




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

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					from ..tf_utils import stable_softmax

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCamelCase__          =		logging.get_logger(__name__)







@add_end_docstrings(__magic_name__	)
class      A__							(     __magic_name__	):






				def __init__(						self					:      int    ,     *a					:      Dict    ,     **a					:      Union[str, Any]   ):






							'''simple docstring'''







							super().__init__(*a    ,     **a   )
							requires_backends(self    ,     'vision'   )
							self.check_model_type(
							    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							    if self.framework == 'tf'
							    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]=None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{}
							if top_k is not None:
										lowerCAmelCase__      :      Tuple         =		top_k
							return {}, {}, postprocess_params






				def __call__(						self					:      Any    ,     a					:      Union[str, List[str], "Image.Image", List["Image.Image"]]    ,     **a					:      List[Any]   ):






							'''simple docstring'''







							return super().__call__(a    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		load_image(a   )
							lowerCAmelCase__      :      Optional[int]         =		self.image_processor(images=a    ,     return_tensors=self.framework   )
							return model_inputs






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.model(**a   )
							return model_outputs






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      List[Any]    ,     a					:      List[Any]=5   ):






							'''simple docstring'''







							if top_k > self.model.config.num_labels:
										lowerCAmelCase__      :      Optional[int]         =		self.model.config.num_labels

							if self.framework == "pt":
										lowerCAmelCase__      :      List[Any]         =		model_outputs.logits.softmax(-1   )[0]
										lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		probs.topk(a   )
							elif self.framework == "tf":
										lowerCAmelCase__      :      Any         =		stable_softmax(model_outputs.logits    ,     axis=-1   )[0]
										lowerCAmelCase__      :      Any         =		tf.math.top_k(a    ,     k=a   )
										lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		topk.values.numpy(), topk.indices.numpy()
							else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''   )

							lowerCAmelCase__      :      List[Any]         =		scores.tolist()
							lowerCAmelCase__      :      List[str]         =		ids.tolist()
							return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a    ,     a   )]