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; y < resize_image.Height; y++)
            {
                for (int x = 0; x < resize_image.Width; x++)
                {
                    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; i < result_array.Length; i++)
            //{
            //    result_array[i] = 1 - result_array[i];
            //}

            float maxVal = result_array.Max();
            float minVal = result_array.Min();

            for (int i = 0; i < result_array.Length; i++)
            {
                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);

            }
        }
    }
}