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126 lines
3.8 KiB
Python
126 lines
3.8 KiB
Python
"""
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PyTest's for Digital Image Processing
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"""
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import numpy as np
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from cv2 import COLOR_BGR2GRAY, cvtColor, imread
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from numpy import array, uint8
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from PIL import Image
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from digital_image_processing import change_contrast as cc
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from digital_image_processing import convert_to_negative as cn
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from digital_image_processing import sepia as sp
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from digital_image_processing.dithering import burkes as bs
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from digital_image_processing.edge_detection import canny
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from digital_image_processing.filters import convolve as conv
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from digital_image_processing.filters import gaussian_filter as gg
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from digital_image_processing.filters import local_binary_pattern as lbp
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from digital_image_processing.filters import median_filter as med
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from digital_image_processing.filters import sobel_filter as sob
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from digital_image_processing.resize import resize as rs
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img = imread(r"digital_image_processing/image_data/lena_small.jpg")
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gray = cvtColor(img, COLOR_BGR2GRAY)
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# Test: convert_to_negative()
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def test_convert_to_negative():
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negative_img = cn.convert_to_negative(img)
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# assert negative_img array for at least one True
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assert negative_img.any()
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# Test: change_contrast()
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def test_change_contrast():
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with Image.open("digital_image_processing/image_data/lena_small.jpg") as img:
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# Work around assertion for response
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assert str(cc.change_contrast(img, 110)).startswith(
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"<PIL.Image.Image image mode=RGB size=100x100 at"
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)
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# canny.gen_gaussian_kernel()
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def test_gen_gaussian_kernel():
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resp = canny.gen_gaussian_kernel(9, sigma=1.4)
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# Assert ambiguous array
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assert resp.all()
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# canny.py
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def test_canny():
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canny_img = imread("digital_image_processing/image_data/lena_small.jpg", 0)
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# assert ambiguous array for all == True
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assert canny_img.all()
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canny_array = canny.canny(canny_img)
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# assert canny array for at least one True
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assert canny_array.any()
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# filters/gaussian_filter.py
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def test_gen_gaussian_kernel_filter():
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assert gg.gaussian_filter(gray, 5, sigma=0.9).all()
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def test_convolve_filter():
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# laplace diagonals
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laplace = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
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res = conv.img_convolve(gray, laplace).astype(uint8)
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assert res.any()
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def test_median_filter():
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assert med.median_filter(gray, 3).any()
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def test_sobel_filter():
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grad, theta = sob.sobel_filter(gray)
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assert grad.any() and theta.any()
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def test_sepia():
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sepia = sp.make_sepia(img, 20)
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assert sepia.all()
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def test_burkes(file_path: str = "digital_image_processing/image_data/lena_small.jpg"):
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burkes = bs.Burkes(imread(file_path, 1), 120)
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burkes.process()
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assert burkes.output_img.any()
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def test_nearest_neighbour(
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file_path: str = "digital_image_processing/image_data/lena_small.jpg",
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):
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nn = rs.NearestNeighbour(imread(file_path, 1), 400, 200)
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nn.process()
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assert nn.output.any()
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def test_local_binary_pattern():
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file_path: str = "digital_image_processing/image_data/lena.jpg"
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# Reading the image and converting it to grayscale.
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image = imread(file_path, 0)
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# Test for get_neighbors_pixel function() return not None
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x_coordinate = 0
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y_coordinate = 0
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center = image[x_coordinate][y_coordinate]
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neighbors_pixels = lbp.get_neighbors_pixel(
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image, x_coordinate, y_coordinate, center
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)
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assert neighbors_pixels is not None
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# Test for local_binary_pattern function()
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# Create a numpy array as the same height and width of read image
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lbp_image = np.zeros((image.shape[0], image.shape[1]))
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# Iterating through the image and calculating the local binary pattern value
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# for each pixel.
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for i in range(0, image.shape[0]):
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for j in range(0, image.shape[1]):
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lbp_image[i][j] = lbp.local_binary_value(image, i, j)
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assert lbp_image.any()
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