





"""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

lowerCAmelCase_			=							imread(r'''digital_image_processing/image_data/lena_small.jpg''')
lowerCAmelCase_			=							cvtColor(img, COLOR_BGR2GRAY)

def       __lowerCamelCase    (       )		->       List[Any]:






       """simple docstring"""




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

def       __lowerCamelCase    (       )		->       Optional[int]:






       """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(a_,110							)							).startswith(
                  '<PIL.Image.Image image mode=RGB size=100x100 at'							)

def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




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

def       __lowerCamelCase    (       )		->       int:






       """simple docstring"""




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

def       __lowerCamelCase    (       )		->       List[str]:






       """simple docstring"""




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

def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




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

def       __lowerCamelCase    (       )		->       Any:






       """simple docstring"""




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

def       __lowerCamelCase    (       )		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     sob.sobel_filter(a_							)
       assert grad.any() and theta.any()

def       __lowerCamelCase    (       )		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     sp.make_sepia(a_,20							)
       assert sepia.all()

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"							)		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     bs.Burkes(imread(a_,1							),120							)
       burkes.process()
       assert burkes.output_img.any()

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg",)		->       Optional[int]:






       """simple docstring"""




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


def       __lowerCamelCase    (       )		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     '''digital_image_processing/image_data/lena.jpg'''

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

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

       _UpperCAmelCase     =     lbp.get_neighbors_pixel(
           a_,a_,a_,a_							)

       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
       _UpperCAmelCase     =     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]							):
                     _UpperCAmelCase     =     lbp.local_binary_value(a_,a_,a_							)

       assert lbp_image.any()













"""simple docstring"""






from datetime import datetime

import matplotlib.pyplot as plt
import torch

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Any:






       """simple docstring"""




       for param in module.parameters():
              _UpperCAmelCase     =     False

def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              _UpperCAmelCase     =     'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'							)
       return device

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     plt.imshow(SCREAMING_SNAKE_CASE							)
       fig.axes.get_xaxis().set_visible(SCREAMING_SNAKE_CASE							)
       fig.axes.get_yaxis().set_visible(SCREAMING_SNAKE_CASE							)
       plt.show()


def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     datetime.now()
       _UpperCAmelCase     =     current_time.strftime('%H:%M:%S'							)
       return timestamp






