




"""simple docstring"""






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							_lowerCamelCase  :    Tuple					=					''
							_lowerCamelCase  :    Any					=					''
							_lowerCamelCase  :    List[Any]					=					[]
							_lowerCamelCase  :    Tuple					=					0
							_lowerCamelCase  :    Dict					=					256
							_lowerCamelCase  :    Optional[Any]					=					0
							_lowerCamelCase  :    List[Any]					=					0
							_lowerCamelCase  :    int					=					0
							_lowerCamelCase  :    Dict					=					0
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					cva.imread(lowercase						,   0						)
							_lowerCamelCase  :    Optional[int]					=					copy.deepcopy(self.img						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					plt.hist(self.img.ravel()						,   256						,   [0, 256]						,   label='x'						)
							_lowerCamelCase  :    Optional[int]					=					np.sum(lowercase						)
							for i in range(len(lowercase						)						):
											_lowerCamelCase  :    List[Any]					=					x[i] / self.k
											self.sk += prk
											_lowerCamelCase  :    int					=					(self.L - 1) * self.sk
											if self.rem != 0:
															_lowerCamelCase  :    Union[str, Any]					=					int(last % last						)
											_lowerCamelCase  :    int					=					int(last + 1 if self.rem >= 0.5 else last						)
											self.last_list.append(lowercase						)
											_lowerCamelCase  :    List[str]					=					int(np.ma.count(self.img						) / self.img[1].size						)
											_lowerCamelCase  :    int					=					self.img[1].size
							for i in range(self.number_of_cols						):
											for j in range(self.number_of_rows						):
															_lowerCamelCase  :    Optional[Any]					=					self.img[j][i]
															if num != self.last_list[num]:
																			_lowerCamelCase  :    Any					=					self.last_list[num]
							cva.imwrite('output_data/output.jpg'						,   self.img						)
			def       A_					(						self						):
							plt.hist(self.img.ravel()						,   256						,   [0, 256]						)






			def       A_					(						self						):
							cva.imshow('Output-Image'						,   self.img						)
							cva.imshow('Input-Image'						,   self.original_image						)
							cva.waitKey(5000						)
							cva.destroyAllWindows()


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()





"""simple docstring"""






from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
			import torch

if is_tf_available():
			import tensorflow as tf






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    ["""image_processor"""]
			lowerCamelCase__   =    """SamImageProcessor"""
			def __init__(						self						,   lowercase						):
							super().__init__(lowercase						)
							_lowerCamelCase  :    str					=					self.image_processor
							_lowerCamelCase  :    Optional[int]					=					-10
							_lowerCamelCase  :    Optional[Any]					=					self.image_processor.size['longest_edge']
			def __call__(						self						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    int					=					self.image_processor(
							    lowercase						,   return_tensors=lowercase						,   **lowercase						,   )

							# pop arguments that are not used in the foward but used nevertheless
							_lowerCamelCase  :    int					=					encoding_image_processor['original_sizes']

							if hasattr(lowercase						,   'numpy'						):  # Checks if Torch or TF tensor
											_lowerCamelCase  :    Tuple					=					original_sizes.numpy()

							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					self._check_and_preprocess_points(
							    input_points=lowercase						,   input_labels=lowercase						,   input_boxes=lowercase						,   )

							_lowerCamelCase  :    Tuple					=					self._normalize_and_convert(
							    lowercase						,   lowercase						,   input_points=lowercase						,   input_labels=lowercase						,   input_boxes=lowercase						,   return_tensors=lowercase						,   )

							return encoding_image_processor
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase="pt"						,   ):
							if input_points is not None:
											if len(lowercase						) != len(lowercase						):
															_lowerCamelCase  :    str					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   original_sizes[0]						) for point in input_points
															]
											else:
															_lowerCamelCase  :    Optional[Any]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   lowercase						)
															    for point, original_size in zip(lowercase						,   lowercase						)
															]
											# check that all arrays have the same shape
											if not all(point.shape == input_points[0].shape for point in input_points						):
															if input_labels is not None:
																			_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self._pad_points_and_labels(lowercase						,   lowercase						)

											_lowerCamelCase  :    Optional[int]					=					np.array(lowercase						)

							if input_labels is not None:
											_lowerCamelCase  :    List[Any]					=					np.array(lowercase						)

							if input_boxes is not None:
											if len(lowercase						) != len(lowercase						):
															_lowerCamelCase  :    Optional[int]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   original_sizes[0]						,   is_bounding_box=lowercase						)
															    for box in input_boxes
															]
											else:
															_lowerCamelCase  :    Optional[int]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   lowercase						,   is_bounding_box=lowercase						)
															    for box, original_size in zip(lowercase						,   lowercase						)
															]
											_lowerCamelCase  :    Any					=					np.array(lowercase						)

							if input_boxes is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    List[str]					=					torch.from_numpy(lowercase						)
															# boxes batch size of 1 by default
															_lowerCamelCase  :    str					=					input_boxes.unsqueeze(1						) if len(input_boxes.shape						) != 3 else input_boxes
											elif return_tensors == "tf":
															_lowerCamelCase  :    int					=					tf.convert_to_tensor(lowercase						)
															# boxes batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					tf.expand_dims(lowercase						,   1						) if len(input_boxes.shape						) != 3 else input_boxes
											encoding_image_processor.update({'input_boxes': input_boxes}						)
							if input_points is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    Optional[Any]					=					torch.from_numpy(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					input_points.unsqueeze(1						) if len(input_points.shape						) != 4 else input_points
											elif return_tensors == "tf":
															_lowerCamelCase  :    List[str]					=					tf.convert_to_tensor(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					tf.expand_dims(lowercase						,   1						) if len(input_points.shape						) != 4 else input_points
											encoding_image_processor.update({'input_points': input_points}						)
							if input_labels is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    Union[str, Any]					=					torch.from_numpy(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Optional[Any]					=					input_labels.unsqueeze(1						) if len(input_labels.shape						) != 3 else input_labels
											elif return_tensors == "tf":
															_lowerCamelCase  :    Dict					=					tf.convert_to_tensor(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Any					=					tf.expand_dims(lowercase						,   1						) if len(input_labels.shape						) != 3 else input_labels
											encoding_image_processor.update({'input_labels': input_labels}						)

							return encoding_image_processor
			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    Optional[int]					=					max([point.shape[0] for point in input_points]						)
							_lowerCamelCase  :    Optional[int]					=					[]
							for i, point in enumerate(lowercase						):
											if point.shape[0] != expected_nb_points:
															_lowerCamelCase  :    Tuple					=					np.concatenate(
															    [point, np.zeros((expected_nb_points - point.shape[0], 2)						) + self.point_pad_value]						,   axis=0						)
															_lowerCamelCase  :    Optional[Any]					=					np.append(input_labels[i]						,   [self.point_pad_value]						)
											processed_input_points.append(lowercase						)
							_lowerCamelCase  :    Any					=					processed_input_points
							return input_points, input_labels
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase=False						):
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					original_size
							_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					self.image_processor._get_preprocess_shape(lowercase						,   longest_edge=lowercase						)
							_lowerCamelCase  :    Tuple					=					deepcopy(lowercase						).astype(lowercase						)

							if is_bounding_box:
											_lowerCamelCase  :    List[str]					=					coords.reshape(-1						,   2						,   2						)

							_lowerCamelCase  :    Optional[Any]					=					coords[..., 0] * (new_w / old_w)
							_lowerCamelCase  :    List[Any]					=					coords[..., 1] * (new_h / old_h)

							if is_bounding_box:
											_lowerCamelCase  :    Union[str, Any]					=					coords.reshape(-1						,   4						)

							return coords
			def       A_					(						self						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   ):
							if input_points is not None:
											if hasattr(lowercase						,   'numpy'						):  # Checks for TF or Torch tensor
															_lowerCamelCase  :    int					=					input_points.numpy().tolist()

											if not isinstance(lowercase						,   lowercase						) or not isinstance(input_points[0]						,   lowercase						):
															raise ValueError('Input points must be a list of list of floating points.'						)
											_lowerCamelCase  :    Optional[int]					=					[np.array(lowercase						) for input_point in input_points]
							else:
											_lowerCamelCase  :    Optional[Any]					=					None

							if input_labels is not None:
											if hasattr(lowercase						,   'numpy'						):
															_lowerCamelCase  :    Any					=					input_labels.numpy().tolist()

											if not isinstance(lowercase						,   lowercase						) or not isinstance(input_labels[0]						,   lowercase						):
															raise ValueError('Input labels must be a list of list integers.'						)
											_lowerCamelCase  :    List[str]					=					[np.array(lowercase						) for label in input_labels]
							else:
											_lowerCamelCase  :    List[str]					=					None

							if input_boxes is not None:
											if hasattr(lowercase						,   'numpy'						):
															_lowerCamelCase  :    Optional[int]					=					input_boxes.numpy().tolist()

											if (
											    not isinstance(lowercase						,   lowercase						)
											    or not isinstance(input_boxes[0]						,   lowercase						)
											    or not isinstance(input_boxes[0][0]						,   lowercase						)
											):
															raise ValueError('Input boxes must be a list of list of list of floating points.'						)
											_lowerCamelCase  :    int					=					[np.array(lowercase						).astype(np.floataa						) for box in input_boxes]
							else:
											_lowerCamelCase  :    str					=					None

							return input_points, input_labels, input_boxes
			@property
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.image_processor.model_input_names
							return list(dict.fromkeys(lowercase						)						)






			def       A_					(						self						,   *lowercase						,   **lowercase						):
							return self.image_processor.post_process_masks(*lowercase						,   **lowercase						)