
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
snake_case_ :  Union[str, Any]     =					(720, 1_280)  # Height, Width
snake_case_ :  Optional[int]     =					(0.4, 0.6)  # if height or width lower than this scale, drop it.
snake_case_ :  Optional[Any]     =					1 / 100
snake_case_ :  str     =					''
snake_case_ :  List[Any]     =					''
snake_case_ :  Optional[Any]     =					''
snake_case_ :  List[str]     =					250



def  __snake_case     (    ):
   UpperCamelCase     , UpperCamelCase       =  get_dataset(_UpperCAmelCase,      _UpperCAmelCase)
   for index in range(_UpperCAmelCase):
      UpperCamelCase       =  random.sample(range(len(_UpperCAmelCase)),      4)
      UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  update_image_and_anno(
          _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      filter_scale=_UpperCAmelCase,      )

      # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
      UpperCamelCase       =  random_chars(32)
      UpperCamelCase       =  path.split(os.sep)[-1].rsplit('''.''',      1)[0]
      UpperCamelCase       =  f'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
      cva.imwrite(f'{file_root}.jpg',      _UpperCAmelCase,      [cva.IMWRITE_JPEG_QUALITY, 85])
      print(f'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}')
      UpperCamelCase       =  []
      for anno in new_annos:
         UpperCamelCase       =  anno[3] - anno[1]
         UpperCamelCase       =  anno[4] - anno[2]
         UpperCamelCase       =  anno[1] + width / 2
         UpperCamelCase       =  anno[2] + height / 2
         UpperCamelCase       =  f'{anno[0]} {x_center} {y_center} {width} {height}'
         annos_list.append(_UpperCAmelCase)
      with open(f'{file_root}.txt',      '''w''') as outfile:
         outfile.write('''\n'''.join(line for line in annos_list))



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str):
   UpperCamelCase       =  []
   UpperCamelCase       =  []
   for label_file in glob.glob(os.path.join(_UpperCAmelCase,      '''*.txt''')):
      UpperCamelCase       =  label_file.split(os.sep)[-1].rsplit('''.''',      1)[0]
      with open(_UpperCAmelCase) as in_file:
         UpperCamelCase       =  in_file.readlines()
      UpperCamelCase       =  os.path.join(_UpperCAmelCase,      f'{label_name}.jpg')

      UpperCamelCase       =  []
      for obj_list in obj_lists:
         UpperCamelCase       =  obj_list.rstrip('''\n''').split(''' ''')
         UpperCamelCase       =  float(obj[1]) - float(obj[3]) / 2
         UpperCamelCase       =  float(obj[2]) - float(obj[4]) / 2
         UpperCamelCase       =  float(obj[1]) + float(obj[3]) / 2
         UpperCamelCase       =  float(obj[2]) + float(obj[4]) / 2

         boxes.append([int(obj[0]), xmin, ymin, xmax, ymax])
      if not boxes:
         continue
      img_paths.append(_UpperCAmelCase)
      labels.append(_UpperCAmelCase)
   return img_paths, labels



def  __snake_case     (    _UpperCAmelCase					:       list,      _UpperCAmelCase					:       list,      _UpperCAmelCase					:       list[int],      _UpperCAmelCase					:       tuple[int, int],      _UpperCAmelCase					:       tuple[float, float],      _UpperCAmelCase					:       float = 0.0,      ):
   UpperCamelCase       =  np.zeros([output_size[0], output_size[1], 3],      dtype=np.uinta)
   UpperCamelCase       =  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   UpperCamelCase       =  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   UpperCamelCase       =  int(scale_x * output_size[1])
   UpperCamelCase       =  int(scale_y * output_size[0])

   UpperCamelCase       =  []
   UpperCamelCase       =  []
   for i, index in enumerate(_UpperCAmelCase):
      UpperCamelCase       =  all_img_list[index]
      path_list.append(_UpperCAmelCase)
      UpperCamelCase       =  all_annos[index]
      UpperCamelCase       =  cva.imread(_UpperCAmelCase)
      if i == 0:  # top-left
         UpperCamelCase       =  cva.resize(_UpperCAmelCase,      (divid_point_x, divid_point_y))
         UpperCamelCase       =  img
         for bbox in img_annos:
            UpperCamelCase       =  bbox[1] * scale_x
            UpperCamelCase       =  bbox[2] * scale_y
            UpperCamelCase       =  bbox[3] * scale_x
            UpperCamelCase       =  bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
      elif i == 1:  # top-right
         UpperCamelCase       =  cva.resize(_UpperCAmelCase,      (output_size[1] - divid_point_x, divid_point_y))
         UpperCamelCase       =  img
         for bbox in img_annos:
            UpperCamelCase       =  scale_x + bbox[1] * (1 - scale_x)
            UpperCamelCase       =  bbox[2] * scale_y
            UpperCamelCase       =  scale_x + bbox[3] * (1 - scale_x)
            UpperCamelCase       =  bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
      elif i == 2:  # bottom-left
         UpperCamelCase       =  cva.resize(_UpperCAmelCase,      (divid_point_x, output_size[0] - divid_point_y))
         UpperCamelCase       =  img
         for bbox in img_annos:
            UpperCamelCase       =  bbox[1] * scale_x
            UpperCamelCase       =  scale_y + bbox[2] * (1 - scale_y)
            UpperCamelCase       =  bbox[3] * scale_x
            UpperCamelCase       =  scale_y + bbox[4] * (1 - scale_y)
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
      else:  # bottom-right
         UpperCamelCase       =  cva.resize(
             _UpperCAmelCase,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y))
         UpperCamelCase       =  img
         for bbox in img_annos:
            UpperCamelCase       =  scale_x + bbox[1] * (1 - scale_x)
            UpperCamelCase       =  scale_y + bbox[2] * (1 - scale_y)
            UpperCamelCase       =  scale_x + bbox[3] * (1 - scale_x)
            UpperCamelCase       =  scale_y + bbox[4] * (1 - scale_y)
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax])

    # Remove bounding box small than scale of filter
   if filter_scale > 0:
      UpperCamelCase       =  [
          anno
          for anno in new_anno
          if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
      ]

   return output_img, new_anno, path_list[0]



def  __snake_case     (    _UpperCAmelCase					:       int):
   assert number_char > 1, "The number of character should greater than 1"
   UpperCamelCase       =  ascii_lowercase + digits
   return "".join(random.choice(_UpperCAmelCase) for _ in range(_UpperCAmelCase))


if __name__ == "__main__":
   main()
   print('DONE ✅')







'''simple docstring'''


from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
   import PIL

# soft dependency
if is_pytesseract_available():
   import pytesseract

snake_case_ :  Tuple     =					logging.get_logger(__name__)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[int]):
   return [
       int(1000 * (box[0] / width)),
       int(1000 * (box[1] / height)),
       int(1000 * (box[2] / width)),
       int(1000 * (box[3] / height)),
   ]



def  __snake_case     (    _UpperCAmelCase					:       np.ndarray,      _UpperCAmelCase					:       Optional[str],      _UpperCAmelCase					:       Optional[str]):
   UpperCamelCase       =  to_pil_image(_UpperCAmelCase)
   UpperCamelCase     , UpperCamelCase       =  pil_image.size
   UpperCamelCase       =  pytesseract.image_to_data(_UpperCAmelCase,      lang=_UpperCAmelCase,      output_type='''dict''',      config=_UpperCAmelCase)
   UpperCamelCase     , UpperCamelCase     , UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

   # filter empty words and corresponding coordinates
   UpperCamelCase       =  [idx for idx, word in enumerate(_UpperCAmelCase) if not word.strip()]
   UpperCamelCase       =  [word for idx, word in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]

   # turn coordinates into (left, top, left+width, top+height) format
   UpperCamelCase       =  []
   for x, y, w, h in zip(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase):
      UpperCamelCase       =  [x, y, x + w, y + h]
      actual_boxes.append(_UpperCAmelCase)

   # finally, normalize the bounding boxes
   UpperCamelCase       =  []
   for box in actual_boxes:
      normalized_boxes.append(normalize_box(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase))

   assert len(_UpperCAmelCase) == len(_UpperCAmelCase), "Not as many words as there are bounding boxes"

   return words, normalized_boxes


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''pixel_values''']







   def __init__(			self ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = PILImageResampling.BILINEAR ,   lowerCamelCase__ = True ,   lowerCamelCase__ = 1 / 2_5_5 ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "" ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      super().__init__(**lowerCamelCase__   )
      UpperCamelCase       =  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
      UpperCamelCase       =  get_size_dict(lowerCamelCase__   )

      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size
      UpperCamelCase       =  resample
      UpperCamelCase       =  do_rescale
      UpperCamelCase       =  rescale_value
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCamelCase       =  image_std if image_std is not None else IMAGENET_STANDARD_STD
      UpperCamelCase       =  apply_ocr
      UpperCamelCase       =  ocr_lang
      UpperCamelCase       =  tesseract_config







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = PILImageResampling.BILINEAR ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  get_size_dict(lowerCamelCase__   )
      if "height" not in size or "width" not in size:
         raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'   )
      UpperCamelCase       =  (size['''height'''], size['''width'''])
      return resize(lowerCamelCase__ ,   size=lowerCamelCase__ ,   resample=lowerCamelCase__ ,   data_format=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      return rescale(lowerCamelCase__ ,   scale=lowerCamelCase__ ,   data_format=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      return normalize(lowerCamelCase__ ,   mean=lowerCamelCase__ ,   std=lowerCamelCase__ ,   data_format=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__=None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = ChannelDimension.FIRST ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase       =  size if size is not None else self.size
      UpperCamelCase       =  get_size_dict(lowerCamelCase__   )
      UpperCamelCase       =  resample if resample is not None else self.resample
      UpperCamelCase       =  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase       =  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase       =  do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase       =  image_mean if image_mean is not None else self.image_mean
      UpperCamelCase       =  image_std if image_std is not None else self.image_std
      UpperCamelCase       =  apply_ocr if apply_ocr is not None else self.apply_ocr
      UpperCamelCase       =  ocr_lang if ocr_lang is not None else self.ocr_lang
      UpperCamelCase       =  tesseract_config if tesseract_config is not None else self.tesseract_config

      UpperCamelCase       =  make_list_of_images(lowerCamelCase__   )

      if not valid_images(lowerCamelCase__   ):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

      if do_resize and size is None:
         raise ValueError('''Size must be specified if do_resize is True.'''   )

      if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

      if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''   )

      # All transformations expect numpy arrays.
      UpperCamelCase       =  [to_numpy_array(lowerCamelCase__   ) for image in images]

      # Tesseract OCR to get words + normalized bounding boxes
      if apply_ocr:
         requires_backends(self ,   '''pytesseract'''   )
         UpperCamelCase       =  []
         UpperCamelCase       =  []
         for image in images:
            UpperCamelCase     , UpperCamelCase       =  apply_tesseract(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
            words_batch.append(lowerCamelCase__   )
            boxes_batch.append(lowerCamelCase__   )

      if do_resize:
         UpperCamelCase       =  [self.resize(image=lowerCamelCase__ ,   size=lowerCamelCase__ ,   resample=lowerCamelCase__   ) for image in images]

      if do_rescale:
         UpperCamelCase       =  [self.rescale(image=lowerCamelCase__ ,   scale=lowerCamelCase__   ) for image in images]

      if do_normalize:
         UpperCamelCase       =  [self.normalize(image=lowerCamelCase__ ,   mean=lowerCamelCase__ ,   std=lowerCamelCase__   ) for image in images]

      UpperCamelCase       =  [to_channel_dimension_format(lowerCamelCase__ ,   lowerCamelCase__   ) for image in images]

      UpperCamelCase       =  BatchFeature(data={'''pixel_values''': images} ,   tensor_type=lowerCamelCase__   )

      if apply_ocr:
         UpperCamelCase       =  words_batch
         UpperCamelCase       =  boxes_batch
      return data





