






"""simple docstring"""




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

import cva
UpperCamelCase							       =  """"""
UpperCamelCase							       =  """"""
UpperCamelCase							       =  """"""
UpperCamelCase							       =  1  # (0 is vertical, 1 is horizontal)






def        lowerCAmelCase (      )       ->							None:







  '''simple docstring'''







  _a							=	get_dataset(UpperCamelCase_     , UpperCamelCase_					)
  print("Processing..."					)
  _a							=	update_image_and_anno(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)

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






def        lowerCAmelCase (      UpperCamelCase_:							Any     , UpperCamelCase_:							Dict					)       ->							tuple[list, list]:







  '''simple docstring'''







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

    _a							=	[]
    for obj_list in obj_lists:
      _a							=	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(UpperCamelCase_					)
    labels.append(UpperCamelCase_					)
  return img_paths, labels






def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict = 1					)       ->							tuple[list, list, list]:







  '''simple docstring'''







  _a							=	[]
  _a							=	[]
  _a							=	[]
  for idx in range(len(UpperCamelCase_					)					):
    _a							=	[]
    _a							=	img_list[idx]
    path_list.append(UpperCamelCase_					)
    _a							=	anno_list[idx]
    _a							=	cva.imread(UpperCamelCase_					)
    if flip_type == 1:
      _a							=	cva.flip(UpperCamelCase_     , UpperCamelCase_					)
      for bbox in img_annos:
        _a							=	1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]					)
    elif flip_type == 0:
      _a							=	cva.flip(UpperCamelCase_     , UpperCamelCase_					)
      for bbox in img_annos:
        _a							=	1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]					)
    new_annos_lists.append(UpperCamelCase_					)
    new_imgs_list.append(UpperCamelCase_					)
  return new_imgs_list, new_annos_lists, path_list






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any] = 32					)       ->							str:







  '''simple docstring'''







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


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










"""simple docstring"""




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def        lowerCAmelCase (      UpperCamelCase_:							ndarray					)       ->							float:







  '''simple docstring'''







  return np.dot(UpperCamelCase_     , UpperCamelCase_					)




class    lowercase_    :






      def __init__(    self     ,     *,
          a_ = np.inf     ,     a_ = "linear"     ,     a_ = 0.0     ,     )	->None:







        '''simple docstring'''


        _a							=	regularization
        _a							=	gamma
        if kernel == "linear":
          _a							=	self.__linear
        elif kernel == "rbf":
          if self.gamma == 0:
            raise ValueError("rbf kernel requires gamma"	)
          if not isinstance(self.gamma     ,     (float, int)	):
            raise ValueError("gamma must be float or int"	)
          if not self.gamma > 0:
            raise ValueError("gamma must be > 0"	)
          _a							=	self.__rbf
          # in the future, there could be a default value like in sklearn
          # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
          # previously it was 1/(n_features)
        else:
          _a							=	f'''Unknown kernel: {kernel}'''
          raise ValueError(a_	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->float:







        '''simple docstring'''


        return np.dot(a_     ,     a_	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->float:







        '''simple docstring'''


        return np.exp(-(self.gamma * norm_squared(vectora - vectora	))	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->None:







        '''simple docstring'''


        _a							=	observations
        _a							=	classes

        # using Wolfe's Dual to calculate w.
        # Primal problem: minimize 1/2*norm_squared(w)
        #   constraint: yn(w . xn + b) >= 1
        #
        # With l a vector
        # Dual problem: maximize sum_n(ln) -
        #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
        #   constraint: self.C >= ln >= 0
        #           and sum_n(ln*yn) = 0
        # Then we get w using w = sum_n(ln*yn*xn)
        # At the end we can get b ~= mean(yn - w . xn)
        #
        # Since we use kernels, we only need l_star to calculate b
        # and to classify observations

        ((_a)			,	)							=	np.shape(a_	)

        def to_minimize(a_	) -> float:
          _a							=	0
          ((_a)			,	)							=	np.shape(a_	)
          for i in range(a_	):
            for j in range(a_	):
              s += (
                  candidate[i]
                  * candidate[j]
                  * classes[i]
                  * classes[j]
                  * self.kernel(observations[i]     ,     observations[j]	)
              )
          return 1 / 2 * s - sum(a_	)

        _a							=	LinearConstraint(a_     ,     0     ,     0	)
        _a							=	Bounds(0     ,     self.regularization	)

        _a							=	minimize(
            a_     ,     np.ones(a_	)     ,     bounds=a_     ,     constraints=[ly_contraint]	).x
        _a							=	l_star

        # calculating mean offset of separation plane to points
        _a							=	0
        for i in range(a_	):
          for j in range(a_	):
            s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                observations[i]     ,     observations[j]	)
        _a							=	s / n






      def 			lowerCamelCase__    (    self     ,     a_	)	->int:







        '''simple docstring'''


        _a							=	sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n]     ,     a_	)
            for n in range(len(self.classes	)	)	)
        return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()


