import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

UpperCAmelCase							= 1_80


def SCREAMING_SNAKE_CASE (   lowercase_:		Any						,  lowercase_:		str ):
       _UpperCamelCase   :				str       =     k_size // 2
       _UpperCamelCase  ,					_UpperCamelCase   :				List[str]       =     np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _UpperCamelCase   :				str       =     (
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(lowercase_ ) + np.square(lowercase_ )) / (2 * np.square(lowercase_ )) )
       )
       return g


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]						,  lowercase_:		str ):
       _UpperCamelCase   :				List[str]       =     np.zeros(lowercase_ )

       for row in range(1						,  image_shape[0] - 1 ):
              for col in range(1						,  image_shape[1] - 1 ):
                     _UpperCamelCase   :				Tuple       =     gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _UpperCamelCase   :				Any       =     sobel_grad[row, col - 1]
                            _UpperCamelCase   :				Tuple       =     sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _UpperCamelCase   :				int       =     sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _UpperCamelCase   :				Tuple       =     sobel_grad[row + 1, col - 1]
                            _UpperCamelCase   :				int       =     sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _UpperCamelCase   :				List[Any]       =     sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _UpperCamelCase   :				int       =     sobel_grad[row - 1, col]
                            _UpperCamelCase   :				List[str]       =     sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _UpperCamelCase   :				Any       =     sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _UpperCamelCase   :				List[Any]       =     sobel_grad[row - 1, col - 1]
                            _UpperCamelCase   :				int       =     sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _UpperCamelCase   :				Dict       =     sobel_grad[row, col]

       return destination


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		Any						,  lowercase_:		Any						,  lowercase_:		Optional[Any]						,  lowercase_:		Optional[Any]						,  lowercase_:		Any ):
       for row in range(1						,  image_shape[0] - 1 ):
              for col in range(1						,  image_shape[1] - 1 ):
                     if destination[row, col] >= threshold_high:
                            _UpperCamelCase   :				Any       =     strong
                     elif destination[row, col] <= threshold_low:
                            _UpperCamelCase   :				int       =     0
                     else:
                            _UpperCamelCase   :				str       =     weak


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Any]						,  lowercase_:		Optional[int]						,  lowercase_:		Optional[Any]						,  lowercase_:		Tuple ):
       for row in range(1						,  image_shape[0] ):
              for col in range(1						,  image_shape[1] ):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _UpperCamelCase   :				Any       =     strong
                            else:
                                   _UpperCamelCase   :				Tuple       =     0


def SCREAMING_SNAKE_CASE (   lowercase_:		Dict						,  lowercase_:		List[Any]=15						,  lowercase_:		Union[str, Any]=30						,  lowercase_:		List[Any]=128						,  lowercase_:		List[Any]=255 ):
       # gaussian_filter
       _UpperCamelCase   :				str       =     img_convolve(lowercase_						,  gen_gaussian_kernel(9						,  sigma=1.4 ) )
       # get the gradient and degree by sobel_filter
       _UpperCamelCase  ,					_UpperCamelCase   :				Optional[int]       =     sobel_filter(lowercase_ )
       _UpperCamelCase   :				Union[str, Any]       =     PI + np.radadeg(lowercase_ )

       _UpperCamelCase   :				Union[str, Any]       =     suppress_non_maximum(image.shape						,  lowercase_						,  lowercase_ )

       detect_high_low_threshold(
           image.shape						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_ )

       track_edge(image.shape						,  lowercase_						,  lowercase_						,  lowercase_ )

       return destination


if __name__ == "__main__":
 # read original image in gray mode
 UpperCAmelCase							= cva.imread(r'''../image_data/lena.jpg''', 0)
 # canny edge detection
 UpperCAmelCase							= canny(lena)
 cva.imshow('''canny''', canny_destination)
 cva.waitKey(0)




from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
 import torch




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict 			=       ['image_processor', 'tokenizer']
 _lowerCamelCase  :		Tuple 			=       'OneFormerImageProcessor'
 _lowerCamelCase  :		Dict 			=       ('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__(	self				:						str				,	lowerCamelCase				:						Any=None				,	lowerCamelCase				:						List[Any]=None				,	lowerCamelCase				:						int = 7_7				,	lowerCamelCase				:						int = 7_7				,	**lowerCamelCase				:						Union[str, Any]     )       ->							Any:
        if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.'     )
        if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.'     )

        _UpperCamelCase   :				int       =     max_seq_length
        _UpperCamelCase   :				Optional[Any]       =     task_seq_length

        super().__init__(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						Optional[int]=7_7     )       ->							Tuple:
        if text_list is None:
               raise ValueError('tokens cannot be None.'     )

        _UpperCamelCase   :				Dict       =     self.tokenizer(lowerCamelCase				,	padding='max_length'				,	max_length=lowerCamelCase				,	truncation=lowerCamelCase     )

        _UpperCamelCase  ,					_UpperCamelCase   :				List[str]       =     tokens['attention_mask'], tokens['input_ids']

        _UpperCamelCase   :				Optional[int]       =     []
        for attn_mask, input_id in zip(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     torch.tensor(lowerCamelCase     ) * torch.tensor(lowerCamelCase     )
               token_inputs.append(token.unsqueeze(0     )     )

        _UpperCamelCase   :				Dict       =     torch.cat(lowerCamelCase				,	dim=0     )
        return token_inputs

 def __call__(	self				:						Union[str, Any]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						Any=None				,	**lowerCamelCase				:						List[Any]     )       ->							List[Any]:

        if task_inputs is None:
               raise ValueError('You have to specify the task_input. Found None.'     )
        elif images is None:
               raise ValueError('You have to specify the image. Found None.'     )

        if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
               raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

        _UpperCamelCase   :				Optional[Any]       =     self.image_processor(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				str       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				Optional[Any]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				Dict       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				str       =     encoded_inputs.text_inputs

               _UpperCamelCase   :				Optional[Any]       =     []
               for texts in texts_list:
                      _UpperCamelCase   :				Optional[int]       =     self._preprocess_text(lowerCamelCase				,	max_length=self.max_seq_length     )
                      text_inputs.append(text_input_list.unsqueeze(0     )     )

               _UpperCamelCase   :				Optional[int]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						int=None				,	**lowerCamelCase				:						List[str]     )       ->							Tuple:

        if task_inputs is None:
               raise ValueError('You have to specify the task_input. Found None.'     )
        elif images is None:
               raise ValueError('You have to specify the image. Found None.'     )

        if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
               raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

        _UpperCamelCase   :				Any       =     self.image_processor.encode_inputs(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				int       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				List[str]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				int       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				Optional[int]       =     encoded_inputs.text_inputs

               _UpperCamelCase   :				Any       =     []
               for texts in texts_list:
                      _UpperCamelCase   :				Any       =     self._preprocess_text(lowerCamelCase				,	max_length=self.max_seq_length     )
                      text_inputs.append(text_input_list.unsqueeze(0     )     )

               _UpperCamelCase   :				Optional[Any]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						Tuple				,	**lowerCamelCase				:						Any     )       ->							str:
        return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Union[str, Any]				,	*lowerCamelCase				:						Optional[int]				,	**lowerCamelCase				:						Any     )       ->							Any:
        return self.image_processor.post_process_instance_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						List[str]				,	**lowerCamelCase				:						int     )       ->							List[Any]:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )



