

"""simple docstring"""





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    Tuple,      UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    int			):
    __lowercase			=		0
    # max greyscale value for #FFFFFF
    __lowercase			=		int(self.get_greyscale(2_5_5,      2_5_5,      2_5_5			)			)

    if not self.min_threshold < threshold < self.max_threshold:
       __lowercase			=		F"""Factor value should be from 0 to {self.max_threshold}"""
       raise ValueError(UpperCAmelCase__			)

    __lowercase			=		input_img
    __lowercase			=		threshold
    __lowercase	,__lowercase			=		self.input_img.shape[1], self.input_img.shape[0]

    # error table size (+4 columns and +1 row) greater than input image because of
    # lack of if statements
    __lowercase			=		[
        [0 for _ in range(self.height + 4			)] for __ in range(self.width + 1			)
    ]
    __lowercase			=		np.ones((self.width, self.height, 3),      np.uinta			) * 2_5_5


 @classmethod
 def       _lowercase				(    cls							:    Dict,      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    int			):
    return 0.114 * blue + 0.587 * green + 0.2_126 * red



 def       _lowercase				(    self							:    List[str]			):
    for y in range(self.height			):
       for x in range(self.width			):
          __lowercase			=		int(self.get_greyscale(*self.input_img[y][x]			)			)
          if self.threshold > greyscale + self.error_table[y][x]:
             __lowercase			=		(0, 0, 0)
             __lowercase			=		greyscale + self.error_table[x][y]
          else:
             __lowercase			=		(2_5_5, 2_5_5, 2_5_5)
             __lowercase			=		greyscale + self.error_table[x][y] - 2_5_5
          self.error_table[y][x + 1] += int(8 / 3_2 * current_error			)
          self.error_table[y][x + 2] += int(4 / 3_2 * current_error			)
          self.error_table[y + 1][x] += int(8 / 3_2 * current_error			)
          self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error			)
          self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error			)
          self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error			)
          self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error			)


if __name__ == "__main__":
   # create Burke's instances with original images in greyscale
   _a				   =							[
       Burkes(imread('image_data/lena.jpg', 1), threshold)
       for threshold in (1, 1_26, 1_30, 1_40)
   ]

   for burkes in burkes_instances:
      burkes.process()

   for burkes in burkes_instances:
      imshow(
          F"Original image with dithering threshold: {burkes.threshold}",
          burkes.output_img,
      )

   waitKey(0)
   destroyAllWindows()







"""simple docstring"""





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, 20_48), 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							:    Optional[int],      UpperCAmelCase__							:    Optional[Any]=sys.argv[1:]			):
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		1
    __lowercase	,__lowercase			=		getopt.getopt(UpperCAmelCase__,      "i:o:b:s",      ["inputdir=", "outfile=", "batch_size=", "subset_list="]			)
    for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
          __lowercase			=		arg
       elif opt in ("-o", "--outfile"):
          __lowercase			=		arg
       elif opt in ("-b", "--batch_size"):
          __lowercase			=		int(UpperCAmelCase__			)
       elif opt in ("-s", "--subset_list"):
          __lowercase			=		arg

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

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


 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    List[Any]			):
    __lowercase			=		int(file.split("."			)[0].split("_"			)[-1]			)
    __lowercase			=		os.path.join(self.inputdir,      UpperCAmelCase__			)
    return (img_id, filepath)


 @property
 def       _lowercase				(    self							:    int			):
    __lowercase			=		[]
    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(UpperCAmelCase__			)			)
       if len(UpperCAmelCase__			) == self.batch:
          __lowercase			=		batch
          __lowercase			=		[]
          yield list(map(UpperCAmelCase__,      zip(*UpperCAmelCase__			)			)			)

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



 def __call__(    self							:    Optional[Any]			):
    # make writer
    if not TEST:
       __lowercase			=		datasets.ArrowWriter(features=self.schema,      path=self.outputfile			)
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator			):
       __lowercase	,__lowercase	,__lowercase			=		self.preprocess(UpperCAmelCase__			)
       __lowercase			=		self.model(
           UpperCAmelCase__,      UpperCAmelCase__,      scales_yx=UpperCAmelCase__,      padding="max_detections",      max_detections=self.config.MAX_DETECTIONS,      pad_value=0,      return_tensors="np",      location="cpu",      )
       __lowercase			=		output_dict.pop("normalized_boxes"			)
       if not TEST:
          __lowercase			=		np.array(UpperCAmelCase__			)
          __lowercase			=		self.schema.encode_batch(UpperCAmelCase__			)
          writer.write_batch(UpperCAmelCase__			)
       if TEST:
          break
       # finalizer the writer
    if not TEST:
       __lowercase	,__lowercase			=		writer.finalize()
       print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)







def 						_A						(						UpperCamelCase_  : int)			->		Optional[Any]:
   '''simple docstring'''


   try:
      __lowercase			=		json.load(UpperCamelCase_)
      try:
         __lowercase			=		list(data.keys())
      except Exception:
         __lowercase			=		[d["img_id"] for d in data]
   except Exception:
      try:
         __lowercase			=		eval(stream.read())
      except Exception:
         __lowercase			=		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)




