




import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
a_										=       False
a_										=       Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a_										=       datasets.Features(
    OrderedDict(
        {
            "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
            "img_id": datasets.Value("int32"),
            "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)

class 	_lowerCamelCase  :

      """simple docstring"""

      def __init__(       self   :	Dict	,    snake_case   :	int=sys.argv[1:]			):
          __UpperCamelCase											=  None
          __UpperCamelCase											=  None
          __UpperCamelCase											=  None
          __UpperCamelCase											=  1
          __UpperCamelCase						, __UpperCamelCase											=  getopt.getopt(snake_case	,    '''i:o:b:s'''	,    ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']			)
          for opt, arg in opts:
              if opt in ("-i", "--inputdir"):
                  __UpperCamelCase											=  arg
              elif opt in ("-o", "--outfile"):
                  __UpperCamelCase											=  arg
              elif opt in ("-b", "--batch_size"):
                  __UpperCamelCase											=  int(snake_case			)
              elif opt in ("-s", "--subset_list"):
                  __UpperCamelCase											=  arg

          assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
          assert outputfile is not None and not os.path.isfile(snake_case			), F"{outputfile}"
          if subset_list is not None:
              with open(os.path.realpath(snake_case			)			) as f:
                  __UpperCamelCase											=  {self._vqa_file_split()[0] for x in tryload(snake_case			)}
          else:
              __UpperCamelCase											=  None

          __UpperCamelCase											=  CONFIG
          if torch.cuda.is_available():
              __UpperCamelCase											=  '''cuda'''
          __UpperCamelCase											=  os.path.realpath(snake_case			)
          __UpperCamelCase											=  os.path.realpath(snake_case			)
          __UpperCamelCase											=  Preprocess(self.config			)
          __UpperCamelCase											=  GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''	,    config=self.config			)
          __UpperCamelCase											=  batch_size if batch_size != 0 else 1
          __UpperCamelCase											=  DEFAULT_SCHEMA







      def  snake_case		(       self   :	Dict	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  int(file.split('''.'''			)[0].split('''_'''			)[-1]			)
          __UpperCamelCase											=  os.path.join(self.inputdir	,    snake_case			)
          return (img_id, filepath)







      @property
      def  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  []
          for i, file in enumerate(os.listdir(self.inputdir			)			):
              if self.subset_list is not None and i not in self.subset_list:
                  continue
              batch.append(self._vqa_file_split(snake_case			)			)
              if len(snake_case			) == self.batch:
                  __UpperCamelCase											=  batch
                  __UpperCamelCase											=  []
                  yield list(map(snake_case	,    zip(*snake_case			)			)			)

          for i in range(1			):
              yield list(map(snake_case	,    zip(*snake_case			)			)			)







      def __call__(       self   :	Optional[Any]			):
          # make writer
          if not TEST:
              __UpperCamelCase											=  datasets.ArrowWriter(features=self.schema	,    path=self.outputfile			)
          # do file generator
          for i, (img_ids, filepaths) in enumerate(self.file_generator			):
              __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  self.preprocess(snake_case			)
              __UpperCamelCase											=  self.model(
                  snake_case	,    snake_case	,    scales_yx=snake_case	,    padding='''max_detections'''	,    max_detections=self.config.MAX_DETECTIONS	,    pad_value=0	,    return_tensors='''np'''	,    location='''cpu'''	,    )
              __UpperCamelCase											=  output_dict.pop('''normalized_boxes'''			)
              if not TEST:
                  __UpperCamelCase											=  np.array(snake_case			)
                  __UpperCamelCase											=  self.schema.encode_batch(snake_case			)
                  writer.write_batch(snake_case			)
              if TEST:
                  break
              # finalizer the writer
          if not TEST:
              __UpperCamelCase						, __UpperCamelCase											=  writer.finalize()
              print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"			)







def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Optional[int]:

    """simple docstring"""


    try:
        __UpperCamelCase											=  json.load(lowercase_ )
        try:
            __UpperCamelCase											=  list(data.keys() )
        except Exception:
            __UpperCamelCase											=  [d['''img_id'''] for d in data]
    except Exception:
        try:
            __UpperCamelCase											=  eval(stream.read() )
        except Exception:
            __UpperCamelCase											=  stream.read().split('''\n''' )
    return data


if __name__ == "__main__":
 a_										=       Extract(sys.argv[1:])
 extract()
 if not TEST:
  a_										=       datasets.Dataset.from_file(extract.outputfile)
  # wala!
  # print(np.array(dataset[0:2]["roi_features"]).shape)












import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center )
    except (IndexError, TypeError):
        return 0




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [1, 2, 4, 8, 16, 32, 64, 1_28]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    __UpperCamelCase											=  [
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate - 1   ,       lowercase_ ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(lowercase_   ,       lowercase_ ) )


if __name__ == "__main__":
 # Reading the image and converting it to grayscale.
 a_										=       cva.imread(
     "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
 )

 # 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_										=       local_binary_value(image, i, j)

 cva.imshow("local binary pattern", lbp_image)
 cva.waitKey(0)
 cva.destroyAllWindows()






