






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

import cva
import numpy as np

# Parrameters
__lowerCamelCase :      Any										=				(720, 1280)  # Height, Width
__lowerCamelCase :      int										=				(0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCamelCase :      str										=				1 / 100
__lowerCamelCase :      List[Any]										=				""""""
__lowerCamelCase :      int										=				""""""
__lowerCamelCase :      List[str]										=				""""""
__lowerCamelCase :      Dict										=				250







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__,						snake_case__			:				Optional[int]       =  get_dataset(snake_case_					,  snake_case_       )
	for index in range(snake_case_       ):
		snake_case__			:				str       =  random.sample(range(len(snake_case_       )       )					,  4       )
		snake_case__,						snake_case__,						snake_case__			:				List[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(32       )
		snake_case__			:				List[str]       =  path.split(os.sep       )[-1].rsplit("."					,  1       )[0]
		snake_case__			:				int       =  F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
		cva.imwrite(F'''{file_root}.jpg'''					,  snake_case_					,  [cva.IMWRITE_JPEG_QUALITY, 85]       )
		print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''       )
		snake_case__			:				Tuple       =  []
		for anno in new_annos:
			snake_case__			:				Tuple       =  anno[3] - anno[1]
			snake_case__			:				Optional[Any]       =  anno[4] - anno[2]
			snake_case__			:				Any       =  anno[1] + width / 2
			snake_case__			:				Dict       =  anno[2] + height / 2
			snake_case__			:				List[Any]       =  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_ :							str					,  snake_case_ :							str       ):
	snake_case__			:				Dict       =  []
	snake_case__			:				str       =  []
	for label_file in glob.glob(os.path.join(snake_case_					,  "*.txt"       )       ):
		snake_case__			:				Optional[int]       =  label_file.split(os.sep       )[-1].rsplit("."					,  1       )[0]
		with open(snake_case_       ) as in_file:
			snake_case__			:				Dict       =  in_file.readlines()
		snake_case__			:				List[Any]       =  os.path.join(snake_case_					,  F'''{label_name}.jpg'''       )

		snake_case__			:				List[Any]       =  []
		for obj_list in obj_lists:
			snake_case__			:				List[Any]       =  obj_list.rstrip("\n"       ).split(" "       )
			snake_case__			:				Optional[int]       =  float(obj[1]       ) - float(obj[3]       ) / 2
			snake_case__			:				str       =  float(obj[2]       ) - float(obj[4]       ) / 2
			snake_case__			:				List[Any]       =  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    (			snake_case_ :							list					,  snake_case_ :							list					,  snake_case_ :							list[int]					,  snake_case_ :							tuple[int, int]					,  snake_case_ :							tuple[float, float]					,  snake_case_ :							float = 0.0					,  ):
	snake_case__			:				Optional[Any]       =  np.zeros([output_size[0], output_size[1], 3]					,  dtype=np.uinta       )
	snake_case__			:				Optional[int]       =  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case__			:				Tuple       =  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case__			:				Any       =  int(scale_x * output_size[1]       )
	snake_case__			:				Tuple       =  int(scale_y * output_size[0]       )

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


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











# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)



