YOLOv5超详细的入门级教程（训练篇）（二）——VOC2007模型复现

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
 
# 这里就体现出来了咱们在1.2步骤的时候我说的尽量按照那个目录名进行操作的优势，
# 在这可以剩下很多去修改名称的精力
# sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
sets=[ ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]  # 我只用了VOC2007
 
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle",
           "bus", "car", "cat", "chair", "cow",
           "diningtable", "dog", "horse", "motorbike", "person",
           "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
#classes = ["face"]  # 修改为自己的label
 
def convert(size, box):
    dw = 1./(size[0])  # 有的人运行这个脚本可能报错，说不能除以0什么的，你可以变成dw = 1./((size[0])+0.1)
    dh = 1./(size[1])  # 有的人运行这个脚本可能报错，说不能除以0什么的，你可以变成dh = 1./((size[0])+0.1)
    x = (box[0] + box[1])/2.0 - 1
    y = (box[2] + box[3])/2.0 - 1
    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(year, image_id):
    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
    out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
    tree=ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)
 
    for obj in root.iter('object'):
        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)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
 
wd = getcwd()
 
for year, image_set in sets:
    if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
        os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
    list_file = open('%s_%s.txt'%(year, image_set), 'w')
    for image_id in image_ids:
        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
        convert_annotation(year, image_id)
    list_file.close()
 
# 这块是路径拼接，暂时用不上，先都注释了
# os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt")
# os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt")

# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
# Download command: bash ./data/get_voc.sh
# Train command: python train.py --data voc.yaml
# Default dataset location is next to /yolov5:
#   /parent_folder
#     /VOC
#     /yolov5
 
 
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: F:\AI\datasets\VOC2007\VOCdevkit\VOC2007\2007_train.txt
val: F:\AI\datasets\VOC2007\VOCdevkit\VOC2007\2007_val.txt
 
# number of classes
nc: 20
 
# class names
names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
        'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']

 list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
 换成
  list_file.write('%s\VOCdevkit\VOC%s\JPEGImages\%s.jpg\n'%(wd, year, image_id))