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+using System;
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+using System.IO;
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+using System.Collections.Generic;
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+using System.ComponentModel;
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+using System.Data;
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+using System.Drawing;
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+using System.Linq;
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+using System.Text;
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+using System.Threading;
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+using System.Threading.Tasks;
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+using System.Windows.Forms;
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+using System.Runtime.InteropServices;
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+using System.Drawing.Imaging;
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+
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+using OpenCvSharp;
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+
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+
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+namespace WinFormsApp_final
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+{
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+ public partial class Form1 : Form
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+ {
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+ /**********************************************************************/
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+ /***************** 1.推理DLL导入实现 ****************/
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+ /**********************************************************************/
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+ // 加载推理相关方法
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+ [DllImport("model_infer.dll", EntryPoint = "InitModel")] // 模型统一初始化方法: 需要yml、pdmodel、pdiparams
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+ public static extern void InitModel(string model_type, string model_filename, string params_filename, string cfg_file, bool use_gpu, int gpu_id, ref byte paddlex_model_type);
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+
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+ [DllImport("model_infer.dll", EntryPoint = "Det_ModelPredict")] // PaddleDetection模型推理方法
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+ public static extern void Det_ModelPredict(byte[] img, int W, int H, int C, IntPtr output, int[] BoxesNum, ref byte label);
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+
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+ [DllImport("model_infer.dll", EntryPoint = "Seg_ModelPredict")] // PaddleSeg模型推理方法
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+ public static extern void Seg_ModelPredict(byte[] img, int W, int H, int C, ref byte output);
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+
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+ [DllImport("model_infer.dll", EntryPoint = "Cls_ModelPredict")] // PaddleClas模型推理方法
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+ public static extern void Cls_ModelPredict(byte[] img, int W, int H, int C, ref float score, ref byte category, ref int category_id);
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+
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+ [DllImport("model_infer.dll", EntryPoint = "Mask_ModelPredict")] // Paddlex的MaskRCNN模型推理方法
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+ public static extern void Mask_ModelPredict(byte[] img, int W, int H, int C, IntPtr output, ref byte Mask_output, int[] BoxesNum, ref byte label);
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+
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+ [DllImport("model_infer.dll", EntryPoint = "DestructModel")] // 分割、检测、识别模型销毁方法
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+ public static extern void DestructModel();
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+
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+
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+ /**********************************************************************/
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+ /****************** 2.控制参数的声明 *****************/
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+ /**********************************************************************/
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+ // 模型基本参数
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+ string imgfile = null; // 推理的图片路径 -- 单张图片路径
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+ List<string> imgfiles = new List<string>(); // 推理的图片路径 -- 多张图片路径
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+ string videofile = null; // 推理的视频路径
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+ bool use_gpu = false; // 是否使用gpu
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+ int gpu_id = 0; // 默认GPU_ID为0
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+ float det_threshold = 0.5F; // 设置阈值 -- 默认0.5
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+ string model_type = "det"; // 模型类型 -- 检测: det / paddlex
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+ string model_filename = null; // *.pdmodel -- 模型文件
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+ string params_filename = null; // *.pdiparams == 参数文件
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+ string cfg_file = null; // *.yml -- 配置文件
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+
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+ // paddlex模型下的实际模型类型
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+ byte[] paddlex_model_type = new byte[10]; // det/seg/clas + \0
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+ // 记录paddlex模式存在
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+ bool paddlex_doing = false;
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+ // 图片类型
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+ string[] img_type = {"jpg", "png", "JPEG", "jpeg"};
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+
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+ // 模型已完成初始化的标志
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+ static int has_model_init = 0;
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+ // 是否正在进行推理预测
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+ static int is_infer = 0;
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+ // 是否中断推理
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+ static int isBreakInfer = 0;
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+ // 推理线程进行标志
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+ static int infer_one_img_flag = 0;
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+ static int infer_many_img_flag = 0;
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+ static int infer_video_img_flag = 0;
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+ // 连续推理的间隔ms
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+ int continue_infer_delay = 50;
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+
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+
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+ /**********************************************************************/
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+ /************** 3.窗体加载与关闭的实现 ***************/
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+ /**********************************************************************/
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+ public Form1()
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+ {
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+ InitializeComponent();
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+ }
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+
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+ private void Form1_Load(object sender, EventArgs e)
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+ {
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+ comboBox1.SelectedIndex = 0; // 初始运行环境 -- cpu
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+ comboBox2.SelectedIndex = 0; // 初始模型 -- det
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+ comboBox3.SelectedIndex = 5; // 初始阈值 -- 0.5
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+ numericUpDown1.Value = 50; // 设置初始连续推理间隔为50ms
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+
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+ label7.Text = "0.00"; // 默认推理耗时
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+ textBox1.Text = "0"; // 默认GPU_ID为0
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+ }
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+
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+ private void Form1_FromClosing(object sender, EventArgs e)
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+ {
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+ while (is_infer != 0) // 有推理进程在运行
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+ {
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+ isBreakInfer = 1; // 关掉进程
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+ } // 等待推理进程完全结束
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+
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+ if (has_model_init == 1) // 有初始化的模型存在，销毁模型后正常退出
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+ {
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+ DestructModel(); // 销毁模型
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+ }
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+ }
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+
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+
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+ /**********************************************************************/
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+ /***************** 4.细节参数选项实现 ***************/
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+ /**********************************************************************/
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+ int comboBox_clicked = 0;
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+ int comboBox1_last_index = 0;
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+
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+ // 选择运行环境 - 是否使用GPU
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+ private void comboBox1_SelectedIndexChanged(object sender, EventArgs e)
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+ {
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+
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+ // 推理过程中，不支持运行环境选择
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+ if (is_infer == 1 && comboBox_clicked == 0)
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+ {
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+ MessageBox.Show("正在推理中，请推理完成后再选择运行环境重新初始化!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Error);
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+ comboBox_clicked = 1;
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+ comboBox1.SelectedIndex = comboBox1_last_index; // 使用上一次改变后得到的index
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+ return;
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+ }
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+
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+ // 已经初始化，不支持运行环境选择
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+ if (has_model_init == 1 && comboBox_clicked == 0)
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+ {
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+ MessageBox.Show("模型已初始化，请销毁模型后再进行运行环境选择!\n(CPU,GPU)", "提示", MessageBoxButtons.OK, MessageBoxIcon.Error);
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+ comboBox_clicked = 1;
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+ comboBox1.SelectedIndex = comboBox1_last_index; // 使用上一次改变后得到的index
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+ return;
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+ }
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+
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+ // 对应两种运行环境
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+ if (comboBox1.SelectedItem.ToString() == "GPU") // 使用gpu
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+ {
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+ use_gpu = true;
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+ }
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+ else if (comboBox1.SelectedItem.ToString() == "CPU")
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+ {
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+ use_gpu = false;
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+ }
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+ comboBox1_last_index = comboBox1.SelectedIndex;
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+
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+
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+ comboBox_clicked = 0;
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+ }
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+
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+ // 修改GPU_ID -- 指定gpu
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+ int last_gpu_id = 0;
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+ int gpu_id_done = 0;
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+ private void textBox1_TextChanged(object sender, EventArgs e)
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+ {
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+ // 推理过程中，不支持GPU指定 -- 未定义操作
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+ if (is_infer == 1 && gpu_id_done == 0)
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+ {
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+ MessageBox.Show("正在推理中，请推理完成后再选择指定GPU重新初始化!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Error);
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+ gpu_id_done = 1;
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+ gpu_id = last_gpu_id;
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+ textBox1.Text = $"{gpu_id}";
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+ return;
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+ }
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+
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+ // 已经初始化，不支持GPU指定 -- 未定义操作
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+ if (has_model_init == 1 && gpu_id_done == 0)
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+ {
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+ MessageBox.Show("模型已初始化，请销毁模型后再进行GPU指定!\n(GPU:x)", "提示", MessageBoxButtons.OK, MessageBoxIcon.Error);
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+ gpu_id_done = 1;
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+ gpu_id = last_gpu_id;
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+ textBox1.Text = $"{gpu_id}";
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+ return;
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+ }
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+
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+ if (gpu_id_done == 0) // 定义操作下的修改才会执行以下内容
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+ {
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+ string gpu_id_str = textBox1.Text.ToString();
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+ if (gpu_id_str.Length != 0)
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+ {
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+ last_gpu_id = gpu_id;
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+ try
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+ {
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+ gpu_id = Int32.Parse(gpu_id_str); // 获取新的GPU_id
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+ }
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+ catch (Exception ex)
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+ {
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+ gpu_id = last_gpu_id;
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+ textBox1.Text = $"{gpu_id}";
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+ MessageBox.Show("GPU_ID只能输入数字!");
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+ }
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+ }
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+ }
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+
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+ gpu_id_done = 0; // 复原状态值
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+ }
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+
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+
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+ int comboBox2_last_index = 0;
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+
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+ // 执行推理的模型的类型选择
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+ private void comboBox2_SelectedIndexChanged(object sender, EventArgs e)
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+ {
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+
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+ // 推理过程中，不支持模型类型选择
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+ if (is_infer == 1 && comboBox_clicked == 0)
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+ {
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+ MessageBox.Show("正在推理中，请推理完成后再选择模型类型重新初始化!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Error);
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+ comboBox_clicked = 1;
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+ comboBox2.SelectedIndex = comboBox2_last_index; // 使用上一次改变后得到的index
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+ return;
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+ }
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+
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+ // 已经初始化，发出警告，提示重新初始化，模型类型的修改才会生效
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+ if (has_model_init == 1 && comboBox_clicked == 0)
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+ {
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+ MessageBox.Show("模型已初始化，请销毁模型后再进行模型类型选择!\n(det,seg,clas)", "提示", MessageBoxButtons.OK, MessageBoxIcon.Error);
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+ comboBox_clicked = 1;
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+ comboBox2.SelectedIndex = comboBox2_last_index; // 使用上一次改变后得到的index
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+ return;
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+ }
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+
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+ // 对应三种类型
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+ if (comboBox2.SelectedItem.ToString() == "det") // 加载检测模型 -- 推理已实现
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+ {
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+ model_type = comboBox2.SelectedItem.ToString();
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+ paddlex_doing = false; // 进入非paddlex模式 -- 检测
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+ }
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+ else if (comboBox2.SelectedItem.ToString() == "seg") // 加载分割模型 -- 推理已实现
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+ {
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+ model_type = comboBox2.SelectedItem.ToString();
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+ paddlex_doing = false; // 进入非paddlex模式 -- 分割
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+ }
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+ else if (comboBox2.SelectedItem.ToString() == "clas") // 加载识别模型 -- 推理已实现
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+ {
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+ model_type = comboBox2.SelectedItem.ToString();
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+ paddlex_doing = false; // 进入非paddlex模式 -- 识别
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+ }
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+ else if (comboBox2.SelectedItem.ToString() == "mask") // 加载实例分割MaskRCNN模型 -- 推理已实现
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+ {
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+ model_type = comboBox2.SelectedItem.ToString();
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+ paddlex_doing = false; // 进入非paddlex模式 -- 实则也为paddlex导出的模型
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+ }
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+ else if (comboBox2.SelectedItem.ToString() == "paddlex") // 加载识别模型 -- 推理已实现
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+ {
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+ model_type = comboBox2.SelectedItem.ToString();
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+ // 重复选中paddlex，不修改状态
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+ }
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+ comboBox2_last_index = comboBox2.SelectedIndex;
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+
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+
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+ comboBox_clicked = 0;
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+ }
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+
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+
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+ int comboBox3_last_index = 0;
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+ // 设置目标检测的检测阈值
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+ private void comboBox3_SelectedIndexChanged(object sender, EventArgs e)
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+ {
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+ // 推理过程中，不支持检测阈值选择
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+ if (is_infer == 1 && comboBox_clicked == 0)
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+ {
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+ MessageBox.Show("正在推理中，检测阈值修改将在本次模型推理完成后生效!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Error);
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+
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+ comboBox_clicked = 1;
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+ comboBox3.SelectedIndex = comboBox3_last_index; // 使用上一次改变后得到的index
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+ return;
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+ }
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+
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+
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+ // 修改检测阈值
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+ det_threshold = float.Parse(comboBox3.SelectedItem.ToString());
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+ comboBox3_last_index = comboBox3.SelectedIndex; // 保存本次的索引
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+
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+
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+ comboBox_clicked = 0;
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+ }
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+
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+
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+ // 连续推理的间隔时间长度 -- 图片文件夹推理
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+ private void numericUpDown1_ValueChanged(object sender, EventArgs e)
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+ {
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+ // 配置连续推理的延时
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+ continue_infer_delay = ((int)numericUpDown1.Value);
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+ }
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+
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+
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+ /**********************************************************************/
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+ /***************** 5.选择控制组件实现 ***************/
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+ /**********************************************************************/
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+ // 加载模型相关文件的文件夹 -- 测试完成
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+ private void button1_Click(object sender, EventArgs e)
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+ {
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+ // 推理过程中，不支持模型初始化
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+ if (is_infer == 1)
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+ {
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+ MessageBox.Show("正在推理中，请推理完成后再初始化加载模型!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Warning);
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+ return;
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+ }
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+
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+ // 先检查MaskRCNN启动状态
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+ if (!CheckMaskRCNN_workOnGpu(model_type, use_gpu)) // MaskRCNN环境不在GPU，则报错提醒
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+ {
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+ MessageBox.Show("MaskRCNN推理仅支持GPU环境，请重新选择启动环境!\n(因为CPU环境可能存在内存不足，导致推理失败。)", "提示", MessageBoxButtons.OK, MessageBoxIcon.Warning);
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+ return;
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+ }
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+
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+ int dir_load_flag = 0; // 文件夹选择的标志位
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+ string dir_path = null;
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+ folderBrowserDialog1.Description = "请选择模型文件夹";
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+ DialogResult folder = folderBrowserDialog1.ShowDialog();
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+ if (folder == DialogResult.OK || folder == DialogResult.Yes)
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+ {
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+ dir_path = folderBrowserDialog1.SelectedPath;
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+ if (string.IsNullOrEmpty(dir_path)) // 判断是否选择了模型文件
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+ {
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+ MessageBox.Show("请选择模型路径/模型路径为空!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Warning);
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+ dir_load_flag = 0;
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+ }
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+ else
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+ {
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+ dir_load_flag = 1;
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+ }
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+ }
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+
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+ if (dir_load_flag == 1) // 寻找模型文件
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+ {
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+ List<FileInfo> model_lst = new List<FileInfo>();
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+ List<FileInfo> params_lst = new List<FileInfo>();
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+ List<FileInfo> cfg_lst = new List<FileInfo>();
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+ model_lst = getFile(dir_path, ".pdmodel", model_lst); // 返回匹配的文件
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+ params_lst = getFile(dir_path, ".pdiparams", params_lst); // 返回匹配的文件
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+ cfg_lst = getFile(dir_path, ".yml", cfg_lst); // 返回匹配的文件
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+ if (cfg_lst.Count == 0)
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+ cfg_lst = getFile(dir_path, ".yaml", cfg_lst); // 返回匹配的文件
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+
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+ if (model_lst.Count != 1 || params_lst.Count != 1)
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+ {
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+ MessageBox.Show("模型文件加载失败!\n请注意模型文件夹下应包含以下文件各一个:\n*.pdmodel, *.pdiparams", "提示");
|
|
|
+ return;
|
|
|
+ }
|
|
|
+
|
|
|
+ model_filename = model_lst[0].FullName;
|
|
|
+ params_filename = params_lst[0].FullName;
|
|
|
+
|
|
|
+ if (cfg_lst.Count == 0) // 没有yml文件
|
|
|
+ {
|
|
|
+ MessageBox.Show("模型文件加载失败!\n请注意模型文件夹下应包含以下文件:\nmodel.yml/model.yaml", "提示");
|
|
|
+ return;
|
|
|
+ }
|
|
|
+ else if (cfg_lst.Count > 2) // yml过多， > 2
|
|
|
+ {
|
|
|
+ MessageBox.Show("模型文件加载失败!\n请注意模型文件夹下应至多包含以下文件(yml文件个数不得超过2):\nmodel.yml/model.yaml，pipeline.yml/pipeline.yaml", "提示");
|
|
|
+ return;
|
|
|
+ }
|
|
|
+ else if (cfg_lst.Count == 2) // 对于包含多个yml文件的情况的处理 ， == 2, 及针对paddlex的处理
|
|
|
+ {
|
|
|
+ // 筛选yml文件 -- 只需要model.yml
|
|
|
+ for (int i = 0; i < 2; i++)
|
|
|
+ {
|
|
|
+ if (cfg_lst[i].Name == "model.yml" || cfg_lst[i].Name == "model.yaml")
|
|
|
+ {
|
|
|
+ cfg_file = cfg_lst[i].FullName;
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else if (cfg_lst.Count == 1) // 直接取出唯一的yml文件
|
|
|
+ {
|
|
|
+ cfg_file = cfg_lst[0].FullName;
|
|
|
+ }
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ int is_Mask = 0; // 当前初始化模型是否未MaskRCNN
|
|
|
+ if (has_model_init == 1) // 已经初始化，再次初始化前要完成上一个模型的销毁
|
|
|
+ {
|
|
|
+ // 销毁模型
|
|
|
+ DestructModel();
|
|
|
+
|
|
|
+ // 初始化模型
|
|
|
+ try
|
|
|
+ {
|
|
|
+ // 保持paddlex模式
|
|
|
+ if (paddlex_doing == true) model_type = "paddlex";
|
|
|
+ if (model_type == "mask")
|
|
|
+ {
|
|
|
+ model_type = "paddlex"; // 因为MaskRCNN来自paddlex训练，所以这里先转未paddlex
|
|
|
+ is_Mask = 1;
|
|
|
+ }
|
|
|
+ InitModel(model_type, model_filename, params_filename, cfg_file, use_gpu, gpu_id, ref paddlex_model_type[0]);
|
|
|
+
|
|
|
+ if (is_Mask == 1) // 初始化完成后还原model_type
|
|
|
+ {
|
|
|
+ model_type = "mask";
|
|
|
+ }
|
|
|
+ if (model_type == "paddlex") // 如果当前初始模型类型为paddlex，则初始化完成后，转为paddlex模型的实际类型
|
|
|
+ {
|
|
|
+ paddlex_doing = true; // 进入paddlex类型模式
|
|
|
+ model_type = System.Text.Encoding.UTF8.GetString(paddlex_model_type).Split('\0')[0]; // 得到实际运行的模型类型 -- Split去掉多余的\0（原byte[]长度为10，有许多多余的\0）
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception ex)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1; // 发生了异常
|
|
|
+ MessageBox.Show("1.请确定文件中包含有效的模型文件(*.pdmodel, *.pdiparams, *.yml)!\n2.请检查模型文件与模型类型是否一致!\n3.其它原因:GPU号有误，yml中预处理有误...", "模型初始化失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ // 初始化模型
|
|
|
+ try
|
|
|
+ {
|
|
|
+ // 保持paddlex模式
|
|
|
+ if (paddlex_doing == true) model_type = "paddlex";
|
|
|
+ if (model_type == "mask")
|
|
|
+ {
|
|
|
+ model_type = "paddlex"; // 因为MaskRCNN来自paddlex训练，所以这里先转未paddlex
|
|
|
+ is_Mask = 1;
|
|
|
+ }
|
|
|
+ InitModel(model_type, model_filename, params_filename, cfg_file, use_gpu, gpu_id, ref paddlex_model_type[0]);
|
|
|
+
|
|
|
+ if (is_Mask == 1) // 初始化完成后还原model_type
|
|
|
+ {
|
|
|
+ model_type = "mask";
|
|
|
+ }
|
|
|
+ if (model_type == "paddlex") // 如果当前初始模型类型为paddlex，则初始化完成后，转为paddlex模型的实际类型
|
|
|
+ {
|
|
|
+ paddlex_doing = true; // 进入paddlex类型模式
|
|
|
+
|
|
|
+ model_type = System.Text.Encoding.UTF8.GetString(paddlex_model_type).Split('\0')[0]; // 得到实际运行的模型类型
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception ex)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1; // 发生了异常
|
|
|
+ MessageBox.Show("1.请确定文件中包含有效的模型文件(*.pdmodel, *.pdiparams, *.yml)!\n2.请检查模型文件与模型类型是否一致!\n3.其它原因:GPU号有误，yml中预处理有误...", "模型初始化失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if (raise_ex_flag == 0) // 未发生异常时，才进行正常的运行提示
|
|
|
+ {
|
|
|
+ has_model_init = 1; // 已经完成初始化
|
|
|
+ if (use_gpu)
|
|
|
+ {
|
|
|
+ MessageBox.Show($"模型文件已加载到GPU:{gpu_id}!\n(模型类型为: {model_type.Split('\0')[0]})", "提示");
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ MessageBox.Show($"模型类型为: {model_type.Split('\0')[0]}", "提示");
|
|
|
+ }
|
|
|
+ button1.Text = "模型已初始化"; // 更改按键提示信息
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 加载单张图片 -- 测试完成
|
|
|
+ private void button2_Click(object sender, EventArgs e)
|
|
|
+ {
|
|
|
+ // 推理过程中，不支持推理数据集选择与加载
|
|
|
+ if (is_infer == 1)
|
|
|
+ {
|
|
|
+ MessageBox.Show("正在推理中，请推理完成后再选择推理数据!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ return;
|
|
|
+ }
|
|
|
+
|
|
|
+ int has_load_img_file_flag = 1;
|
|
|
+ openFileDialog1.Filter = "(*.png;*.jpg;*.JPEG;*.jpeg)|*.*"; // 设置打开的文件类型
|
|
|
+ DialogResult dr = openFileDialog1.ShowDialog();
|
|
|
+ //获取所打开文件的文件名
|
|
|
+ string filename = openFileDialog1.FileName;
|
|
|
+ if (dr != System.Windows.Forms.DialogResult.OK || string.IsNullOrEmpty(filename)) // 检验文件是否选择成功
|
|
|
+ {
|
|
|
+ has_load_img_file_flag = 0; // 没有读取到图片路径
|
|
|
+ }
|
|
|
+
|
|
|
+ if (has_load_img_file_flag==1) // 正确加载图片才有以下执行
|
|
|
+ {
|
|
|
+ // 划分文件，获取后缀
|
|
|
+ string[] final_tag = filename.Split('.');
|
|
|
+ int flag = 0;
|
|
|
+ foreach (string type in img_type)
|
|
|
+ {
|
|
|
+ if (final_tag[1] == type)
|
|
|
+ {
|
|
|
+ flag = 1; // 类型满足预置图片类型时，flag为1
|
|
|
+
|
|
|
+ imgfile = filename; // 单张图片
|
|
|
+
|
|
|
+ // 显示加载的图片
|
|
|
+ pictureBox1.Image = Image.FromFile(imgfile);
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 清空其它推理数据的来源
|
|
|
+ videofile = null;
|
|
|
+ imgfiles.Clear(); // 加载单张图片，清空文件夹图片索引
|
|
|
+
|
|
|
+ MessageBox.Show("图片加载完成!", "提示");
|
|
|
+
|
|
|
+ button2.Text = "图片已加载"; // 更改按键提示信息
|
|
|
+ button3.Text = "加载图片文件夹"; // 更改按键提示信息
|
|
|
+ button4.Text = "加载视频流"; // 更改按键提示信息
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 加载文件夹图片
|
|
|
+ private void button3_Click(object sender, EventArgs e)
|
|
|
+ {
|
|
|
+ // 推理过程中，不支持推理数据集选择与加载
|
|
|
+ if (is_infer == 1)
|
|
|
+ {
|
|
|
+ MessageBox.Show("正在推理中，请推理完成后再选择推理数据!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ return;
|
|
|
+ }
|
|
|
+
|
|
|
+ int img_dir_load_flag = 0;
|
|
|
+ string img_dir_path = null;
|
|
|
+ folderBrowserDialog1.Description = "请选择模型文件夹";
|
|
|
+ DialogResult folder = folderBrowserDialog1.ShowDialog();
|
|
|
+ if (folder == DialogResult.OK || folder == DialogResult.Yes)
|
|
|
+ {
|
|
|
+ img_dir_path = folderBrowserDialog1.SelectedPath;
|
|
|
+ if (string.IsNullOrEmpty(img_dir_path)) // 判断是否选择了模型文件
|
|
|
+ {
|
|
|
+ MessageBox.Show("请选择图片文件夹路径/图片文件夹路径为空!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ img_dir_load_flag = 0;
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ img_dir_load_flag = 1;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if (img_dir_load_flag == 1) // 读取文件夹中的指定图片文件
|
|
|
+ {
|
|
|
+ List<FileInfo> lst = new List<FileInfo>();
|
|
|
+ lst = getFile(img_dir_path, ".jpg", lst); // 返回匹配的文件
|
|
|
+ lst = getFile(img_dir_path, ".png", lst); // 返回匹配的文件
|
|
|
+ lst = getFile(img_dir_path, ".JPEG", lst); // 返回匹配的文件
|
|
|
+
|
|
|
+ foreach (FileInfo Image_File in lst) // 添加文件
|
|
|
+ {
|
|
|
+ imgfiles.Add(Image_File.FullName);
|
|
|
+ }
|
|
|
+
|
|
|
+ if (imgfiles.Count == 0)
|
|
|
+ {
|
|
|
+ MessageBox.Show("请输入选择非空/包含正确图片类型的图片文件夹!\n(*.png, *.jpg, *.JPEG)", "图片解析失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ return; // 提前终止该函数操作 -- 保留原有加载数据
|
|
|
+ }
|
|
|
+
|
|
|
+ // 展示第一张图片
|
|
|
+ // 显示加载的图片
|
|
|
+ pictureBox1.Image = Image.FromFile(imgfiles[0]);
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 清空其它推理数据的来源
|
|
|
+ imgfile = null; // 既然加载文件夹，则单张图片的索引应该清空
|
|
|
+ videofile = null;
|
|
|
+ MessageBox.Show("图片文件夹加载完成!", "提示");
|
|
|
+
|
|
|
+ button3.Text = "图片文件夹已加载"; // 更改按键提示信息
|
|
|
+ button2.Text = "加载图片"; // 更改按键提示信息
|
|
|
+ button4.Text = "加载视频流"; // 更改按键提示信息
|
|
|
+ }
|
|
|
+
|
|
|
+ }
|
|
|
+
|
|
|
+ // 加载视频流
|
|
|
+ private void button4_Click(object sender, EventArgs e)
|
|
|
+ {
|
|
|
+ // 推理过程中，不支持模型初始化
|
|
|
+ if (is_infer == 1)
|
|
|
+ {
|
|
|
+ MessageBox.Show("正在推理中，请推理完成后再选择推理数据!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ return;
|
|
|
+ }
|
|
|
+
|
|
|
+ int has_load_mp4_file_flag = 1;
|
|
|
+ openFileDialog1.Filter = "(*.mp4)|*.*"; // 设置打开的文件类型
|
|
|
+ DialogResult dr = openFileDialog1.ShowDialog();
|
|
|
+ //获取所打开文件的文件名
|
|
|
+ string filename = openFileDialog1.FileName;
|
|
|
+ if (dr != System.Windows.Forms.DialogResult.OK || string.IsNullOrEmpty(filename)) // 检验文件是否选择成功
|
|
|
+ {
|
|
|
+ if (string.IsNullOrEmpty(filename)) MessageBox.Show("请选择视频(*.mp4)文件!", "视频路径为空", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ has_load_mp4_file_flag = 0;
|
|
|
+ }
|
|
|
+
|
|
|
+ if (has_load_mp4_file_flag==1) // 读取到视频mp4
|
|
|
+ {
|
|
|
+ // 划分文件，获取后缀
|
|
|
+ string[] final_tag = filename.Split('.');
|
|
|
+
|
|
|
+ if (final_tag[1] == "mp4")
|
|
|
+ {
|
|
|
+ videofile = filename; // mp4视频路径
|
|
|
+
|
|
|
+ Bitmap image = null;
|
|
|
+ Mat frame = new Mat();
|
|
|
+ VideoCapture capture = new VideoCapture(); // 创建一个摄像头
|
|
|
+ capture.Open(videofile);
|
|
|
+ bool read_success = capture.Read(frame); // 帧是否读取成功
|
|
|
+ if (!read_success)
|
|
|
+ {
|
|
|
+ MessageBox.Show("无法读取视频的帧！！！", "提示");
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame);
|
|
|
+
|
|
|
+ // 显示加载的视频的第一帧
|
|
|
+ pictureBox1.Image = image;
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ capture = null; // 收回内存
|
|
|
+ frame = null; // 收回内存
|
|
|
+ image = null; // 收回内存
|
|
|
+
|
|
|
+ // 清空其它推理数据的来源
|
|
|
+ imgfile = null;
|
|
|
+ imgfiles.Clear(); // 加载单张图片，清空文件夹图片索引
|
|
|
+ MessageBox.Show("视频加载完成!", "提示");
|
|
|
+
|
|
|
+ button4.Text = "视频流已加载"; // 更改按键提示信息
|
|
|
+ button2.Text = "加载图片"; // 更改按键提示信息
|
|
|
+ button3.Text = "加载图片文件夹"; // 更改按键提示信息
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ // 保持原有数据加载情况，并发出错误警告
|
|
|
+ MessageBox.Show("请选择mp4视频文件!", "视频资源加载失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 执行推理
|
|
|
+ private void button5_Click(object sender, EventArgs e)
|
|
|
+ {
|
|
|
+ // has_model_init: 确保模型已经初始化
|
|
|
+ if (imgfile != null && is_infer==0 && has_model_init == 1) // 单张图片的预测 -- is_infer 等于 0， 表示没有任何进程在运行
|
|
|
+ {
|
|
|
+ Thread infer_one_img_thread = null;
|
|
|
+ if (model_type == "det") infer_one_img_thread = new Thread(new ThreadStart(delegate { det_infer_one_img(); }));
|
|
|
+ else if (model_type == "seg") infer_one_img_thread = new Thread(new ThreadStart(delegate { seg_infer_one_img(); }));
|
|
|
+ else if (model_type == "clas") infer_one_img_thread = new Thread(new ThreadStart(delegate { cls_infer_one_img(); }));
|
|
|
+ else if (model_type == "mask") infer_one_img_thread = new Thread(new ThreadStart(delegate { mask_infer_one_img(); }));
|
|
|
+ MessageBox.Show("开始图片推理任务!", "提示");
|
|
|
+ infer_one_img_thread.Start(); // 启动任务
|
|
|
+ infer_one_img_flag = 1; // 标志着图片正在推理执行
|
|
|
+
|
|
|
+ }
|
|
|
+ else if (imgfiles.Count != 0 && is_infer == 0 && has_model_init == 1) // 图片文件夹的预测
|
|
|
+ {
|
|
|
+ Thread infer_many_img_thread = null;
|
|
|
+ if (model_type == "det") infer_many_img_thread = new Thread(new ThreadStart(delegate { det_infer_many_img(); }));
|
|
|
+ else if (model_type == "seg") infer_many_img_thread = new Thread(new ThreadStart(delegate { seg_infer_many_img(); }));
|
|
|
+ else if (model_type == "clas") infer_many_img_thread = new Thread(new ThreadStart(delegate { cls_infer_many_img(); }));
|
|
|
+ else if (model_type == "mask") infer_many_img_thread = new Thread(new ThreadStart(delegate { mask_infer_many_img(); }));
|
|
|
+ MessageBox.Show("开始图片文件夹推理任务!", "提示");
|
|
|
+ infer_many_img_thread.Start(); // 启动任务
|
|
|
+ infer_many_img_flag = 1; // 标志着图片文件夹正在推理执行
|
|
|
+ }
|
|
|
+ else if (videofile != null && is_infer == 0 && has_model_init == 1)
|
|
|
+ {
|
|
|
+ Thread infer_video_img_thread = null;
|
|
|
+ if (model_type == "det") infer_video_img_thread = new Thread(new ThreadStart(delegate { det_infer_video_img(); }));
|
|
|
+ else if (model_type == "seg") infer_video_img_thread = new Thread(new ThreadStart(delegate { seg_infer_video_img(); }));
|
|
|
+ else if (model_type == "clas") infer_video_img_thread = new Thread(new ThreadStart(delegate { cls_infer_video_img(); }));
|
|
|
+ else if (model_type == "mask") infer_video_img_thread = new Thread(new ThreadStart(delegate { mask_infer_video_img(); }));
|
|
|
+ MessageBox.Show("开始视频推理任务!", "提示");
|
|
|
+ infer_video_img_thread.Start(); // 启动任务
|
|
|
+ infer_video_img_flag = 1; // 标志着视频正在推理执行
|
|
|
+ }
|
|
|
+ else if (is_infer == 1 && has_model_init == 1)
|
|
|
+ {
|
|
|
+ if (infer_one_img_flag == 1) MessageBox.Show("正在进行推理任务!", "请勿再执行图片推理任务", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ if (infer_many_img_flag == 1) MessageBox.Show("正在进行推理任务!", "请勿再执行图片文件夹推理任务", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ if (infer_video_img_flag == 1) MessageBox.Show("正在进行推理任务!", "请勿再执行视频推理任务", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ }
|
|
|
+
|
|
|
+ if (has_model_init == 0 && (imgfile == null && imgfiles.Count == 0 && videofile == null)) // 模型未初始化，数据未加载
|
|
|
+ {
|
|
|
+ MessageBox.Show("请先初始化模型，并选择加载的推理数据后，再点击模型推理!", "推理执行失败", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ }
|
|
|
+ else if (has_model_init == 0 && (imgfile != null || imgfiles.Count != 0 || videofile != null)) // 模型未初始化，数据加载
|
|
|
+ {
|
|
|
+ MessageBox.Show("请初始化模型，再点击模型推理!", "推理执行失败", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ }
|
|
|
+ else if (has_model_init != 0 && (imgfile == null && imgfiles.Count == 0 && videofile == null)) // 模型初始化，数据未加载
|
|
|
+ {
|
|
|
+ MessageBox.Show("请选择加载的推理数据，再点击模型推理!", "推理执行失败", MessageBoxButtons.OK, MessageBoxIcon.Warning);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 终止推理
|
|
|
+ private void button6_Click(object sender, EventArgs e)
|
|
|
+ {
|
|
|
+ isBreakInfer = 1; // 发出推理终止的信号 -- 线程会开始终止(非kill终止)
|
|
|
+ }
|
|
|
+
|
|
|
+ // 销毁已初始化好的模型
|
|
|
+ private void button7_Click(object sender, EventArgs e)
|
|
|
+ {
|
|
|
+ if (is_infer == 0)
|
|
|
+ {
|
|
|
+ if (has_model_init == 1)
|
|
|
+ {
|
|
|
+ // 销毁模型
|
|
|
+ try // 进行未定义的模型销毁时的异常处理
|
|
|
+ {
|
|
|
+ DestructModel();
|
|
|
+ }
|
|
|
+ catch (Exception ex)
|
|
|
+ {
|
|
|
+ MessageBox.Show("当前未初始化模型，无需销毁!", "模型销毁失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+ has_model_init = 0;
|
|
|
+ }
|
|
|
+
|
|
|
+ button1.Text = "初始化模型"; // 重置按键状态
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ MessageBox.Show("请先中断模型推理，再销毁已初始化的模型!", "提示", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+ /***********************************************************************/
|
|
|
+ /***************** 6.可视化推理实现部分 **************/
|
|
|
+ /***********************************************************************/
|
|
|
+ // 检测单张图片
|
|
|
+ private void det_infer_one_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ //Bitmap bmp = new Bitmap(imgfile);
|
|
|
+ Bitmap bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(imgfile));
|
|
|
+ byte[] inputData = GetBGRValues(bmp, out int stride);
|
|
|
+
|
|
|
+ float[] resultlist = new float[600];
|
|
|
+ IntPtr results = FloatToIntptr(resultlist);
|
|
|
+ int[] boxesInfo = new int[1]; // 10 boundingbox
|
|
|
+ byte[] labellist = new byte[1000]; //新建字节数组：label1_str label2_str
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ // 第四个参数为输入图像的通道数
|
|
|
+ Det_ModelPredict(inputData, bmp.Width, bmp.Height, 3, results, boxesInfo, ref labellist[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ string strGet = System.Text.Encoding.Default.GetString(labellist, 0, labellist.Length); //将字节数组转换为字符串
|
|
|
+ string[] predict_Label_List = strGet.Split(' '); // 预测的类别情况
|
|
|
+ // MessageBox.Show($"Box_Number: {boxesInfo[0]}");
|
|
|
+ using OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);//用bitmap转换为mat
|
|
|
+ for (int i = 0; i < boxesInfo[0]; i++) // 未绘制图像
|
|
|
+ {
|
|
|
+ int labelindex = Convert.ToInt32(resultlist[i * 6 + 0]);
|
|
|
+ float score = resultlist[i * 6 + 1];
|
|
|
+ float left = resultlist[i * 6 + 2];
|
|
|
+ float top = resultlist[i * 6 + 3];
|
|
|
+ float right = resultlist[i * 6 + 4];
|
|
|
+ float down = resultlist[i * 6 + 5];
|
|
|
+
|
|
|
+ if (score > det_threshold)
|
|
|
+ {
|
|
|
+ int[] color_ = { (int)(color_map[(labelindex%256)*3]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 2]) };
|
|
|
+
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{predict_Label_List[i]}-{labelindex}-{score:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline); // 1倍大小的HersheySimplex，高度为22
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = (int)left + 22; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = (int)top + text_size.Height;
|
|
|
+
|
|
|
+ // 绘制矩形，书写类别
|
|
|
+ Cv2.Rectangle(mat, new OpenCvSharp.Rect((int)left, (int)top, (int)right, (int)down), new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.AntiAlias);//LineTypes.AntiAlias:反锯齿效果
|
|
|
+ Cv2.PutText(mat, $"{predict_Label_List[i]}-{labelindex}-: {score:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+ // 反馈到另一个picturebox上
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1;
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_one_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("图片推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ // 检测图片文件夹
|
|
|
+ private void det_infer_many_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ foreach (string img_file in imgfiles)
|
|
|
+ {
|
|
|
+ if (isBreakInfer == 1) break; // 中断推理
|
|
|
+
|
|
|
+ Bitmap show_image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(img_file));
|
|
|
+
|
|
|
+ Bitmap bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(img_file));
|
|
|
+ byte[] inputData = GetBGRValues(bmp, out int stride);
|
|
|
+
|
|
|
+ float[] resultlist = new float[600];
|
|
|
+ IntPtr results = FloatToIntptr(resultlist);
|
|
|
+ int[] boxesInfo = new int[1];
|
|
|
+ byte[] labellist = new byte[1000]; //新建字节数组
|
|
|
+
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Det_ModelPredict(inputData, bmp.Width, bmp.Height, 3, results, boxesInfo, ref labellist[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ string strGet = System.Text.Encoding.Default.GetString(labellist, 0, labellist.Length); //将字节数组转换为字符串
|
|
|
+ string[] predict_Label_List = strGet.Split(' '); // 预测的类别情况
|
|
|
+ //MessageBox.Show($"Box_Number: {boxesInfo[0]}");
|
|
|
+ //Console.WriteLine("labellist: {0}", strGet);
|
|
|
+ // 转换为mat数据，方便opencv处理
|
|
|
+ using OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);//用bitmap转换为mat
|
|
|
+ for (int i = 0; i < boxesInfo[0]; i++) // 未绘制图像
|
|
|
+ {
|
|
|
+ int labelindex = Convert.ToInt32(resultlist[i * 6 + 0]);
|
|
|
+ float score = resultlist[i * 6 + 1];
|
|
|
+ float left = resultlist[i * 6 + 2];
|
|
|
+ float top = resultlist[i * 6 + 3];
|
|
|
+ float right = resultlist[i * 6 + 4]; // det -- right down
|
|
|
+ float down = resultlist[i * 6 + 5];
|
|
|
+
|
|
|
+ if (score > det_threshold)
|
|
|
+ {
|
|
|
+ int[] color_ = { (int)(color_map[(labelindex%256)*3]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 2]) };
|
|
|
+
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{predict_Label_List[i]}-{labelindex}-{score:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline); // 1倍大小的HersheySimplex，高度为22
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = (int)left + 22; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = (int)top + text_size.Height;
|
|
|
+
|
|
|
+ // 绘制矩形，书写类别
|
|
|
+ Cv2.Rectangle(mat, new OpenCvSharp.Rect((int)left, (int)top, (int)right, (int)down), new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.AntiAlias);//LineTypes.AntiAlias:反锯齿效果
|
|
|
+ Cv2.PutText(mat, $"{predict_Label_List[i]}-{labelindex}-: {score:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+ }
|
|
|
+ }
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+
|
|
|
+ // 显示图片
|
|
|
+ if (pictureBox1.Image != null) pictureBox1.Image.Dispose();
|
|
|
+ pictureBox1.Image = show_image; //显示原始图片到box1
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom; //显示原始图片到box1
|
|
|
+ // 反馈到另一个picturebox上
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+
|
|
|
+ Thread.Sleep(continue_infer_delay); // 连续识别时，每张图片间隔continue_infer_delay毫秒
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1;
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+
|
|
|
+ // DestructModel(); // 销毁模型
|
|
|
+
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_many_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("图片文件夹推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ // 检测视频流
|
|
|
+ private void det_infer_video_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ VideoCapture capture = new VideoCapture();
|
|
|
+ capture.Open(videofile); // 读取视频
|
|
|
+
|
|
|
+ using Mat frame = new Mat();
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ while (true)
|
|
|
+ {
|
|
|
+ if (isBreakInfer == 1) break;
|
|
|
+ capture.Read(frame);//图像存储一帧数据
|
|
|
+ if (frame.Empty()) break;
|
|
|
+
|
|
|
+ // ------------- 原始图片 ----------
|
|
|
+ Bitmap image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame); //显示原始图片到box1
|
|
|
+ if (pictureBox1.Image != null) pictureBox1.Image.Dispose();
|
|
|
+ pictureBox1.Image = image; //显示原始图片到box1
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom; //显示原始图片到box1
|
|
|
+
|
|
|
+ // ------------- 送入推理的图片以及数据 ----------
|
|
|
+ image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame);
|
|
|
+ byte[] inputData = GetBGRValues(image, out int stride);
|
|
|
+
|
|
|
+ float[] resultlist = new float[600];
|
|
|
+ IntPtr results = FloatToIntptr(resultlist);
|
|
|
+ int[] boxesInfo = new int[1];
|
|
|
+ byte[] labellist = new byte[1000]; //新建字节数组
|
|
|
+
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Det_ModelPredict(inputData, image.Width, image.Height, 3, results, boxesInfo, ref labellist[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ string strGet = System.Text.Encoding.Default.GetString(labellist, 0, labellist.Length); //将字节数组转换为字符串
|
|
|
+ string[] predict_Label_List = strGet.Split(' '); // 预测的类别情况
|
|
|
+ //Console.WriteLine("labellist: {0}", strGet);
|
|
|
+ // 转换为mat数据，方便opencv处理
|
|
|
+ using OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(image);//用bitmap转换为mat
|
|
|
+ for (int i = 0; i < boxesInfo[0]; i++) // 未绘制图像
|
|
|
+ {
|
|
|
+ int labelindex = Convert.ToInt32(resultlist[i * 6 + 0]);
|
|
|
+ float score = resultlist[i * 6 + 1];
|
|
|
+ float left = resultlist[i * 6 + 2];
|
|
|
+ float top = resultlist[i * 6 + 3];
|
|
|
+ float right = resultlist[i * 6 + 4];
|
|
|
+ float down = resultlist[i * 6 + 5];
|
|
|
+
|
|
|
+ if (score > det_threshold)
|
|
|
+ {
|
|
|
+ int[] color_ = { (int)(color_map[(labelindex%256)*3]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 2]) };
|
|
|
+
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{predict_Label_List[i]}-{labelindex}-{score:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline); // 1倍大小的HersheySimplex，高度为22
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = (int)left + 22; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = (int)top + text_size.Height;
|
|
|
+
|
|
|
+ // 绘制矩形
|
|
|
+ Cv2.Rectangle(mat, new OpenCvSharp.Rect((int)left, (int)top, (int)right, (int)down), new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.AntiAlias);//LineTypes.AntiAlias:反锯齿效果
|
|
|
+ Cv2.PutText(mat, $"{predict_Label_List[i]}-{labelindex}-: {score:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+ // 反馈到另一个picturebox上
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = image;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1; // 发生了异常
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+
|
|
|
+ // DestructModel(); // 销毁模型
|
|
|
+
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_video_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("视频推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+ // 识别单张图片 -- 固定大小展示:short_side: 512
|
|
|
+ private void cls_infer_one_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ //Bitmap bmp = new Bitmap(imgfile);
|
|
|
+ Bitmap bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(imgfile));
|
|
|
+
|
|
|
+ // resize()
|
|
|
+ var short_side = bmp.Width > bmp.Height ? bmp.Height : bmp.Width;
|
|
|
+ double resize_scale = 512.0 / short_side;
|
|
|
+
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);
|
|
|
+ OpenCvSharp.Mat output_mat = new Mat();
|
|
|
+
|
|
|
+ int new_height = (int)(bmp.Height * resize_scale);
|
|
|
+ int new_width = (int)(bmp.Width * resize_scale);
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(new_width, new_height));
|
|
|
+ bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+ byte[] inputData = GetBGRValues(bmp, out int stride);
|
|
|
+
|
|
|
+ float[] pre_score = new float[1];
|
|
|
+ int[] pre_category_id = new int[1];
|
|
|
+ byte[] pre_category = new byte[200]; //新建字节数组
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Cls_ModelPredict(inputData, bmp.Width, bmp.Height, 3, ref pre_score[0], ref pre_category[0], ref pre_category_id[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ string category_strGet = System.Text.Encoding.Default.GetString(pre_category, 0, pre_category.Length).Split('\0')[0]; //将类别字节数组转换为字符串
|
|
|
+ OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);//用bitmap转换为mat
|
|
|
+
|
|
|
+ // 对应类别的颜色
|
|
|
+ int[] color_ = { (int)(color_map[(pre_category_id[0]%256)*3]),
|
|
|
+ (int)(color_map[(pre_category_id[0] % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(pre_category_id[0] % 256) * 3 + 2]) };
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{category_strGet}-{pre_category_id[0]}-{pre_score[0]:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline); // 1倍大小的HersheySimplex，高度为22
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = bmp.Width - text_size.Width; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = text_size.Height;
|
|
|
+
|
|
|
+ // 书写类别
|
|
|
+ Cv2.PutText(mat, $"{category_strGet}-{pre_category_id[0]}-{pre_score[0]:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+ // 反馈到另一个picturebox上
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 收回内存
|
|
|
+ input_mat = null;
|
|
|
+ output_mat = null;
|
|
|
+ mat = null;
|
|
|
+ inputData = null;
|
|
|
+ pre_score = null;
|
|
|
+ pre_category_id = null;
|
|
|
+ pre_category = null;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1;
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_one_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("图片推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ // 识别图片文件夹 -- 固定大小展示:short_side: 512
|
|
|
+ private void cls_infer_many_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ foreach (string img_file in imgfiles)
|
|
|
+ {
|
|
|
+ if (isBreakInfer == 1) break; // 中断推理
|
|
|
+
|
|
|
+ //Bitmap bmp = new Bitmap(img_file);
|
|
|
+ Bitmap bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(img_file));
|
|
|
+
|
|
|
+ // resize()
|
|
|
+ var short_side = bmp.Width > bmp.Height ? bmp.Height : bmp.Width;
|
|
|
+ double resize_scale = 512.0 / short_side;
|
|
|
+
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);
|
|
|
+ OpenCvSharp.Mat output_mat = new Mat();
|
|
|
+
|
|
|
+ int new_height = (int)(bmp.Height * resize_scale);
|
|
|
+ int new_width = (int)(bmp.Width * resize_scale);
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(new_width, new_height));
|
|
|
+ bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+ byte[] inputData = GetBGRValues(bmp, out int stride);
|
|
|
+
|
|
|
+ float[] pre_score = new float[1];
|
|
|
+ int[] pre_category_id = new int[1];
|
|
|
+ byte[] pre_category = new byte[200]; //新建字节数组
|
|
|
+
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Cls_ModelPredict(inputData, bmp.Width, bmp.Height, 3, ref pre_score[0], ref pre_category[0], ref pre_category_id[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ string category_strGet = System.Text.Encoding.Default.GetString(pre_category, 0, pre_category.Length).Split('\0')[0]; //将类别字节数组转换为字符串
|
|
|
+ OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);//用bitmap转换为mat
|
|
|
+
|
|
|
+ // 对应类别的颜色
|
|
|
+ int[] color_ = { (int)(color_map[(pre_category_id[0]%256)*3]),
|
|
|
+ (int)(color_map[(pre_category_id[0] % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(pre_category_id[0] % 256) * 3 + 2]) };
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{category_strGet}-{pre_category_id[0]}-:{pre_score[0]:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline);
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = bmp.Width - text_size.Width; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = text_size.Height;
|
|
|
+
|
|
|
+ // 书写类别
|
|
|
+ Cv2.PutText(mat, $"{category_strGet}-{pre_category_id[0]}-:{pre_score[0]:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+
|
|
|
+ // 原图显示
|
|
|
+ Bitmap show_image = new Bitmap(img_file);
|
|
|
+ if (pictureBox1.Image != null) pictureBox1.Image.Dispose();
|
|
|
+ pictureBox1.Image = show_image; //显示原始图片到box1
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom; //显示原始图片到box1
|
|
|
+
|
|
|
+ // 反馈到另一个picturebox上
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 收回内存
|
|
|
+ mat = null;
|
|
|
+ inputData = null;
|
|
|
+ pre_score = null;
|
|
|
+ pre_category_id = null;
|
|
|
+ pre_category = null;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+
|
|
|
+ Thread.Sleep(continue_infer_delay); // 连续识别时，每张图片间隔continue_infer_delay毫秒
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1;
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_many_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("图片文件夹推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ // 识别视频 -- 固定大小展示:short_side: 512
|
|
|
+ private void cls_infer_video_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ VideoCapture capture = new VideoCapture();
|
|
|
+ capture.Open(videofile); // 读取视频
|
|
|
+
|
|
|
+ using Mat frame = new Mat();
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ while (true)
|
|
|
+ {
|
|
|
+ if (isBreakInfer == 1) break;
|
|
|
+ capture.Read(frame);//图像存储一帧数据
|
|
|
+ if (frame.Empty()) break;
|
|
|
+
|
|
|
+ // ------------- 原始图片 ----------
|
|
|
+ Bitmap image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame); //显示原始图片到box1
|
|
|
+ if (pictureBox1.Image != null) pictureBox1.Image.Dispose();
|
|
|
+ pictureBox1.Image = image; //显示原始图片到box1
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom; //显示原始图片到box1
|
|
|
+
|
|
|
+ // ------------- 送入推理的图片以及数据 ----------
|
|
|
+ image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame);
|
|
|
+
|
|
|
+ // resize()
|
|
|
+ var short_side = image.Width > image.Height ? image.Height : image.Width;
|
|
|
+ double resize_scale = 512.0 / short_side;
|
|
|
+
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(image);
|
|
|
+ OpenCvSharp.Mat output_mat = new Mat();
|
|
|
+
|
|
|
+ int new_height = (int)(image.Height * resize_scale);
|
|
|
+ int new_width = (int)(image.Width * resize_scale);
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(new_width, new_height));
|
|
|
+ image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+ byte[] inputData = GetBGRValues(image, out int stride);
|
|
|
+
|
|
|
+ float[] pre_score = new float[1];
|
|
|
+ int[] pre_category_id = new int[1];
|
|
|
+ byte[] pre_category = new byte[200]; //新建字节数组
|
|
|
+
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Cls_ModelPredict(inputData, image.Width, image.Height, 3, ref pre_score[0], ref pre_category[0], ref pre_category_id[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ string category_strGet = System.Text.Encoding.Default.GetString(pre_category, 0, pre_category.Length).Split('\0')[0]; //将类别字节数组转换为字符串
|
|
|
+ OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(image);//用bitmap转换为mat
|
|
|
+
|
|
|
+ // 对应类别的颜色
|
|
|
+ int[] color_ = { (int)(color_map[(pre_category_id[0]%256)*3]),
|
|
|
+ (int)(color_map[(pre_category_id[0] % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(pre_category_id[0] % 256) * 3 + 2]) };
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{category_strGet}-{pre_category_id[0]}-:{pre_score[0]:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline);
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = image.Width - text_size.Width; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = text_size.Height;
|
|
|
+ // 书写类别
|
|
|
+ Cv2.PutText(mat, $"{category_strGet}-{pre_category_id[0]}-:{pre_score[0]:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+ // 反馈到另一个picturebox上
|
|
|
+ pictureBox2.Image = image;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 收回内存
|
|
|
+ mat = null;
|
|
|
+ inputData = null;
|
|
|
+ pre_score = null;
|
|
|
+ pre_category_id = null;
|
|
|
+ pre_category = null;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1; // 发生了异常
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+
|
|
|
+ // DestructModel(); // 销毁模型
|
|
|
+
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_video_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("视频推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+ // 分割图片 -- 固定大小展示:512 X 512
|
|
|
+ private void seg_infer_one_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+
|
|
|
+ //Bitmap origin_bmp = new Bitmap(imgfile);
|
|
|
+ Bitmap origin_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(imgfile));
|
|
|
+ Bitmap input_bmp = null;
|
|
|
+
|
|
|
+ // resize()
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+ OpenCvSharp.Mat output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(512, 512));
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+ input_mat = null;
|
|
|
+ output_mat = null;
|
|
|
+
|
|
|
+ byte[] inputData = GetBGRValues(input_bmp, out int stride);
|
|
|
+
|
|
|
+ byte[] output_map = new byte[input_bmp.Height * input_bmp.Width]; //新建字节数组
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Seg_ModelPredict(inputData, input_bmp.Width, input_bmp.Height, 3, ref output_map[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ // 还原原始图像大小
|
|
|
+ input_bmp = CreateBitmap(output_map, input_bmp.Width, input_bmp.Height, color_map); // 还原512的输入大小的图像
|
|
|
+
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp);
|
|
|
+ output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp); // 获取处理后的图像
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(origin_bmp.Width, origin_bmp.Height)); // 还原到与输入一致的图像大小
|
|
|
+ input_mat = null; // 回收内存
|
|
|
+
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp); // 获取原始图像
|
|
|
+ //OpenCvSharp.Mat add_mat = new Mat(); // 叠加后的图像
|
|
|
+
|
|
|
+ Cv2.AddWeighted(output_mat, 1.0, input_mat, 0.35, 1, output_mat); // 执行叠加
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+ // 反馈到另一个picturebox上
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = input_bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1;
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_one_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("图片推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ // 分割图片文件夹 -- 固定大小展示:512 X 512
|
|
|
+ private void seg_infer_many_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ foreach (string img_file in imgfiles)
|
|
|
+ {
|
|
|
+ if (isBreakInfer == 1) break; // 中断推理
|
|
|
+
|
|
|
+ Bitmap origin_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(img_file));
|
|
|
+ Bitmap input_bmp = null;
|
|
|
+
|
|
|
+ // resize()
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+ OpenCvSharp.Mat output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(512, 512));
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+ input_mat = null;
|
|
|
+ output_mat = null;
|
|
|
+
|
|
|
+ byte[] inputData = GetBGRValues(input_bmp, out int stride);
|
|
|
+
|
|
|
+ byte[] output_map = new byte[input_bmp.Height * input_bmp.Width]; //新建字节数组
|
|
|
+
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Seg_ModelPredict(inputData, input_bmp.Width, input_bmp.Height, 3, ref output_map[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ // 还原原始图像大小
|
|
|
+ input_bmp = CreateBitmap(output_map, input_bmp.Width, input_bmp.Height, color_map); // 还原512的输入大小的图像
|
|
|
+
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp);
|
|
|
+ output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp); // 获取处理后的图像
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(origin_bmp.Width, origin_bmp.Height)); // 还原到与输入一致的图像大小
|
|
|
+ input_mat = null; // 回收内存
|
|
|
+
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp); // 获取原始图像
|
|
|
+ //OpenCvSharp.Mat add_mat = new Mat(); // 叠加后的图像
|
|
|
+
|
|
|
+ Cv2.AddWeighted(output_mat, 1.0, input_mat, 0.35, 1, output_mat); // 执行叠加
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+ // 显示图片
|
|
|
+ if (pictureBox1.Image != null) pictureBox1.Image.Dispose();
|
|
|
+ pictureBox1.Image = origin_bmp; //显示原始图片到box1
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom; //显示原始图片到box1
|
|
|
+
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = input_bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+
|
|
|
+ Thread.Sleep(continue_infer_delay); // 连续识别时，每张图片间隔continue_infer_delay毫秒
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1;
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_many_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("图片文件夹推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ // 分割视频流 -- 固定大小展示:512 X 512
|
|
|
+ private void seg_infer_video_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ VideoCapture capture = new VideoCapture();
|
|
|
+ capture.Open(videofile); // 读取视频
|
|
|
+
|
|
|
+ using Mat frame = new Mat();
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ while (true)
|
|
|
+ {
|
|
|
+ if (isBreakInfer == 1) break;
|
|
|
+ capture.Read(frame);//图像存储一帧数据
|
|
|
+ if (frame.Empty()) break;
|
|
|
+
|
|
|
+ // ------------- 原始图片 ----------
|
|
|
+ Bitmap origin_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame);
|
|
|
+ // ------------- 送入推理的图片以及数据 ----------
|
|
|
+ Bitmap input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame);
|
|
|
+ // resize()
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+ OpenCvSharp.Mat output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(512, 512));
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+ input_mat = null;
|
|
|
+ output_mat = null;
|
|
|
+
|
|
|
+ byte[] inputData = GetBGRValues(input_bmp, out int stride);
|
|
|
+
|
|
|
+ byte[] output_map = new byte[input_bmp.Height * input_bmp.Width]; //新建字节数组
|
|
|
+
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Seg_ModelPredict(inputData, input_bmp.Width, input_bmp.Height, 3, ref output_map[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ // 还原原始图像大小
|
|
|
+ input_bmp = CreateBitmap(output_map, input_bmp.Width, input_bmp.Height, color_map); // 还原512的输入大小的图像
|
|
|
+
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp);
|
|
|
+ output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp); // 获取处理后的图像
|
|
|
+ Cv2.Resize(input_mat, output_mat, new OpenCvSharp.Size(origin_bmp.Width, origin_bmp.Height)); // 还原到与输入一致的图像大小
|
|
|
+ input_mat = null; // 回收内存
|
|
|
+
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp); // 获取原始图像
|
|
|
+ //OpenCvSharp.Mat add_mat = new Mat(); // 叠加后的图像
|
|
|
+
|
|
|
+ Cv2.AddWeighted(output_mat, 1.0, input_mat, 0.35, 1, output_mat); // 执行叠加
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+
|
|
|
+ // 显示图片
|
|
|
+ if (pictureBox1.Image != null) pictureBox1.Image.Dispose();
|
|
|
+ pictureBox1.Image = origin_bmp; //显示原始图片到box1
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom; //显示原始图片到box1
|
|
|
+
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = input_bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1; // 发生了异常
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+
|
|
|
+ // DestructModel(); // 销毁模型
|
|
|
+
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_video_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("视频推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+ // MaskRCNN检测单张图片 -- GPU推理正常
|
|
|
+ private void mask_infer_one_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ Bitmap origin_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(imgfile));
|
|
|
+ Bitmap input_bmp = null;
|
|
|
+
|
|
|
+ // resize()
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+ OpenCvSharp.Mat output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+ byte[] inputData = GetBGRValues(origin_bmp, out int stride);
|
|
|
+
|
|
|
+ float[] resultlist = new float[600];
|
|
|
+ IntPtr results = FloatToIntptr(resultlist);
|
|
|
+
|
|
|
+ byte[] mask_results = new byte[input_bmp.Height * input_bmp.Width]; //新建字节数组
|
|
|
+
|
|
|
+ int[] boxesInfo = new int[1]; // 10 boundingbox
|
|
|
+ byte[] labellist = new byte[1000]; //新建字节数组：label1_str label2_str
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ Mask_ModelPredict(inputData, input_bmp.Width, input_bmp.Height, 3, results, ref mask_results[0], boxesInfo, ref labellist[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ input_bmp = CreateBitmap(mask_results, input_bmp.Width, input_bmp.Height, color_map);
|
|
|
+
|
|
|
+ output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp); // 获取处理后的图像
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp); // 获取原始图像
|
|
|
+ //OpenCvSharp.Mat add_mat = new Mat(); // 叠加后的图像
|
|
|
+
|
|
|
+ Cv2.AddWeighted(output_mat, 0.65, input_mat, 0.35, 1, output_mat); // 执行叠加
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+ string strGet = System.Text.Encoding.Default.GetString(labellist, 0, labellist.Length); //将字节数组转换为字符串
|
|
|
+ string[] predict_Label_List = strGet.Split(' '); // 预测的类别情况
|
|
|
+ // MessageBox.Show($"Box_Number: {boxesInfo[0]}");
|
|
|
+ using OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp);//用bitmap转换为mat
|
|
|
+ for (int i = 0; i < boxesInfo[0]; i++) // 未绘制图像
|
|
|
+ {
|
|
|
+ int labelindex = Convert.ToInt32(resultlist[i * 6 + 0]);
|
|
|
+ float score = resultlist[i * 6 + 1];
|
|
|
+ float left = resultlist[i * 6 + 2];
|
|
|
+ float top = resultlist[i * 6 + 3];
|
|
|
+ float right = resultlist[i * 6 + 4];
|
|
|
+ float down = resultlist[i * 6 + 5];
|
|
|
+
|
|
|
+ if (score > det_threshold)
|
|
|
+ {
|
|
|
+ labelindex += 1; // Mask RCNN包含背景，故而加1
|
|
|
+ int[] color_ = { (int)(color_map[(labelindex%256)*3]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 2]) };
|
|
|
+
|
|
|
+ labelindex -= 1; // 还原类别
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{predict_Label_List[i]}-{labelindex}-{score:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline); // 1倍大小的HersheySimplex，高度为22
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = (int)left + 22; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = (int)top + text_size.Height;
|
|
|
+
|
|
|
+ // 绘制矩形，书写类别
|
|
|
+ Cv2.Rectangle(mat, new OpenCvSharp.Rect((int)left, (int)top, (int)right, (int)down), new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.AntiAlias);//LineTypes.AntiAlias:反锯齿效果
|
|
|
+ Cv2.PutText(mat, $"{predict_Label_List[i]}-{labelindex}-: {score:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = input_bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1;
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_one_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("图片推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ // MaskRCNN检测图片文件夹
|
|
|
+ private void mask_infer_many_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ foreach (string img_file in imgfiles)
|
|
|
+ {
|
|
|
+ if (isBreakInfer == 1) break; // 中断推理
|
|
|
+
|
|
|
+ Bitmap origin_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Cv2.ImRead(img_file));
|
|
|
+ Bitmap input_bmp = null;
|
|
|
+
|
|
|
+ // resize()
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+ OpenCvSharp.Mat output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+ byte[] inputData = GetBGRValues(origin_bmp, out int stride);
|
|
|
+
|
|
|
+ float[] resultlist = new float[600];
|
|
|
+ IntPtr results = FloatToIntptr(resultlist);
|
|
|
+
|
|
|
+ byte[] mask_results = new byte[input_bmp.Height * input_bmp.Width]; //新建字节数组
|
|
|
+
|
|
|
+ int[] boxesInfo = new int[1]; // 10 boundingbox
|
|
|
+ byte[] labellist = new byte[1000]; //新建字节数组：label1_str label2_str
|
|
|
+
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Mask_ModelPredict(inputData, input_bmp.Width, input_bmp.Height, 3, results, ref mask_results[0], boxesInfo, ref labellist[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ input_bmp = CreateBitmap(mask_results, input_bmp.Width, input_bmp.Height, color_map);
|
|
|
+
|
|
|
+ output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp); // 获取处理后的图像
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp); // 获取原始图像
|
|
|
+ //OpenCvSharp.Mat add_mat = new Mat(); // 叠加后的图像
|
|
|
+
|
|
|
+ Cv2.AddWeighted(output_mat, 1.0, input_mat, 0.35, 1, output_mat); // 执行叠加
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+ string strGet = System.Text.Encoding.Default.GetString(labellist, 0, labellist.Length); //将字节数组转换为字符串
|
|
|
+ string[] predict_Label_List = strGet.Split(' '); // 预测的类别情况
|
|
|
+ // MessageBox.Show($"Box_Number: {boxesInfo[0]}");
|
|
|
+ using OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp);//用bitmap转换为mat
|
|
|
+ for (int i = 0; i < boxesInfo[0]; i++) // 未绘制图像
|
|
|
+ {
|
|
|
+ int labelindex = Convert.ToInt32(resultlist[i * 6 + 0]);
|
|
|
+ float score = resultlist[i * 6 + 1];
|
|
|
+ float left = resultlist[i * 6 + 2];
|
|
|
+ float top = resultlist[i * 6 + 3];
|
|
|
+ float right = resultlist[i * 6 + 4];
|
|
|
+ float down = resultlist[i * 6 + 5];
|
|
|
+
|
|
|
+ if (score > det_threshold)
|
|
|
+ {
|
|
|
+ labelindex += 1; // Mask RCNN包含背景，故而加1
|
|
|
+ int[] color_ = { (int)(color_map[(labelindex%256)*3]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 2]) };
|
|
|
+
|
|
|
+ labelindex -= 1; // 还原类别
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{predict_Label_List[i]}-{labelindex}-{score:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline); // 1倍大小的HersheySimplex，高度为22
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = (int)left + 22; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = (int)top + text_size.Height;
|
|
|
+
|
|
|
+ // 绘制矩形，书写类别
|
|
|
+ Cv2.Rectangle(mat, new OpenCvSharp.Rect((int)left, (int)top, (int)right, (int)down), new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.AntiAlias);//LineTypes.AntiAlias:反锯齿效果
|
|
|
+ Cv2.PutText(mat, $"{predict_Label_List[i]}-{labelindex}-: {score:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 显示图片
|
|
|
+ if (pictureBox1.Image != null) pictureBox1.Image.Dispose();
|
|
|
+ pictureBox1.Image = origin_bmp; //显示原始图片到box1
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom; //显示原始图片到box1
|
|
|
+
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = input_bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+
|
|
|
+ Thread.Sleep(continue_infer_delay); // 连续识别时，每张图片间隔continue_infer_delay毫秒
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1;
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_many_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("图片文件夹推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ // MaskRCNN检测视频流
|
|
|
+ private void mask_infer_video_img()
|
|
|
+ {
|
|
|
+ is_infer = 1; // 进入推理
|
|
|
+
|
|
|
+ byte[] color_map = get_color_map_list(256);
|
|
|
+
|
|
|
+ VideoCapture capture = new VideoCapture();
|
|
|
+ capture.Open(videofile); // 读取视频
|
|
|
+
|
|
|
+ using Mat frame = new Mat();
|
|
|
+
|
|
|
+ int raise_ex_flag = 0; // 是否发生了异常
|
|
|
+ try
|
|
|
+ {
|
|
|
+ while (true)
|
|
|
+ {
|
|
|
+ if (isBreakInfer == 1) break;
|
|
|
+ capture.Read(frame);//图像存储一帧数据
|
|
|
+ if (frame.Empty()) break;
|
|
|
+
|
|
|
+ // ------------- 原始图片 ----------
|
|
|
+ Bitmap origin_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame);
|
|
|
+ // ------------- 送入推理的图片以及数据 ----------
|
|
|
+ Bitmap input_bmp = null;
|
|
|
+
|
|
|
+ // resize()
|
|
|
+ OpenCvSharp.Mat input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+ OpenCvSharp.Mat output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp);
|
|
|
+
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+ byte[] inputData = GetBGRValues(origin_bmp, out int stride);
|
|
|
+
|
|
|
+ float[] resultlist = new float[600];
|
|
|
+ IntPtr results = FloatToIntptr(resultlist);
|
|
|
+
|
|
|
+ byte[] mask_results = new byte[input_bmp.Height * input_bmp.Width]; //新建字节数组
|
|
|
+
|
|
|
+ int[] boxesInfo = new int[1]; // 10 boundingbox
|
|
|
+ byte[] labellist = new byte[1000]; //新建字节数组：label1_str label2_str
|
|
|
+
|
|
|
+ TimeSpan infer_start_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+ //第四个参数为输入图像的通道数
|
|
|
+ Mask_ModelPredict(inputData, input_bmp.Width, input_bmp.Height, 3, results, ref mask_results[0], boxesInfo, ref labellist[0]);
|
|
|
+ TimeSpan infer_end_time = new TimeSpan(DateTime.Now.Ticks);
|
|
|
+
|
|
|
+ input_bmp = CreateBitmap(mask_results, input_bmp.Width, input_bmp.Height, color_map);
|
|
|
+
|
|
|
+ output_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp); // 获取处理后的图像
|
|
|
+ input_mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(origin_bmp); // 获取原始图像
|
|
|
+ //OpenCvSharp.Mat add_mat = new Mat(); // 叠加后的图像
|
|
|
+
|
|
|
+ Cv2.AddWeighted(output_mat, 1.0, input_mat, 0.35, 1, output_mat); // 执行叠加
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(output_mat);
|
|
|
+
|
|
|
+ string strGet = System.Text.Encoding.Default.GetString(labellist, 0, labellist.Length); //将字节数组转换为字符串
|
|
|
+ string[] predict_Label_List = strGet.Split(' '); // 预测的类别情况
|
|
|
+ // MessageBox.Show($"Box_Number: {boxesInfo[0]}");
|
|
|
+ using OpenCvSharp.Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(input_bmp);//用bitmap转换为mat
|
|
|
+ for (int i = 0; i < boxesInfo[0]; i++) // 未绘制图像
|
|
|
+ {
|
|
|
+ int labelindex = Convert.ToInt32(resultlist[i * 6 + 0]);
|
|
|
+ float score = resultlist[i * 6 + 1];
|
|
|
+ float left = resultlist[i * 6 + 2];
|
|
|
+ float top = resultlist[i * 6 + 3];
|
|
|
+ float right = resultlist[i * 6 + 4];
|
|
|
+ float down = resultlist[i * 6 + 5];
|
|
|
+
|
|
|
+ if (score > det_threshold)
|
|
|
+ {
|
|
|
+ labelindex += 1; // Mask RCNN包含背景，故而加1
|
|
|
+ int[] color_ = { (int)(color_map[(labelindex%256)*3]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 1]),
|
|
|
+ (int)(color_map[(labelindex % 256) * 3 + 2]) };
|
|
|
+
|
|
|
+ labelindex -= 1; // 还原类别
|
|
|
+ // 获取文本区域的大小
|
|
|
+ var text_size = Cv2.GetTextSize($"{predict_Label_List[i]}-{labelindex}-{score:f2}",
|
|
|
+ HersheyFonts.HersheySimplex, 1, 2, out int baseline); // 1倍大小的HersheySimplex，高度为22
|
|
|
+ // 获取文本区域的左下顶点 -- 右上角
|
|
|
+ int left_down_x = (int)left + 22; // 小偏移调整量: (int)(text_size.Width/10)
|
|
|
+ int left_down_y = (int)top + text_size.Height;
|
|
|
+
|
|
|
+ // 绘制矩形，书写类别
|
|
|
+ Cv2.Rectangle(mat, new OpenCvSharp.Rect((int)left, (int)top, (int)right, (int)down), new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.AntiAlias);//LineTypes.AntiAlias:反锯齿效果
|
|
|
+ Cv2.PutText(mat, $"{predict_Label_List[i]}-{labelindex}-: {score:f2}", new OpenCvSharp.Point(left_down_x, left_down_y), HersheyFonts.HersheySimplex, 1, new OpenCvSharp.Scalar(color_[0], color_[1], color_[2]), 2, LineTypes.Link4);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 显示图片
|
|
|
+ if (pictureBox1.Image != null) pictureBox1.Image.Dispose();
|
|
|
+ pictureBox1.Image = origin_bmp; //显示原始图片到box1
|
|
|
+ pictureBox1.SizeMode = PictureBoxSizeMode.Zoom; //显示原始图片到box1
|
|
|
+
|
|
|
+ // 转换回bitmap进行显示
|
|
|
+ input_bmp = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
|
|
|
+ if (pictureBox2.Image != null) pictureBox2.Image.Dispose();
|
|
|
+ pictureBox2.Image = input_bmp;
|
|
|
+ pictureBox2.SizeMode = PictureBoxSizeMode.Zoom;
|
|
|
+
|
|
|
+ // 展示推理耗时
|
|
|
+ TimeSpan start2end_time = infer_end_time.Subtract(infer_start_time).Duration();
|
|
|
+ double cost_milliseconds = start2end_time.TotalMilliseconds;
|
|
|
+ // 通过委托展示到label上
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { $"{cost_milliseconds:f2}" });
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch (Exception e)
|
|
|
+ {
|
|
|
+ raise_ex_flag = 1; // 发生了异常
|
|
|
+
|
|
|
+ // 默认耗时为0ms
|
|
|
+ Action<String> AsyncUIDelegate = delegate (string n) { label7.Text = n; };//定义一个委托
|
|
|
+ label7.Invoke(AsyncUIDelegate, new object[] { "0.00" });
|
|
|
+ MessageBox.Show("1.请检查模型文件与模型类型是否一致!\n2.内存溢出，yml预处理有误，图片格式确保为1/3通道...", "模型运行失败", MessageBoxButtons.OK, MessageBoxIcon.Error);
|
|
|
+ }
|
|
|
+
|
|
|
+ isBreakInfer = 0; // 清空标志
|
|
|
+
|
|
|
+ is_infer = 0; // 退出推理 -- 解除推理状态
|
|
|
+ infer_video_img_flag = 0; // 重置当前推理状态 -- 解除图片推理状态
|
|
|
+ if (raise_ex_flag == 0) MessageBox.Show("视频推理完成!"); // 未发生异常，正常显示推理完成提示
|
|
|
+ }
|
|
|
+
|
|
|
+ /**********************************************************************/
|
|
|
+ /***************** 7.部分推理组件函数 ***************/
|
|
|
+ /**********************************************************************/
|
|
|
+ /// <summary>
|
|
|
+ /// 从内存流中指定位置，读取数据
|
|
|
+ /// </summary>
|
|
|
+ /// <param name="curStream"></param>
|
|
|
+ /// <param name="startPosition"></param>
|
|
|
+ /// <param name="length"></param>
|
|
|
+ /// <returns></returns>
|
|
|
+ public static int ReadData(MemoryStream curStream, int startPosition, int length)
|
|
|
+ {
|
|
|
+ int result = -1;
|
|
|
+
|
|
|
+ byte[] tempData = new byte[length];
|
|
|
+ curStream.Position = startPosition;
|
|
|
+ curStream.Read(tempData, 0, length);
|
|
|
+ result = BitConverter.ToInt32(tempData, 0);
|
|
|
+
|
|
|
+ return result;
|
|
|
+ }
|
|
|
+
|
|
|
+ /// <summary>
|
|
|
+ /// 使用byte[]数据，生成三通道 BMP 位图
|
|
|
+ /// </summary>
|
|
|
+ /// <param name="originalImageData"></param>
|
|
|
+ /// <param name="originalWidth"></param>
|
|
|
+ /// <param name="originalHeight"></param>
|
|
|
+ /// <returns></returns>
|
|
|
+ public static Bitmap CreateBitmap(byte[] originalImageData, int originalWidth, int originalHeight, byte[] color_map)
|
|
|
+ {
|
|
|
+ // 指定8位格式，即256色
|
|
|
+ Bitmap resultBitmap = new Bitmap(originalWidth, originalHeight, System.Drawing.Imaging.PixelFormat.Format8bppIndexed);
|
|
|
+
|
|
|
+ // 将该位图存入内存中
|
|
|
+ MemoryStream curImageStream = new MemoryStream();
|
|
|
+ resultBitmap.Save(curImageStream, System.Drawing.Imaging.ImageFormat.Bmp);
|
|
|
+ curImageStream.Flush();
|
|
|
+
|
|
|
+ // 由于位图数据需要DWORD对齐（4byte倍数），计算需要补位的个数
|
|
|
+ int curPadNum = ((originalWidth * 8 + 31) / 32 * 4) - originalWidth;
|
|
|
+
|
|
|
+ // 最终生成的位图数据大小
|
|
|
+ int bitmapDataSize = ((originalWidth * 8 + 31) / 32 * 4) * originalHeight;
|
|
|
+
|
|
|
+ // 数据部分相对文件开始偏移，具体可以参考位图文件格式
|
|
|
+ int dataOffset = ReadData(curImageStream, 10, 4);
|
|
|
+
|
|
|
+
|
|
|
+ // 改变调色板，因为默认的调色板是32位彩色的，需要修改为256色的调色板
|
|
|
+ int paletteStart = 54;
|
|
|
+ int paletteEnd = dataOffset;
|
|
|
+ int color = 0;
|
|
|
+
|
|
|
+ for (int i = paletteStart; i < paletteEnd; i += 4)
|
|
|
+ {
|
|
|
+ byte[] tempColor = new byte[4];
|
|
|
+ tempColor[0] = (byte)color;
|
|
|
+ tempColor[1] = (byte)color;
|
|
|
+ tempColor[2] = (byte)color;
|
|
|
+ tempColor[3] = (byte)0;
|
|
|
+ color++;
|
|
|
+
|
|
|
+ curImageStream.Position = i;
|
|
|
+ curImageStream.Write(tempColor, 0, 4);
|
|
|
+ }
|
|
|
+
|
|
|
+ // 最终生成的位图数据，以及大小，高度没有变，宽度需要调整
|
|
|
+ byte[] destImageData = new byte[bitmapDataSize];
|
|
|
+ int destWidth = originalWidth + curPadNum;
|
|
|
+
|
|
|
+ // 生成最终的位图数据，注意的是，位图数据 从左到右，从下到上，所以需要颠倒
|
|
|
+ for (int originalRowIndex = originalHeight - 1; originalRowIndex >= 0; originalRowIndex--)
|
|
|
+ {
|
|
|
+ int destRowIndex = originalHeight - originalRowIndex - 1;
|
|
|
+
|
|
|
+ for (int dataIndex = 0; dataIndex < originalWidth; dataIndex++)
|
|
|
+ {
|
|
|
+ // 同时还要注意，新的位图数据的宽度已经变化destWidth，否则会产生错位
|
|
|
+ destImageData[destRowIndex * destWidth + dataIndex] = originalImageData[originalRowIndex * originalWidth + dataIndex];
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 将流的Position移到数据段
|
|
|
+ curImageStream.Position = dataOffset;
|
|
|
+
|
|
|
+ // 将新位图数据写入内存中
|
|
|
+ curImageStream.Write(destImageData, 0, bitmapDataSize);
|
|
|
+
|
|
|
+ curImageStream.Flush();
|
|
|
+
|
|
|
+ // 将内存中的位图写入Bitmap对象
|
|
|
+ resultBitmap = new Bitmap(curImageStream);
|
|
|
+
|
|
|
+ resultBitmap = transForm8to24(resultBitmap, color_map); // 转为3通道图像
|
|
|
+
|
|
|
+ return resultBitmap;
|
|
|
+ }
|
|
|
+
|
|
|
+ // 实现bitmap单通道到三通道(分割生成掩码图像(单通道) ==> RGB图像)
|
|
|
+ public static Bitmap transForm8to24(Bitmap bmp, byte[] color_map)
|
|
|
+ {
|
|
|
+
|
|
|
+ System.Drawing.Rectangle rect = new System.Drawing.Rectangle(0, 0, bmp.Width, bmp.Height);
|
|
|
+
|
|
|
+ System.Drawing.Imaging.BitmapData bitmapData = bmp.LockBits(rect, System.Drawing.Imaging.ImageLockMode.ReadOnly, bmp.PixelFormat);
|
|
|
+
|
|
|
+ //计算实际8位图容量
|
|
|
+ int size8 = bitmapData.Stride * bmp.Height;
|
|
|
+ byte[] grayValues = new byte[size8];
|
|
|
+
|
|
|
+ //// 申请目标位图的变量，并将其内存区域锁定
|
|
|
+ Bitmap TempBmp = new Bitmap(bmp.Width, bmp.Height, PixelFormat.Format24bppRgb);
|
|
|
+ BitmapData TempBmpData = TempBmp.LockBits(new Rectangle(0, 0, bmp.Width, bmp.Height), ImageLockMode.WriteOnly, PixelFormat.Format24bppRgb);
|
|
|
+
|
|
|
+
|
|
|
+ //// 获取图像参数以及设置24位图信息
|
|
|
+ int stride = TempBmpData.Stride; // 扫描线的宽度
|
|
|
+ int offset = stride - TempBmp.Width; // 显示宽度与扫描线宽度的间隙
|
|
|
+ IntPtr iptr = TempBmpData.Scan0; // 获取bmpData的内存起始位置
|
|
|
+ int scanBytes = stride * TempBmp.Height;// 用stride宽度，表示这是内存区域的大小
|
|
|
+
|
|
|
+ //// 下面把原始的显示大小字节数组转换为内存中实际存放的字节数组
|
|
|
+
|
|
|
+ byte[] pixelValues = new byte[scanBytes]; //为目标数组分配内存
|
|
|
+ System.Runtime.InteropServices.Marshal.Copy(bitmapData.Scan0, grayValues, 0, size8);
|
|
|
+
|
|
|
+ for (int i = 0; i < bmp.Height; i++)
|
|
|
+ {
|
|
|
+
|
|
|
+ for (int j = 0; j < bitmapData.Stride; j++)
|
|
|
+ {
|
|
|
+
|
|
|
+ if (j >= bmp.Width)
|
|
|
+ continue;
|
|
|
+
|
|
|
+
|
|
|
+ int indexSrc = i * bitmapData.Stride + j;
|
|
|
+ int realIndex = i * TempBmpData.Stride + j * 3;
|
|
|
+
|
|
|
+ // color_id：就是预测出来的结果
|
|
|
+ int color_id = (int)grayValues[indexSrc] % 256;
|
|
|
+
|
|
|
+ if (color_id == 0) // 分割中类别1对应值1，而背景往往为0，因此这里就将背景置为[0, 0, 0]
|
|
|
+ {
|
|
|
+ // 空白
|
|
|
+ pixelValues[realIndex] = 0;
|
|
|
+ pixelValues[realIndex + 1] = 0;
|
|
|
+ pixelValues[realIndex + 2] = 0;
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ // 替换为color_map中的颜色值
|
|
|
+ pixelValues[realIndex] = color_map[color_id * 3];
|
|
|
+ pixelValues[realIndex + 1] = color_map[color_id * 3 + 1];
|
|
|
+ pixelValues[realIndex + 2] = color_map[color_id * 3 + 2];
|
|
|
+ }
|
|
|
+
|
|
|
+ }
|
|
|
+
|
|
|
+ }
|
|
|
+
|
|
|
+ //// 用Marshal的Copy方法，将刚才得到的内存字节数组复制到BitmapData中
|
|
|
+ System.Runtime.InteropServices.Marshal.Copy(pixelValues, 0, iptr, scanBytes);
|
|
|
+ TempBmp.UnlockBits(TempBmpData); // 解锁内存区域
|
|
|
+ bmp.UnlockBits(bitmapData);
|
|
|
+
|
|
|
+ return TempBmp;
|
|
|
+ }
|
|
|
+
|
|
|
+ // 生成伪彩色图的RGB值集合(color_map) -- 同时也是适用于检测框分类颜色
|
|
|
+ private byte[] get_color_map_list(int num_classes = 256)
|
|
|
+ {
|
|
|
+ num_classes += 1;
|
|
|
+ byte[] color_map = new byte[num_classes * 3];
|
|
|
+ for (int i = 0; i < num_classes; i++)
|
|
|
+ {
|
|
|
+ int j = 0;
|
|
|
+ int lab = i;
|
|
|
+ while (lab != 0)
|
|
|
+ {
|
|
|
+ color_map[i * 3] |= (byte)(((lab >> 0) & 1) << (7 - j));
|
|
|
+ color_map[i * 3 + 1] |= (byte)(((lab >> 1) & 1) << (7 - j));
|
|
|
+ color_map[i * 3 + 2] |= (byte)(((lab >> 2) & 1) << (7 - j));
|
|
|
+
|
|
|
+ j += 1;
|
|
|
+ lab >>= 3;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 去掉底色
|
|
|
+ color_map = color_map.Skip(3).ToArray();
|
|
|
+
|
|
|
+ return color_map;
|
|
|
+ }
|
|
|
+
|
|
|
+ /// <summary>
|
|
|
+ /// 获得目录下所有文件或指定文件类型文件(包含所有子文件夹)
|
|
|
+ /// </summary>
|
|
|
+ /// <param name="path">文件夹路径</param>
|
|
|
+ /// <param name="extName">扩展名可以多个 例如 .mp3.wma.rm</param>
|
|
|
+ /// <returns>List<FileInfo></returns>
|
|
|
+ public static List<FileInfo> getFile(string path, string extName, List<FileInfo> lst)
|
|
|
+ {
|
|
|
+ try
|
|
|
+ {
|
|
|
+ DirectoryInfo fdir = new DirectoryInfo(path);
|
|
|
+ FileInfo[] file = fdir.GetFiles();
|
|
|
+ //FileInfo[] file = Directory.GetFiles(path); //文件列表
|
|
|
+ if (file.Length != 0) //当前目录文件或文件夹不为空
|
|
|
+ {
|
|
|
+ foreach (FileInfo f in file) //显示当前目录所有文件
|
|
|
+ {
|
|
|
+ if (extName.ToLower().IndexOf(f.Extension.ToLower()) >= 0)
|
|
|
+ {
|
|
|
+ lst.Add(f);
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ return lst;
|
|
|
+ }
|
|
|
+ catch (Exception ex)
|
|
|
+ {
|
|
|
+ throw ex;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // 将Btimap类转换为byte[]类函数
|
|
|
+ public static byte[] GetBGRValues(Bitmap bmp, out int stride)
|
|
|
+ {
|
|
|
+ var rect = new Rectangle(0, 0, bmp.Width, bmp.Height);
|
|
|
+ var bmpData = bmp.LockBits(rect, ImageLockMode.ReadOnly, bmp.PixelFormat);
|
|
|
+ stride = bmpData.Stride;
|
|
|
+ var rowBytes = bmpData.Width * Image.GetPixelFormatSize(bmp.PixelFormat) / 8;
|
|
|
+ var imgBytes = bmp.Height * rowBytes;
|
|
|
+ byte[] rgbValues = new byte[imgBytes];
|
|
|
+ IntPtr ptr = bmpData.Scan0;
|
|
|
+ for (var i = 0; i < bmp.Height; i++)
|
|
|
+ {
|
|
|
+ Marshal.Copy(ptr, rgbValues, i * rowBytes, rowBytes);
|
|
|
+ ptr += bmpData.Stride;
|
|
|
+ }
|
|
|
+ bmp.UnlockBits(bmpData);
|
|
|
+ return rgbValues;
|
|
|
+ }
|
|
|
+
|
|
|
+ // 创建指向float数组类型的IntPtr指针
|
|
|
+ public static IntPtr FloatToIntptr(float[] bytes)
|
|
|
+ {
|
|
|
+ GCHandle hObject = GCHandle.Alloc(bytes, GCHandleType.Pinned);
|
|
|
+ return hObject.AddrOfPinnedObject();
|
|
|
+ }
|
|
|
+
|
|
|
+ // 检查MaskRCNN模型是否启动在GPU上 -- 只支持GPU推理，因为内存占用较大，CPU可能溢出，导致无法连续推理
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+ public static bool CheckMaskRCNN_workOnGpu(string model_type, bool use_gpu)
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+ {
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+ if (model_type == "mask")
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+ {
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+ if (use_gpu == false) // 当且仅当为MaskRCNN时，没有使用GPU会返回false
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+ return false;
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+ }
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+ return true;
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+ }
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+ }
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+}
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Jinghui Cai