import cva
import numpy as np



class        lowercase_   :



							def __init__(						self				,    lowercase_				,    lowercase_    ):

													if k in (0.04, 0.06):
																			_snake_case						:					List[str]    =   k
																			_snake_case						:					Optional[Any]    =   window_size
													else:
																			raise ValueError("invalid k value"    )



							def __str__(						self    ):
													return str(self.k    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					int    =   cva.imread(lowercase_				,    0    )
													_snake_case    ,_snake_case						:					List[Any]    =   img.shape
													_snake_case						:					list[list[int]]    =   []
													_snake_case						:					Tuple    =   img.copy()
													_snake_case						:					str    =   cva.cvtColor(lowercase_				,    cva.COLOR_GRAY2RGB    )
													_snake_case    ,_snake_case						:					Any    =   np.gradient(lowercase_    )
													_snake_case						:					Union[str, Any]    =   dx**2
													_snake_case						:					List[str]    =   dy**2
													_snake_case						:					Any    =   dx * dy
													_snake_case						:					List[str]    =   0.04
													_snake_case						:					Union[str, Any]    =   self.window_size // 2
													for y in range(lowercase_				,    h - offset    ):
																			for x in range(lowercase_				,    w - offset    ):
																									_snake_case						:					Dict    =   ixx[
																									    y - offset : y + offset + 1, x - offset : x + offset + 1
																									].sum()
																									_snake_case						:					str    =   iyy[
																									    y - offset : y + offset + 1, x - offset : x + offset + 1
																									].sum()
																									_snake_case						:					Any    =   ixy[
																									    y - offset : y + offset + 1, x - offset : x + offset + 1
																									].sum()

																									_snake_case						:					int    =   (wxx * wyy) - (wxy**2)
																									_snake_case						:					List[Any]    =   wxx + wyy
																									_snake_case						:					Dict    =   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__":
				__SCREAMING_SNAKE_CASE						:   Dict         =			HarrisCorner(0.04, 3)
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			edge_detect.detect('path_to_image')
				cva.imwrite('detect.png', color_img)
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
				import PIL

if is_torch_available():
				import torch


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			logging.get_logger(__name__)



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		['pixel_values']



							def __init__(						self				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = PILImageResampling.BILINEAR				,    lowercase_ = True				,    lowercase_ = 1 / 255				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = True				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )
													_snake_case						:					Dict    =   size if size is not None else {"shortest_edge": 224}
													_snake_case						:					Optional[int]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													_snake_case						:					Any    =   crop_size if crop_size is not None else {"height": 256, "width": 256}
													_snake_case						:					str    =   get_size_dict(lowercase_				,    param_name="crop_size"    )

													_snake_case						:					List[Any]    =   do_resize
													_snake_case						:					Tuple    =   size
													_snake_case						:					Union[str, Any]    =   resample
													_snake_case						:					str    =   do_rescale
													_snake_case						:					Dict    =   rescale_factor
													_snake_case						:					int    =   do_center_crop
													_snake_case						:					int    =   crop_size
													_snake_case						:					List[Any]    =   do_flip_channel_order



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = PIL.Image.BILINEAR				,    lowercase_ = None				,    **lowercase_				,    ):
													_snake_case						:					Optional[Any]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													if "shortest_edge" not in size:
																			raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""    )
													_snake_case						:					int    =   get_resize_output_image_size(lowercase_				,    size=size["shortest_edge"]				,    default_to_square=lowercase_    )
													return resize(lowercase_				,    size=lowercase_				,    resample=lowercase_				,    data_format=lowercase_				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_				,    ):
													_snake_case						:					List[str]    =   get_size_dict(lowercase_    )
													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()}"""    )
													return center_crop(lowercase_				,    size=(size["height"], size["width"])				,    data_format=lowercase_				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_				,    ):
													return rescale(lowercase_				,    scale=lowercase_				,    data_format=lowercase_				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													return flip_channel_order(lowercase_				,    data_format=lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = ChannelDimension.FIRST				,    **lowercase_				,    ):
													_snake_case						:					Dict    =   do_resize if do_resize is not None else self.do_resize
													_snake_case						:					Union[str, Any]    =   resample if resample is not None else self.resample
													_snake_case						:					Dict    =   do_rescale if do_rescale is not None else self.do_rescale
													_snake_case						:					str    =   rescale_factor if rescale_factor is not None else self.rescale_factor
													_snake_case						:					Optional[int]    =   do_center_crop if do_center_crop is not None else self.do_center_crop
													_snake_case						:					List[str]    =   (
													    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
													)

													_snake_case						:					Optional[int]    =   size if size is not None else self.size
													_snake_case						:					Optional[int]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													_snake_case						:					Optional[int]    =   crop_size if crop_size is not None else self.crop_size
													_snake_case						:					Union[str, Any]    =   get_size_dict(lowercase_				,    param_name="crop_size"    )

													_snake_case						:					Tuple    =   make_list_of_images(lowercase_    )

													if not valid_images(lowercase_    ):
																			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:
																			raise ValueError("Size 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_center_crop and crop_size is None:
																			raise ValueError("Crop size must be specified if do_center_crop is True."    )

													# All transformations expect numpy arrays.
													_snake_case						:					Tuple    =   [to_numpy_array(lowercase_    ) for image in images]

													if do_resize:
																			_snake_case						:					int    =   [self.resize(image=lowercase_				,    size=lowercase_				,    resample=lowercase_    ) for image in images]

													if do_center_crop:
																			_snake_case						:					List[Any]    =   [self.center_crop(image=lowercase_				,    size=lowercase_    ) for image in images]

													if do_rescale:
																			_snake_case						:					Optional[int]    =   [self.rescale(image=lowercase_				,    scale=lowercase_    ) for image in images]

													# the pretrained checkpoints assume images are BGR, not RGB
													if do_flip_channel_order:
																			_snake_case						:					int    =   [self.flip_channel_order(image=lowercase_    ) for image in images]

													_snake_case						:					str    =   [to_channel_dimension_format(lowercase_				,    lowercase_    ) for image in images]

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



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													_snake_case						:					int    =   outputs.logits

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

																			if is_torch_tensor(lowercase_    ):
																									_snake_case						:					Union[str, Any]    =   target_sizes.numpy()

																			_snake_case						:					int    =   []

																			for idx in range(len(lowercase_    )    ):
																									_snake_case						:					Optional[Any]    =   torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0    )				,    size=target_sizes[idx]				,    mode="bilinear"				,    align_corners=lowercase_    )
																									_snake_case						:					List[str]    =   resized_logits[0].argmax(dim=0    )
																									semantic_segmentation.append(lowercase_    )
													else:
																			_snake_case						:					List[Any]    =   logits.argmax(dim=1    )
																			_snake_case						:					Optional[Any]    =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

													return semantic_segmentation