



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

import cva
import numpy as np

# Parrameters
__lowerCamelCase						=       (7_2_0, 1_2_8_0)  # Height, Width
__lowerCamelCase						=       (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCamelCase						=       1 / 1_0_0
__lowerCamelCase						=       """"""
__lowerCamelCase						=       """"""
__lowerCamelCase						=       """"""
__lowerCamelCase						=       2_5_0




def      UpperCAmelCase__			(				) ->			None:
  _A     , _A							     =     get_dataset(SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__				)
  for index in range(SCREAMING_SNAKE_CASE__				):
    _A							     =     random.sample(range(len(SCREAMING_SNAKE_CASE__				)				)    ,					4				)
    _A     , _A     , _A							     =     update_image_and_anno(
        SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					filter_scale=SCREAMING_SNAKE_CASE__    ,					)

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




def      UpperCAmelCase__			(				__snake_case    ,					__snake_case				) ->			tuple[list, list]:
  _A							     =     []
  _A							     =     []
  for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE__    ,					'''*.txt'''				)				):
    _A							     =     label_file.split(os.sep				)[-1].rsplit('''.'''    ,					1				)[0]
    with open(SCREAMING_SNAKE_CASE__				) as in_file:
      _A							     =     in_file.readlines()
    _A							     =     os.path.join(SCREAMING_SNAKE_CASE__    ,					F'''{label_name}.jpg'''				)

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

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




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

  _A							     =     []
  _A							     =     []
  for i, index in enumerate(SCREAMING_SNAKE_CASE__				):
    _A							     =     all_img_list[index]
    path_list.append(SCREAMING_SNAKE_CASE__				)
    _A							     =     all_annos[index]
    _A							     =     cva.imread(SCREAMING_SNAKE_CASE__				)
    if i == 0:  # top-left
      _A							     =     cva.resize(SCREAMING_SNAKE_CASE__    ,					(divid_point_x, divid_point_y)				)
      _A							     =     img
      for bbox in img_annos:
        _A							     =     bbox[1] * scale_x
        _A							     =     bbox[2] * scale_y
        _A							     =     bbox[3] * scale_x
        _A							     =     bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
    elif i == 1:  # top-right
      _A							     =     cva.resize(SCREAMING_SNAKE_CASE__    ,					(output_size[1] - divid_point_x, divid_point_y)				)
      _A							     =     img
      for bbox in img_annos:
        _A							     =     scale_x + bbox[1] * (1 - scale_x)
        _A							     =     bbox[2] * scale_y
        _A							     =     scale_x + bbox[3] * (1 - scale_x)
        _A							     =     bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
    elif i == 2:  # bottom-left
      _A							     =     cva.resize(SCREAMING_SNAKE_CASE__    ,					(divid_point_x, output_size[0] - divid_point_y)				)
      _A							     =     img
      for bbox in img_annos:
        _A							     =     bbox[1] * scale_x
        _A							     =     scale_y + bbox[2] * (1 - scale_y)
        _A							     =     bbox[3] * scale_x
        _A							     =     scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
    else:  # bottom-right
      _A							     =     cva.resize(
          SCREAMING_SNAKE_CASE__    ,					(output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
      _A							     =     img
      for bbox in img_annos:
        _A							     =     scale_x + bbox[1] * (1 - scale_x)
        _A							     =     scale_y + bbox[2] * (1 - scale_y)
        _A							     =     scale_x + bbox[3] * (1 - scale_x)
        _A							     =     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:
    _A							     =     [
        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      UpperCAmelCase__			(				__snake_case				) ->			str:
  assert number_char > 1, "The number of character should greater than 1"
  _A							     =     ascii_lowercase + digits
  return "".join(random.choice(SCREAMING_SNAKE_CASE__				) for _ in range(SCREAMING_SNAKE_CASE__				)				)


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







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						PriorityQueue,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						float | int,       )	->      float | int:

   """simple docstring"""
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      __a					  =     cst_fwd.get(SCREAMING_SNAKE_CASE__,       np.inf     )
      __a					  =     cst_fwd[v] + d
      if new_cost_f < old_cost_f:
         queue.put((new_cost_f, nxt)     )
         __a					  =     new_cost_f
         __a					  =     v
      if nxt in visited_backward:
         if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
            __a					  =     cst_fwd[v] + d + cst_bwd[nxt]
   return shortest_distance



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict     )	->      int:

   """simple docstring"""
   __a					  =     -1

   __a					  =     set()
   __a					  =     set()
   __a					  =     {source: 0}
   __a					  =     {destination: 0}
   __a					  =     {source: None}
   __a					  =     {destination: None}
   __a					  =     PriorityQueue()
   __a					  =     PriorityQueue()

   __a					  =     np.inf

   queue_forward.put((0, source)     )
   queue_backward.put((0, destination)     )

   if source == destination:
      return 0

   while not queue_forward.empty() and not queue_backward.empty():
      __a  ,      __a					  =     queue_forward.get()
      visited_forward.add(SCREAMING_SNAKE_CASE__     )

      __a  ,      __a					  =     queue_backward.get()
      visited_backward.add(SCREAMING_SNAKE_CASE__     )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
         break

   if shortest_distance != np.inf:
      __a					  =     shortest_distance
   return shortest_path_distance


__UpperCamelCase   :							Dict         =   {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__UpperCamelCase   :							int         =   {
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()