yolov7模型部署——代码实现（python版和c#版）

柔情只为你懂 2024-04-01 18:53 87阅读 0赞

忙了两个星期，终于把c\#版onnx调用整合到项目中，并和UI功能结合起来了~~~也终于腾出时间来总结一下，都快忘记踩过什么坑了T\_T。

## 一，python版 ##

python版比较容易，毕竟有官方的detect.py指导，据说之前官方放了个使用onnx推理的ipynb文件，但很快就删了~~~参考[这篇博客][Link 1]。通过[模型结构图][Link 2]查看模型输入要求尺寸为\[1 3 640 640\]。

![e11a75d3116c4fc4b5c461d00dfd6bdc.png][]

onnx调用的基本思想步骤就是：

1.  根据EP建立session：
    
        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
        session = ort.InferenceSession(w, providers=providers)
2.  调用session.run:
    
        outputs = session.run(outname, inp)[0]
3.  解析并可视化结果

具体实现步骤为：

*  Resize原始图片，处理成onnx模型需要的大小，这个过程是通过letterbox()函数实现的。公主的模型输入尺寸为\[640, 640\]。根据官方提供的思想resize的时候需要保持原始图片的长宽比。因此需要首先计算与所需尺寸相差的最小ratio（即根据原始长度长，宽较长一边计算出的ratio），将图片resize之后，剩下的空隙用灰色来填充。公主输入图片原始尺寸为\[512, 555, 3\],转换之后为\[640, 640, 3\]，如下图所示：

![6e624b359fa146d2be49a28c49bf6e19.png][]

*  将图像转为RGB格式，并增加一维以符合输入尺寸，通过以下命令实现
    
        image = image.transpose((2, 0, 1))
        image = np.expand_dims(image, 0)
 *  调用onnx模型
 *  可视化输出结果，输出是个二维n\*7数组，n代表检测结果数，每一条结果的7个值分别代表：\[batch\_id, bbox左上角横坐标，bbox左上角纵坐标，bbox右下角横坐标，bbox右下角纵坐标，classIndex，置信度\]。这里可视化bbox时注意，得出的bbox坐标为resize之后的图像中坐标，需要根据resize时用到的ratio和padding数来还原出原始坐标，才可以在原始图像中显示出来。

python代码：

import cv2
    import time
    import requests
    import random
    import numpy as np
    import onnxruntime as ort
    import onnx
    import torch
    
    # import torchvision
    # from PIL import Image
    # from pathlib import Path
    # from collections import OrderedDict, namedtuple
    
    cuda = torch.cuda.is_available()
    w = "bestE2E.onnx"  # 模型名称
    
    # 判断模型是否有效，这步可以省略
    onnx_model = onnx.load(w)
    try:
        onnx.checker.check_model(onnx_model)
    except Exception:
        print("incorrect model")
        exit()
    else:
        print("correct model")
    
    # 创建session
    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
    session = ort.InferenceSession(w, providers=providers)
    
    
    # Resize原始图像，预处理
    def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
        # Resize and pad image while meeting stride-multiple constraints
        shape = im.shape[:2]  # current shape [height, width]
        if isinstance(new_shape, int):
            new_shape = (new_shape, new_shape)
    
        # Scale ratio (new / old)
        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
        if not scaleup:  # only scale down, do not scale up (for better val mAP)
            r = min(r, 1.0)
    
        # Compute padding
        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
    
        if auto:  # minimum rectangle
            dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
    
        dw /= 2  # divide padding into 2 sides
        dh /= 2
        print(shape[::-1])
        if shape[::-1] != new_unpad:  # resize
            im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
        im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
        return im, r, (dw, dh)
    
    
    # 类别名称，根据需要自己修改
    names = ['bubbles']
    colors = {name: [random.randint(0, 255) for _ in range(3)] for i, name in enumerate(names)}
    
    
    # 读入待检测图像
    img_path = r"D:/YOLOV7/img/test.bmp"
    img = cv2.imread(img_path)
    
    # BGR to RGB，并增加一维
    image = img.copy()
    image, ratio, dwdh = letterbox(image, auto=False)
    image = image.transpose((2, 0, 1))
    image = np.expand_dims(image, 0)
    image = np.ascontiguousarray(image)
    
    # 归一化
    im = image.astype(np.float32)
    im /= 255
    
    outname = [i.name for i in session.get_outputs()]
    inname = [i.name for i in session.get_inputs()]
    
    # 调用模型得出结果，并记录时间
    inp = {inname[0]: im}
    t1 = time.time()
    outputs = session.run(outname, inp)[0]
    print('inference time :%.4f' % (time.time() - t1))
    
    ori_images = [img.copy()]
    
    # 可视化结果
    for i, (batch_id, x0, y0, x1, y1, cls_id, score) in enumerate(outputs):
        image = ori_images[int(batch_id)]
        box = np.array([x0, y0, x1, y1])
        box -= np.array(dwdh * 2)
        box /= ratio
        box = box.round().astype(np.int32).tolist()
        cls_id = int(cls_id)
        score = round(float(score), 3)
        name = names[cls_id]
        color = colors[name]
        name += ' ' + str(score)
        cv2.rectangle(image, box[:2], box[2:], color, 2)
        cv2.putText(image, name, (box[0], box[1] - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.75, [225, 255, 255], thickness=2)
    
    cv2.imshow('dddd', ori_images[0])
    cv2.waitKey(0)
    cv2.destroyAllWindows()

结果显示：

![851982dc3c474f1d93e021f92f845974.png][]

## 二，C\# 版 ##

公主做的demo界面如下：

![a20893039d654095a2f670511ee59b8c.png][]

步骤与python版一模一样，公主一句一句翻译过来的。中间遇到过很多坑，但事情太多忘记一条一条记录了，有问题随时欢迎评论区讨论，完整代码如下：

private void btn_analyze_Click(object sender, EventArgs e){
                
                // 原始图像切割为3份，分成1024*1024
                Mat img_img = this.img_mat;
                Mat[] subImg = new Mat[3];
                int w = Width / 3;
    
                subImg[0] = img_img[0, 1024, startX, startX + w];
                subImg[1] = img_img[0, 1024, startX + w, startX + w * 2];
                subImg[2] = img_img[0, 1024, startX + w * 2, startX + rect.Width - edge * 2];
    
                // pictureBox对象“们”存为数组
                PictureBox[] picbox = new PictureBox[3] { this.pictureBox2, this.pictureBox3, this.pictureBox4 };
                for(int i = 0; i < 3; i++)
                {
                    int top = 0, left = 0;
                    double ratio = 0.0;
                    // after resize and padding
                    Mat img_copy = new Mat();
                    subImg[i].CopyTo(img_copy);
                    
                    // letterbox实现Resize图像
                    Mat imageRgb = letterbox(img_copy, imgSize, out top, out left, out ratio);
    
                    // 将Mat格式图像转化为onnx模型需要的tensor张量
                    Bitmap imageBmp = BitmapConverter.ToBitmap(imageRgb);
                    Tensor<float> input = FastToOnnxTensor(imageBmp);
                    var inputs = new List<NamedOnnxValue>
                    {
                        NamedOnnxValue.CreateFromTensor("images", input)
                    };
    
                    // 调用onnx模型，保存结果
                    IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = session.Run(inputs);
    
                    // 图像后处理，并可视化
                    var resultsArray = results.ToArray()[0];
                    Tensor<float> tensors = resultsArray.AsTensor<float>();
                    var array = tensors.ToArray();
    
                    Mat newMat = new Mat();
                    Cv2.CvtColor(subImg[i], newMat, ColorConversionCodes.GRAY2RGB);
    
                    if (array.Length > 0)
                    {
                        for (int j = 0; j < tensors.Dimensions[0]; j++)
                        {
                            int startIndex = j * tensors.Dimensions[1];
                            Scalar color = new Scalar(127, 255, 0);  // 灰度颜色                        
                            int lefttop_x = (int)((array[startIndex + 1] - left) / ratio);
                            int lefttop_y = (int)((array[startIndex + 2] - top) / ratio);
                            int rightbottom_x = (int)((array[startIndex + 3] - left) / ratio);
                            int rightbottom_y = (int)((array[startIndex + 4] - top) / ratio);
                            Cv2.Rectangle(newMat, new OpenCvSharp.Point(lefttop_x, lefttop_y),
                                            new OpenCvSharp.Point(rightbottom_x, rightbottom_y),
                                            color, 2);
                            Cv2.PutText(newMat, LabelMap.Labels[(int)array[startIndex + 5]],
                                        new OpenCvSharp.Point(lefttop_x, lefttop_y - 5),
                                        HersheyFonts.HersheySimplex, 0.75, color, 2);
                        }
                    }
                    picbox[i].Image = BitmapConverter.ToBitmap(newMat);
                }
    }
    
    
    // Load表格时创建session
    private void OnnxDemo_Load(object sender, EventArgs e){
                options = SessionOptions.MakeSessionOptionWithCudaProvider(0);
    
                session = new InferenceSession(path, options);
    }
    
    
    // letterbox预处理图像
    private Mat letterbox(Mat subImg, int newSize, out int top, out int left, out double ratio)
            {
                int width = subImg.Cols;
                int height = subImg.Rows;
    
                ratio = Math.Min(((double)newSize / (double)width), ((double)newSize / (double)height));
                int[] new_unpad = { (int)(Math.Round(height * ratio)), (int)(Math.Round(width * ratio)) };
                int[] dwh = { (newSize - new_unpad[0]) / 2, (newSize - new_unpad[1]) / 2 };
    
                if (dwh != new_unpad)
                {
                    Cv2.Resize(subImg, subImg, new OpenCvSharp.Size(new_unpad[1], new_unpad[0]));
                }
    
                top = (int)(Math.Round(dwh[0] - 0.1));
                int bottom = newSize - top - new_unpad[0];
                left = (int)(Math.Round(dwh[1] - 0.1));
                int right = newSize - left - new_unpad[1];
    
                Cv2.CopyMakeBorder(subImg, subImg, top, bottom, left, right, BorderTypes.Constant, Scalar.Gray);
    
                Cv2.CvtColor(subImg, subImg, ColorConversionCodes.GRAY2RGB);  //Gray to RGB
    
                return subImg;
    }
    
    
    // Bitmap格式转化为Tensor张量
    public Tensor<float> FastToOnnxTensor(Bitmap source)
            {
                var floatArray = new float[source.Width * source.Height * 3];
    
                var bitmap_data = source.LockBits(new Rectangle(0, 0, source.Width, source.Height), ImageLockMode.ReadOnly, System.Drawing.Imaging.PixelFormat.Format24bppRgb);
                var bitmap_bytes = new byte[Math.Abs(bitmap_data.Stride) * source.Height];
    
                Marshal.Copy(bitmap_data.Scan0, bitmap_bytes, 0, bitmap_bytes.Length);
    
                int total_pixels_count = source.Width * source.Height;
    
    
                Parallel.For(0, total_pixels_count, (p_idx, state) =>
                {
    
                    var g_idx = p_idx + total_pixels_count;
                    var b_idx = p_idx + total_pixels_count * 2;
    
                    floatArray[p_idx] = bitmap_bytes[p_idx * 3] / 255f;//R
                    floatArray[g_idx] = bitmap_bytes[p_idx * 3 + 1] / 255f;//G
                    floatArray[b_idx] = bitmap_bytes[p_idx * 3 + 2] / 255f;//B
    
                });
    
                source.UnlockBits(bitmap_data);
    
                return new DenseTensor<float>(new Memory<float>(floatArray), new int[] { 1, 3, source.Height, source.Width });
            }

静态变量：

// 静态变量，用来保存类别名称
    
    using System;
    using System.Collections.Generic;
    using System.Linq;
    using System.Text;
    using System.Threading.Tasks;
    
    namespace OnnxDemo
    {
        public static class LabelMap
        {
            static LabelMap()
            {
                Labels = new string[]
                {
                    "bubble"
                };
            }
            public static string[] Labels { set; get; }
        }
    }

运行结果：

![ee255680f5db48e7a1af515a0f4b79fb.png][]

Nice~Nice~Nice~~~公主对结果还是灰常满意哒！！！

[Link 1]: https://blog.csdn.net/athrunsunny/article/details/126306363
[Link 2]: https://netron.app/
[e11a75d3116c4fc4b5c461d00dfd6bdc.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/b1614f44860646f3b91f9dd65a4fcd76.png
[6e624b359fa146d2be49a28c49bf6e19.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/e9f6be156a5049a48043484a2e79a9ac.png
[851982dc3c474f1d93e021f92f845974.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/18cc19ed21184612b45d01199ca3f233.png
[a20893039d654095a2f670511ee59b8c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/4090542b50a344a0b669c90acb6f391a.png
[ee255680f5db48e7a1af515a0f4b79fb.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/8ea0b6a658484083b9129e7816454357.png