


"""simple docstring"""







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

import cva
import numpy as np

# Parrameters
lowerCamelCase_       :       int      						=  (7_2_0, 1_2_8_0)  # Height, Width
lowerCamelCase_       :       Dict      						=  (0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCamelCase_       :       Dict      						=  1 / 1_0_0
lowerCamelCase_       :       Union[str, Any]      						=  """"""
lowerCamelCase_       :       List[str]      						=  """"""
lowerCamelCase_       :       str      						=  """"""
lowerCamelCase_       :       List[Any]      						=  2_5_0
def _A						(						):







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

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        a					=random_chars(32		)
        a					=path.split(os.sep		)[-1].rsplit('''.'''	,   1		)[0]
        a					=f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
        cva.imwrite(f'''{file_root}.jpg'''	,   lowercase	,   [cva.IMWRITE_JPEG_QUALITY, 85]		)
        print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''		)
        a					=[]
        for anno in new_annos:
            a					=anno[3] - anno[1]
            a					=anno[4] - anno[2]
            a					=anno[1] + width / 2
            a					=anno[2] + height / 2
            a					=f'''{anno[0]} {x_center} {y_center} {width} {height}'''
            annos_list.append(lowercase		)
        with open(f'''{file_root}.txt'''	,   '''w'''		) as outfile:
            outfile.write('''\n'''.join(line for line in annos_list		)		)
def _A						(						lowercase	,   lowercase		):







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

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

            boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
        if not boxes:
            continue
        img_paths.append(lowercase		)
        labels.append(lowercase		)
    return img_paths, labels
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase	,   lowercase	,   lowercase = 0.0	,   ):







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

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







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


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



"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase_       :       List[Any]      						=  TypeVar("""T""")






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self			,   __A						) ->					None:
     a					=data
     a					=self
     a					=0






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self						) ->					None:
     # map from node name to the node object
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					None:
     # create a new set with x as its member
     a					=DisjointSetTreeNode(__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					DisjointSetTreeNode[T]:
     # find the set x belongs to (with path-compression)
     a					=self.map[data]
     if elem_ref != elem_ref.parent:
         a					=self.find_set(elem_ref.parent.data						)
     return elem_ref.parent


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					None:
     # helper function for union operation
     if nodea.rank > nodea.rank:
         a					=nodea
     else:
         a					=nodea
         if nodea.rank == nodea.rank:
             nodea.rank += 1





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					None:
     # merge 2 disjoint sets
     self.link(self.find_set(__A						)			,   self.find_set(__A						)						)






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self						) ->					None:
     # connections: map from the node to the neighbouring nodes (with weights)
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					None:
     # add a node ONLY if its not present in the graph
     if node not in self.connections:
         a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					None:
     # add an edge with the given weight
     self.add_node(__A						)
     self.add_node(__A						)
     a					=weight
     a					=weight





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					GraphUndirectedWeighted[T]:
     a					=[]
     a					=set()
     for start in self.connections:
         for end in self.connections[start]:
             if (start, end) not in seen:
                 seen.add((end, start)						)
                 edges.append((start, end, self.connections[start][end])						)
     edges.sort(key=lambda __A						: x[2]						)

     # creating the disjoint set
     a					=DisjointSetTree[T]()
     for node in self.connections:
         disjoint_set.make_set(__A						)

     # MST generation
     a					=0
     a					=0
     a					=GraphUndirectedWeighted[T]()
     while num_edges < len(self.connections						) - 1:
         a		,				a		,				a					=edges[index]
         index += 1
         a					=disjoint_set.find_set(__A						)
         a					=disjoint_set.find_set(__A						)
         if parent_u != parent_v:
             num_edges += 1
             graph.add_edge(__A			,   __A			,   __A						)
             disjoint_set.union(__A			,   __A						)
     return graph