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 = 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外汇跟单gendan5.com = 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()