


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


A   :					str	=      logging.get_logger(__name__)

def    lowercase_	(	_A    :					Optional[Any]					,      _A    :					str					,      _A    :					Any					,      _A    :					str		):

      """simple docstring"""




      def constraint_to_multiple_of(_A    :					Optional[Any]					,      _A    :					Tuple					,      _A    :					int=0					,      _A    :					List[Any]=None		):
            lowerCamelCase__	:       str					=						round(val / multiple		) * multiple

            if max_val is not None and x > max_val:
                  lowerCamelCase__	:       List[str]					=						math.floor(val / multiple		) * multiple

            if x < min_val:
                  lowerCamelCase__	:       int					=						math.ceil(val / multiple		) * multiple

            return x

      lowerCamelCase__	:       List[str]					=						(output_size, output_size) if isinstance(lowerCamelCase__					,      lowerCamelCase__		) else output_size

      lowerCamelCase__	:       Tuple					=						get_image_size(lowerCamelCase__		)
      lowerCamelCase__	:       Any					=						output_size

      # determine new height and width
      lowerCamelCase__	:       int					=						output_height / input_height
      lowerCamelCase__	:       List[str]					=						output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width		) < abs(1 - scale_height		):
                  # fit width
                  lowerCamelCase__	:       List[str]					=						scale_width
            else:
                  # fit height
                  lowerCamelCase__	:       Union[str, Any]					=						scale_height

      lowerCamelCase__	:       str					=						constraint_to_multiple_of(scale_height * input_height					,      multiple=lowerCamelCase__		)
      lowerCamelCase__	:       int					=						constraint_to_multiple_of(scale_width * input_width					,      multiple=lowerCamelCase__		)

      return (new_height, new_width)







class     _lowercase				(							lowercase__):
       A__      		=    ["pixel_values"]





       def __init__(     self							:    Tuple      ,   __lowerCamelCase							:    List[str] = True      ,   __lowerCamelCase							:    Any = None      ,   __lowerCamelCase							:    int = PILImageResampling.BILINEAR      ,   __lowerCamelCase							:    Optional[Any] = False      ,   __lowerCamelCase							:    Optional[Any] = 1      ,   __lowerCamelCase							:    Any = True      ,   __lowerCamelCase							:    Dict = 1 / 255      ,   __lowerCamelCase							:    Tuple = True      ,   __lowerCamelCase							:    str = None      ,   __lowerCamelCase							:    Optional[Any] = None      ,   **__lowerCamelCase							:    Union[str, Any]      ,   ):


             '''simple docstring'''



             super().__init__(**UpperCamelCase_ )
             lowerCamelCase__	:       Optional[Any]					=						size if size is not None else {"height": 384, "width": 384}
             lowerCamelCase__	:       int					=						get_size_dict(UpperCamelCase_ )
             lowerCamelCase__	:       str					=						do_resize
             lowerCamelCase__	:       List[str]					=						size
             lowerCamelCase__	:       Dict					=						keep_aspect_ratio
             lowerCamelCase__	:       Optional[Any]					=						ensure_multiple_of
             lowerCamelCase__	:       List[Any]					=						resample
             lowerCamelCase__	:       str					=						do_rescale
             lowerCamelCase__	:       str					=						rescale_factor
             lowerCamelCase__	:       Tuple					=						do_normalize
             lowerCamelCase__	:       Dict					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             lowerCamelCase__	:       Dict					=						image_std if image_std is not None else IMAGENET_STANDARD_STD





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any] = False      ,   __lowerCamelCase							:    Optional[int] = 1      ,   __lowerCamelCase							:    Union[str, Any] = PILImageResampling.BICUBIC      ,   __lowerCamelCase							:    List[str] = None      ,   **__lowerCamelCase							:    Any      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						get_size_dict(UpperCamelCase_ )
             if "height" not in size or "width" not in size:
                   raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
             lowerCamelCase__	:       Optional[Any]					=						get_resize_output_image_size(
                 UpperCamelCase_      ,   output_size=(size["height"], size["width"])      ,   keep_aspect_ratio=UpperCamelCase_      ,   multiple=UpperCamelCase_      ,   )
             return resize(UpperCamelCase_      ,   size=UpperCamelCase_      ,   resample=UpperCamelCase_      ,   data_format=UpperCamelCase_      ,   **UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    str = None      ,   **__lowerCamelCase							:    List[str]      ,   ):


             '''simple docstring'''



             return rescale(UpperCamelCase_      ,   scale=UpperCamelCase_      ,   data_format=UpperCamelCase_      ,   **UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Optional[Any] = None      ,   **__lowerCamelCase							:    str      ,   ):


             '''simple docstring'''



             return normalize(UpperCamelCase_      ,   mean=UpperCamelCase_      ,   std=UpperCamelCase_      ,   data_format=UpperCamelCase_      ,   **UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Union[str, Any] = None      ,   __lowerCamelCase							:    str = None      ,   __lowerCamelCase							:    Any = None      ,   __lowerCamelCase							:    Dict = None      ,   __lowerCamelCase							:    List[Any] = None      ,   __lowerCamelCase							:    Any = None      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    Union[str, Any] = None      ,   __lowerCamelCase							:    List[Any] = None      ,   __lowerCamelCase							:    str = None      ,   __lowerCamelCase							:    int = None      ,   __lowerCamelCase							:    Any = ChannelDimension.FIRST      ,   **__lowerCamelCase							:    Union[str, Any]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						do_resize if do_resize is not None else self.do_resize
             lowerCamelCase__	:       List[str]					=						size if size is not None else self.size
             lowerCamelCase__	:       Any					=						get_size_dict(UpperCamelCase_ )
             lowerCamelCase__	:       List[str]					=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
             lowerCamelCase__	:       Dict					=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
             lowerCamelCase__	:       Any					=						resample if resample is not None else self.resample
             lowerCamelCase__	:       Any					=						do_rescale if do_rescale is not None else self.do_rescale
             lowerCamelCase__	:       List[str]					=						rescale_factor if rescale_factor is not None else self.rescale_factor
             lowerCamelCase__	:       Dict					=						do_normalize if do_normalize is not None else self.do_normalize
             lowerCamelCase__	:       List[Any]					=						image_mean if image_mean is not None else self.image_mean
             lowerCamelCase__	:       int					=						image_std if image_std is not None else self.image_std

             lowerCamelCase__	:       int					=						make_list_of_images(UpperCamelCase_ )

             if not valid_images(UpperCamelCase_ ):
                   raise ValueError(
                       "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                       "torch.Tensor, tf.Tensor or jax.ndarray." )

             if do_resize and size is None or resample is None:
                   raise ValueError("Size and resample must be specified if do_resize is True." )

             if do_rescale and rescale_factor is None:
                   raise ValueError("Rescale factor must be specified if do_rescale is True." )

             if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError("Image mean and std must be specified if do_normalize is True." )

             # All transformations expect numpy arrays.
             lowerCamelCase__	:       Optional[int]					=						[to_numpy_array(UpperCamelCase_ ) for image in images]

             if do_resize:
                   lowerCamelCase__	:       str					=						[self.resize(image=UpperCamelCase_      ,   size=UpperCamelCase_      ,   resample=UpperCamelCase_ ) for image in images]

             if do_rescale:
                   lowerCamelCase__	:       Tuple					=						[self.rescale(image=UpperCamelCase_      ,   scale=UpperCamelCase_ ) for image in images]

             if do_normalize:
                   lowerCamelCase__	:       List[str]					=						[self.normalize(image=UpperCamelCase_      ,   mean=UpperCamelCase_      ,   std=UpperCamelCase_ ) for image in images]

             lowerCamelCase__	:       Dict					=						[to_channel_dimension_format(UpperCamelCase_      ,   UpperCamelCase_ ) for image in images]

             lowerCamelCase__	:       List[Any]					=						{"pixel_values": images}
             return BatchFeature(data=UpperCamelCase_      ,   tensor_type=UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Dict = None ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						outputs.logits

             # Resize logits and compute semantic segmentation maps
             if target_sizes is not None:
                   if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
                         raise ValueError(
                             "Make sure that you pass in as many target sizes as the batch dimension of the logits" )

                   if is_torch_tensor(UpperCamelCase_ ):
                         lowerCamelCase__	:       List[str]					=						target_sizes.numpy()

                   lowerCamelCase__	:       Optional[int]					=						[]

                   for idx in range(len(UpperCamelCase_ ) ):
                         lowerCamelCase__	:       int					=						torch.nn.functional.interpolate(
                             logits[idx].unsqueeze(dim=0 )      ,   size=target_sizes[idx]      ,   mode="bilinear"      ,   align_corners=UpperCamelCase_ )
                         lowerCamelCase__	:       Dict					=						resized_logits[0].argmax(dim=0 )
                         semantic_segmentation.append(UpperCamelCase_ )
             else:
                   lowerCamelCase__	:       int					=						logits.argmax(dim=1 )
                   lowerCamelCase__	:       Tuple					=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

             return semantic_segmentation






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if k in (0.0_4, 0.0_6):
                   lowerCamelCase__	:       int					=						k
                   lowerCamelCase__	:       List[str]					=						window_size
             else:
                   raise ValueError("invalid k value" )





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						cva.imread(__lowerCamelCase      ,   0 )
             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						img.shape
             lowerCamelCase__	:       list[list[int]]					=						[]
             lowerCamelCase__	:       List[Any]					=						img.copy()
             lowerCamelCase__	:       int					=						cva.cvtColor(__lowerCamelCase      ,   cva.COLOR_GRAY2RGB )
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						np.gradient(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						dx**2
             lowerCamelCase__	:       Optional[Any]					=						dy**2
             lowerCamelCase__	:       int					=						dx * dy
             lowerCamelCase__	:       Union[str, Any]					=						0.0_4
             lowerCamelCase__	:       Any					=						self.window_size // 2
             for y in range(__lowerCamelCase      ,   h - offset ):
                   for x in range(__lowerCamelCase      ,   w - offset ):
                         lowerCamelCase__	:       Optional[Any]					=						ixx[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       Optional[Any]					=						iyy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       str					=						ixy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[Any]					=						det - k * (trace**2)
                         # Can change the value
                         if r > 0.5:
                               corner_list.append([x, y, r] )
                               color_img.itemset((y, x, 0)      ,   0 )
                               color_img.itemset((y, x, 1)      ,   0 )
                               color_img.itemset((y, x, 2)      ,   255 )
             return color_img, corner_list


if __name__ == "__main__":
    A   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


