jinko.yikatong/Src/Dlhis.InfoCheck/Controllers/FaceController.cs

using Microsoft.AspNetCore.Mvc;
using SkiaSharp;
using ViewFaceCore.Core;
using ViewFaceCore.Model;
using ViewFaceCore;
using ViewFaceCore.Configs;
using Dlhis.InfoCheck.Request;
using Dlhis.InfoCheck.Response;
using Kurisu.AspNetCore.UnifyResultAndValidation.Aop;
using Kurisu.AspNetCore.Utils.Extensions;

namespace Dlhis.InfoCheck.Controllers;

[ApiController]
[Route("api/[controller]")]
public class FaceController : ControllerBase
{
    private readonly ILogger<FaceController> _logger;
    private readonly IHttpContextAccessor _httpContextAccessor;

    public FaceController(ILogger<FaceController> logger, IHttpContextAccessor httpContextAccessor)
    {
        _logger = logger;
        _httpContextAccessor = httpContextAccessor;
    }

    /// <summary>
    /// 人脸质量检测
    /// </summary>
    /// <param name="request"></param>
    /// <returns></returns>
    [DisableApiLog]
    [HttpPost("detection")]
    public IEnumerable<string> FacialDetection(FaceDetectRequest request)
    {
        _logger.LogInformation("currentIP:{IP}", _httpContextAccessor.HttpContext.Connection.RemoteIpAddress);
        
        var base64Bytes = Convert.FromBase64String(request.Base64);
        using var stream = new MemoryStream(base64Bytes);
        using var bitmap = SKBitmap.Decode(stream);
        using var faceDetector = new FaceDetector();

        using var faceMark = new FaceLandmarker(new FaceLandmarkConfig
        {
            MarkType = MarkType.Light
        });

        var faces = faceDetector.Detect(bitmap);
        if (faces.Length == 0)
        {
            return new List<string> { "未检测到人脸" };
        }

        var info = faces.First();
        var markPoints = faceMark.Mark(bitmap, info);

        using var faceQuality = new FaceQuality();
        var qualityResult = new List<QualityResultEx>(6);
        qualityResult.Add(faceQuality.DetectEx(bitmap, info, markPoints, QualityType.Brightness));

        qualityResult.Add(faceQuality.DetectEx(bitmap, info, markPoints, QualityType.Resolution));
        //var clarityResult = faceQuality.Detect(bitmap, info, markPoints, QualityType.Clarity);
        qualityResult.Add(faceQuality.DetectEx(bitmap, info, markPoints, QualityType.ClarityEx));
        qualityResult.Add(faceQuality.DetectEx(bitmap, info, markPoints, QualityType.Integrity));
        qualityResult.Add(faceQuality.DetectEx(bitmap, info, markPoints, QualityType.Structure));
        //var poseResult = faceQuality.Detect(bitmap, info, markPoints, QualityType.Pose);
        qualityResult.Add(faceQuality.DetectEx(bitmap, info, markPoints, QualityType.PoseEx));

        return request.CheckList.Where(x =>
        {
            var r = qualityResult.First(item => item.Type == x.Type);
            return r.Level < x.Level;
        }).Select(x => x.Type.GetDescription().Replace("评估", string.Empty) + "不符合");
    }

    /// <summary>
    /// 图片识别对比
    /// </summary>
    /// <param name="request"></param>
    /// <returns></returns>
    [HttpPost("recognize")]
    public FaceRecognizeResponse FacialRecognize(FaceRecognizeRequest request)
    {
        var firstBase64Bytes = Convert.FromBase64String(request.FirstBase64);
        using var firstStream = new MemoryStream(firstBase64Bytes);
        using var firstBitmap = SKBitmap.Decode(firstStream);

        var secondBase64Bytes = Convert.FromBase64String(request.SecondBase64);
        using var secondStream = new MemoryStream(secondBase64Bytes);
        using var secondBitmap = SKBitmap.Decode(secondStream);

        using var recognizer = new FaceRecognizer(new FaceRecognizeConfig());

        using var faceDetector = new FaceDetector();
        using var faceMark = new FaceLandmarker();

        //todo
        var lft = recognizer.Extract(firstBitmap, faceMark.Mark(firstBitmap, faceDetector.Detect(firstBitmap).First()));
        var rft = recognizer.Extract(secondBitmap, faceMark.Mark(secondBitmap, faceDetector.Detect(secondBitmap).First()));

        var similarity = recognizer.Compare(lft, rft);
        var isSelf = recognizer.IsSelf(similarity);

        return new FaceRecognizeResponse
        {
            Similarity = similarity,
            IsSelf = isSelf
        };
    }
}