



'''simple docstring'''


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

import cva
import numpy as np

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







def 				__lowerCamelCase				(   )       ->	None:






   """simple docstring"""
   UpperCamelCase				,      UpperCamelCase					=  get_dataset(A__    ,						A__    )
   for index in range(A__    ):
      UpperCamelCase					=  random.sample(range(len(A__    )    )    ,						4    )
      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  update_image_and_anno(
          A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						filter_scale=A__    ,						)

      # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
      UpperCamelCase					=  random_chars(32    )
      UpperCamelCase					=  path.split(os.sep    )[-1].rsplit('.'    ,						1    )[0]
      UpperCamelCase					=  F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
      cva.imwrite(F"""{file_root}.jpg"""    ,						A__    ,						[cva.IMWRITE_JPEG_QUALITY, 85]    )
      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(A__    )
      with open(F"""{file_root}.txt"""    ,						'w'    ) as outfile:
         outfile.write('\n'.join(line for line in annos_list    )    )







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	tuple[list, list]:






   """simple docstring"""
   UpperCamelCase					=  []
   UpperCamelCase					=  []
   for label_file in glob.glob(os.path.join(A__    ,						'*.txt'    )    ):
      UpperCamelCase					=  label_file.split(os.sep    )[-1].rsplit('.'    ,						1    )[0]
      with open(A__    ) as in_file:
         UpperCamelCase					=  in_file.readlines()
      UpperCamelCase					=  os.path.join(A__    ,						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(A__    )
      labels.append(A__    )
   return img_paths, labels







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__ = 0.0    ,						)       ->	tuple[list, list, str]:






   """simple docstring"""
   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(A__    ):
      UpperCamelCase					=  all_img_list[index]
      path_list.append(A__    )
      UpperCamelCase					=  all_annos[index]
      UpperCamelCase					=  cva.imread(A__    )
      if i == 0:  # top-left
         UpperCamelCase					=  cva.resize(A__    ,						(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(A__    ,						(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(A__    ,						(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(
             A__    ,						(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 				__lowerCamelCase				(   A__    )       ->	str:






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


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






'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:       List[str]      	=	5_0000
_lowerCamelCase	:       Optional[int]      	=	5000

_lowerCamelCase	,_lowerCamelCase	:       int      	=	os.path.split(__file__)
_lowerCamelCase	:       str      	=	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   for i in range(A__    ):
      UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   for i in range(0    ,						len(A__    )    ,						A__    ):
      UpperCamelCase					=  dataset[i : i + batch_size]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(A__    ):
         UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(0    ,						A__    ,						A__    ):
         UpperCamelCase					=  dataset[i : i + batch_size]







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]

   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print('generating dataset'    )
      UpperCamelCase					=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
      UpperCamelCase					=  generate_example_dataset(
          os.path.join(A__    ,						'dataset.arrow'    )    ,						A__    ,						num_examples=A__    ,						seq_shapes={'list': (100,)}    ,						)
      print('first set of iterations'    )
      for func, kwargs in functions:
         print(func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(A__    ,						**A__    )

      print('shuffling dataset'    )
      UpperCamelCase					=  dataset.shuffle()
      print('Second set of iterations (after shuffling'    )
      for func, kwargs in functions_shuffled:
         print('shuffled '    ,						func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(
             A__    ,						**A__    )

   with open(A__    ,						'wb'    ) as f:
      f.write(json.dumps(A__    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()

