基于YoloV5的智能零售柜商品识别_用yolo开发一个商品识别功能

下载yolov5：

git clone https://github.com/ultralytics/yolov5

进入yolov5文件夹,下载依赖文件

pip install -r requirements.txt

深度学习第一步 准备好数据集：

附件中的train.zip。

图片数据集为稀疏商品图片， 格式为jpg，标注文件符合VOC数据集类型，文件格式为xml。

数据集分为 训练集，验证集，测试集，

数据集分割

首先从数据集中抽取一百张图片以及对应的标签文件，作为测试集

接下来是对训练集和验证集进行分类

以下是分类的代码

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 = ['3+2-2', '3jia2', 'aerbeisi', 'anmuxi', 'aoliao', 'asamu', 'baicha', 'baishikele', 'baishikele-2', 'baokuangli', 'binghongcha', 'bingqilinniunai', 'bingtangxueli', 'buding', 'chacui', 'chapai', 'chapai2', 'damaicha', 'daofandian1', 'daofandian2', 'daofandian3', 'daofandian4', 'dongpeng', 'dongpeng-b', 'fenda', 'gudasao', 'guolicheng', 'guolicheng2', 'haitai', 'haochidian', 'haoliyou', 'heweidao', 'heweidao2', 'heweidao3', 'hongniu', 'hongniu2', 'hongshaoniurou', 'jianjiao', 'jianlibao', 'jindian', 'kafei', 'kaomo_gali', 'kaomo_jiaoyan', 'kaomo_shaokao', 'kaomo_xiangcon', 'kebike', 'kele', 'kele-b', 'kele-b-2', 'laotansuancai', 'liaomian', 'libaojian', 'lingdukele', 'lingdukele-b', 'liziyuan', 'lujiaoxiang', 'lujikafei', 'luxiangniurou', 'maidong', 'mangguoxiaolao', 'meiniye', 'mengniu', 'mengniuzaocan', 'moliqingcha', 'nfc', 'niudufen', 'niunai', 'nongfushanquan', 'qingdaowangzi-1', 'qingdaowangzi-2', 'qinningshui', 'quchenshixiangcao', 'rancha-1', 'rancha-2', 'rousongbing', 'rusuanjunqishui', 'suanlafen', 'suanlaniurou', 'taipingshuda', 'tangdaren', 'tangdaren2', 'tangdaren3', 'ufo', 'ufo2', 'wanglaoji', 'wanglaoji-c', 'wangzainiunai', 'weic', 'weitanai', 'weitanai2', 'weitanaiditang', 'weitaningmeng', 'weitaningmeng-bottle', 'weiweidounai', 'wuhounaicha', 'wulongcha', 'xianglaniurou', 'xianguolao', 'xianxiayuban', 'xuebi', 'xuebi-b', 'xuebi2', 'yezhi', 'yibao', 'yida', 'yingyangkuaixian', 'yitengyuan', 'youlemei', 'yousuanru', 'youyanggudong', 'yuanqishui', 'zaocanmofang', 'zihaiguo', '']
 
TRAIN_RATIO = 99  
 
 
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)
 
wd = os.getcwd()
data_base_dir = os.path.join(wd, "train/")
if not os.path.isdir(data_base_dir):
    os.mkdir(data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, "train/")
if not os.path.isdir(work_sapce_dir):
    os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, "xml/")
if not os.path.isdir(annotation_dir):
    os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, "img/")
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, "labels/")
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',encoding='utf-8')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w',encoding='utf-8')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a',encoding='utf-8')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a',encoding='utf-8')
list_imgs = os.listdir(image_dir)  # list image files
prob = random.randint(1, 100)
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)
    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()

分割好的数据集文件夹结构：

- train
    - labels
        - train
        - val
    - images
        - train
        - val

为了符合coco数据集格式，需要将xml标签文件转化为txt格式：

def convert_annotation(image_id):
    in_file = open('train/train/xml/%s.xml' % image_id) # xml路径
    out_file = open('train/train/labels/%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()
 
def convert(size, box): # 边界框的转换
    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)

配置YAML文件

architecture: yolov5s  ##使用yolov5预训练
batch_size: 16
epochs: 50
lr: 0.001
names: ##类别
- 3+2-2
- 3jia2
- aerbeisi
- anmuxi
- aoliao
- asamu
- baicha
- baishikele
- baishikele-2
...
nc: 113  ##类别数目
train: train/images/train ##训练集路径
val: train/images/val ##验证集路径

训练模型：

python train.py --img 640 --batch 16 --epochs 50 --data ./data/my.yaml --cfg ./models/yolov5s.yaml --weights yolov5s.pt  

训练好的模型存在runs/exp/best.pt

TensorRT加速

下载好tensorrt之后

python export.py --weights best.pt --data data/my.yaml --include engine --device 0 --half    

将best.pt导出为best.engine

目标识别

python detect.py --source train/images/val --data data/my.yaml --weights best.engine --device 0