


import unittest

import numpy as np

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 ConvNextImageProcessor





class     __a     (       unittest.TestCase ):
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__=7							,					lowerCAmelCase__=3							,					lowerCAmelCase__=18							,					lowerCAmelCase__=30							,					lowerCAmelCase__=400							,					lowerCAmelCase__=True							,					lowerCAmelCase__=None							,					lowerCAmelCase__=0.8_7_5							,					lowerCAmelCase__=True							,					lowerCAmelCase__=[0.5, 0.5, 0.5]							,					lowerCAmelCase__=[0.5, 0.5, 0.5]							,					)   ->							Any:




        '''simple docstring'''
        lowercase__:  List[Any]         =  size if size is not None else {'shortest_edge': 20}
        lowercase__:  List[str]         =  parent
        lowercase__:  List[Any]         =  batch_size
        lowercase__:  Optional[int]         =  num_channels
        lowercase__:  List[str]         =  image_size
        lowercase__:  int         =  min_resolution
        lowercase__:  str         =  max_resolution
        lowercase__:  List[Any]         =  do_resize
        lowercase__:  int         =  size
        lowercase__:  Optional[int]         =  crop_pct
        lowercase__:  Any         =  do_normalize
        lowercase__:  Any         =  image_mean
        lowercase__:  Optional[int]         =  image_std
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_pct": self.crop_pct,
        }





@require_torch
@require_vision
class     __a     (       __UpperCamelCase			,			unittest.TestCase ):
 __lowercase					:				Optional[int]  	=					ConvNextImageProcessor if is_vision_available() else None
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  List[str]         =  ConvNextImageProcessingTester(self					)
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(lowerCAmelCase__							,					'do_resize'					)					)
        self.assertTrue(hasattr(lowerCAmelCase__							,					'size'					)					)
        self.assertTrue(hasattr(lowerCAmelCase__							,					'crop_pct'					)					)
        self.assertTrue(hasattr(lowerCAmelCase__							,					'do_normalize'					)					)
        self.assertTrue(hasattr(lowerCAmelCase__							,					'image_mean'					)					)
        self.assertTrue(hasattr(lowerCAmelCase__							,					'image_std'					)					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  int         =  self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size							,					{'shortest_edge': 20}					)

        lowercase__:  int         =  self.image_processing_class.from_dict(self.image_processor_dict							,					size=42					)
        self.assertEqual(image_processor.size							,					{'shortest_edge': 42}					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        pass
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							int:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:  Union[str, Any]         =  self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        lowercase__:  Any         =  prepare_image_inputs(self.image_processor_tester							,					equal_resolution=lowerCAmelCase__					)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__							,					Image.Image					)

        # Test not batched input
        lowercase__:  int         =  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.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )							,					)

        # Test batched
        lowercase__:  Union[str, Any]         =  image_processing(lowerCAmelCase__							,					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.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )							,					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:  List[Any]         =  self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        lowercase__:  List[str]         =  prepare_image_inputs(self.image_processor_tester							,					equal_resolution=lowerCAmelCase__							,					numpify=lowerCAmelCase__					)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__							,					np.ndarray					)

        # Test not batched input
        lowercase__:  int         =  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.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )							,					)

        # Test batched
        lowercase__:  str         =  image_processing(lowerCAmelCase__							,					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.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )							,					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:  Tuple         =  self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        lowercase__:  str         =  prepare_image_inputs(self.image_processor_tester							,					equal_resolution=lowerCAmelCase__							,					torchify=lowerCAmelCase__					)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__							,					torch.Tensor					)

        # Test not batched input
        lowercase__:  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.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )							,					)

        # Test batched
        lowercase__:  Union[str, Any]         =  image_processing(lowerCAmelCase__							,					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.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )							,					)






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def   snake_case_	(    snake_case	)    ->							List[str]:
       lowercase__:  Any         =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
       lowercase__:  Optional[Any]         =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

       lowercase__:  Optional[int]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       lowercase__:  List[Any]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       # modify the pix within [0, 255]
       lowercase__:  Optional[Any]         =  dst_x * 2_55 / np.max(snake_case	)
       lowercase__:  Dict         =  dst_y * 2_55 / np.max(snake_case	)

       lowercase__:  Tuple         =  np.sqrt((np.square(snake_case	)) + (np.square(snake_case	))	)
       lowercase__:  str         =  dst_xy * 2_55 / np.max(snake_case	)
       lowercase__:  Tuple         =  dst_xy.astype(np.uinta	)

       lowercase__:  List[Any]         =  np.arctana(snake_case     ,				snake_case	)
       return dst, theta


if __name__ == "__main__":
 # read original image
 __lowerCAmelCase									=				imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 __lowerCAmelCase									=				cvtColor(img, COLOR_BGR2GRAY)

 __lowerCAmelCase			,__lowerCAmelCase									=				sobel_filter(gray)

 # show result images
 imshow('''sobel filter''', sobel_grad)
 imshow('''sobel theta''', sobel_theta)
 waitKey(0)


