![]() imageDir # Now we are ready to start the iteration iterate_dir ( args. add_argument ( '-x', '-xml', help = 'Set this flag if you want the xml annotation files to be processed and copied over.', action = 'store_true' ) args = parser. The default is 0.1.', default = 0.1, type = float ) parser. add_argument ( '-r', '-ratio', help = 'The ratio of the number of test images over the total number of images. ' 'Defaults to the same directory as IMAGEDIR.', type = str, default = None ) parser. add_argument ( '-o', '-outputDir', help = 'Path to the output folder where the train and test dirs should be created. If not specified, the CWD will be used.', type = str, default = os. add_argument ( '-i', '-imageDir', help = 'Path to the folder where the image dataset is stored. ![]() ArgumentParser ( description = "Partition dataset of images into training and testing sets", formatter_class = argparse. join ( train_dir, xml_filename )) def main (): # Initiate argument parser parser = argparse. splitext ( filename ) + '.xml' copyfile ( os. join ( train_dir, filename )) if copy_xml : xml_filename = os. remove ( images ) for filename in images : copyfile ( os. join ( test_dir, xml_filename )) images. join ( test_dir, filename )) if copy_xml : xml_filename = os. randint ( 0, len ( images ) - 1 ) filename = images copyfile ( os. ![]() ceil ( ratio * num_images ) for i in range ( num_test_images ): idx = random. makedirs ( test_dir ) images = )+(?i)(.jpg|.jpeg|.png)$', f )] num_images = len ( images ) num_test_images = math. """ import os import re from shutil import copyfile import argparse import math import random def iterate_dir ( source, dest, ratio, copy_xml ): source = source. x, -xml Set this flag if you want the xml annotation files to be processed and copied over. r RATIO, -ratio RATIO The ratio of the number of test images over the total number of images. Defaults to the same directory as IMAGEDIR. o OUTPUTDIR, -outputDir OUTPUTDIR Path to the output folder where the train and test dirs should be created. ![]() """ usage: partition_dataset.py Partition dataset of images into training and testing sets optional arguments: -h, -help show this help message and exit -i IMAGEDIR, -imageDir IMAGEDIR Path to the folder where the image dataset is stored. If you do not understand most of the things mentioned above, no need to worry, as we’ll see how all the files are generated further down. It is not used by TensorFlow in any way, but it generally helps when you have a few training folders and/or you are revisiting a trained model after some time. README.md: This is an optional file which provides some general information regarding the training conditions of our model. Pre-trained-models: This folder will contain the downloaded pre-trained models, which shall be used as a starting checkpoint for our training jobs. Each subfolder will contain the training pipeline configuration file *.config, as well as all files generated during the training and evaluation of our model. Models: This folder will contain a sub-folder for each of training job. Images/test: This folder contains a copy of all images, and the respective *.xml files, which will be used to test our model. Images/train: This folder contains a copy of all images, and the respective *.xml files, which will be used to train our model. ![]()
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