



"""simple docstring"""

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__   (		):



					"""simple docstring"""





					snake_case_       ,      snake_case_       :		Tuple			       =       get_dataset(snake_case__					,     snake_case__      )
					for index in range(snake_case__      ):
										snake_case_       :		Any			       =       random.sample(range(len(snake_case__      )      )					,     4      )
										snake_case_       ,      snake_case_       ,      snake_case_       :		Any			       =       update_image_and_anno(
										    snake_case__					,     snake_case__					,     snake_case__					,     snake_case__					,     snake_case__					,     filter_scale=snake_case__					,     )

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										snake_case_       :		int			       =       random_chars(3_2      )
										snake_case_       :		Any			       =       path.split(os.sep      )[-1].rsplit("""."""					,     1      )[0]
										snake_case_       :		List[str]			       =       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}'      )
										snake_case_       :		List[Any]			       =       []
										for anno in new_annos:
															snake_case_       :		Optional[int]			       =       anno[3] - anno[1]
															snake_case_       :		Optional[Any]			       =       anno[4] - anno[2]
															snake_case_       :		Any			       =       anno[1] + width / 2
															snake_case_       :		Optional[Any]			       =       anno[2] + height / 2
															snake_case_       :		List[str]			       =       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__   (		SCREAMING_SNAKE_CASE__			:	str					,     SCREAMING_SNAKE_CASE__			:	str      ):



					"""simple docstring"""





					snake_case_       :		List[Any]			       =       []
					snake_case_       :		int			       =       []
					for label_file in glob.glob(os.path.join(snake_case__					,     """*.txt"""      )      ):
										snake_case_       :		Any			       =       label_file.split(os.sep      )[-1].rsplit("""."""					,     1      )[0]
										with open(snake_case__      ) as in_file:
															snake_case_       :		List[str]			       =       in_file.readlines()
										snake_case_       :		Optional[Any]			       =       os.path.join(snake_case__					,     f'{label_name}.jpg'      )

										snake_case_       :		str			       =       []
										for obj_list in obj_lists:
															snake_case_       :		Optional[int]			       =       obj_list.rstrip("""\n"""      ).split(""" """      )
															snake_case_       :		str			       =       float(obj[1]      ) - float(obj[3]      ) / 2
															snake_case_       :		Any			       =       float(obj[2]      ) - float(obj[4]      ) / 2
															snake_case_       :		Optional[int]			       =       float(obj[1]      ) + float(obj[3]      ) / 2
															snake_case_       :		Dict			       =       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__   (		SCREAMING_SNAKE_CASE__			:	list					,     SCREAMING_SNAKE_CASE__			:	list					,     SCREAMING_SNAKE_CASE__			:	list[int]					,     SCREAMING_SNAKE_CASE__			:	tuple[int, int]					,     SCREAMING_SNAKE_CASE__			:	tuple[float, float]					,     SCREAMING_SNAKE_CASE__			:	float = 0.0					,     ):



					"""simple docstring"""





					snake_case_       :		int			       =       np.zeros([output_size[0], output_size[1], 3]					,     dtype=np.uinta      )
					snake_case_       :		int			       =       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					snake_case_       :		Optional[int]			       =       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					snake_case_       :		Tuple			       =       int(scale_x * output_size[1]      )
					snake_case_       :		Any			       =       int(scale_y * output_size[0]      )

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



					"""simple docstring"""





					assert number_char > 1, "The number of character should greater than 1"
					snake_case_       :		Any			       =       ascii_lowercase + digits
					return "".join(random.choice(snake_case__      ) for _ in range(snake_case__      )      )


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






import os

# Precomputes a list of the 100 first triangular numbers
__a         =   [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   os.path.dirname(os.path.realpath(snake_case__   )   )
				lowercase_	    =   os.path.join(snake_case__		,  '''words.txt'''   )

				lowercase_	    =   ''''''
				with open(snake_case__   ) as f:
								lowercase_	    =   f.readline()

				lowercase_	    =   [word.strip('''"'''   ) for word in words.strip('''\r\n'''   ).split(''','''   )]
				lowercase_	    =   [
				    word
				    for word in [sum(ord(snake_case__   ) - 64 for x in word   ) for word in words]
				    if word in TRIANGULAR_NUMBERS
				]
				return len(snake_case__   )


if __name__ == "__main__":
						print(solution())




