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

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* 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

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Co-authored-by: Christian Clauss <cclauss@me.com>
This commit is contained in:
zhexuanl
2022-08-24 12:48:54 +08:00
committed by GitHub
parent f31fa4ea7e
commit b1818af517
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digital_image_processing/test_digital_image_processing.py
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@ -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()