






"""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

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

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       Optional[int]             =	cn.convert_to_negative(UpperCamelCase__				)
  # assert negative_img array for at least one True
  assert negative_img.any()

def 			lowerCAmelCase__     (     ):



  '''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(UpperCamelCase__     ,   1_1_0				)				).startswith(
        """<PIL.Image.Image image mode=RGB size=100x100 at"""				)

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       int             =	canny.gen_gaussian_kernel(9     ,   sigma=1.4				)
  # Assert ambiguous array
  assert resp.all()

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       Optional[Any]             =	imread("""digital_image_processing/image_data/lena_small.jpg"""     ,   0				)
  # assert ambiguous array for all == True
  assert canny_img.all()
  _a  :       Dict             =	canny.canny(UpperCamelCase__				)
  # assert canny array for at least one True
  assert canny_array.any()

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  assert gg.gaussian_filter(UpperCamelCase__     ,   5     ,   sigma=0.9				).all()

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  # laplace diagonals
  _a  :       Dict             =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
  _a  :       str             =	conv.img_convolve(UpperCamelCase__     ,   UpperCamelCase__				).astype(UpperCamelCase__				)
  assert res.any()

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  assert med.median_filter(UpperCamelCase__     ,   3				).any()

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a      ,  _a  :       Any             =	sob.sobel_filter(UpperCamelCase__				)
  assert grad.any() and theta.any()

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       List[str]             =	sp.make_sepia(UpperCamelCase__     ,   2_0				)
  assert sepia.all()

def 			lowerCAmelCase__     (     UpperCamelCase__ = "digital_image_processing/image_data/lena_small.jpg"				):



  '''simple docstring'''
  _a  :       Union[str, Any]             =	bs.Burkes(imread(UpperCamelCase__     ,   1				)     ,   1_2_0				)
  burkes.process()
  assert burkes.output_img.any()

def 			lowerCAmelCase__     (     UpperCamelCase__ = "digital_image_processing/image_data/lena_small.jpg"     ,   ):



  '''simple docstring'''
  _a  :       Optional[int]             =	rs.NearestNeighbour(imread(UpperCamelCase__     ,   1				)     ,   4_0_0     ,   2_0_0				)
  nn.process()
  assert nn.output.any()



def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       Any             =	"""digital_image_processing/image_data/lena.jpg"""

  # Reading the image and converting it to grayscale.
  _a  :       int             =	imread(UpperCamelCase__     ,   0				)

  # Test for get_neighbors_pixel function() return not None
  _a  :       Tuple             =	0
  _a  :       int             =	0
  _a  :       Dict             =	image[x_coordinate][y_coordinate]

  _a  :       Optional[int]             =	lbp.get_neighbors_pixel(
      UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  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
  _a  :       Optional[int]             =	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]				):
      _a  :       Dict             =	lbp.local_binary_value(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  assert lbp_image.any()









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case          =   {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   ['PerceiverFeatureExtractor']
 _snake_case          =   ['PerceiverImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'PerceiverForImageClassificationConvProcessing',
     'PerceiverForImageClassificationFourier',
     'PerceiverForImageClassificationLearned',
     'PerceiverForMaskedLM',
     'PerceiverForMultimodalAutoencoding',
     'PerceiverForOpticalFlow',
     'PerceiverForSequenceClassification',
     'PerceiverLayer',
     'PerceiverModel',
     'PerceiverPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

