2020-1-capstone-design1 / PET_Project1

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김성주
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annotation and dataset text files

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import xml.etree.ElementTree as elemTree
import os
CLASSES = ['dog']
def parseFile(path):
result = ""
annotation = elemTree.parse(path)
filename = annotation.find('filename')
size = annotation.find('size')
width = size.find('width')
height = size.find('height')
result += filename.text + ' ' + width.text + ' ' + height.text
for obj in annotation.findall('./object'):
name = obj.find('name')
box = obj.find('bndbox')
label_index = CLASSES.index(name.text)
xmin = box.find('xmin')
ymin = box.find('ymin')
xmax = box.find('xmax')
ymax = box.find('ymax')
result += ' ' + str(label_index) + ' ' + xmin.text + ' ' + ymin.text + ' ' + xmax.text + ' ' + ymax.text
return result
def parseDir(path):
result = ""
for file in os.listdir(path):
if file.split('.')[-1] != 'xml':
continue
result += parseFile(path+'/'+file) + '\n'
return result
directory = input('Input directory, or 0 to exit: ')
if directory != '0':
result = parseDir(directory)
file = open(directory+'/'+'annotation.txt','w')
file.write(result)
file.close()
print("Complete!")
import os
import tensorflow as tf
all_path = '../data/annotation.txt'
train_path = '../data/train.txt'
val_path = '../data/val.txt'
test_path = '../data/test.txt'
allFile = open(all_path, 'r')
allFile_lines = allFile.readlines()
allFile.close()
dataset_size = len(allFile_lines)
train_size = int(0.7 * dataset_size)
val_size = int(0.15 * dataset_size)
test_size = int(0.15 * dataset_size)
full_dataset = tf.data.TextLineDataset(all_path)
full_dataset = full_dataset.shuffle(dataset_size)
train_dataset = full_dataset.take(train_size)
test_dataset = full_dataset.skip(train_size)
val_dataset = test_dataset.skip(test_size)
test_dataset = test_dataset.take(test_size)
train_dataset = list(train_dataset)
val_dataset = list(val_dataset)
test_dataset = list(test_dataset)
writer = open(train_path, 'w')
for data in train_dataset:
writer.write(data.numpy().decode('ascii') + '\n')
writer.close()
writer = open(val_path, 'w')
for data in val_dataset:
writer.write(data.numpy().decode('ascii') + '\n')
writer.close()
writer = open(test_path, 'w')
for data in test_dataset:
writer.write(data.numpy().decode('ascii') + '\n')
writer.close()
print('train dataset counts:', len(train_dataset))
print('validation dataset counts:', len(val_dataset))
print('test dataset counts:', len(test_dataset))
reader = open(train_path, 'r')
train_file = reader.readlines()
reader.close()
train_file.sort()
writer = open(train_path, 'w')
for i in range(len(train_file)):
writer.write(str(i) + ' ' + train_file[i])
writer.close()
reader = open(val_path, 'r')
val_file = reader.readlines()
reader.close()
val_file.sort()
writer = open(val_path, 'w')
for i in range(len(val_file)):
writer.write(str(i) + ' ' + val_file[i])
writer.close()
reader = open(test_path, 'r')
test_file = reader.readlines()
reader.close()
test_file.sort()
writer = open(test_path, 'w')
for i in range(len(test_file)):
writer.write(str(i) + ' ' + test_file[i])
writer.close()
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