


"""simple docstring"""







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

import cva
import numpy as np

# Parrameters
__lowercase    					=      (720, 1280)  # Height, Width
__lowercase    					=      (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowercase    					=      1 / 100
__lowercase    					=      """"""
__lowercase    					=      """"""
__lowercase    					=      """"""
__lowercase    					=      250


def      lowercase			(				)->      None:
  '''simple docstring'''







  a							,		a						:		Optional[Any]      							=			get_dataset(A_  ,   A_       )
  for index in range(A_       ):
    a						:		Optional[int]      							=			random.sample(range(len(A_       )       )  ,   4       )
    a							,		a							,		a						:		str      							=			update_image_and_anno(
        A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   filter_scale=A_  ,   )

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


def      lowercase			(				A_  ,   A_       )->      tuple[list, list]:
  '''simple docstring'''







  a						:		Any      							=			[]
  a						:		Dict      							=			[]
  for label_file in glob.glob(os.path.join(A_  ,   "*.txt"       )       ):
    a						:		Union[str, Any]      							=			label_file.split(os.sep       )[-1].rsplit("."  ,   1       )[0]
    with open(A_       ) as in_file:
      a						:		Union[str, Any]      							=			in_file.readlines()
    a						:		Tuple      							=			os.path.join(A_  ,   F'''{label_name}.jpg'''       )

    a						:		Optional[Any]      							=			[]
    for obj_list in obj_lists:
      a						:		str      							=			obj_list.rstrip("\n"       ).split(" "       )
      a						:		str      							=			float(obj[1]       ) - float(obj[3]       ) / 2
      a						:		Optional[Any]      							=			float(obj[2]       ) - float(obj[4]       ) / 2
      a						:		Optional[int]      							=			float(obj[1]       ) + float(obj[3]       ) / 2
      a						:		Optional[Any]      							=			float(obj[2]       ) + float(obj[4]       ) / 2

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


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_ = 0.0  ,   )->      tuple[list, list, str]:
  '''simple docstring'''







  a						:		Union[str, Any]      							=			np.zeros([output_size[0], output_size[1], 3]  ,   dtype=np.uinta       )
  a						:		Dict      							=			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  a						:		List[Any]      							=			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  a						:		Union[str, Any]      							=			int(scale_x * output_size[1]       )
  a						:		Tuple      							=			int(scale_y * output_size[0]       )

  a						:		List[Any]      							=			[]
  a						:		int      							=			[]
  for i, index in enumerate(A_       ):
    a						:		Union[str, Any]      							=			all_img_list[index]
    path_list.append(A_       )
    a						:		List[str]      							=			all_annos[index]
    a						:		Union[str, Any]      							=			cva.imread(A_       )
    if i == 0:  # top-left
      a						:		Any      							=			cva.resize(A_  ,   (divid_point_x, divid_point_y)       )
      a						:		Tuple      							=			img
      for bbox in img_annos:
        a						:		List[Any]      							=			bbox[1] * scale_x
        a						:		Dict      							=			bbox[2] * scale_y
        a						:		List[str]      							=			bbox[3] * scale_x
        a						:		Any      							=			bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
    elif i == 1:  # top-right
      a						:		Any      							=			cva.resize(A_  ,   (output_size[1] - divid_point_x, divid_point_y)       )
      a						:		int      							=			img
      for bbox in img_annos:
        a						:		List[Any]      							=			scale_x + bbox[1] * (1 - scale_x)
        a						:		int      							=			bbox[2] * scale_y
        a						:		List[str]      							=			scale_x + bbox[3] * (1 - scale_x)
        a						:		Union[str, Any]      							=			bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
    elif i == 2:  # bottom-left
      a						:		Optional[Any]      							=			cva.resize(A_  ,   (divid_point_x, output_size[0] - divid_point_y)       )
      a						:		Optional[int]      							=			img
      for bbox in img_annos:
        a						:		Any      							=			bbox[1] * scale_x
        a						:		Tuple      							=			scale_y + bbox[2] * (1 - scale_y)
        a						:		List[Any]      							=			bbox[3] * scale_x
        a						:		Optional[Any]      							=			scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
    else:  # bottom-right
      a						:		int      							=			cva.resize(
          A_  ,   (output_size[1] - divid_point_x, output_size[0] - divid_point_y)       )
      a						:		List[Any]      							=			img
      for bbox in img_annos:
        a						:		Tuple      							=			scale_x + bbox[1] * (1 - scale_x)
        a						:		Dict      							=			scale_y + bbox[2] * (1 - scale_y)
        a						:		Tuple      							=			scale_x + bbox[3] * (1 - scale_x)
        a						:		List[str]      							=			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						:		Dict      							=			[
        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      lowercase			(				A_       )->      str:
  '''simple docstring'''







  assert number_char > 1, "The number of character should greater than 1"
  a						:		Optional[int]      							=			ascii_lowercase + digits
  return "".join(random.choice(A_       ) for _ in range(A_       )       )


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










"""simple docstring"""







from __future__ import annotations

from PIL import Image

# Define glider example
__lowercase    					=      [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__lowercase    					=      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def      lowercase			(				A_       )->      list[list[int]]:
  '''simple docstring'''







  a						:		str      							=			[]
  for i in range(len(A_       )       ):
    a						:		str      							=			[]
    for j in range(len(cells[i]       )       ):
      # Get the number of live neighbours
      a						:		Union[str, Any]      							=			0
      if i > 0 and j > 0:
        neighbour_count += cells[i - 1][j - 1]
      if i > 0:
        neighbour_count += cells[i - 1][j]
      if i > 0 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i - 1][j + 1]
      if j > 0:
        neighbour_count += cells[i][j - 1]
      if j < len(cells[i]       ) - 1:
        neighbour_count += cells[i][j + 1]
      if i < len(A_       ) - 1 and j > 0:
        neighbour_count += cells[i + 1][j - 1]
      if i < len(A_       ) - 1:
        neighbour_count += cells[i + 1][j]
      if i < len(A_       ) - 1 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i + 1][j + 1]

      # Rules of the game of life (excerpt from Wikipedia):
      # 1. Any live cell with two or three live neighbours survives.
      # 2. Any dead cell with three live neighbours becomes a live cell.
      # 3. All other live cells die in the next generation.
      #    Similarly, all other dead cells stay dead.
      a						:		Tuple      							=			cells[i][j] == 1
      if (
          (alive and 2 <= neighbour_count <= 3)
          or not alive
          and neighbour_count == 3
      ):
        next_generation_row.append(1       )
      else:
        next_generation_row.append(0       )

    next_generation.append(A_       )
  return next_generation


def      lowercase			(				A_  ,   A_       )->      list[Image.Image]:
  '''simple docstring'''







  a						:		List[str]      							=			[]
  for _ in range(A_       ):
    # Create output image
    a						:		str      							=			Image.new("RGB"  ,   (len(cells[0]       ), len(A_       ))       )
    a						:		Union[str, Any]      							=			img.load()

    # Save cells to image
    for x in range(len(A_       )       ):
      for y in range(len(cells[0]       )       ):
        a						:		Optional[Any]      							=			255 - cells[y][x] * 255
        a						:		str      							=			(colour, colour, colour)

        # Save image
    images.append(A_       )
    a						:		Tuple      							=			new_generation(A_       )
  return images


if __name__ == "__main__":
 __lowercase    					=      generate_images(GLIDER, 16)
 images[0].save("""out.gif""", save_all=True, append_images=images[1:])






