

'''simple docstring'''







from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__lowerCamelCase		       =    logging.get_logger(__name__)


@add_end_docstrings(_snake_case			)
class 					A__      (      _snake_case			):



    def __init__(					self		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__       )
           requires_backends(self		,				"""vision"""       )
           requires_backends(self		,				"""torch"""       )

           if self.framework != "pt":
                  raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''       )

           self.check_model_type(UpperCamelCase__       )



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      {}
           A_						=      {}
           A_						=      {}
           # preprocess args
           if "points_per_batch" in kwargs:
                  A_						=      kwargs["""points_per_batch"""]
           if "points_per_crop" in kwargs:
                  A_						=      kwargs["""points_per_crop"""]
           if "crops_n_layers" in kwargs:
                  A_						=      kwargs["""crops_n_layers"""]
           if "crop_overlap_ratio" in kwargs:
                  A_						=      kwargs["""crop_overlap_ratio"""]
           if "crop_n_points_downscale_factor" in kwargs:
                  A_						=      kwargs["""crop_n_points_downscale_factor"""]
           # postprocess args
           if "pred_iou_thresh" in kwargs:
                  A_						=      kwargs["""pred_iou_thresh"""]
           if "stability_score_offset" in kwargs:
                  A_						=      kwargs["""stability_score_offset"""]
           if "mask_threshold" in kwargs:
                  A_						=      kwargs["""mask_threshold"""]
           if "stability_score_thresh" in kwargs:
                  A_						=      kwargs["""stability_score_thresh"""]
           if "crops_nms_thresh" in kwargs:
                  A_						=      kwargs["""crops_nms_thresh"""]
           if "output_rle_mask" in kwargs:
                  A_						=      kwargs["""output_rle_mask"""]
           if "output_bboxes_mask" in kwargs:
                  A_						=      kwargs["""output_bboxes_mask"""]
           return preprocess_kwargs, forward_params, postprocess_kwargs



    def __call__(					self		,				UpperCamelCase__		,				*UpperCamelCase__		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				**UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           return super().__call__(UpperCamelCase__		,				*UpperCamelCase__		,				num_workers=UpperCamelCase__		,				batch_size=UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=64		,				UpperCamelCase__ = 0		,				UpperCamelCase__ = 512 / 1500		,				UpperCamelCase__ = 32		,				UpperCamelCase__ = 1		,				)						->      List[Any]:





           '''simple docstring'''
           A_						=      load_image(UpperCamelCase__       )
           A_						=      self.image_processor.size["""longest_edge"""]
           A_					,			A_					,			A_					,			A_						=      self.image_processor.generate_crop_boxes(
               UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )
           A_						=      self.image_processor(images=UpperCamelCase__		,				return_tensors="""pt"""       )

           with self.device_placement():
                  if self.framework == "pt":
                         A_						=      self.get_inference_context()
                         with inference_context():
                                A_						=      self._ensure_tensor_on_device(UpperCamelCase__		,				device=self.device       )
                                A_						=      self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""       )       )
                                A_						=      image_embeddings

           A_						=      grid_points.shape[1]
           A_						=      points_per_batch if points_per_batch is not None else n_points

           if points_per_batch <= 0:
                  raise ValueError(
                      """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
                      """To return all points at once, set points_per_batch to None"""       )

           for i in range(0		,				UpperCamelCase__		,				UpperCamelCase__       ):
                  A_						=      grid_points[:, i : i + points_per_batch, :, :]
                  A_						=      input_labels[:, i : i + points_per_batch]
                  A_						=      i == n_points - points_per_batch
                  yield {
                      "input_points": batched_points,
                      "input_labels": labels,
                      "input_boxes": crop_boxes,
                      "is_last": is_last,
                      **model_inputs,
                  }



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=0.88		,				UpperCamelCase__=0.95		,				UpperCamelCase__=0		,				UpperCamelCase__=1		,				)						->      Optional[Any]:





           '''simple docstring'''
           A_						=      model_inputs.pop("""input_boxes"""       )
           A_						=      model_inputs.pop("""is_last"""       )
           A_						=      model_inputs.pop("""original_sizes"""       ).tolist()
           A_						=      model_inputs.pop("""reshaped_input_sizes"""       ).tolist()

           A_						=      self.model(**UpperCamelCase__       )

           # post processing happens here in order to avoid CPU GPU copies of ALL the masks
           A_						=      model_outputs["""pred_masks"""]
           A_						=      self.image_processor.post_process_masks(
               UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				binarize=UpperCamelCase__       )
           A_						=      model_outputs["""iou_scores"""]
           A_					,			A_					,			A_						=      self.image_processor.filter_masks(
               masks[0]		,				iou_scores[0]		,				original_sizes[0]		,				input_boxes[0]		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				)
           return {
               "masks": masks,
               "is_last": is_last,
               "boxes": boxes,
               "iou_scores": iou_scores,
           }







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=False		,				UpperCamelCase__=False		,				UpperCamelCase__=0.7		,				)						->      str:





           '''simple docstring'''
           A_						=      []
           A_						=      []
           A_						=      []
           for model_output in model_outputs:
                  all_scores.append(model_output.pop("""iou_scores"""       )       )
                  all_masks.extend(model_output.pop("""masks"""       )       )
                  all_boxes.append(model_output.pop("""boxes"""       )       )

           A_						=      torch.cat(UpperCamelCase__       )
           A_						=      torch.cat(UpperCamelCase__       )
           A_					,			A_					,			A_					,			A_						=      self.image_processor.post_process_for_mask_generation(
               UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )

           A_						=      defaultdict(UpperCamelCase__       )
           for output in model_outputs:
                  for k, v in output.items():
                         extra[k].append(UpperCamelCase__       )

           A_						=      {}
           if output_rle_mask:
                  A_						=      rle_mask

           if output_bboxes_mask:
                  A_						=      bounding_boxes

           return {"masks": output_masks, "scores": iou_scores, **optional, **extra}



'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
              # Work around assertion for response
              assert str(cc.change_contrast(UpperCAmelCase__,       1_10				)				).startswith(
                  """<PIL.Image.Image image mode=RGB size=100x100 at"""				)
def       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

       # Reading the image and converting it to grayscale.
       A_						=      imread(UpperCAmelCase__,       0				)

       # Test for get_neighbors_pixel function() return not None
       A_						=      0
       A_						=      0
       A_						=      image[x_coordinate][y_coordinate]

       A_						=      lbp.get_neighbors_pixel(
           UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert neighbors_pixels is not None

       # Test for local_binary_pattern function()
       # Create a numpy array as the same height and width of read image
       A_						=      np.zeros((image.shape[0], image.shape[1])				)

       # Iterating through the image and calculating the local binary pattern value
       # for each pixel.
       for i in range(0,       image.shape[0]				):
              for j in range(0,       image.shape[1]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
