


"""simple docstring"""

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

import cva
import numpy as np

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


def lowerCAmelCase_   (					):






 """simple docstring"""







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

  # 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'''					, UpperCamelCase__					, [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(UpperCamelCase__			)
  with open(f'''{file_root}.txt'''					, """w"""			) as outfile:
   outfile.write("""\n""".join(line for line in annos_list			)			)


def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			str			):






 """simple docstring"""







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


def lowerCAmelCase_   (					UpperCamelCase__						:			list					, UpperCamelCase__						:			list					, UpperCamelCase__						:			list[int]					, UpperCamelCase__						:			tuple[int, int]					, UpperCamelCase__						:			tuple[float, float]					, UpperCamelCase__						:			float = 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(UpperCamelCase__			):
  __lowercase						=						all_img_list[index]
  path_list.append(UpperCamelCase__			)
  __lowercase						=						all_annos[index]
  __lowercase						=						cva.imread(UpperCamelCase__			)
  if i == 0:  # top-left
   __lowercase						=						cva.resize(UpperCamelCase__					, (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(UpperCamelCase__					, (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(UpperCamelCase__					, (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(
       UpperCamelCase__					, (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 lowerCAmelCase_   (					UpperCamelCase__						:			int			):






 """simple docstring"""







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


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




"""simple docstring"""

class  lowerCamelCase__    :
      def __init__(		self						:						Optional[Any]   ,							A_						:						Dict   ,							A_						:						str   ,							A_						:						Any				):






       '''simple docstring'''


       __lowercase						=						None
       __lowercase						=						None
       __lowercase						=						graph

       self._normalize_graph(A_   ,							A_				)
       __lowercase						=						len(A_				)
       __lowercase						=						None
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str   ,							A_						:						List[Any]				):






       '''simple docstring'''


       if sources is int:
        __lowercase						=						[sources]
       if sinks is int:
        __lowercase						=						[sinks]

       if len(A_				) == 0 or len(A_				) == 0:
        return

       __lowercase						=						sources[0]
       __lowercase						=						sinks[0]

       # make fake vertex if there are more
       # than one source or sink
       if len(A_				) > 1 or len(A_				) > 1:
        __lowercase						=						0
        for i in sources:
         max_input_flow += sum(self.graph[i]				)

        __lowercase						=						len(self.graph				) + 1
        for room in self.graph:
         room.insert(0   ,							0				)
        self.graph.insert(0   ,							[0] * size				)
        for i in sources:
         __lowercase						=						max_input_flow
        __lowercase						=						0

        __lowercase						=						len(self.graph				) + 1
        for room in self.graph:
         room.append(0				)
        self.graph.append([0] * size				)
        for i in sinks:
         __lowercase						=						max_input_flow
        __lowercase						=						size - 1
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       if self.maximum_flow_algorithm is None:
        raise Exception("""You need to set maximum flow algorithm before."""				)
       if self.source_index is None or self.sink_index is None:
        return 0

       self.maximum_flow_algorithm.execute()
       return self.maximum_flow_algorithm.getMaximumFlow()






      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						algorithm(self				)






class  lowerCamelCase__    :
      def __init__(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						flow_network
       __lowercase						=						flow_network.verticesCount
       __lowercase						=						flow_network.sourceIndex
       __lowercase						=						flow_network.sinkIndex
       # it's just a reference, so you shouldn't change
       # it in your algorithms, use deep copy before doing that
       __lowercase						=						flow_network.graph
       __lowercase						=						False
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       if not self.executed:
        self._algorithm()
        __lowercase						=						True






      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       pass






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Union[str, Any]   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__(A_				)
       # use this to save your result
       __lowercase						=						-1






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       if not self.executed:
        raise Exception("""You should execute algorithm before using its result!"""				)

       return self.maximum_flow






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						List[str]   ,							A_						:						Tuple				):






       '''simple docstring'''


       super().__init__(A_				)

       __lowercase						=						[[0] * self.verticies_count for i in range(self.verticies_count				)]

       __lowercase						=						[0] * self.verticies_count
       __lowercase						=						[0] * self.verticies_count
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.verticies_count

       # push some substance to graph
       for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]				):
        self.preflow[self.source_index][nextvertex_index] += bandwidth
        self.preflow[nextvertex_index][self.source_index] -= bandwidth
        self.excesses[nextvertex_index] += bandwidth

       # Relabel-to-front selection rule
       __lowercase						=						[
           i
           for i in range(self.verticies_count				)
           if i != self.source_index and i != self.sink_index
       ]

       # move through list
       __lowercase						=						0
       while i < len(A_				):
        __lowercase						=						vertices_list[i]
        __lowercase						=						self.heights[vertex_index]
        self.process_vertex(A_				)
        if self.heights[vertex_index] > previous_height:
         # if it was relabeled, swap elements
         # and start from 0 index
         vertices_list.insert(0   ,							vertices_list.pop(A_				)				)
         __lowercase						=						0
        else:
         i += 1

       __lowercase						=						sum(self.preflow[self.source_index]				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       while self.excesses[vertex_index] > 0:
        for neighbour_index in range(self.verticies_count				):
         # if it's neighbour and current vertex is higher
         if (
             self.graph[vertex_index][neighbour_index]
             - self.preflow[vertex_index][neighbour_index]
             > 0
             and self.heights[vertex_index] > self.heights[neighbour_index]
         ):
          self.push(A_   ,							A_				)

        self.relabel(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Union[str, Any]   ,							A_						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						min(
           self.excesses[from_index]   ,							self.graph[from_index][to_index] - self.preflow[from_index][to_index]   ,							)
       self.preflow[from_index][to_index] += preflow_delta
       self.preflow[to_index][from_index] -= preflow_delta
       self.excesses[from_index] -= preflow_delta
       self.excesses[to_index] += preflow_delta






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						None
       for to_index in range(self.verticies_count				):
        if (
            self.graph[vertex_index][to_index]
            - self.preflow[vertex_index][to_index]
            > 0
        ) and (min_height is None or self.heights[to_index] < min_height):
         __lowercase						=						self.heights[to_index]

       if min_height is not None:
        __lowercase						=						min_height + 1


if __name__ == "__main__":
       UpperCAmelCase__					=[0]
       UpperCAmelCase__					=[3]
       # graph = [
       #     [0, 0, 4, 6, 0, 0],
       #     [0, 0, 5, 2, 0, 0],
       #     [0, 0, 0, 0, 4, 4],
       #     [0, 0, 0, 0, 6, 6],
       #     [0, 0, 0, 0, 0, 0],
       #     [0, 0, 0, 0, 0, 0],
       # ]
       UpperCAmelCase__					=[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

       # prepare our network
       UpperCAmelCase__					=FlowNetwork(graph, entrances, exits)
       # set algorithm
       flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
       # and calculate
       UpperCAmelCase__					=flow_network.find_maximum_flow()

       print(f"""maximum flow is {maximum_flow}""")
