+ # Applying gaussian filter
+ blur_image = gaussian_filter(gray, 3, sigma=1)
+
+ # Apply multiple Kernel to detect edges
+ laplacian_image = my_laplacian(ksize=3, src=blur_image)
+
+ imshow("Original image", img)
+ imshow("Detected edges using laplacian filter", laplacian_image)
+
+ waitKey(0)

digital_image_processing/filters/laplacian_filter.py

@@ -0,0 +1,81 @@
+# @Author : ojas-wani
+# @File : laplacian_filter.py
+# @Date : 10/04/2023
+
+import numpy as np
+from cv2 import (
+ BORDER_DEFAULT,
+ COLOR_BGR2GRAY,
+ CV_64F,
+ cvtColor,
+ filter2D,
+ imread,
+ imshow,
+ waitKey,
+)
+
+from digital_image_processing.filters.gaussian_filter import gaussian_filter
+
+
+def my_laplacian(src: np.ndarray, ksize: int) -> np.ndarray:
+ """
+ :param src: the source image, which should be a grayscale or color image.
+ :param ksize: the size of the kernel used to compute the Laplacian filter,
+ which can be 1, 3, 5, or 7.
+
+ >>> my_laplacian(src=np.array([]), ksize=0)
+ Traceback (most recent call last):
+ ...
+ ValueError: ksize must be in (1, 3, 5, 7)
+ """
+ kernels = {
+ 1: np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+ 3: np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]),
+ 5: np.array(
+ [
+ [0, 0, -1, 0, 0],
+ [0, -1, -2, -1, 0],
+ [-1, -2, 16, -2, -1],
+ [0, -1, -2, -1, 0],
+ [0, 0, -1, 0, 0],
+ ]
+ ),
+ 7: np.array(
+ [
+ [0, 0, 0, -1, 0, 0, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, -2, -7, -10, -7, -2, 0],
+ [-1, -3, -10, 68, -10, -3, -1],
+ [0, -2, -7, -10, -7, -2, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, 0, 0, -1, 0, 0, 0],
+ ]
+ ),
+ }
+ if ksize not in kernels:
+ msg = f"ksize must be in {tuple(kernels)}"
+ raise ValueError(msg)
+
+ # Apply the Laplacian kernel using convolution
+ return filter2D(
+ src, CV_64F, kernels[ksize], 0, borderType=BORDER_DEFAULT, anchor=(0, 0)
+ )
+
+
+if __name__ == "__main__":
+ # read original image
+ img = imread(r"../image_data/lena.jpg")
+
+ # turn image in gray scale value
+ gray = cvtColor(img, COLOR_BGR2GRAY)
+
+ # Applying gaussian filter
+ blur_image = gaussian_filter(gray, 3, sigma=1)
+
+ # Apply multiple Kernel to detect edges
+ laplacian_image = my_laplacian(ksize=3, src=blur_image)
+
+ imshow("Original image", img)
+ imshow("Detected edges using laplacian filter", laplacian_image)
+
+ waitKey(0)

digital_image_processing/filters/laplacian_filter.py

@@ -0,0 +1,81 @@
+# @Author : ojas-wani
+# @File : laplacian_filter.py
+# @Date : 10/04/2023
+
+import numpy as np
+from cv2 import (
+ BORDER_DEFAULT,
+ COLOR_BGR2GRAY,
+ CV_64F,
+ cvtColor,
+ filter2D,
+ imread,
+ imshow,
+ waitKey,
+)
+
+from digital_image_processing.filters.gaussian_filter import gaussian_filter
+
+
+def my_laplacian(src: np.ndarray, ksize: int) -> np.ndarray:
+ """
+ :param src: the source image, which should be a grayscale or color image.
+ :param ksize: the size of the kernel used to compute the Laplacian filter,
+ which can be 1, 3, 5, or 7.
+
+ >>> my_laplacian(src=np.array([]), ksize=0)
+ Traceback (most recent call last):
+ ...
+ ValueError: ksize must be in (1, 3, 5, 7)
+ """
+ kernels = {
+ 1: np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+ 3: np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]),
+ 5: np.array(
+ [
+ [0, 0, -1, 0, 0],
+ [0, -1, -2, -1, 0],
+ [-1, -2, 16, -2, -1],
+ [0, -1, -2, -1, 0],
+ [0, 0, -1, 0, 0],
+ ]
+ ),
+ 7: np.array(
+ [
+ [0, 0, 0, -1, 0, 0, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, -2, -7, -10, -7, -2, 0],
+ [-1, -3, -10, 68, -10, -3, -1],
+ [0, -2, -7, -10, -7, -2, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, 0, 0, -1, 0, 0, 0],
+ ]
+ ),
+ }
+ if ksize not in kernels:
+ msg = f"ksize must be in {tuple(kernels)}"
+ raise ValueError(msg)
+
+ # Apply the Laplacian kernel using convolution
+ return filter2D(
+ src, CV_64F, kernels[ksize], 0, borderType=BORDER_DEFAULT, anchor=(0, 0)
+ )
+
+
+if __name__ == "__main__":
+ # read original image
+ img = imread(r"../image_data/lena.jpg")
+
+ # turn image in gray scale value
+ gray = cvtColor(img, COLOR_BGR2GRAY)
+
+ # Applying gaussian filter
+ blur_image = gaussian_filter(gray, 3, sigma=1)
+
+ # Apply multiple Kernel to detect edges
+ laplacian_image = my_laplacian(ksize=3, src=blur_image)
+
+ imshow("Original image", img)
+ imshow("Detected edges using laplacian filter", laplacian_image)
+
+ waitKey(0)

digital_image_processing/filters/laplacian_filter.py

@@ -0,0 +1,81 @@
+# @Author : ojas-wani
+# @File : laplacian_filter.py
+# @Date : 10/04/2023
+
+import numpy as np
+from cv2 import (
+ BORDER_DEFAULT,
+ COLOR_BGR2GRAY,
+ CV_64F,
+ cvtColor,
+ filter2D,
+ imread,
+ imshow,
+ waitKey,
+)
+
+from digital_image_processing.filters.gaussian_filter import gaussian_filter
+
+
+def my_laplacian(src: np.ndarray, ksize: int) -> np.ndarray:
+ """
+ :param src: the source image, which should be a grayscale or color image.
+ :param ksize: the size of the kernel used to compute the Laplacian filter,
+ which can be 1, 3, 5, or 7.
+
+ >>> my_laplacian(src=np.array([]), ksize=0)
+ Traceback (most recent call last):
+ ...
+ ValueError: ksize must be in (1, 3, 5, 7)
+ """
+ kernels = {
+ 1: np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+ 3: np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]),
+ 5: np.array(
+ [
+ [0, 0, -1, 0, 0],
+ [0, -1, -2, -1, 0],
+ [-1, -2, 16, -2, -1],
+ [0, -1, -2, -1, 0],
+ [0, 0, -1, 0, 0],
+ ]
+ ),
+ 7: np.array(
+ [
+ [0, 0, 0, -1, 0, 0, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, -2, -7, -10, -7, -2, 0],
+ [-1, -3, -10, 68, -10, -3, -1],
+ [0, -2, -7, -10, -7, -2, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, 0, 0, -1, 0, 0, 0],
+ ]
+ ),
+ }
+ if ksize not in kernels:
+ msg = f"ksize must be in {tuple(kernels)}"
+ raise ValueError(msg)
+
+ # Apply the Laplacian kernel using convolution
+ return filter2D(
+ src, CV_64F, kernels[ksize], 0, borderType=BORDER_DEFAULT, anchor=(0, 0)
+ )
+
+
+if __name__ == "__main__":
+ # read original image
+ img = imread(r"../image_data/lena.jpg")
+
+ # turn image in gray scale value
+ gray = cvtColor(img, COLOR_BGR2GRAY)
+
+ # Applying gaussian filter
+ blur_image = gaussian_filter(gray, 3, sigma=1)
+
+ # Apply multiple Kernel to detect edges
+ laplacian_image = my_laplacian(ksize=3, src=blur_image)
+
+ imshow("Original image", img)
+ imshow("Detected edges using laplacian filter", laplacian_image)
+
+ waitKey(0)

digital_image_processing/filters/laplacian_filter.py

@@ -0,0 +1,81 @@
+# @Author : ojas-wani
+# @File : laplacian_filter.py
+# @Date : 10/04/2023
+
+import numpy as np
+from cv2 import (
+ BORDER_DEFAULT,
+ COLOR_BGR2GRAY,
+ CV_64F,
+ cvtColor,
+ filter2D,
+ imread,
+ imshow,
+ waitKey,
+)
+
+from digital_image_processing.filters.gaussian_filter import gaussian_filter
+
+
+def my_laplacian(src: np.ndarray, ksize: int) -> np.ndarray:
+ """
+ :param src: the source image, which should be a grayscale or color image.
+ :param ksize: the size of the kernel used to compute the Laplacian filter,
+ which can be 1, 3, 5, or 7.
+
+ >>> my_laplacian(src=np.array([]), ksize=0)
+ Traceback (most recent call last):
+ ...
+ ValueError: ksize must be in (1, 3, 5, 7)
+ """
+ kernels = {
+ 1: np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
+ 3: np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]),
+ 5: np.array(
+ [
+ [0, 0, -1, 0, 0],
+ [0, -1, -2, -1, 0],
+ [-1, -2, 16, -2, -1],
+ [0, -1, -2, -1, 0],
+ [0, 0, -1, 0, 0],
+ ]
+ ),
+ 7: np.array(
+ [
+ [0, 0, 0, -1, 0, 0, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, -2, -7, -10, -7, -2, 0],
+ [-1, -3, -10, 68, -10, -3, -1],
+ [0, -2, -7, -10, -7, -2, 0],
+ [0, 0, -2, -3, -2, 0, 0],
+ [0, 0, 0, -1, 0, 0, 0],
+ ]
+ ),
+ }
+ if ksize not in kernels:
+ msg = f"ksize must be in {tuple(kernels)}"
+ raise ValueError(msg)
+
+ # Apply the Laplacian kernel using convolution
+ return filter2D(
+ src, CV_64F, kernels[ksize], 0, borderType=BORDER_DEFAULT, anchor=(0, 0)
+ )
+
+
+if __name__ == "__main__":
+ # read original image
+ img = imread(r"../image_data/lena.jpg")
+
+ # turn image in gray scale value
+ gray = cvtColor(img, COLOR_BGR2GRAY)
+
+ # Applying gaussian filter
+ blur_image = gaussian_filter(gray, 3, sigma=1)
+
+ # Apply multiple Kernel to detect edges
+ laplacian_image = my_laplacian(ksize=3, src=blur_image)
+
+ imshow("Original image", img)
+ imshow("Detected edges using laplacian filter", laplacian_image)
+
+ waitKey(0)