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Add PP_Human_Fall_Detection project.
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tutorial_examples/PP-Human_Fall_Detection/HumanFallDown.cs
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| using OpenCvSharp; | ||
| using PP_Human; | ||
| using System; | ||
| using System.Collections.Generic; | ||
| using System.Linq; | ||
| using System.Text; | ||
| using System.Threading.Tasks; | ||
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| namespace PP_Human_Fall_Detection | ||
| { | ||
| /// <summary> | ||
| /// 测试人体摔倒检测 | ||
| /// </summary> | ||
| public class HumanFallDown | ||
| { | ||
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| /// <summary> | ||
| /// 人体摔倒检测 | ||
| /// </summary> | ||
| static public void human_fall_down(string yoloe_path, string tinypose_path, string STGCN_path, string device_name) | ||
| { | ||
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| // 定义模型预测器 | ||
| // yoloe 模型预测 | ||
| YOLOE yoloe_predictor = new YOLOE(yoloe_path, device_name); | ||
| Console.WriteLine("目标检测模型加载成功！！"); | ||
| // tinypose 预测器 | ||
| TinyPose tinyPose_predictor = new TinyPose(tinypose_path, device_name); | ||
| Console.WriteLine("关键点识别模型加载成功！！"); | ||
| // STGCN 模型 | ||
| STGCN stgcn_predictor = new STGCN(STGCN_path, device_name); | ||
| Console.WriteLine("摔倒识别模型加载成功！！"); | ||
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| // 测试视频信息 | ||
| // 视频路径 | ||
| string test_video = "/home/ygj/Program/PP-Human_Fall_Detection/demo/摔倒.mp4"; | ||
| //string test_video = @"E:\Git_space\基于Csharp和OpenVINO部署PP-Human\demo\摔倒2.mp4"; | ||
| // 视频读取器 | ||
| VideoCapture video_capture = new VideoCapture(test_video); | ||
| // 视频帧率 | ||
| double fps = video_capture.Fps; | ||
| // 视频帧数 | ||
| int frame_count = video_capture.FrameCount; | ||
| Console.WriteLine("video fps: {0}, frame_count: {1}", Math.Round(fps), frame_count); | ||
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| // 定义相关变量 | ||
| // 视频帧 | ||
| int frame_id = 0; | ||
| // 视频帧图像 | ||
| Mat frame = new Mat(); | ||
| // 可视化 | ||
| Mat visualize_frame = new Mat(); | ||
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| // 关键点处理类 | ||
| MotPoint mot_point = new MotPoint(); | ||
| // 关键点快开始预测标志位 | ||
| bool flag_stgcn = false; | ||
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| // 可视化窗口 | ||
| Window window = new Window("image", WindowFlags.AutoSize); | ||
| //window.Resize(100, 100); | ||
| // 摔倒预测结果 | ||
| KeyValuePair<string, float> fall_down_result = new KeyValuePair<string, float>("unfalling", 1.0f); | ||
| // 设置起始帧 | ||
| //video_capture.Set(VideoCaptureProperties.PosFrames, 100); | ||
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| // 创建视频保存器 | ||
| VideoWriter video_writer = new VideoWriter(@"output.avi", | ||
| FourCC.MP42, 30, new Size(video_capture.FrameWidth, video_capture.FrameHeight)); | ||
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| while (true) | ||
| { | ||
| // 判断视频是否打开 | ||
| if (!video_capture.IsOpened()) | ||
| { | ||
| Console.WriteLine("视频打开失败！！"); | ||
| break; | ||
| } | ||
| // 每10帧打印一次过程 | ||
| if (frame_id % 10 == 0) | ||
| { | ||
| Console.WriteLine("检测进程 frame id: {0} - {1}", frame_id, frame_id + 10); | ||
| } | ||
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| // 读取视频帧 | ||
| if (!video_capture.Read(frame)) | ||
| { | ||
| Console.WriteLine("视频读取完毕！！{0}", frame_id); | ||
| break; | ||
| } | ||
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| // 复制可视化图片 | ||
| visualize_frame = frame.Clone(); | ||
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| //****************************1. 行人区域识别 ******************************// | ||
| ResBboxs person_result = yoloe_predictor.predict(frame); | ||
| // 判断是否识别到人 | ||
| if (person_result.bboxs.Count < 1) | ||
| { | ||
| continue; | ||
| } | ||
| // 绘制行人区域 | ||
| yoloe_predictor.draw_boxes(person_result, ref visualize_frame); | ||
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| //****************************2. 行人关键点识别 ******************************// | ||
| //裁剪行人区域 | ||
| List<Rect> point_rects; | ||
| List<Mat> person_rois = tinyPose_predictor.get_point_roi(frame, person_result.bboxs, out point_rects); | ||
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| for (int p = 0; p < person_rois.Count; p++) | ||
| { | ||
| Cv2.ImShow("www", person_rois[p]); | ||
| // 关键点识别 | ||
| float[,] person_point = tinyPose_predictor.predict(person_rois[p]); | ||
| KeyPoints key_point = new KeyPoints(frame_id, person_point, point_rects[p]); | ||
| //for (int i = 0; i < 17; ++i) { | ||
| // Console.WriteLine(key_point.points[i,0] + " " + key_point.points[i, 1]); | ||
| //} | ||
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| flag_stgcn = mot_point.add_point(key_point); | ||
| tinyPose_predictor.draw_poses(key_point, ref visualize_frame); | ||
| } | ||
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| //****************************3. 行人摔倒识别 ******************************// | ||
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| if (flag_stgcn) | ||
| { | ||
| List<List<KeyPoints>> predict_points = mot_point.get_points(); | ||
| for (int p = 0; p < predict_points.Count; p++) | ||
| { | ||
| Console.WriteLine(predict_points[p].Count); | ||
| fall_down_result = stgcn_predictor.predict(predict_points[p]); | ||
| } | ||
| } | ||
| stgcn_predictor.draw_result(ref visualize_frame, fall_down_result, person_result.bboxs[0]); | ||
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| window.ShowImage(visualize_frame); | ||
| video_writer.Write(visualize_frame); | ||
| Cv2.WaitKey(1); | ||
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| frame_id++; // 帧号累加 | ||
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| } | ||
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| yoloe_predictor.release(); | ||
| tinyPose_predictor.release(); | ||
| stgcn_predictor.release(); | ||
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| visualize_frame.Release(); | ||
| video_capture.Release(); | ||
| } | ||
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| } | ||
| } |
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tutorial_examples/PP-Human_Fall_Detection/OpenVINO-CSharp-API/代码资源.txt
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| @@ -0,0 +1 @@ | ||
| 请将OpenVINO C# API 中的代码放置在当前文件夹。 |
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tutorial_examples/PP-Human_Fall_Detection/PP-Human/Common.cs
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| Original file line number | Diff line number | Diff line change |
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| using System; | ||
| using System.Collections.Generic; | ||
| using System.Linq; | ||
| using System.Text; | ||
| using System.Threading.Tasks; | ||
| using OpenCvSharp; | ||
|
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| namespace PP_Human | ||
| { | ||
| /// <summary> | ||
| /// 识别检测结果类 | ||
| /// </summary> | ||
| public class ResBboxs | ||
| { | ||
| public List<Rect> bboxs; // 预测框 | ||
| public float[] scores; // 预测分数 | ||
| public int[] classes; // 预测分类 | ||
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| /// <summary> | ||
| /// 构造函数 | ||
| /// </summary> | ||
| /// <param name="bboxs">预测框</param> | ||
| /// <param name="scores">预测分数</param> | ||
| /// <param name="classes">预测分类</param> | ||
| public ResBboxs(List<Rect> bboxs, float[] scores, int[] classes) | ||
| { | ||
| this.bboxs = bboxs; | ||
| this.scores = scores; | ||
| this.classes = classes; | ||
| } | ||
| } | ||
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| public class KeyPoints | ||
| { | ||
| // 视频帧 | ||
| public int mot_id { get; set; } | ||
| // 关键点和分数 | ||
| public float[,] points { get; set; } | ||
| // 行人位置框 | ||
| public Rect bbox { get; set; } | ||
| // 识别框中心 | ||
| public Point center { get; set; } | ||
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| public KeyPoints(int mot_id, float[,] points, Rect bbox) | ||
| { | ||
| this.mot_id = mot_id; | ||
| this.points = (float[,])points.Clone(); | ||
| this.bbox = bbox; | ||
| this.center = new Point((int)(bbox.X + bbox.Width / 2), (int)(bbox.Y + bbox.Height / 2)); | ||
| } | ||
| } | ||
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| public class MotPoint | ||
| { | ||
| public List<List<KeyPoints>> mot_point; | ||
| public bool flag_predict=false; | ||
| public List<int> predict_id = new List<int>(); | ||
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| public MotPoint() | ||
| { | ||
| mot_point = new List<List<KeyPoints>>(); | ||
| } | ||
| public bool add_point(KeyPoints point) | ||
| { | ||
| bool flag = false; | ||
| for (int c = 0; c < mot_point.Count; c++) | ||
| { | ||
| List<KeyPoints> points = mot_point[c]; | ||
| // 添加新的的关键点 | ||
| Point center1 = point.center; | ||
| Point center2 = points[points.Count - 1].center; | ||
| if (Math.Sqrt(Math.Pow((center1.X - center2.X), 2.0) + Math.Pow((center1.Y - center2.Y), 2.0)) < 500) | ||
| { | ||
| mot_point[c].Add(point); | ||
| flag = true; | ||
| } | ||
| // 判断是否满足预测条件 | ||
| if (mot_point[c].Count >= 50) | ||
| { | ||
| flag_predict = true; | ||
| predict_id.Add(c); | ||
| } | ||
| } | ||
| if (!flag) | ||
| { | ||
| mot_point.Add(new List<KeyPoints>()); | ||
| mot_point[mot_point.Count-1].Add(point); | ||
| return false; | ||
| } | ||
| //Console.WriteLine(mot_point.Count); | ||
| return flag_predict; | ||
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| } | ||
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| public List<List<KeyPoints>> get_points() | ||
| { | ||
| List <List<KeyPoints>> points = new List<List <KeyPoints>> (); | ||
| if (flag_predict) | ||
| { | ||
| for (int l = 0; l < predict_id.Count; l++) | ||
| { | ||
| List<KeyPoints> new_points = new List<KeyPoints> (mot_point[predict_id[l]]); | ||
| points.Add(new_points); | ||
| Console.WriteLine(mot_point.Count); | ||
| for (int i = 0; i < 20; i++) | ||
| { | ||
| mot_point[predict_id[l]].RemoveAt(0); | ||
| } | ||
| flag_predict = false; | ||
| } | ||
| flag_predict = false; | ||
| predict_id.Clear(); | ||
| } | ||
| return points; | ||
| } | ||
| } | ||
| } |
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