/// <summary> /// 活体检测器。 /// <para> /// 视频帧图片,由 <paramref name="global"/> 指定是否启用全局检测能力 <br /> /// </para> /// <para>如果返回结果为 <see cref="AntiSpoofingStatus.Detecting"/>,则说明需要继续调用此方法,传入更多的图片</para> /// </summary> /// <param name="viewFace"></param> /// <param name="bitmaps">一组图片,即视频帧的 <see cref="Bitmap"/> 数组</param> /// <param name="faceIndex">指定要识别的人脸索引</param> /// <param name="global">是否启用全局检测能力</param> /// <returns></returns> public static AntiSpoofingStatus AntiSpoofingVideo(this ViewFace viewFace, Bitmap[] bitmaps, int faceIndex, bool global) { var result = AntiSpoofingStatus.Detecting; bool haveFace = false; foreach (var bitmap in bitmaps) { var infos = viewFace.FaceDetector(bitmap); if (faceIndex >= 0 && faceIndex < infos.Length) { haveFace = true; var points = viewFace.FaceMark(bitmap, infos[faceIndex]); var status = viewFace.AntiSpoofingVideo(bitmap, infos[faceIndex], points, global); if (status == AntiSpoofingStatus.Detecting) { continue; } else { result = status; } } } if (haveFace) { return(result); } else { return(AntiSpoofingStatus.Error); } }
public void TestAntiSpoofingVideo(bool global) { if (!(Image.FromFile(images[0]) is Bitmap bitmap)) { throw new ArgumentNullException(nameof(bitmap)); } ViewFace viewFace = new ViewFace((str) => { System.Console.WriteLine(str); }); var infos = viewFace.FaceDetector(bitmap); if (infos.Length > 0) { var points = viewFace.FaceMark(bitmap, infos[0]); AntiSpoofingStatus status = AntiSpoofingStatus.Error; while (true) { status = viewFace.AntiSpoofingVideo(bitmap, infos[0], points, global); if (status != AntiSpoofingStatus.Detecting) { break; } } Console.WriteLine(status); } else { Console.WriteLine("No Face!"); } bitmap.Dispose(); }
static void Main() { // 老图片路径 string oldImgPath = @"C:/Users/yangw/OneDrive/桌面/image/0.jpg"; string newImgPath = @"C:/Users/yangw/OneDrive/桌面/image/1.jpg"; ViewFace viewFace = new ViewFace((str) => { Debug.WriteLine(str); }); // 初始化人脸识别类,并设置 日志回调函数 // 系统默认使用的轻量级识别模型。如果对精度有要求,请切换到 Normal 模式;并下载需要模型文件 放入生成目录的 model 文件夹中 viewFace.FaceType = FaceType.Normal; // 系统默认使用5个人脸关键点。//不建议改动,除非是使用口罩模型。 viewFace.MarkType = MarkType.Light; #region 识别老照片 float[] oldEigenValues; Bitmap oldImg = (Bitmap)Image.FromFile(oldImgPath); // 从文件中加载照片 // 或者视频帧等 var oldFaceInfos = viewFace.FaceDetector(oldImg); // 检测图片中包含的人脸信息。(置信度、位置、大小) oldFaceInfos = viewFace.FaceDetectorAsync(oldImg).GetAwaiter().GetResult(); // 异步检测图片中的人脸信息 if (oldFaceInfos.Length > 0) //识别到人脸 { { // 打印人脸信息 Console.WriteLine($"识别到的人脸数量:{oldFaceInfos.Length} 。人脸信息:\n"); Console.WriteLine($"序号\t人脸置信度\t位置X\t位置Y\t宽度\t高度"); for (int i = 0; i < oldFaceInfos.Length; i++) { Console.WriteLine($"{i + 1}\t{oldFaceInfos[i].Score:f8}\t{oldFaceInfos[i].Location.X}\t{oldFaceInfos[i].Location.Y}\t{oldFaceInfos[i].Location.Width}\t{oldFaceInfos[i].Location.Height}"); } Console.WriteLine(); } var oldPoints = viewFace.FaceMark(oldImg, oldFaceInfos[0]); // 获取 第一个人脸 的识别关键点。(人脸识别的关键点数据) oldEigenValues = viewFace.Extract(oldImg, oldPoints); // 获取 指定的关键点 的特征值。 var state = viewFace.AntiSpoofing(oldImg, oldFaceInfos[0], oldPoints); Console.WriteLine($"单帧 活体检测结果:{state}"); state = viewFace.AntiSpoofingVideo(oldImg, oldFaceInfos[0], oldPoints); Console.WriteLine($"视频 活体检测结果:{state}"); Console.WriteLine(); #region 质量评估 var qualityBrightness = viewFace.FaceQuality(oldImg, oldFaceInfos[0], oldPoints, QualityType.Brightness); Console.WriteLine($"亮度:Level [{qualityBrightness.Level}] - Score [{qualityBrightness.Score}]"); var qualityClarity = viewFace.FaceQuality(oldImg, oldFaceInfos[0], oldPoints, QualityType.Clarity); Console.WriteLine($"清晰度:Level [{qualityClarity.Level}] - Score [{qualityClarity.Score}]"); var qualityIntegrity = viewFace.FaceQuality(oldImg, oldFaceInfos[0], oldPoints, QualityType.Integrity); Console.WriteLine($"完整度:Level [{qualityIntegrity.Level}] - Score [{qualityIntegrity.Score}]"); var qualityPose = viewFace.FaceQuality(oldImg, oldFaceInfos[0], oldPoints, QualityType.Pose); Console.WriteLine($"姿态:Level [{qualityPose.Level}] - Score [{qualityPose.Score}]"); var qualityPoseEx = viewFace.FaceQuality(oldImg, oldFaceInfos[0], oldPoints, QualityType.PoseEx); Console.WriteLine($"姿态 (深度):Level [{qualityPoseEx.Level}] - Score [{qualityPoseEx.Score}]"); var qualityResolution = viewFace.FaceQuality(oldImg, oldFaceInfos[0], oldPoints, QualityType.Resolution); Console.WriteLine($"分辨率:Level [{qualityResolution.Level}] - Score [{qualityResolution.Score}]"); var qualityStructure = viewFace.FaceQuality(oldImg, oldFaceInfos[0], oldPoints, QualityType.Structure); Console.WriteLine($"遮挡:Level [{qualityStructure.Level}] - Score [{qualityStructure.Score}]"); var qualityClarityEx = viewFace.FaceQuality(oldImg, oldFaceInfos[0], oldPoints, QualityType.ClarityEx); Console.WriteLine($"清晰度 (深度):Level [{qualityClarityEx.Level}] - Score [{qualityClarityEx.Score}]"); #endregion } else { oldEigenValues = new float[0]; /*未识别到人脸*/ } #endregion Console.WriteLine(); #region 人脸追踪 var trackFaceInfos = viewFace.FaceTrack(oldImg); // 人脸追踪 if (trackFaceInfos.Length > 0) { { // 打印人脸信息 Console.WriteLine($"\r\n跟踪到的人脸数量:{trackFaceInfos.Length} 。人脸信息:\n"); Console.WriteLine($"PID\t人脸置信度\t位置X\t位置Y\t宽度\t高度"); for (int i = 0; i < trackFaceInfos.Length; i++) { Console.WriteLine($"{trackFaceInfos[i].Pid}\t{trackFaceInfos[i].Score:f8}\t{trackFaceInfos[i].Location.X}\t{trackFaceInfos[i].Location.Y}\t{trackFaceInfos[i].Location.Width}\t{trackFaceInfos[i].Location.Height}"); } } } #endregion Console.WriteLine(); #region 识别新照片 float[] newEigenValues; Bitmap newImg = (Bitmap)Image.FromFile(newImgPath /*新图片路径*/); // 从文件中加载照片 // 或者视频帧等 newImg = (Bitmap)newImg.ChangeSize(new Size(1024, 768)); var newFaces = viewFace.FaceDetector(newImg); // 检测图片中包含的人脸信息。(置信度、位置、大小) if (newFaces.Length > 0) //识别到人脸 { { // 打印人脸信息 Console.WriteLine($"识别到的人脸数量:{newFaces.Length} 。人脸信息:\n"); Console.WriteLine($"序号\t人脸置信度\t位置X\t位置Y\t宽度\t高度"); for (int i = 0; i < newFaces.Length; i++) { Console.WriteLine($"{i + 1}\t{newFaces[i].Score:f8}\t{newFaces[i].Location.X}\t{newFaces[i].Location.Y}\t{newFaces[i].Location.Width}\t{newFaces[i].Location.Height}"); } } var newPoints = viewFace.FaceMark(newImg, newFaces[0]); // 获取 第一个人脸 的识别关键点。(人脸识别的关键点数据) newEigenValues = viewFace.Extract(newImg, newPoints); // 获取 指定的关键点 的特征值。 } else { newEigenValues = new float[0]; /*未识别到人脸*/ } #endregion Console.WriteLine(); try { float similarity = viewFace.Similarity(oldEigenValues, newEigenValues); // 对比两张照片上的数据,确认是否是同一个人。 Console.WriteLine($"阈值 = {FaceCompareConfig.GetThreshold(viewFace.FaceType)}\t相似度 = {similarity}"); Console.WriteLine($"是否是同一个人:{viewFace.IsSelf(similarity)}"); } catch (Exception e) { Console.WriteLine(e.Message); } Console.ReadKey(); }