






'''simple docstring'''
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor






class    __SCREAMING_SNAKE_CASE					(	unittest.TestCase ):




 def __init__(					self		:							str    ,					__lowercase		:							Optional[Any]    ,					__lowercase		:							List[str]=7    ,					__lowercase		:							Any=3    ,					__lowercase		:							List[str]=18    ,					__lowercase		:							Optional[int]=30    ,					__lowercase		:							Any=4_00    ,					__lowercase		:							Any=True    ,					__lowercase		:							Tuple=None    ,					__lowercase		:							Union[str, Any]=True    ,					__lowercase		:							Tuple=None    ,					__lowercase		:							Union[str, Any]=True    ,					__lowercase		:							Tuple=[0.5, 0.5, 0.5]    ,					__lowercase		:							Union[str, Any]=[0.5, 0.5, 0.5]    ,					__lowercase		:							List[Any]=False    ,					)		->  Optional[int]:
       SCREAMING_SNAKE_CASE__   : Tuple													=size if size is not None else {'''height''': 20, '''width''': 20}
       SCREAMING_SNAKE_CASE__   : str													=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
       SCREAMING_SNAKE_CASE__   : Dict													=parent
       SCREAMING_SNAKE_CASE__   : int													=batch_size
       SCREAMING_SNAKE_CASE__   : str													=num_channels
       SCREAMING_SNAKE_CASE__   : Tuple													=image_size
       SCREAMING_SNAKE_CASE__   : Tuple													=min_resolution
       SCREAMING_SNAKE_CASE__   : int													=max_resolution
       SCREAMING_SNAKE_CASE__   : Dict													=do_resize
       SCREAMING_SNAKE_CASE__   : str													=size
       SCREAMING_SNAKE_CASE__   : Tuple													=do_center_crop
       SCREAMING_SNAKE_CASE__   : Optional[int]													=crop_size
       SCREAMING_SNAKE_CASE__   : List[str]													=do_normalize
       SCREAMING_SNAKE_CASE__   : str													=image_mean
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=image_std
       SCREAMING_SNAKE_CASE__   : int													=do_reduce_labels




 def    __magic_name__		(					self		:							Tuple					)		->  Optional[Any]:
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_reduce_labels": self.do_reduce_labels,
       }





def        _a(     ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Dict													=load_dataset('''hf-internal-testing/fixtures_ade20k''',		split='''test'''							)

      SCREAMING_SNAKE_CASE__   : Any													=Image.open(dataset[0]['''file''']							)
      SCREAMING_SNAKE_CASE__   : Dict													=Image.open(dataset[1]['''file''']							)

      return image, map





def        _a(     ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Dict													=load_dataset('''hf-internal-testing/fixtures_ade20k''',		split='''test'''							)

      SCREAMING_SNAKE_CASE__   : Tuple													=Image.open(ds[0]['''file''']							)
      SCREAMING_SNAKE_CASE__   : List[Any]													=Image.open(ds[1]['''file''']							)
      SCREAMING_SNAKE_CASE__   : Union[str, Any]													=Image.open(ds[2]['''file''']							)
      SCREAMING_SNAKE_CASE__   : Union[str, Any]													=Image.open(ds[3]['''file''']							)

      return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class    __SCREAMING_SNAKE_CASE					(	lowerCamelCase		, unittest.TestCase ):
 snake_case_     							=							BeitImageProcessor if is_vision_available() else None




 def    __magic_name__		(					self		:							str					)		->  str:
       SCREAMING_SNAKE_CASE__   : str													=BeitImageProcessingTester(self					)




 @property
 def    __magic_name__		(					self		:							Tuple					)		->  Tuple:
       return self.image_processor_tester.prepare_image_processor_dict()




 def    __magic_name__		(					self		:							Tuple					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : str													=self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(__lowercase    ,					'''do_resize'''					)					)
       self.assertTrue(hasattr(__lowercase    ,					'''size'''					)					)
       self.assertTrue(hasattr(__lowercase    ,					'''do_center_crop'''					)					)
       self.assertTrue(hasattr(__lowercase    ,					'''center_crop'''					)					)
       self.assertTrue(hasattr(__lowercase    ,					'''do_normalize'''					)					)
       self.assertTrue(hasattr(__lowercase    ,					'''image_mean'''					)					)
       self.assertTrue(hasattr(__lowercase    ,					'''image_std'''					)					)




 def    __magic_name__		(					self		:							str					)		->  List[str]:
       SCREAMING_SNAKE_CASE__   : str													=self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,					{'''height''': 20, '''width''': 20}					)
       self.assertEqual(image_processor.crop_size    ,					{'''height''': 18, '''width''': 18}					)
       self.assertEqual(image_processor.do_reduce_labels    ,					__lowercase					)

       SCREAMING_SNAKE_CASE__   : int													=self.image_processing_class.from_dict(
           self.image_processor_dict    ,					size=42    ,					crop_size=84    ,					reduce_labels=__lowercase					)
       self.assertEqual(image_processor.size    ,					{'''height''': 42, '''width''': 42}					)
       self.assertEqual(image_processor.crop_size    ,					{'''height''': 84, '''width''': 84}					)
       self.assertEqual(image_processor.do_reduce_labels    ,					__lowercase					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Dict:
       pass




 def    __magic_name__		(					self		:							Optional[Any]					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : List[Any]													=self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE__   : Dict													=prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__lowercase					)
       for image in image_inputs:
             self.assertIsInstance(__lowercase    ,					Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE__   : List[str]													=image_processing(image_inputs[0]    ,					return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)

       # Test batched
       SCREAMING_SNAKE_CASE__   : List[Any]													=image_processing(__lowercase    ,					return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)




 def    __magic_name__		(					self		:							List[str]					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__   : List[str]													=prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__lowercase    ,					numpify=__lowercase					)
       for image in image_inputs:
             self.assertIsInstance(__lowercase    ,					np.ndarray					)

       # Test not batched input
       SCREAMING_SNAKE_CASE__   : Dict													=image_processing(image_inputs[0]    ,					return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)

       # Test batched
       SCREAMING_SNAKE_CASE__   : str													=image_processing(__lowercase    ,					return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)




 def    __magic_name__		(					self		:							Tuple					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : List[Any]													=self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__lowercase    ,					torchify=__lowercase					)
       for image in image_inputs:
             self.assertIsInstance(__lowercase    ,					torch.Tensor					)

       # Test not batched input
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=image_processing(image_inputs[0]    ,					return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)

       # Test batched
       SCREAMING_SNAKE_CASE__   : List[str]													=image_processing(__lowercase    ,					return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__   : List[str]													=self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__lowercase    ,					torchify=__lowercase					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=[]
       for image in image_inputs:
             self.assertIsInstance(__lowercase    ,					torch.Tensor					)
             maps.append(torch.zeros(image.shape[-2:]					).long()					)

       # Test not batched input
       SCREAMING_SNAKE_CASE__   : List[Any]													=image_processing(image_inputs[0]    ,					maps[0]    ,					return_tensors='''pt'''					)
       self.assertEqual(
           encoding['''pixel_values'''].shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)
       self.assertEqual(
           encoding['''labels'''].shape    ,					(
               1,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)
       self.assertEqual(encoding['''labels'''].dtype    ,					torch.long					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 2_55					)

       # Test batched
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=image_processing(__lowercase    ,					__lowercase    ,					return_tensors='''pt'''					)
       self.assertEqual(
           encoding['''pixel_values'''].shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)
       self.assertEqual(
           encoding['''labels'''].shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)
       self.assertEqual(encoding['''labels'''].dtype    ,					torch.long					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 2_55					)

       # Test not batched input (PIL images)
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : Dict													=prepare_semantic_single_inputs()

       SCREAMING_SNAKE_CASE__   : Dict													=image_processing(__lowercase    ,					__lowercase    ,					return_tensors='''pt'''					)
       self.assertEqual(
           encoding['''pixel_values'''].shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)
       self.assertEqual(
           encoding['''labels'''].shape    ,					(
               1,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)
       self.assertEqual(encoding['''labels'''].dtype    ,					torch.long					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 2_55					)

       # Test batched input (PIL images)
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : List[Any]													=prepare_semantic_batch_inputs()

       SCREAMING_SNAKE_CASE__   : Dict													=image_processing(__lowercase    ,					__lowercase    ,					return_tensors='''pt'''					)
       self.assertEqual(
           encoding['''pixel_values'''].shape    ,					(
               2,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)
       self.assertEqual(
           encoding['''labels'''].shape    ,					(
               2,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,					)
       self.assertEqual(encoding['''labels'''].dtype    ,					torch.long					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 2_55					)




 def    __magic_name__		(					self		:							Optional[Any]					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : Dict													=self.image_processing_class(**self.image_processor_dict					)

       # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : Tuple													=prepare_semantic_single_inputs()
       SCREAMING_SNAKE_CASE__   : Optional[int]													=image_processing(__lowercase    ,					__lowercase    ,					return_tensors='''pt'''					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 1_50					)

       SCREAMING_SNAKE_CASE__   : str													=True
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=image_processing(__lowercase    ,					__lowercase    ,					return_tensors='''pt'''					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 2_55					)


import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


