目标检测 的标注数据 .xml 转为 tfrecord 的格式用于 TensorFlow 训练

将目标检测 的标注数据 .xml 转为 tfrecord 的格式用于 TensorFlow 训练。

import xml.etree.ElementTree as ET
import numpy as np
import os
import tensorflow as tf
from PIL import Image

classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
           "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

def convert(size, box):
    dw = 1./size[0]
    dh = 1./size[1]
    x = (box[0] + box[1])/2.0
    y = (box[2] + box[3])/2.0
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return [x, y, w, h]

def convert_annotation(image_id):
    in_file = open(‘F:/xml/%s.xml‘%(image_id))

    tree = ET.parse(in_file)
    root = tree.getroot()
    size = root.find(‘size‘)
    w = int(size.find(‘width‘).text)
    h = int(size.find(‘height‘).text)
    bboxes = []
    for i, obj in enumerate(root.iter(‘object‘)):
        if i > 29:
            break
        difficult = obj.find(‘difficult‘).text
        cls = obj.find(‘name‘).text
        if cls not in classes or int(difficult) == 1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find(‘bndbox‘)
        b = (float(xmlbox.find(‘xmin‘).text), float(xmlbox.find(‘xmax‘).text), float(xmlbox.find(‘ymin‘).text), float(xmlbox.find(‘ymax‘).text))
        bb = convert((w, h), b) + [cls_id]
        bboxes.extend(bb)
    if len(bboxes) < 30*5:
        bboxes = bboxes + [0, 0, 0, 0, 0]*(30-int(len(bboxes)/5))

    return np.array(bboxes, dtype=np.float32).flatten().tolist()

def convert_img(image_id):
    image = Image.open(‘F:/snow leopard/test_im/%s.jpg‘ % (image_id))
    resized_image = image.resize((416, 416), Image.BICUBIC)
    image_data = np.array(resized_image, dtype=‘float32‘)/255
    img_raw = image_data.tobytes()
    return img_raw

filename = os.path.join(‘test‘+‘.tfrecords‘)
writer = tf.python_io.TFRecordWriter(filename)
# image_ids = open(‘F:/snow leopard/test_im/%s.txt‘ % (
#     year, year, image_set)).read().strip().split()

image_ids = os.listdir(‘F:/snow leopard/test_im/‘)
# print(filename)
for image_id in image_ids:
    print (image_id)
    image_id = image_id.split(‘.‘)[0]
    print (image_id)

    xywhc = convert_annotation(image_id)
    img_raw = convert_img(image_id)

    example = tf.train.Example(features=tf.train.Features(feature={
        ‘xywhc‘:
                tf.train.Feature(float_list=tf.train.FloatList(value=xywhc)),
        ‘img‘:
                tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
        }))
    writer.write(example.SerializeToString())
writer.close()

Python读取文件夹下图片的两种方法：

import os
imagelist = os.listdir(‘./images/‘)      #读取images文件夹下所有文件的名字

import glob
imagelist= sorted(glob.glob(‘./images/‘ + ‘frame_*.png‘))      #读取带有相同关键字的图片名字，比上一中方法好

参考：

https://blog.csdn.net/CV_YOU/article/details/80778392

https://github.com/raytroop/YOLOv3_tf

原文地址：https://www.cnblogs.com/Allen-rg/p/10245729.html