






import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												if dst_width < 0 or dst_height < 0:
																		raise ValueError('Destination width/height should be > 0'   )

												lowerCAmelCase__    						=						img
												lowerCAmelCase__    						=						img.shape[1]
												lowerCAmelCase__    						=						img.shape[0]
												lowerCAmelCase__    						=						dst_width
												lowerCAmelCase__    						=						dst_height

												lowerCAmelCase__    						=						self.src_w / self.dst_w
												lowerCAmelCase__    						=						self.src_h / self.dst_h

												lowerCAmelCase__    						=						lowerCAmelCase__    						=						(
												    np.ones((self.dst_h, self.dst_w, 3)     ,						np.uinta   ) * 2_55
												)






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for i in range(self.dst_h   ):
																		for j in range(self.dst_w   ):
																								lowerCAmelCase__    						=						self.img[self.get_y(_UpperCamelCase   )][self.get_x(_UpperCamelCase   )]






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return int(self.ratio_x * x   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return int(self.ratio_y * y   )


if __name__ == "__main__":
					__snake_case     , __snake_case    :    Dict         =     8_00, 6_00
					__snake_case    :    Tuple         =     imread("""image_data/lena.jpg""", 1)
					__snake_case    :    Tuple         =     NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
					)
					waitKey(0)
					destroyAllWindows()













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

import cva
import numpy as np

# Parrameters
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataset(UpperCamelCase_    ,       UpperCamelCase_		)
						for index in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						random.sample(range(len(UpperCamelCase_		)		)    ,       4		)
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						update_image_and_anno(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       filter_scale=UpperCamelCase_    ,       )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__    						=						random_chars(32		)
												lowerCAmelCase__    						=						path.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												lowerCAmelCase__    						=						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}"		)
												lowerCAmelCase__    						=						[]
												for anno in new_annos:
																		lowerCAmelCase__    						=						anno[3] - anno[1]
																		lowerCAmelCase__    						=						anno[4] - anno[2]
																		lowerCAmelCase__    						=						anno[1] + width / 2
																		lowerCAmelCase__    						=						anno[2] + height / 2
																		lowerCAmelCase__    						=						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 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   tuple[list, list]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for label_file in glob.glob(os.path.join(UpperCamelCase_    ,       '*.txt'		)		):
												lowerCAmelCase__    						=						label_file.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												with open(UpperCamelCase_		) as in_file:
																		lowerCAmelCase__    						=						in_file.readlines()
												lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       F"{label_name}.jpg"		)

												lowerCAmelCase__    						=						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__    						=						obj_list.rstrip('\n'		).split(' '		)
																		lowerCAmelCase__    						=						float(obj[1]		) - float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) - float(obj[4]		) / 2
																		lowerCAmelCase__    						=						float(obj[1]		) + float(obj[3]		) / 2
																		lowerCAmelCase__    						=						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 	_UpperCamelCase   (    UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					tuple[int, int]    ,       UpperCamelCase_       :					tuple[float, float]    ,       UpperCamelCase_       :					float = 0.0    ,       )  ->   tuple[list, list, str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros([output_size[0], output_size[1], 3]    ,       dtype=np.uinta		)
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						int(scale_x * output_size[1]		)
						lowerCAmelCase__    						=						int(scale_y * output_size[0]		)

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

						"""simple docstring"""



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


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





