asp.net 核心清洁架构
参考代码如下（具体请看源代码）:
namespace SmartAdmin.Application.Photos.Commands
{
public partial class AddPhotoCommand : IRequest<Result<int>>
{
public Stream Stream { get; set; }
public string FileName { get; set; }
public decimal Size { get; set; }
public string Path { get; set; }
}
internal class AddPhotoCommandHandler : IRequestHandler<AddPhotoCommand, Result<int>>
{
private readonly IUnitOfWork unitOfWork;
private readonly IPhotoService photoService;
public AddPhotoCommandHandler(IUnitOfWork unitOfWork,
IPhotoService photoService)
{
this.unitOfWork = unitOfWork;
this.photoService = photoService;
}
public async Task<Result<int>> Handle(AddPhotoCommand request, CancellationToken cancellationToken)
{
var info = new DirectoryInfo(request.Path);
if (!info.Exists)
{
info.Create();
}
using (FileStream outputFileStream = new FileStream(Path.Combine(request.Path,request.FileName), FileMode.Create))
{
request.Stream.CopyTo(outputFileStream);
outputFileStream.Close();
}
var photo = new Photo()
{
Name = Path.GetFileNameWithoutExtension(request.FileName),
Size = request.Size,
Path = $"/photos/{request.FileName}",
};
this.photoService.Insert(photo);
await this.unitOfWork.SaveChangesAsync();
return await Result<int>.SuccessAsync(0, "保存成功");
}
}
}
完成每一张照片中脸部信息的数字转化
参考代码如下：
function predict() {
const img = document.getElementById(‘photo-canvas‘);
facemesh.predict(img).then(faces => {
console.log(faces)
if (faces) {
const canvas = document.getElementById("photo-canvas");
const photoId=canvas.getAttribute("photo-id");
const photoName=canvas.getAttribute("photo-name");
console.log(canvas)
var draw = canvas.getContext("2d");
var mesh = faces[0].scaledMesh;
console.log(mesh);
/* highlight facial landmark points on canvas board */
draw.fillStyle = "#00FF00";
for (i = 0; i < mesh.length; i++) {
var [x, y, z] = mesh[i];
draw.fillRect(Math.round(x), Math.round(y), 2, 2);
}
updateLandmarks(photoId,JSON.stringify(mesh));
knnClassifier.addExample(mesh, photoName);
canvas.setAttribute("photo-mesh", JSON.stringify(mesh));
$(‘#testbutton‘).attr(‘disabled‘, false);
}
});
}
function updateLandmarks(id,landmarks){
$.post(‘/Photos/Update‘,{Id:id,Landmarks:landmarks}).done(res=>{
console.log(res);
reload();
}).fail(res=>{
$.messager.alert(‘更新失败‘, res, ‘error‘);
})
}
添加分类识别样本数据
facemesh模型只负责把照片中面部特征转换成一个数组，如果需要对每一张照片的数据再进行分类就需要用到KNN模型,添加的样本数据越多,识别的就越正确。
参考代码：
let knnClassifier =ml5.KNNClassifier();
function training(){
$.messager.progress({msg:‘training....‘});
$.get(‘/Photos/GetAll‘).done(res=>{
for(let i=0;i<50;i++){
res.map(item=>{
if(item.Landmarks){
knnClassifier.addExample(JSON.parse(item.Landmarks), item.Name);
}
});
}
$.messager.progress(‘close‘)
if(knnClassifier.getNumLabels()>0){
knnClassifier.classify(JSON.parse(res[2].Landmarks),(err,result)=>{
console.log(result);
})
$(‘#testbutton‘).attr(‘disabled‘, false);
}
})
}
function testPredict(){
const img = document.getElementById(‘testphoto_img‘);
facemesh.predict(img).then(faces => {
console.log(faces)
if (faces) {
knnClassifier.classify(faces[0].scaledMesh,(err,result)=>{
console.log(result);
$.messager.alert(‘Result:‘,result.label);
$(‘#testresult‘).text(result.label);
})
}
});
}