

'''simple docstring'''






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

import cva
import numpy as np

# Parrameters
lowerCAmelCase__      		=			(720, 1280)  # Height, Width
lowerCAmelCase__      		=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__      		=			1 / 100
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			250


def 	_A  (					):






 """simple docstring"""



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

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __lowercase													=				random_chars(32   )
  __lowercase													=				path.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  __lowercase													=				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}"   )
  __lowercase													=				[]
  for anno in new_annos:
   __lowercase													=				anno[3] - anno[1]
   __lowercase													=				anno[4] - anno[2]
   __lowercase													=				anno[1] + width / 2
   __lowercase													=				anno[2] + height / 2
   __lowercase													=				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 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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

  __lowercase													=				[]
  for obj_list in obj_lists:
   __lowercase													=				obj_list.rstrip('''\n'''   ).split(''' '''   )
   __lowercase													=				float(obj[1]   ) - float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) - float(obj[4]   ) / 2
   __lowercase													=				float(obj[1]   ) + float(obj[3]   ) / 2
   __lowercase													=				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 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__ = 0.0  ,       ):






 """simple docstring"""



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

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






 """simple docstring"""



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


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




'''simple docstring'''






from scipy.stats import spearmanr

import datasets


lowerCAmelCase__      		=			'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

lowerCAmelCase__      		=			'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

lowerCAmelCase__      		=			R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       , _KWARGS_DESCRIPTION   )
class     lowercase_			(datasets.Metric   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return datasets.MetricInfo(
       description=_DESCRIPTION      ,citation=_CITATION      ,inputs_description=_KWARGS_DESCRIPTION      ,features=datasets.Features(
           {
               '''predictions''': datasets.Value('''float'''    ),
               '''references''': datasets.Value('''float'''    ),
           }    )      ,reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html''']      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]=False    ):
   __lowercase													=				spearmanr(lowercase__      ,lowercase__    )
   if return_pvalue:
    return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
    return {"spearmanr": results[0]}

