修改github上yolo源码实现自建数据集的目标检测任务_yolo目标检测+猫狗大战

本项目自建猫狗数据集，搭建Yolov5，实现猫狗检测

一、环境搭建

1.在Anaconda中创建pytorch环境

conda create -n pytorch python=3.8

2.激活pytorch环境

conda activate pytorch

3.进入PyTorch

 复制命令，安装支持包

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

4.安装pycharmDownload PyCharm: Python IDE for Professional Developers by JetBrains

5.在pycharm中创建pytorch环境的工程项目文件

 二、数据集制作

采用labelimg进行数据集标注

1.安装labelimg

cmd中输入指令

pip install labelimg -i https://pypi.tuna.tsinghua.edu.cn/simple

2.创建数据集文件夹VOC2007并创建子文件

JPEGImages存放需要打标签的图片文件

Annotations存放标注的标签文件

predefined_classes.txt存放类别名称

3.在JPEGImages中放入待标注的图片，分别是猫、狗，然后在predefined_classes.txt内输入定义的类别

 

4.在cmd中进入数据集文件夹目录下打开labelimg

labelimg JPEGImages predefined_classes.txt

  在view中勾选

Auto Save mode 切换下一张图时自动保存标签

Display Labels 显示标注框和标签

Advanced Mode 标注的十字架一直悬浮在窗口

 5.开始标注

 三、VOC标签格式转YOLO格式并划分训练集和测试集

1.Yolov5训练所需要的文件格式是yolo(txt)格式的，在此对xml格式的标签文件转换为txt文件，同时训练将数据集按比例划分为训练集和验证集。(注：需要修改标签名称)

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
 
#标签名称
classes = ["dog", "cat"]
 
#80%划分给训练集，20%划分给验证集
TRAIN_RATIO = 80
 
 
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,encoding='UTF-8')
    out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' % image_id, 'w',encoding='UTF-8')
    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 = 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()

2.将之前做好的数据集复制到VOCdevkit-VOC2007下的Annotations和JPEGImages中

（注：运行完上述代码后，会建立空的文件，将数据集复制进去后，再次运行文件即可完成转换与划分）

 四、利用Yolov5训练目标检测模型

1.在github上下载yolov5源码

GitHub - ultralytics/yolov5 at v5.0

 

2.pycharm的命令终端中输入如下命令，安装依赖包

pip install -r requirements.txt

  3.将数据集（VOCdevkit文件）复制到工程文件中

   4.在下载预训练权重，以yolov5s.pt为例，下载后放入weights文件中

 Releases · ultralytics/yolov5 · GitHub

   5.修改data目录下的yaml文件

复制VOC.yaml文件，将复制后的文件重命名为dog_cat.yaml，之后对dog_cat.yaml中的参数进行修改（路径修改方式如下）

  6.修改models下的yaml文件

复制yolov5s.yaml文件，将复制后的文件重命名为yolo5s_dog_cat.yaml，之后对yolo5s_dog_cat.yaml中的参数进行修改

 7.修改train.py中的参数后运行train.py

（注：遇到“页面文件太小，无法完成操作”的红字不用管，只要程序在运行，等待一会就会开始训练）

可以通过如下命令查看训练参数

tensorboard --logdir=runs/train  # 训练过程中查看参数
tensorboard --logdir=runs  # 训练完查看参数

 8.训练完成后，最佳参数保存在runs-train-exp40-weights中

 9.修改detect.py中参数并运行

（注：以照片为例进行预测）

 10.检测结果保存在runs-detect-exp20中

（注：由于本实例只演示操作，数据集很少，训练轮数也很少，所以精度很低，调小detect.py中的置信度参数，如果不调可能无法框出预测结果）