




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

import cva
import numpy as np

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




def        _SCREAMING_SNAKE_CASE      (		)						->				None:
						_UpperCAmelCase    ,					_UpperCAmelCase      =			get_dataset(snake_case			,     snake_case       )
						for index in range(snake_case       ):
												_UpperCAmelCase      =			random.sample(range(len(snake_case       )       )			,     4       )
												_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			update_image_and_anno(
												    snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     filter_scale=snake_case			,     )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												_UpperCAmelCase      =			random_chars(3_2       )
												_UpperCAmelCase      =			path.split(os.sep       )[-1].rsplit("""."""			,     1       )[0]
												_UpperCAmelCase      =			f"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
												cva.imwrite(f"{file_root}.jpg"			,     snake_case			,     [cva.IMWRITE_JPEG_QUALITY, 8_5]       )
												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(snake_case       )
												with open(f"{file_root}.txt"			,     """w"""       ) as outfile:
																		outfile.write("""\n""".join(line for line in annos_list       )       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				tuple[list, list]:
						_UpperCAmelCase      =			[]
						_UpperCAmelCase      =			[]
						for label_file in glob.glob(os.path.join(snake_case			,     """*.txt"""       )       ):
												_UpperCAmelCase      =			label_file.split(os.sep       )[-1].rsplit("""."""			,     1       )[0]
												with open(snake_case       ) as in_file:
																		_UpperCAmelCase      =			in_file.readlines()
												_UpperCAmelCase      =			os.path.join(snake_case			,     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(snake_case       )
												labels.append(snake_case       )
						return img_paths, labels




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case = 0.0			,     )						->				tuple[list, list, str]:
						_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(snake_case       ):
												_UpperCAmelCase      =			all_img_list[index]
												path_list.append(snake_case       )
												_UpperCAmelCase      =			all_annos[index]
												_UpperCAmelCase      =			cva.imread(snake_case       )
												if i == 0:  # top-left
																		_UpperCAmelCase      =			cva.resize(snake_case			,     (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(snake_case			,     (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(snake_case			,     (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(
																		    snake_case			,     (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        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						assert number_char > 1, "The number of character should greater than 1"
						_UpperCAmelCase      =			ascii_lowercase + digits
						return "".join(random.choice(snake_case       ) for _ in range(snake_case       )       )


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





from typing import Union

import fire
import torch
from tqdm import tqdm




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case = "cpu"			,     snake_case = None       )						->				None:
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location=snake_case       )
						for k, v in tqdm(state_dict.items()       ):
												if not isinstance(snake_case			,     torch.Tensor       ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""       )
												_UpperCAmelCase      =			v.half()
						if save_path is None:  # overwrite src_path
												_UpperCAmelCase      =			src_path
						torch.save(snake_case			,     snake_case       )


if __name__ == "__main__":
				fire.Fire(convert)