Python/digital_image_processing/test_digital_image_processing.py

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"""
PyTest's for Digital Image Processing
"""
import numpy as np
from cv2 import COLOR_BGR2GRAY, cvtColor, imread
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from numpy import array, uint8
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import 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
img = imread(r"digital_image_processing/image_data/lena_small.jpg")
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gray = cvtColor(img, COLOR_BGR2GRAY)
# Test: convert_to_negative()
def test_convert_to_negative():
negative_img = cn.convert_to_negative(img)
# assert negative_img array for at least one True
assert negative_img.any()
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# Test: change_contrast()
def test_change_contrast():
with Image.open("digital_image_processing/image_data/lena_small.jpg") as img:
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# Work around assertion for response
assert str(cc.change_contrast(img, 110)).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at"
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)
# canny.gen_gaussian_kernel()
def test_gen_gaussian_kernel():
resp = canny.gen_gaussian_kernel(9, sigma=1.4)
# Assert ambiguous array
assert resp.all()
# canny.py
def test_canny():
canny_img = imread("digital_image_processing/image_data/lena_small.jpg", 0)
# assert ambiguous array for all == True
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assert canny_img.all()
canny_array = canny.canny(canny_img)
# assert canny array for at least one True
assert canny_array.any()
# filters/gaussian_filter.py
def test_gen_gaussian_kernel_filter():
assert gg.gaussian_filter(gray, 5, sigma=0.9).all()
def test_convolve_filter():
# laplace diagonals
laplace = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
res = conv.img_convolve(gray, laplace).astype(uint8)
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assert res.any()
def test_median_filter():
assert med.median_filter(gray, 3).any()
def test_sobel_filter():
grad, theta = sob.sobel_filter(gray)
assert grad.any() and theta.any()
def test_sepia():
sepia = sp.make_sepia(img, 20)
assert sepia.all()
def test_burkes(file_path: str = "digital_image_processing/image_data/lena_small.jpg"):
burkes = bs.Burkes(imread(file_path, 1), 120)
burkes.process()
assert burkes.output_img.any()
def test_nearest_neighbour(
file_path: str = "digital_image_processing/image_data/lena_small.jpg",
):
nn = rs.NearestNeighbour(imread(file_path, 1), 400, 200)
nn.process()
assert nn.output.any()
def test_local_binary_pattern():
file_path = "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()