c++ - OpenCV DNN 不使用 Deeplabv3.onnx 模型产生预期结果

Question

我正在尝试Deeplabv3.onnx在 OpenCV DNN 中使用模型。我使用的模型是从 PyTorch 导出的。即使我没有得到任何编译或运行时错误，实现也不会产生预期的分段结果。我认为来自网络的输出 blob 没有正确解码导致不正确的分割结果。我基本上使用的是 OpenCV DNN Segmentation.cpp示例代码，并在将输入图像传递给网络之前对其进行了一些修改以对其进行预处理。如果您能建议或修复我的代码，那就太好了。提前感谢您的宝贵时间。

Segmention.cpp 代码：

#include <sstream>

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>

std::string keys =
    "{ help  h         |                                                   | Print help message. }"
    "{ model           | deeplabv3.onnx      | Path to input image or video file. Skip this argument to capture frames from a camera. }"
    "{ config          | <none>                                            | Path to model config file}"
    "{ input i         | opencv-samples/data/vtest.avi                                                  | Path to input image or video file. Skip this argument to capture frames from a camera. }"
    "{ device          |  0                                                | camera device number. }"
    "{ initial_width   | 256                                               | Preprocess input image by initial resizing to a specific width.}"
    "{ initial_height  | 256                                               | Preprocess input image by initial resizing to a specific height.}"
    "{ width           | 224                                               | Path to input image or video file. Skip this argument to capture frames from a camera. }"
    "{ height          | 224                                               |  }"
    "{ scale           | 1.0                                               | Scale of the input image }"
    "{ rgb             | true                                              | Path to input image or video file. Skip this argument to capture frames from a camera. }"
    "{ mean            | 0.485 0.456 0.406                                             | Path to input image or video file. Skip this argument to capture frames from a camera. }"
    "{ std             | 0.229 0.224 0.225                                       | Preprocess input image by dividing on a standard deviation.}"
    "{ framework f     |                                                   | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
    "{ classes         |                                                   | Optional path to a text file with names of classes. }"
    "{ colors          |                                                   | Optional path to a text file with colors for an every class. "
                                                                            "An every color is represented with three values from 0 to 255 in BGR channels order. }"
    "{ backend         | 0                                                 | Choose one of computation backends: "
                                                                            "0: automatically (by default), "
                                                                            "1: Halide language (http://halide-lang.org/), "
                                                                            "2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
                                                                            "3: OpenCV implementation }"
    "{ target          | 1                                                 | Choose one of target computation devices: "
                                                                            "0: CPU target (by default), "
                                                                            "1: OpenCL, "
                                                                            "2: OpenCL fp16 (half-float precision), "
                                                                            "3: VPU }";

using namespace cv;
using namespace dnn;

std::vector<std::string> classes;
std::vector<Vec3b> colors;

void showLegend();

void colorizeSegmentation(const Mat &score, Mat &segm);

int main(int argc, char** argv)
{
    CommandLineParser parser(argc, argv, keys);
    parser.about("Semantic segmentation deep learning networks using OpenCV.");

    int rszWidth = parser.get<int>("initial_width");
    int rszHeight = parser.get<int>("initial_height");
    float scale = parser.get<float>("scale");
    Scalar mean = parser.get<Scalar>("mean");
    Scalar std = parser.get<Scalar>("std");
    bool swapRB = parser.get<bool>("rgb");
    int inpWidth = parser.get<int>("width");
    int inpHeight = parser.get<int>("height");
    String model = parser.get<String>("model");
    String config = parser.get<String>("config");
    String framework = parser.get<String>("framework");
    int backendId = parser.get<int>("backend");
    int targetId = parser.get<int>("target");

#ifdef DNDEBUG
    if (argc == 1 || parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }
#endif

    // Open file with classes names.
    if (parser.has("classes"))
    {
        std::string file = parser.get<String>("classes");
        std::ifstream ifs(file.c_str());
        if (!ifs.is_open())
            CV_Error(Error::StsError, "File " + file + " not found");
        std::string line;
        while (std::getline(ifs, line))
        {
            classes.push_back(line);
        }
    }

    // Open file with colors.
    if (parser.has("colors"))
    {
        std::string file = parser.get<String>("colors");
        std::ifstream ifs(file.c_str());
        if (!ifs.is_open())
            CV_Error(Error::StsError, "File " + file + " not found");
        std::string line;
        while (std::getline(ifs, line))
        {
            std::istringstream colorStr(line.c_str());

            Vec3b color;
            for (int i = 0; i < 3 && !colorStr.eof(); ++i)
                colorStr >> color[i];
            colors.push_back(color);
        }
    }

    if (!parser.check())
    {
        parser.printErrors();
        return 1;
    }

    CV_Assert(!model.empty());
    //! [Read and initialize network]
    Net net = readNet(model,config);
    net.setPreferableBackend(backendId);
    net.setPreferableTarget(targetId);
    //! [Read and initialize network]

    // Create a window
    static const std::string kWinName = "Deep learning semantic segmentation in OpenCV";
    namedWindow(kWinName, WINDOW_NORMAL);

    //! [Open a video file or an image file or a camera stream]
    VideoCapture cap;
    if (parser.has("input"))
        cap.open(parser.get<String>("input"));
    else
        cap.open(parser.get<int>("device"));
    //! [Open a video file or an image file or a camera stream]

    // Process frames.
    Mat frame, blob;
    while (waitKey(1) < 0)
    {
        cap >> frame;
        if (frame.empty())
        {
            waitKey();
            break;
        }

        if (rszWidth != 0 && rszHeight != 0)
        {
            resize(frame, frame, Size(rszWidth, rszHeight),0,0,INTER_NEAREST);
        }

        //! [Create a 4D blob from a frame]
        blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false);
        //! [Create a 4D blob from a frame]

                // Check std values.
        if (std.val[0] != 0.0 && std.val[1] != 0.0 && std.val[2] != 0.0)
        {
            // Divide blob by std.
            divide(blob, std, blob);
        }

        //! [Set input blob]
        net.setInput(blob);
        //! [Set input blob]
        //! [Make forward pass]
        Mat score = net.forward();
        //! [Make forward pass]

        Mat segm;
        colorizeSegmentation(score, segm);

        resize(segm, segm, frame.size(), 0, 0, INTER_NEAREST);
        addWeighted(frame, 0.1, segm, 0.9, 0.0, frame);

        // // Put efficiency information.
        std::vector<double> layersTimes;
        double freq = getTickFrequency() / 1000;
        double t = net.getPerfProfile(layersTimes) / freq;
        std::string label = format("Inference time: %.2f ms", t);
        putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));

        imshow(kWinName, frame);
        if (!classes.empty())
            showLegend();
    }
    return 0;
}

void colorizeSegmentation(const Mat &score, Mat &segm)
{
    const int rows = score.size[2];
    const int cols = score.size[3];
    const int chns = score.size[1];

    if (colors.empty())
    {
        // Generate colors.
        colors.push_back(Vec3b());
        for (int i = 1; i < chns; ++i)
        {
            Vec3b color;
            for (int j = 0; j < 3; ++j)
                color[j] = (colors[i - 1][j] + rand() % 256) / 2;
            colors.push_back(color);
        }
    }
    else if (chns != (int)colors.size())
    {
        CV_Error(Error::StsError, format("Number of output classes does not match "
                                         "number of colors (%d != %zu)", chns, colors.size()));
    }

    Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
    Mat maxVal(rows, cols, CV_32FC1, score.data);
    for (int ch = 1; ch < chns; ch++)
    {
        for (int row = 0; row < rows; row++)
        {
            const float *ptrScore = score.ptr<float>(0, ch, row);
            uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
            float *ptrMaxVal = maxVal.ptr<float>(row);
            for (int col = 0; col < cols; col++)
            {
                if (ptrScore[col] > ptrMaxVal[col])
                {
                    ptrMaxVal[col] = ptrScore[col];
                    ptrMaxCl[col] = (uchar)ch;
                }
            }
        }
    }

    segm.create(rows, cols, CV_8UC3);
    for (int row = 0; row < rows; row++)
    {
        const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
        Vec3b *ptrSegm = segm.ptr<Vec3b>(row);
        for (int col = 0; col < cols; col++)
        {
            ptrSegm[col] = colors[ptrMaxCl[col]];
        }
    }
}

void showLegend()
{
    static const int kBlockHeight = 30;
    static Mat legend;
    if (legend.empty())
    {
        const int numClasses = (int)classes.size();
        if ((int)colors.size() != numClasses)
        {
            CV_Error(Error::StsError, format("Number of output classes does not match "
                                             "number of labels (%zu != %zu)", colors.size(), classes.size()));
        }
        legend.create(kBlockHeight * numClasses, 200, CV_8UC3);
        for (int i = 0; i < numClasses; i++)
        {
            Mat block = legend.rowRange(i * kBlockHeight, (i + 1) * kBlockHeight);
            block.setTo(colors[i]);
            putText(block, classes[i], Point(0, kBlockHeight / 2), FONT_HERSHEY_SIMPLEX, 0.5, Vec3b(255, 255, 255));
        }
        namedWindow("Legend", WINDOW_NORMAL);
        imshow("Legend", legend);
    }
}

预训练的 torchvision 模型到 Onnx 模型转换器 python 代码：

import os
import torch
import torch.onnx
from torch.autograd import Variable
from torchvision import models


def get_pytorch_onnx_model(original_model):
    # define the directory for further converted model save
    onnx_model_path = "models"
    # define the name of further converted model
    onnx_model_name = "deeplabv3_resnet101.onnx"

    # create directory for further converted model
    os.makedirs(onnx_model_path, exist_ok=True)

    # get full path to the converted model
    full_model_path = os.path.join(onnx_model_path, onnx_model_name)

    # generate model input
    generated_input = Variable(
        torch.randn(1, 3, 224, 224)
    )

    # model export into ONNX format
    torch.onnx.export(
        original_model,
        generated_input,
        full_model_path,
        verbose=True,
        input_names=["input"],
        output_names=["output"],
        opset_version=11
    )

    return full_model_path


def main():
    # initialize PyTorch ResNet-101 model
    original_model = models.segmentation.deeplabv3_resnet101(pretrained=True)

    # get the path to the converted into ONNX PyTorch model
    full_model_path = get_pytorch_onnx_model(original_model)
    print("PyTorch ResNet-100 model was successfully converted: ", full_model_path)


if __name__ == "__main__":
    main()

标签： c++opencvpytorchonnx