Add Digital Image Processing Algorithm: Local Binary Pattern (#6294)

* add algorithm local binary pattern

* fix failed test for local binary pattern

* updating DIRECTORY.md

* fix detected precommit-error

* fix precommit error

* final check

* Add descriptive name for parameters  x and y

* Update digital_image_processing/filters/local_binary_pattern.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* Update digital_image_processing/filters/local_binary_pattern.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* Update digital_image_processing/filters/local_binary_pattern.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* Update local_binary_pattern.py

* undo changes made on get_neighbors_pixel()

* files formatted by black

* Update digital_image_processing/filters/local_binary_pattern.py

ok thanks

Co-authored-by: Christian Clauss <cclauss@me.com>

* add test for get_neighbors_pixel() function

* reviewed

* fix  get_neighbors_pixel

* Update test_digital_image_processing.py

* updating DIRECTORY.md

* Create code_quality.yml

* Create code_quality.yml

* Delete code_quality.yml

* Update code_quality.yml

* Delete code_quality.yml

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>

DIRECTORY.md

@@ -152,6 +152,7 @@
     * [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
     * [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
     * [Lowest Common Ancestor](data_structures/binary_tree/lowest_common_ancestor.py)
+    * [Maximum Fenwick Tree](data_structures/binary_tree/maximum_fenwick_tree.py)
     * [Merge Two Binary Trees](data_structures/binary_tree/merge_two_binary_trees.py)
     * [Non Recursive Segment Tree](data_structures/binary_tree/non_recursive_segment_tree.py)
     * [Number Of Possible Binary Trees](data_structures/binary_tree/number_of_possible_binary_trees.py)
@@ -229,6 +230,7 @@
     * [Convolve](digital_image_processing/filters/convolve.py)
     * [Gabor Filter](digital_image_processing/filters/gabor_filter.py)
     * [Gaussian Filter](digital_image_processing/filters/gaussian_filter.py)
+    * [Local Binary Pattern](digital_image_processing/filters/local_binary_pattern.py)
     * [Median Filter](digital_image_processing/filters/median_filter.py)
     * [Sobel Filter](digital_image_processing/filters/sobel_filter.py)
   * Histogram Equalization

digital_image_processing/filters/local_binary_pattern.py

@@ -0,0 +1,81 @@
+import cv2
+import numpy as np
+
+
+def get_neighbors_pixel(
+    image: np.ndarray, x_coordinate: int, y_coordinate: int, center: int
+) -> int:
+    """
+    Comparing local neighborhood pixel value with threshold value of centre pixel.
+    Exception is required when neighborhood value of a center pixel value is null.
+    i.e. values present at boundaries.
+
+    :param image: The image we're working with
+    :param x_coordinate: x-coordinate of the  pixel
+    :param y_coordinate: The y coordinate of the pixel
+    :param center: center pixel value
+    :return: The value of the pixel is being returned.
+    """
+
+    try:
+        return int(image[x_coordinate][y_coordinate] >= center)
+    except (IndexError, TypeError):
+        return 0
+
+
+def local_binary_value(image: np.ndarray, x_coordinate: int, y_coordinate: int) -> int:
+    """
+    It takes an image, an x and y coordinate, and returns the
+    decimal value of the local binary patternof the pixel
+    at that coordinate
+
+    :param image: the image to be processed
+    :param x_coordinate: x coordinate of the pixel
+    :param y_coordinate: the y coordinate of the pixel
+    :return: The decimal value of the binary value of the pixels
+    around the center pixel.
+    """
+    center = image[x_coordinate][y_coordinate]
+    powers = [1, 2, 4, 8, 16, 32, 64, 128]
+
+    # skip get_neighbors_pixel if center is null
+    if center is None:
+        return 0
+
+    # Starting from the top right, assigning value to pixels clockwise
+    binary_values = [
+        get_neighbors_pixel(image, x_coordinate - 1, y_coordinate + 1, center),
+        get_neighbors_pixel(image, x_coordinate, y_coordinate + 1, center),
+        get_neighbors_pixel(image, x_coordinate - 1, y_coordinate, center),
+        get_neighbors_pixel(image, x_coordinate + 1, y_coordinate + 1, center),
+        get_neighbors_pixel(image, x_coordinate + 1, y_coordinate, center),
+        get_neighbors_pixel(image, x_coordinate + 1, y_coordinate - 1, center),
+        get_neighbors_pixel(image, x_coordinate, y_coordinate - 1, center),
+        get_neighbors_pixel(image, x_coordinate - 1, y_coordinate - 1, center),
+    ]
+
+    # Converting the binary value to decimal.
+    return sum(
+        binary_value * power for binary_value, power in zip(binary_values, powers)
+    )
+
+
+if __name__ == "main":
+
+    # Reading the image and converting it to grayscale.
+    image = cv2.imread(
+        "digital_image_processing/image_data/lena.jpg", cv2.IMREAD_GRAYSCALE
+    )
+
+    # Create a numpy array as the same height and width of read image
+    lbp_image = np.zeros((image.shape[0], image.shape[1]))
+
+    # Iterating through the image and calculating the
+    # local binary pattern value for each pixel.
+    for i in range(0, image.shape[0]):
+        for j in range(0, image.shape[1]):
+            lbp_image[i][j] = local_binary_value(image, i, j)
+
+    cv2.imshow("local binary pattern", lbp_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()

digital_image_processing/test_digital_image_processing.py

@@ -1,6 +1,7 @@
 """
 PyTest's for Digital Image Processing
 """
+import numpy as np
 from cv2 import COLOR_BGR2GRAY, cvtColor, imread
 from numpy import array, uint8
 from PIL import Image
@@ -12,6 +13,7 @@ from digital_image_processing.dithering import burkes as bs
 from digital_image_processing.edge_detection import canny as canny
 from digital_image_processing.filters import convolve as conv
 from digital_image_processing.filters import gaussian_filter as gg
+from digital_image_processing.filters import local_binary_pattern as lbp
 from digital_image_processing.filters import median_filter as med
 from digital_image_processing.filters import sobel_filter as sob
 from digital_image_processing.resize import resize as rs
@@ -91,3 +93,33 @@ def test_nearest_neighbour(
     nn = rs.NearestNeighbour(imread(file_path, 1), 400, 200)
     nn.process()
     assert nn.output.any()
+
+
+def test_local_binary_pattern():
+    file_path: str = "digital_image_processing/image_data/lena.jpg"
+
+    # Reading the image and converting it to grayscale.
+    image = imread(file_path, 0)
+
+    # Test for get_neighbors_pixel function() return not None
+    x_coordinate = 0
+    y_coordinate = 0
+    center = image[x_coordinate][y_coordinate]
+
+    neighbors_pixels = lbp.get_neighbors_pixel(
+        image, x_coordinate, y_coordinate, center
+    )
+
+    assert neighbors_pixels is not None
+
+    # Test for local_binary_pattern function()
+    # Create a numpy array as the same height and width of read image
+    lbp_image = np.zeros((image.shape[0], image.shape[1]))
+
+    # Iterating through the image and calculating the local binary pattern value
+    # for each pixel.
+    for i in range(0, image.shape[0]):
+        for j in range(0, image.shape[1]):
+            lbp_image[i][j] = lbp.local_binary_value(image, i, j)
+
+    assert lbp_image.any()