关于python:如何配置yolov5并训练自己的模型

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile
classes = [“hat”, “person”] # 自定义类
TRAIN_RATIO = 80 # 8：2 训练集和验证集比率
def clear_hidden_files(path):

dir_list = os.listdir(path)
for i in dir_list:
    abspath = os.path.join(os.path.abspath(path), i)
    if os.path.isfile(abspath):
        if i.startswith("._"):
            os.remove(abspath)
    else:
        clear_hidden_files(abspath)

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('VOCdevkit/VOC2007/Annotations/%s.xml' %image_id)
out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' %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 =[金融期货](https://www.gendan5.com/futures/ff.html) 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')
in_file.close()
out_file.close()

wd = os.getcwd()
wd = os.getcwd()
data_base_dir = os.path.join(wd, “VOCdevkit/”)
if not os.path.isdir(data_base_dir):

os.mkdir(data_base_dir)

work_sapce_dir = os.path.join(data_base_dir, “VOC2007/”)
if not os.path.isdir(work_sapce_dir):

os.mkdir(work_sapce_dir)

annotation_dir = os.path.join(work_sapce_dir, “Annotations/”)
if not os.path.isdir(annotation_dir):

    os.mkdir(annotation_dir)

clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, “JPEGImages/”)
if not os.path.isdir(image_dir):

    os.mkdir(image_dir)

clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, “YOLOLabels/”)
if not os.path.isdir(yolo_labels_dir):

    os.mkdir(yolo_labels_dir)

clear_hidden_files(yolo_labels_dir)
yolov5_images_dir = os.path.join(data_base_dir, “images/”)
if not os.path.isdir(yolov5_images_dir):

    os.mkdir(yolov5_images_dir)

clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir = os.path.join(data_base_dir, “labels/”)
if not os.path.isdir(yolov5_labels_dir):

    os.mkdir(yolov5_labels_dir)

clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir = os.path.join(yolov5_images_dir, “train/”)
if not os.path.isdir(yolov5_images_train_dir):

    os.mkdir(yolov5_images_train_dir)

clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir = os.path.join(yolov5_images_dir, “val/”)
if not os.path.isdir(yolov5_images_test_dir):

    os.mkdir(yolov5_images_test_dir)

clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, “train/”)
if not os.path.isdir(yolov5_labels_train_dir):

    os.mkdir(yolov5_labels_train_dir)

clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, “val/”)
if not os.path.isdir(yolov5_labels_test_dir):

    os.mkdir(yolov5_labels_test_dir)

clear_hidden_files(yolov5_labels_test_dir)
train_file = open(os.path.join(wd, “yolov5_train.txt”), ‘w’)
test_file = open(os.path.join(wd, “yolov5_val.txt”), ‘w’)
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, “yolov5_train.txt”), ‘a’)
test_file = open(os.path.join(wd, “yolov5_val.txt”), ‘a’)
list_imgs = os.listdir(image_dir) # list image files
prob = random.randint(1, 100)
print(“Probability: %d” % prob)
for i in range(0,len(list_imgs)):

path = os.path.join(image_dir,list_imgs[i])
if os.path.isfile(path):
    image_path = image_dir + list_imgs[i]
    voc_path = list_imgs[i]
    (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
    (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
    annotation_name = nameWithoutExtention + '.xml'
    annotation_path = os.path.join(annotation_dir, annotation_name)
    label_name = nameWithoutExtention + '.txt'
    label_path = os.path.join(yolo_labels_dir, label_name)
prob = random.randint(1, 100)
print("Probability: %d" % prob)
if(prob < TRAIN_RATIO): # train dataset
    if os.path.exists(annotation_path):
        train_file.write(image_path + '\n')
        convert_annotation(nameWithoutExtention) # convert label
        copyfile(image_path, yolov5_images_train_dir + voc_path)
        copyfile(label_path, yolov5_labels_train_dir + label_name)
else: # test dataset
    if os.path.exists(annotation_path):
        test_file.write(image_path + '\n')
        convert_annotation(nameWithoutExtention) # convert label
        copyfile(image_path, yolov5_images_test_dir + voc_path)
        copyfile(label_path, yolov5_labels_test_dir + label_name)

train_file.close()
test_file.close()