效果(u2net.onnx)
效果(u2net_human_seg.onnx)
项目
代码
using Microsoft.ML.OnnxRuntime
using Microsoft.ML.OnnxRuntime.Tensors
using OpenCvSharp
using System
using System.Collections.Generic
using System.Drawing
using System.Drawing.Imaging
using System.Linq
using System.Threading.Tasks
using System.Windows.Forms
namespace U2Net
{
public partial class frmMain : Form
{
public frmMain()
{
InitializeComponent()
}
string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png"
string image_path = ""
string startupPath
DateTime dt1 = DateTime.Now
DateTime dt2 = DateTime.Now
string model_path
Mat image
Mat result_image
int modelSize = 512
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
private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog()
ofd.Filter = fileFilter
if (ofd.ShowDialog() != DialogResult.OK) return
pictureBox1.Image = null
image_path = ofd.FileName
pictureBox1.Image = new Bitmap(image_path)
textBox1.Text = ""
image = new Mat(image_path)
pictureBox2.Image = null
}
private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return
}
textBox1.Text = ""
pictureBox2.Image = null
int oldwidth = image.Cols
int oldheight = image.Rows
//缩放图片大小
int maxEdge = Math.Max(image.Rows, image.Cols)
float ratio = 1.0f * modelSize / maxEdge
int newHeight = (int)(image.Rows * ratio)
int newWidth = (int)(image.Cols * ratio)
Mat resize_image = image.Resize(new OpenCvSharp.Size(newWidth, newHeight))
int width = resize_image.Cols
int height = resize_image.Rows
if (width != modelSize || height != modelSize)
{
resize_image = resize_image.CopyMakeBorder(0, modelSize - newHeight, 0, modelSize - newWidth, BorderTypes.Constant, new Scalar(255, 255, 255))
}
Cv2.CvtColor(resize_image, resize_image, ColorConversionCodes.BGR2RGB)
for (int y = 0
{
for (int x = 0
{
input_tensor[0, 0, y, x] = (resize_image.At<Vec3b>(y, x)[0] / 255f - 0.485f) / 0.229f
input_tensor[0, 1, y, x] = (resize_image.At<Vec3b>(y, x)[1] / 255f - 0.456f) / 0.224f
input_tensor[0, 2, y, x] = (resize_image.At<Vec3b>(y, x)[2] / 255f - 0.406f) / 0.225f
}
}
//将 input_tensor 放入一个输入参数的容器，并指定名称
input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input_image", input_tensor))
dt1 = DateTime.Now
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_ontainer)
dt2 = DateTime.Now
//将输出结果转为DisposableNamedOnnxValue数组
results_onnxvalue = result_infer.ToArray()
//读取第一个节点输出并转为Tensor数据
result_tensors = results_onnxvalue[0].AsTensor<float>()
result_array = result_tensors.ToArray()
//黑白色反转
//for (int i = 0
//{
// result_array[i] = 1 - result_array[i]
//}
float maxVal = result_array.Max()
float minVal = result_array.Min()
for (int i = 0
{
result_array[i] = (result_array[i] - minVal) / (maxVal - minVal) * 255
}
result_image = new Mat(modelSize, modelSize, MatType.CV_32F, result_array)
Cv2.CvtColor(result_image, result_image, ColorConversionCodes.RGB2BGR)
//还原图像大小
if (width != modelSize || height != modelSize)
{
Rect rect = new Rect(0, 0, width, height)
result_image = result_image.Clone(rect)
}
result_image = result_image.Resize(new OpenCvSharp.Size(oldwidth, oldheight))
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream())
textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms"
}
private void Form1_Load(object sender, EventArgs e)
{
startupPath = Application.StartupPath
//model_path = startupPath + "\\model\\u2net.onnx"
//model_path = startupPath + "\\model\\u2netp.onnx"
model_path = startupPath + "\\model\\u2net_human_seg.onnx"
modelSize = 320
//创建输出会话，用于输出模型读取信息
options = new SessionOptions()
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO
//设置为CPU上运行
options.AppendExecutionProvider_CPU(0)
//创建推理模型类，读取本地模型文件
onnx_session = new InferenceSession(model_path, options)
//创建输入容器
input_ontainer = new List<NamedOnnxValue>()
//输入Tensor
input_tensor = new DenseTensor<float>(new[] { 1, 3, modelSize, modelSize })
}
private void button3_Click(object sender, EventArgs e)
{
if (pictureBox2.Image == null)
{
return
}
Bitmap output = new Bitmap(pictureBox2.Image)
var sdf = new SaveFileDialog()
sdf.Title = "保存"
sdf.Filter = "Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf"
if (sdf.ShowDialog() == DialogResult.OK)
{
switch (sdf.FilterIndex)
{
case 1:
{
output.Save(sdf.FileName, ImageFormat.Bmp)
break
}
case 2:
{
output.Save(sdf.FileName, ImageFormat.Emf)
break
}
case 3:
{
output.Save(sdf.FileName, ImageFormat.Exif)
break
}
case 4:
{
output.Save(sdf.FileName, ImageFormat.Gif)
break
}
case 5:
{
output.Save(sdf.FileName, ImageFormat.Icon)
break
}
case 6:
{
output.Save(sdf.FileName, ImageFormat.Jpeg)
break
}
case 7:
{
output.Save(sdf.FileName, ImageFormat.Png)
break
}
case 8:
{
output.Save(sdf.FileName, ImageFormat.Tiff)
break
}
case 9:
{
output.Save(sdf.FileName, ImageFormat.Wmf)
break
}
}
MessageBox.Show("保存成功，位置：" + sdf.FileName)
}
}
}
}
参考
GitHub - xuebinqin/U-2-Net: The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
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