






"""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
UpperCamelCase					=     False
UpperCamelCase					=     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
UpperCamelCase					=     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 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_=sys.argv[1:])			->   int:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     None
          lowercase__:							str         =     None
          lowercase__:							Optional[int]         =     None
          lowercase__:							Dict         =     1
          lowercase__    ,      lowercase__:							Optional[Any]         =     getopt.getopt(UpperCAmelCase_					, "i:o:b:s"					, ["inputdir=", "outfile=", "batch_size=", "subset_list="])
          for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                        lowercase__:							Union[str, Any]         =     arg
                 elif opt in ("-o", "--outfile"):
                        lowercase__:							Optional[Any]         =     arg
                 elif opt in ("-b", "--batch_size"):
                        lowercase__:							Any         =     int(UpperCAmelCase_)
                 elif opt in ("-s", "--subset_list"):
                        lowercase__:							str         =     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__:							Tuple         =     {self._vqa_file_split()[0] for x in tryload(UpperCAmelCase_)}
          else:
                 lowercase__:							Tuple         =     None

          lowercase__:							List[Any]         =     CONFIG
          if torch.cuda.is_available():
                 lowercase__:							str         =     "cuda"
          lowercase__:							Tuple         =     os.path.realpath(UpperCAmelCase_)
          lowercase__:							str         =     os.path.realpath(UpperCAmelCase_)
          lowercase__:							Any         =     Preprocess(self.config)
          lowercase__:							int         =     GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"					, config=self.config)
          lowercase__:							int         =     batch_size if batch_size != 0 else 1
          lowercase__:							Tuple         =     DEFAULT_SCHEMA





   def __lowercase							(       self					, UpperCAmelCase_)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							Any         =     int(file.split(".")[0].split("_")[-1])
          lowercase__:							int         =     os.path.join(self.inputdir					, UpperCAmelCase_)
          return (img_id, filepath)





   @property
   def __lowercase							(       self)			->   Optional[int]:




          '''simple docstring'''




          lowercase__:							int         =     []
          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__:							List[str]         =     batch
                        lowercase__:							Union[str, Any]         =     []
                        yield list(map(UpperCAmelCase_					, zip(*UpperCAmelCase_)))

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





   def __call__(       self)			->   str:




          '''simple docstring'''




          if not TEST:
                 lowercase__:							List[str]         =     datasets.ArrowWriter(features=self.schema					, path=self.outputfile)
          # do file generator
          for i, (img_ids, filepaths) in enumerate(self.file_generator):
                 lowercase__    ,      lowercase__    ,      lowercase__:							str         =     self.preprocess(UpperCAmelCase_)
                 lowercase__:							List[str]         =     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__:							List[Any]         =     output_dict.pop("normalized_boxes")
                 if not TEST:
                        lowercase__:							Tuple         =     np.array(UpperCAmelCase_)
                        lowercase__:							int         =     self.schema.encode_batch(UpperCAmelCase_)
                        writer.write_batch(UpperCAmelCase_)
                 if TEST:
                        break
                 # finalizer the writer
          if not TEST:
                 lowercase__    ,      lowercase__:							str         =     writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb""")







def 							A(    snake_case_						):
       """simple docstring"""





       try:
              lowercase__:							List[str]         =     json.load(snake_case_						)
              try:
                     lowercase__:							Optional[int]         =     list(data.keys()						)
              except Exception:
                     lowercase__:							List[str]         =     [d["img_id"] for d in data]
       except Exception:
              try:
                     lowercase__:							Optional[Any]         =     eval(stream.read()						)
              except Exception:
                     lowercase__:							Any         =     stream.read().split("\n"						)
       return data


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














"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like






def 							A(    snake_case_		,  snake_case_=3						):
       """simple docstring"""





       lowercase__:							Tuple         =     int(mask / 2						)
       # copy image size
       lowercase__:							str         =     zeros_like(snake_case_						)
       for i in range(snake_case_		,  gray_img.shape[0] - bd						):
              for j in range(snake_case_		,  gray_img.shape[1] - bd						):
                     # get mask according with mask
                     lowercase__:							Optional[int]         =     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]						)
                     # calculate mask median
                     lowercase__:							Union[str, Any]         =     sort(snake_case_						)[inta(divide((multiply(snake_case_		,  snake_case_						))		,  2						) + 1						)]
                     lowercase__:							Optional[Any]         =     median
       return median_img


if __name__ == "__main__":
  # read original image
  UpperCamelCase					=     imread("""../image_data/lena.jpg""")
  # turn image in gray scale value
  UpperCamelCase					=     cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  UpperCamelCase					=     median_filter(gray, 3)
  UpperCamelCase					=     median_filter(gray, 5)

  # show result images
  imshow("""median filter with 3x3 mask""", medianaxa)
  imshow("""median filter with 5x5 mask""", medianaxa)
  waitKey(0)






