Python/digital_image_processing/test_digital_image_processing.py at master · imanghotbi-python/Python · GitHub

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"""
PyTest's for Digital Image Processing
"""
importnumpyasnp
fromcv2importCOLOR_BGR2GRAY, cvtColor, imread
fromnumpyimportarray, uint8
fromPILimportImage

fromdigital_image_processingimportchange_contrastascc
fromdigital_image_processingimportconvert_to_negativeascn
fromdigital_image_processingimportsepiaassp
fromdigital_image_processing.ditheringimportburkesasbs
fromdigital_image_processing.edge_detectionimportcannyascanny
fromdigital_image_processing.filtersimportconvolveasconv
fromdigital_image_processing.filtersimportgaussian_filterasgg
fromdigital_image_processing.filtersimportlocal_binary_patternaslbp
fromdigital_image_processing.filtersimportmedian_filterasmed
fromdigital_image_processing.filtersimportsobel_filterassob
fromdigital_image_processing.resizeimportresizeasrs

img=imread(r"digital_image_processing/image_data/lena_small.jpg")
gray=cvtColor(img, COLOR_BGR2GRAY)


# Test: convert_to_negative()
deftest_convert_to_negative():
negative_img=cn.convert_to_negative(img)
# assert negative_img array for at least one True
assertnegative_img.any()


# Test: change_contrast()
deftest_change_contrast():
withImage.open("digital_image_processing/image_data/lena_small.jpg") asimg:
# Work around assertion for response
assertstr(cc.change_contrast(img, 110)).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at"
 )


# canny.gen_gaussian_kernel()
deftest_gen_gaussian_kernel():
resp=canny.gen_gaussian_kernel(9, sigma=1.4)
# Assert ambiguous array
assertresp.all()


# canny.py
deftest_canny():
canny_img=imread("digital_image_processing/image_data/lena_small.jpg", 0)
# assert ambiguous array for all == True
assertcanny_img.all()
canny_array=canny.canny(canny_img)
# assert canny array for at least one True
assertcanny_array.any()


# filters/gaussian_filter.py
deftest_gen_gaussian_kernel_filter():
assertgg.gaussian_filter(gray, 5, sigma=0.9).all()


deftest_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)
assertres.any()


deftest_median_filter():
assertmed.median_filter(gray, 3).any()


deftest_sobel_filter():
grad, theta=sob.sobel_filter(gray)
assertgrad.any() andtheta.any()


deftest_sepia():
sepia=sp.make_sepia(img, 20)
assertsepia.all()


deftest_burkes(file_path: str="digital_image_processing/image_data/lena_small.jpg"):
burkes=bs.Burkes(imread(file_path, 1), 120)
burkes.process()
assertburkes.output_img.any()


deftest_nearest_neighbour(
file_path: str="digital_image_processing/image_data/lena_small.jpg",
):
nn=rs.NearestNeighbour(imread(file_path, 1), 400, 200)
nn.process()
assertnn.output.any()


deftest_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
 )

assertneighbors_pixelsisnotNone

# 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.
foriinrange(0, image.shape[0]):
forjinrange(0, image.shape[1]):
lbp_image[i][j] =lbp.local_binary_value(image, i, j)

assertlbp_image.any()