API希望每个对象(bbox)直接在
注释文件
. 由于这一要求,使用等级权重的解决方案似乎是:
1) 如果有自定义数据集,则可以修改每个对象(bbox)的注释,以将权重字段包含为“对象/权重”。
只有tf_记录
文件以包括BBox的重量。
3) 修改API的代码(在我看来相当棘手)
我决定选择#2,所以我把代码放在这里来生成
加重
风俗
数据集包含两个类(“top”、“dress”),权重为(1.0,0.1),文件夹为
xml
注释如下:
import os
import io
import glob
import hashlib
import pandas as pd
import xml.etree.ElementTree as ET
import tensorflow as tf
import random
from PIL import Image
from object_detection.utils import dataset_util
# Define the class names and their weight
class_names = ['top', 'dress', ...]
class_weights = [1.0, 0.1, ...]
def create_example(xml_file):
tree = ET.parse(xml_file)
root = tree.getroot()
image_name = root.find('filename').text
image_path = root.find('path').text
file_name = image_name.encode('utf8')
size=root.find('size')
width = int(size[0].text)
height = int(size[1].text)
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
weights = [] # Important line
for member in root.findall('object'):
xmin.append(float(member[4][0].text) / width)
ymin.append(float(member[4][1].text) / height)
xmax.append(float(member[4][2].text) / width)
ymax.append(float(member[4][3].text) / height)
difficult_obj.append(0)
class_name = member[0].text
class_id = class_names.index(class_name)
weights.append(class_weights[class_id])
if class_name == 'top':
classes_text.append('top'.encode('utf8'))
classes.append(1)
elif class_name == 'dress':
classes_text.append('dress'.encode('utf8'))
classes.append(2)
else:
print('E: class not recognized!')
truncated.append(0)
poses.append('Unspecified'.encode('utf8'))
full_path = image_path
with tf.gfile.GFile(full_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
#create TFRecord Example
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(file_name),
'image/source_id': dataset_util.bytes_feature(file_name),
'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
'image/object/weight': dataset_util.float_list_feature(weights) # Important line
}))
return example
def main(_):
weighted_tf_records_output = 'name_of_records_file.record' # output file
annotations_path = '/path/to/annotations/folder/*.xml' # input annotations
writer_train = tf.python_io.TFRecordWriter(weighted_tf_records_output)
filename_list=tf.train.match_filenames_once(annotations_path)
init = (tf.global_variables_initializer(), tf.local_variables_initializer())
sess=tf.Session()
sess.run(init)
list = sess.run(filename_list)
random.shuffle(list)
for xml_file in list:
print('-> Processing {}'.format(xml_file))
example = create_example(xml_file)
writer_train.write(example.SerializeToString())
writer_train.close()
print('-> Successfully converted dataset to TFRecord.')
if __name__ == '__main__':
tf.app.run()
如果您有其他类型的注释,代码将非常类似,但不幸的是,这一种将无法工作。
