


'''simple docstring'''


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

import cva
UpperCAmelCase_		:   Optional[int]							   =		''
UpperCAmelCase_		:   int							   =		''
UpperCAmelCase_		:   Dict							   =		''
UpperCAmelCase_		:   str							   =		1  # (0 is vertical, 1 is horizontal)







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      get_dataset(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
				print("""Processing..."""     )
				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      update_image_and_anno(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )

				for index, image in enumerate(SCREAMING_SNAKE_CASE__     ):
								# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
								_SCREAMING_SNAKE_CASE :       str     		=      random_chars(32     )
								_SCREAMING_SNAKE_CASE :       Any     		=      paths[index].split(os.sep     )[-1].rsplit("""."""							,      1     )[0]
								_SCREAMING_SNAKE_CASE :       str     		=      f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
								cva.imwrite(f"""/{file_root}.jpg"""							,      SCREAMING_SNAKE_CASE__							,      [cva.IMWRITE_JPEG_QUALITY, 85]     )
								print(f"""Success {index+1}/{len(SCREAMING_SNAKE_CASE__     )} with {file_name}"""     )
								_SCREAMING_SNAKE_CASE :       str     		=      []
								for anno in new_annos[index]:
												_SCREAMING_SNAKE_CASE :       List[Any]     		=      f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
												annos_list.append(SCREAMING_SNAKE_CASE__     )
								with open(f"""/{file_root}.txt"""							,      """w"""     ) as outfile:
												outfile.write("""\n""".join(line for line in annos_list     )     )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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

								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      []
								for obj_list in obj_lists:
												_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      obj_list.rstrip("""\n"""     ).split(""" """     )
												boxes.append(
												    [
												        int(obj[0]     ),
												        float(obj[1]     ),
												        float(obj[2]     ),
												        float(obj[3]     ),
												        float(obj[4]     ),
												    ]     )
								if not boxes:
												continue
								img_paths.append(SCREAMING_SNAKE_CASE__     )
								labels.append(SCREAMING_SNAKE_CASE__     )
				return img_paths, labels







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__ = 1     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[str]     		=      []
				_SCREAMING_SNAKE_CASE :       int     		=      []
				_SCREAMING_SNAKE_CASE :       Dict     		=      []
				for idx in range(len(SCREAMING_SNAKE_CASE__     )     ):
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      []
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      img_list[idx]
								path_list.append(SCREAMING_SNAKE_CASE__     )
								_SCREAMING_SNAKE_CASE :       int     		=      anno_list[idx]
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      cva.imread(SCREAMING_SNAKE_CASE__     )
								if flip_type == 1:
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      cva.flip(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
												for bbox in img_annos:
																_SCREAMING_SNAKE_CASE :       Optional[int]     		=      1 - bbox[1]
																new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]     )
								elif flip_type == 0:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      cva.flip(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
												for bbox in img_annos:
																_SCREAMING_SNAKE_CASE :       Any     		=      1 - bbox[2]
																new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]     )
								new_annos_lists.append(SCREAMING_SNAKE_CASE__     )
								new_imgs_list.append(SCREAMING_SNAKE_CASE__     )
				return new_imgs_list, new_annos_lists, path_list







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 32     ):







				"""simple docstring"""





				assert number_char > 1, "The number of character should greater than 1"
				_SCREAMING_SNAKE_CASE :       List[str]     		=      ascii_lowercase + digits
				return "".join(random.choice(SCREAMING_SNAKE_CASE__     ) for _ in range(SCREAMING_SNAKE_CASE__     )     )


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








'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import MobileNetVaImageProcessor





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=7    ,					__snake_case=3    ,					__snake_case=18    ,					__snake_case=30    ,					__snake_case=400    ,					__snake_case=True    ,					__snake_case=None    ,					__snake_case=True    ,					__snake_case=None    ,					):
											_SCREAMING_SNAKE_CASE :       Any     		=      size if size is not None else {"""shortest_edge""": 20}
											_SCREAMING_SNAKE_CASE :       int     		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											_SCREAMING_SNAKE_CASE :       Dict     		=      parent
											_SCREAMING_SNAKE_CASE :       Any     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_channels
											_SCREAMING_SNAKE_CASE :       Dict     		=      image_size
											_SCREAMING_SNAKE_CASE :       Any     		=      min_resolution
											_SCREAMING_SNAKE_CASE :       Any     		=      max_resolution
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Tuple     		=      size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      do_center_crop
											_SCREAMING_SNAKE_CASE :       str     		=      crop_size






							def UpperCAmelCase_	(    self  ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}





@require_torch
@require_vision
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Union[str, Any]      	=  MobileNetVaImageProcessor if is_vision_available() else None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      MobileNetVaImageProcessingTester(self  )






							@property
							def UpperCAmelCase_	(    self  ):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_resize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""size"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_center_crop"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""crop_size"""  )  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 20}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 18, """width""": 18}  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_SCREAMING_SNAKE_CASE :       List[str]     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					Image.Image  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       int     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE :       Tuple     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					numpify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					np.ndarray  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE :       str     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					torchify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					torch.Tensor  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       str     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)




