





'''simple docstring'''


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
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

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


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   cn.convert_to_negative(SCREAMING_SNAKE_CASE_     )
    # assert negative_img array for at least one True
    assert negative_img.any()


def 						__a():



    '''simple docstring'''





    with Image.open("digital_image_processing/image_data/lena_small.jpg"     ) as img:
        # Work around assertion for response
        assert str(cc.change_contrast(SCREAMING_SNAKE_CASE_		,							110     )     ).startswith(
            "<PIL.Image.Image image mode=RGB size=100x100 at"     )


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   canny.gen_gaussian_kernel(9		,							sigma=1.4     )
    # Assert ambiguous array
    assert resp.all()


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   imread("digital_image_processing/image_data/lena_small.jpg"		,							0     )
    # assert ambiguous array for all == True
    assert canny_img.all()
    _lowerCAmelCase						      =   canny.canny(SCREAMING_SNAKE_CASE_     )
    # assert canny array for at least one True
    assert canny_array.any()


def 						__a():



    '''simple docstring'''





    assert gg.gaussian_filter(SCREAMING_SNAKE_CASE_		,							5		,							sigma=0.9     ).all()


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]     )
    _lowerCAmelCase						      =   conv.img_convolve(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ).astype(SCREAMING_SNAKE_CASE_     )
    assert res.any()


def 						__a():



    '''simple docstring'''





    assert med.median_filter(SCREAMING_SNAKE_CASE_		,							3     ).any()


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase      ,      _lowerCAmelCase						      =   sob.sobel_filter(SCREAMING_SNAKE_CASE_     )
    assert grad.any() and theta.any()


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   sp.make_sepia(SCREAMING_SNAKE_CASE_		,							20     )
    assert sepia.all()


def 						__a(SCREAMING_SNAKE_CASE_	:       str = "digital_image_processing/image_data/lena_small.jpg"     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   bs.Burkes(imread(SCREAMING_SNAKE_CASE_		,							1     )		,							120     )
    burkes.process()
    assert burkes.output_img.any()


def 						__a(SCREAMING_SNAKE_CASE_	:       str = "digital_image_processing/image_data/lena_small.jpg"		,							):



    '''simple docstring'''





    _lowerCAmelCase						      =   rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE_		,							1     )		,							400		,							200     )
    nn.process()
    assert nn.output.any()





def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   "digital_image_processing/image_data/lena.jpg"

    # Reading the image and converting it to grayscale.
    _lowerCAmelCase						      =   imread(SCREAMING_SNAKE_CASE_		,							0     )

    # Test for get_neighbors_pixel function() return not None
    _lowerCAmelCase						      =   0
    _lowerCAmelCase						      =   0
    _lowerCAmelCase						      =   image[x_coordinate][y_coordinate]

    _lowerCAmelCase						      =   lbp.get_neighbors_pixel(
        SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

    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
    _lowerCAmelCase						      =   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]     ):
            _lowerCAmelCase						      =   lbp.local_binary_value(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

    assert lbp_image.any()









'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE									=				{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MCTCTForCTC",
           "MCTCTModel",
           "MCTCTPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
       from .feature_extraction_mctct import MCTCTFeatureExtractor
       from .processing_mctct import MCTCTProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
       import sys

       _SCREAMING_SNAKE_CASE									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


