使用Anaconda来管理Python虚拟环境及包
安装包时一定要注意版本对应关系!!!包列表会在页尾列出
查看本机GPU信息:
nvidia-smi
安装gpu版本Pytorch,注意CUDA版本Release Notes :: CUDA Toolkit Documentation (nvidia.com)
查看CUDA是否安装:
nvcc -V
本人的安装命令,仅供参考:
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
版本对应超级重要!!!目前都是cu117版本,可以使用conda list检查 当前构建版本CUDA 11.7
import torch #导入torch包
torch.cuda.device_count() #查看GPU数,需要大于0
torch.cuda.is_available() #验证GPU是否已激活,需要是true
YoloV5官方的参考训练命令:
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
这里演示的话我就用官方训练好的 yolov5m.pt 模型。
这里我自己的训练命令:
python train.py --data coco.yaml --epochs 10 --weights yolov5m.pt --batch-size 8
这里需要注意的参数是epochs训练轮数,weights预训练模型,以及batch-size每次输入大小 各项参数并非越高越好,需要多次验证,选择最适合的参数
这里因为我只做人流量监测,所以做了筛选,删除即可正常检测80种类别
def detect(self, im):
im0, img = self.preprocess(im)
pred = self.m(img, augment=False)[0]
pred = pred.float()
pred = non_max_suppression(pred, self.threshold, 0.3)
pred_boxes = []
image_info = {}
count = 0
for det in pred:
if det is not None and len(det):
det[:, :4] = scale_coords(
img.shape[2:], det[:, :4], im0.shape).round()
for *x, conf, cls_id in det:
lbl = self.names[int(cls_id)] #用个中间参来保存人物类型名称
x1, y1 = int(x[0]), int(x[1])
x2, y2 = int(x[2]), int(x[3])
pred_boxes.append(
(x1, y1, x2, y2, lbl, conf))
# count += 1
key = ""
if lbl == "person" : #只输出【人】
count += 1
key = '{}-{:02}'.format(lbl, count) #输出类型和编号
image_info[key] = ['{}×{}'.format(
x2-x1, y2-y1), np.round(float(conf), 3)] #输出大小和置信度
#数据库操作模块
db = SQLManager()
keydb = '\'' + key + '\''#目标代号
image_info01db = '\'' + '{}×{}'.format(x2-x1, y2-y1) + '\''#图像大小
image_info02db = '\'' + str(np.round(float(conf), 3)) + '\''#置信度
datatimenow = '\'' + str(datetime.datetime.now().replace(microsecond=0)) + '\''#时间戳
sql = 'INSERT INTO imginfo VALUES(' + keydb + ' ,' + image_info01db + ' ,' + image_info02db + ' ,' + datatimenow + ');'
db.moddify(sql)
db.close()
【app.py】
路由示例
@app.route('/')
def hello_world():
return redirect(url_for('static', filename='./index.html'))
数据库操作示例
@app.route('/testdb', methods=['GET', 'POST'])
def testdb():
db = SQLManager()
show_data_db = db.get_list('select * from imginfo ')
db.close()
return jsonify({'status': 1,
'historical_data': show_data_db})
摄像头模块
getCompetence () {
this.showbutton = false;
var _this = this
this.thisCancas = document.getElementById('canvasCamera')
this.thisContext = this.thisCancas.getContext('2d')
this.thisVideo = document.getElementById('videoCamera')
// this.switchdisvalue = !this.switchdisvalue //必须开启摄像头才可以调整自动模式【功能更新,开关仅作为示意】
// 旧版本浏览器可能根本不支持mediaDevices,我们首先设置一个空对象
if (navigator.mediaDevices === undefined) {
navigator.mediaDevices = {}
}
// 一些浏览器实现了部分mediaDevices,我们不能只分配一个对象
// 使用getUserMedia,因为它会覆盖现有的属性。
// 这里,如果缺少getUserMedia属性,就添加它。
if (navigator.mediaDevices.getUserMedia === undefined) {
navigator.mediaDevices.getUserMedia = function (constraints) {
// 首先获取现存的getUserMedia(如果存在)
var getUserMedia = navigator.webkitGetUserMedia || navigator.mozGetUserMedia || navigator.getUserMedia
// 有些浏览器不支持,会返回错误信息
// 保持接口一致
if (!getUserMedia) {
return Promise.reject(new Error('getUserMedia is not implemented in this browser'))
}
// 否则,使用Promise将调用包装到旧的navigator.getUserMedia
return new Promise(function (resolve, reject) {
getUserMedia.call(navigator, constraints, resolve, reject)
})
}
}
var constraints = { audio: false, video: { width: this.videoWidth, height: this.videoHeight, transform: 'scaleX(-1)' } }
navigator.mediaDevices.getUserMedia(constraints).then(function (stream) {
// 旧的浏览器可能没有srcObject
if ('srcObject' in _this.thisVideo) {
_this.thisVideo.srcObject = stream
} else {
// 避免在新的浏览器中使用它,因为它正在被弃用。
_this.thisVideo.src = window.URL.createObjectURL(stream)
}
_this.thisVideo.onloadedmetadata = function (e) {
_this.thisVideo.play()
}
}).catch(err => {
console.log(err)
})
},
拍照上传模块,文件手动上传与其类似
setImage () {
var _this = this
// 点击,canvas画图
_this.thisContext.drawImage(_this.thisVideo, 0, 0, _this.videoWidth, _this.videoHeight)
// 获取图片base64链接
var image = this.thisCancas.toDataURL('image/png')
_this.imgSrc = image
this.$emit('refreshDataList', this.imgSrc)
this.percentage = 0;
this.dialogTableVisible = true;
this.url_1 = "";
this.url_2 = "";
this.srcList = [];
this.srcList1 = [];
this.wait_return = "";
this.wait_upload = "";
this.feature_list = [];
this.feat_list = [];
this.fullscreenLoading = true;
this.loading = true;
//this.showbutton = false;
this.person_num = 0;
this.camerafilename = this.getCurrentDateTime();
let file = this.dataURLtoFile(this.imgSrc, "camerafile"+ this.camerafilename +".jpg");//存储文件时,重命名打上时间戳留档
this.url_1 = this.$options.methods.getObjectURL(file);
let param = new FormData(); //创建form对象
param.append("file", file, file.name); //通过append向form对象添加数据
var timer = setInterval(() => {
this.myFunc();
}, 30);
let config = {
headers: { "Content-Type": "multipart/form-data" },
}; //添加请求头
axios
.post(this.server_url + "/upload", param, config)
.then((response) => {
this.percentage = 100;
clearInterval(timer);
this.url_1 = response.data.image_url;
this.srcList.push(this.url_1);
this.url_2 = response.data.draw_url;
this.srcList1.push(this.url_2);
this.fullscreenLoading = false;
this.loading = false;
this.feat_list = Object.keys(response.data.image_info);
for (var i = 0; i < this.feat_list.length; i++) {
response.data.image_info[this.feat_list[i]][2] = this.feat_list[i];
this.feature_list.push(response.data.image_info[this.feat_list[i]]);
this.person_num++;//人数加一
}
if (this.person_num < this.numAlert) {
this.noticeSafe();
} else {
this.noticeAlert();
}
this.feature_list.push(response.data.image_info);
this.feature_list_1 = this.feature_list[0];
this.dialogTableVisible = false;
this.percentage = 0;
this.notice1();
});
},
在 Flask 后端项目下启动后端代码:
python app.py
在 VUE 前端项目下,先安装依赖:
npm install
然后运行前端:
npm run serve
如果遇到opensslError,可以使用:
set NODE_OPTIONS=--openssl-legacy-provider
然后在浏览器打开localhost即可:
-V1.0 初始化项目,基础功能构建
-V1.1 修改路径引用方式,支持生成独立部署版本,可大幅度减轻客户机配置压力
前端生成命令
npm run build #先使用npm构建
pkg -t win package.json #再使用pkg打包
后端生成命令
pyinstaller app.spec #app.spec文件可以使用pyinstaller -F app.py命令自动生成,主要需要修改datas和hiddenimports字段
Python 3.10.6
absl-py 1.3.0 <pip>
asttokens 2.1.0 <pip>
backcall 0.2.0 <pip>
bzip2 1.0.8 he774522_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
ca-certificates 2022.10.11 haa95532_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
cachetools 5.2.0 <pip>
certifi 2022.9.24 py310haa95532_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
charset-normalizer 2.1.1 <pip>
click 8.1.3 <pip>
colorama 0.4.6 <pip>
contourpy 1.0.6 <pip>
cycler 0.11.0 <pip>
decorator 5.1.1 <pip>
easydict 1.10 <pip>
executing 1.2.0 <pip>
Flask 2.2.2 <pip>
Flask-Cors 3.0.10 <pip>
Flask-SQLAlchemy 3.0.3 <pip>
fonttools 4.38.0 <pip>
google-auth 2.14.1 <pip>
google-auth-oauthlib 0.4.6 <pip>
greenlet 2.0.2 <pip>
grpcio 1.50.0 <pip>
idna 3.4 <pip>
ipython 8.6.0 <pip>
itsdangerous 2.1.2 <pip>
jedi 0.18.1 <pip>
Jinja2 3.1.2 <pip>
kiwisolver 1.4.4 <pip>
libffi 3.4.2 hd77b12b_4 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
Markdown 3.4.1 <pip>
MarkupSafe 2.1.1 <pip>
matplotlib 3.6.2 <pip>
matplotlib-inline 0.1.6 <pip>
numpy 1.23.4 <pip>
oauthlib 3.2.2 <pip>
opencv-python 4.5.4.60 <pip>
openssl 1.1.1s h2bbff1b_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
packaging 21.3 <pip>
pandas 1.5.1 <pip>
parso 0.8.3 <pip>
pickleshare 0.7.5 <pip>
Pillow 9.3.0 <pip>
pip 22.2.2 py310haa95532_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
prompt-toolkit 3.0.32 <pip>
protobuf 3.20.1 <pip>
psutil 5.9.4 <pip>
pure-eval 0.2.2 <pip>
pyasn1 0.4.8 <pip>
pyasn1-modules 0.2.8 <pip>
Pygments 2.13.0 <pip>
PyMySQL 1.0.2 <pip>
pyparsing 3.0.9 <pip>
python 3.10.6 hbb2ffb3_1 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
python-dateutil 2.8.2 <pip>
pytz 2022.6 <pip>
PyYAML 6.0 <pip>
requests 2.28.1 <pip>
requests-oauthlib 1.3.1 <pip>
rsa 4.9 <pip>
scipy 1.9.3 <pip>
seaborn 0.12.1 <pip>
setuptools 65.5.0 py310haa95532_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
six 1.16.0 <pip>
SQLAlchemy 2.0.5.post1 <pip>
sqlite 3.39.3 h2bbff1b_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
stack-data 0.6.1 <pip>
tensorboard 2.11.0 <pip>
tensorboard-data-server 0.6.1 <pip>
tensorboard-plugin-wit 1.8.1 <pip>
thop-0.1.1 2209072238 <pip>
tk 8.6.12 h2bbff1b_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
torch 1.13.0+cu117 <pip>
torchaudio 0.13.0+cu117 <pip>
torchvision 0.14.0+cu117 <pip>
tqdm 4.64.1 <pip>
traitlets 5.5.0 <pip>
typing_extensions 4.4.0 <pip>
tzdata 2022f h04d1e81_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
urllib3 1.26.12 <pip>
vc 14.2 h21ff451_1 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
vs2015_runtime 14.27.29016 h5e58377_2 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
wcwidth 0.2.5 <pip>
Werkzeug 2.2.2 <pip>
wheel 0.37.1 pyhd3eb1b0_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
wincertstore 0.2 py310haa95532_2 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
xz 5.2.6 h8cc25b3_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
zlib 1.2.13 h8cc25b3_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
🤞YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite