介紹
This model is a lightweight facedetection model designed for edge computing devices.
In terms of model size, the default FP32 precision (.pth) file size is 1.04~1.1MB, and the inference framework int8 quantization size is about 300KB. In terms of the calculation amount of the model, the input resolution of 320x240 is about 90~109 MFlops.
There are two versions of the model, version-slim (network backbone simplification,slightly faster) and version-RFB (with the modified RFB module, higher precision).
Widerface training pre-training model with different input resolutions of 320x240 and 640x480 is provided to better work in different application scenarios.
Support for onnx export for ease of migration and inference.
效果
模型資訊
Inputs
-------------------------
name：input
tensor：Float[1, 3, 240, 320]
---------------------------------------------------------------
Outputs
-------------------------
name：batchno_ classid_score_x1y1x2y2
tensor：Float[-1, 7]
---------------------------------------------------------------
計畫
VS2022
.net framework 4.8
OpenCvSharp 4.8
Microsoft.ML.OnnxRuntime 1.16.2
程式碼
using Microsoft.ML.OnnxRuntime.Tensors;
using Microsoft.ML.OnnxRuntime;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Numerics;
using System.Text;
using System.Threading.Tasks;
namespace Onnx_Demo
{
public class Detect_Face
{
int num_proposal;
float confThreshold;
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
List<NamedOnnxValue> input_ontainer;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
Tensor<float> result_tensors;
float[] result_array;
int inpWidth = 320;
int inpHeight = 240;
public Detect_Face(float confThreshold)
{
this.confThreshold = confThreshold;
string model_path = "model/RFB-320_240x320_post.onnx";
// 輸入Tensor
input_tensor = new DenseTensor<float>(new[] { 1, 3, 240, 320 });
// 建立輸入容器
input_ontainer = new List<NamedOnnxValue>();
// 建立輸出會話
options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);// 設定為CPU上執行
// 建立推理模型類，讀取本地模型檔
onnx_session = new InferenceSession(model_path, options);
}
public List<BoxInfo> detect(Mat frame)
{
Mat dstimg = new Mat();
Cv2.CvtColor(frame, dstimg, ColorConversionCodes.BGR2RGB);
Cv2.Resize(dstimg, dstimg, new Size(inpWidth, inpHeight));
for (int y = 0; y < dstimg.Height; y++)
{
for (int x = 0; x < dstimg.Width; x++)
{
input_tensor[0, 0, y, x] = (float)((dstimg.At<Vec3b>(y, x)[0] - 127.5) / 127.5);
input_tensor[0, 1, y, x] = (float)((dstimg.At<Vec3b>(y, x)[1] - 127.5) / 127.5);
input_tensor[0, 2, y, x] = (float)((dstimg.At<Vec3b>(y, x)[2] - 127.5) / 127.5);
}
}
input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input", input_tensor));
result_infer = onnx_session.Run(input_ontainer);
//將輸出結果轉為DisposableNamedOnnxValue陣列
results_onnxvalue = result_infer.ToArray();
//讀取第一個節點輸出並轉為Tensor數據
result_tensors = results_onnxvalue[0].AsTensor<float>();
num_proposal = result_tensors.Dimensions[0];
result_array = result_tensors.ToArray();
List<BoxInfo> bboxes = new List<BoxInfo>();
int n = 0; //batchno , classid , score , x1y1x2y2
for (n = 0; n < num_proposal; n++) //特征圖尺度
{
float class_socre = result_array[n * 7 + 2];
if ( class_socre >= confThreshold)
{
int xmin = (int)(result_array[n * 7 + 3] * (float)frame.Cols);
int ymin = (int)(result_array[n * 7 + 4] * (float)frame.Rows);
int xmax = (int)(result_array[n * 7 + 5] * (float)frame.Cols);
int ymax = (int)(result_array[n * 7 + 6] * (float)frame.Rows);
bboxes.Add(new BoxInfo(xmin, ymin, xmax, ymax, class_socre, (int)result_array[n * 7 + 1]));
}
}
return bboxes;
}
public void drawPred(Mat frame, List<BoxInfo> bboxes)
{
foreach (BoxInfo box in bboxes)
{
Cv2.Rectangle(frame, new Point(box.x1, box.y1), new Point(box.x2, box.y2), new Scalar(0, 0, 255), 2);
string label = String.Format("Face:{0}", box.score.ToString("0.00"));
Cv2.PutText(frame, label, new Point(box.x1, box.y1 - 5), HersheyFonts.HersheySimplex, 0.75, new Scalar(0, 255, 0), 1);
}
}
}
}
