OpenCV  5.0.0-pre
Open Source Computer Vision
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Load ONNX framework models

Next Tutorial: OpenCV usage with OpenVINO

Original author Vitaliy Lyudvichenko
Compatibility OpenCV >= 4.5.4

Introduction

In this tutorial you will learn how to use opencv_dnn module for image classification by using GoogLeNet trained network from ONNX model zoo.

We will demonstrate results of this example on the following picture.

Source Code

We will be using snippets from the example application, that can be downloaded here.

#include <fstream>
#include <sstream>
#include <iostream>
#include <opencv2/dnn.hpp>
#include "common.hpp"
using namespace cv;
using namespace std;
using namespace dnn;
const string about =
"Use this script to run a classification model on a camera stream, video, image or image list (i.e. .xml or .yaml containing image lists)\n\n"
"Firstly, download required models using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.\n"
"To run:\n"
"\t ./example_dnn_classification model_name --input=path/to/your/input/image/or/video (don't give --input flag if want to use device camera)\n"
"Sample command:\n"
"\t ./example_dnn_classification resnet --input=$OPENCV_SAMPLES_DATA_PATH/baboon.jpg\n"
"\t ./example_dnn_classification squeezenet\n"
"Model path can also be specified using --model argument. "
"Use imagelist_creator to create the xml or yaml list\n";
const string param_keys =
"{ help h | | Print help message. }"
"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
"{ zoo | ../dnn/models.yml | An optional path to file with preprocessing parameters }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
"{ imglist | | Pass this flag if image list (i.e. .xml or .yaml) file is passed}"
"{ crop | false | Preprocess input image by center cropping.}"
//"{ labels | | Path to the text file with labels for detected objects.}"
"{ model | | Path to the model file.}";
const string backend_keys = format(
"{ backend | default | Choose one of computation backends: "
"default: automatically (by default), "
"openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"opencv: OpenCV implementation, "
"vkcom: VKCOM, "
"cuda: CUDA, "
"webnn: WebNN }");
const string target_keys = format(
"{ target | cpu | Choose one of target computation devices: "
"cpu: CPU target (by default), "
"opencl: OpenCL, "
"opencl_fp16: OpenCL fp16 (half-float precision), "
"vpu: VPU, "
"vulkan: Vulkan, "
"cuda: CUDA, "
"cuda_fp16: CUDA fp16 (half-float preprocess) }");
string keys = param_keys + backend_keys + target_keys;
vector<string> classes;
static bool readStringList( const string& filename, vector<string>& l )
{
l.resize(0);
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
size_t dir_pos = filename.rfind('/');
if (dir_pos == string::npos)
dir_pos = filename.rfind('\\');
FileNode n = fs.getFirstTopLevelNode();
if( n.type() != FileNode::SEQ )
return false;
FileNodeIterator it = n.begin(), it_end = n.end();
for( ; it != it_end; ++it )
{
string fname = (string)*it;
if (dir_pos != string::npos)
{
string fpath = samples::findFile(filename.substr(0, dir_pos + 1) + fname, false);
if (fpath.empty())
{
fpath = samples::findFile(fname);
}
fname = fpath;
}
else
{
fname = samples::findFile(fname);
}
l.push_back(fname);
}
return true;
}
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv, keys);
if (!parser.has("@alias") || parser.has("help"))
{
cout << about << endl;
parser.printMessage();
return -1;
}
const string modelName = parser.get<String>("@alias");
const string zooFile = findFile(parser.get<String>("zoo"));
keys += genPreprocArguments(modelName, zooFile);
parser = CommandLineParser(argc, argv, keys);
parser.about(about);
if (argc == 1 || parser.has("help"))
{
parser.printMessage();
return 0;
}
String sha1 = parser.get<String>("sha1");
float scale = parser.get<float>("scale");
Scalar mean = parser.get<Scalar>("mean");
Scalar std = parser.get<Scalar>("std");
bool swapRB = parser.get<bool>("rgb");
bool crop = parser.get<bool>("crop");
int inpWidth = parser.get<int>("width");
int inpHeight = parser.get<int>("height");
String model = findModel(parser.get<String>("model"), sha1);
String backend = parser.get<String>("backend");
String target = parser.get<String>("target");
bool isImgList = parser.has("imglist");
// Open file with labels.
string labels_filename = parser.get<String>("labels");
string file = findFile(labels_filename);
ifstream ifs(file.c_str());
if (!ifs.is_open()){
cout<<"File " << file << " not found";
exit(1);
}
string line;
while (getline(ifs, line))
{
classes.push_back(line);
}
if (!parser.check())
{
parser.printErrors();
return 1;
}
CV_Assert(!model.empty());
Net net = readNetFromONNX(model);
net.setPreferableBackend(getBackendID(backend));
net.setPreferableTarget(getTargetID(target));
// Create a window
static const std::string kWinName = "Deep learning image classification in OpenCV";
namedWindow(kWinName, WINDOW_NORMAL);
//Create FontFace for putText
FontFace sans("sans");
vector<string> imageList;
size_t currentImageIndex = 0;
if (parser.has("input")) {
string input = findFile(parser.get<String>("input"));
if (isImgList) {
bool check = readStringList(samples::findFile(input), imageList);
if (imageList.empty() || !check) {
cout << "Error: No images found or the provided file is not a valid .yaml or .xml file." << endl;
return -1;
}
} else {
// Input is not a directory, try to open as video or image
cap.open(input);
if (!cap.isOpened()) {
cout << "Failed to open the input." << endl;
return -1;
}
}
} else {
cap.open(0); // Open default camera
}
Mat frame, blob;
for(;;)
{
if (!imageList.empty()) {
// Handling directory of images
if (currentImageIndex >= imageList.size()) {
break; // Exit if all images are processed
}
frame = imread(imageList[currentImageIndex++]);
if(frame.empty()){
cout<<"Cannot open file"<<endl;
continue;
}
} else {
// Handling video or single image
cap >> frame;
}
if (frame.empty())
{
break;
}
blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, crop);
// 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);
}
net.setInput(blob);
TickMeter timeRecorder;
timeRecorder.reset();
Mat prob = net.forward();
double t1;
timeRecorder.start();
prob = net.forward();
timeRecorder.stop();
int N = (int)prob.total(), K = std::min(5, N);
std::vector<std::pair<float, int> > prob_vec;
for (int i = 0; i < N; i++) {
prob_vec.push_back(std::make_pair(-prob.at<float>(i), i));
}
std::sort(prob_vec.begin(), prob_vec.end());
t1 = timeRecorder.getTimeMilli();
timeRecorder.reset();
string label = format("Inference time: %.1f ms", t1);
Mat subframe = frame(Rect(0, 0, std::min(1000, frame.cols), std::min(300, frame.rows)));
subframe *= 0.3f;
putText(frame, label, Point(20, 50), Scalar(0, 255, 0), sans, 25, 800);
// Print predicted class.
for (int i = 0; i < K; i++) {
int classId = prob_vec[i].second;
float confidence = -prob_vec[i].first;
label = format("%d. %s: %.2f", i+1, (classes.empty() ? format("Class #%d", classId).c_str() :
classes[classId].c_str()), confidence);
putText(frame, label, Point(20, 110 + i*35), Scalar(0, 255, 0), sans, 25, 500);
}
imshow(kWinName, frame);
int key = waitKey(isImgList ? 1000 : 100);
if (key == ' ')
key = waitKey();
if (key == 'q' || key == 27) // Check if 'q' or 'ESC' is pressed
return 0;
}
return 0;
}
Designed for command line parsing.
Definition utility.hpp:890
used to iterate through sequences and mappings.
Definition persistence.hpp:591
File Storage Node class.
Definition persistence.hpp:439
FileNodeIterator begin() const
returns iterator pointing to the first node element
FileNodeIterator end() const
returns iterator pointing to the element following the last node element
int type() const
Returns type of the node.
XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or readi...
Definition persistence.hpp:261
Wrapper on top of a truetype/opentype/etc font, i.e. Freetype's FT_Face.
Definition imgproc.hpp:4991
n-dimensional dense array class
Definition mat.hpp:950
_Tp & at(int i0=0)
Returns a reference to the specified array element.
int cols
Definition mat.hpp:2424
size_t total() const
Returns the total number of array elements.
bool empty() const
Returns true if the array has no elements.
int rows
the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
Definition mat.hpp:2424
Template class for 2D rectangles.
Definition types.hpp:447
Template class for specifying the size of an image or rectangle.
Definition types.hpp:338
a Class to measure passing time.
Definition utility.hpp:326
void start()
starts counting ticks.
Definition utility.hpp:335
void stop()
stops counting ticks.
Definition utility.hpp:341
void reset()
resets internal values.
Definition utility.hpp:430
double getTimeMilli() const
returns passed time in milliseconds.
Definition utility.hpp:365
Class for video capturing from video files, image sequences or cameras.
Definition videoio.hpp:727
virtual bool open(const String &filename, int apiPreference=CAP_ANY)
Opens a video file or a capturing device or an IP video stream for video capturing.
virtual bool isOpened() const
Returns true if video capturing has been initialized already.
Scalar mean(InputArray src, InputArray mask=noArray())
Calculates an average (mean) of array elements.
void divide(InputArray src1, InputArray src2, OutputArray dst, double scale=1, int dtype=-1)
Performs per-element division of two arrays or a scalar by an array.
std::string String
Definition cvstd.hpp:151
cv::String findFile(const cv::String &relative_path, bool required=true, bool silentMode=false)
Try to find requested data file.
String format(const char *fmt,...)
Returns a text string formatted using the printf-like expression.
#define CV_Assert(expr)
Checks a condition at runtime and throws exception if it fails.
Definition exception.hpp:198
Mat blobFromImage(InputArray image, double scalefactor=1.0, const Size &size=Size(), const Scalar &mean=Scalar(), bool swapRB=false, bool crop=false, int ddepth=CV_32F)
Creates 4-dimensional blob from image. Optionally resizes and crops image from center,...
Net readNetFromONNX(CV_WRAP_FILE_PATH const String &onnxFile, int engine=ENGINE_AUTO)
Reads a network model ONNX.
cv::gapi::GBackend backend()
Get a reference to CPU (OpenCV) backend.
GMat crop(const GMat &src, const Rect &rect)
Crops a 2D matrix.
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
int waitKey(int delay=0)
Waits for a pressed key.
void namedWindow(const String &winname, int flags=WINDOW_AUTOSIZE)
Creates a window.
CV_EXPORTS_W Mat imread(const String &filename, int flags=IMREAD_COLOR_BGR)
Loads an image from a file.
void putText(InputOutputArray img, const String &text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=LINE_8, bool bottomLeftOrigin=false)
Draws a text string.
void line(InputOutputArray img, Point pt1, Point pt2, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
Draws a line segment connecting two points.
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
void scale(cv::Mat &mat, const cv::Mat &range, const T min, const T max)
Definition quality_utils.hpp:90
Definition core.hpp:107
STL namespace.

Explanation

  1. Firstly, download GoogLeNet model files:

    python download_models.py googlenet

    Also you need file with names of ILSVRC2012 classes: classification_classes_ILSVRC2012.txt.

    Put these files into working dir of this program example.

  2. Read and initialize network using path to .onnx file
    Net net = readNetFromONNX(model);
    net.setPreferableBackend(getBackendID(backend));
    net.setPreferableTarget(getTargetID(target));
  3. Read input image and convert to the blob, acceptable by GoogleNet

    vector<string> imageList;
    size_t currentImageIndex = 0;
    if (parser.has("input")) {
    string input = findFile(parser.get<String>("input"));
    if (isImgList) {
    bool check = readStringList(samples::findFile(input), imageList);
    if (imageList.empty() || !check) {
    cout << "Error: No images found or the provided file is not a valid .yaml or .xml file." << endl;
    return -1;
    }
    } else {
    // Input is not a directory, try to open as video or image
    cap.open(input);
    if (!cap.isOpened()) {
    cout << "Failed to open the input." << endl;
    return -1;
    }
    }
    } else {
    cap.open(0); // Open default camera
    }

    cv::VideoCapture can load both images and videos.

    blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, crop);
    // 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);
    }

    We convert the image to a 4-dimensional blob (so-called batch) with 1x3x224x224 shape after applying necessary pre-processing like resizing and mean subtraction for each blue, green and red channels correspondingly using cv::dnn::blobFromImage function.

  4. Pass the blob to the network
    net.setInput(blob);
  5. Make forward pass
    timeRecorder.start();
    prob = net.forward();
    timeRecorder.stop();
    During the forward pass output of each network layer is computed, but in this example we need output from the last layer only.
  6. Determine the best class
    int N = (int)prob.total(), K = std::min(5, N);
    std::vector<std::pair<float, int> > prob_vec;
    for (int i = 0; i < N; i++) {
    prob_vec.push_back(std::make_pair(-prob.at<float>(i), i));
    }
    std::sort(prob_vec.begin(), prob_vec.end());
    We put the output of network, which contain probabilities for each of 1000 ILSVRC2012 image classes, to the prob blob. And find the index of element with maximal value in this one. This index corresponds to the class of the image.
  7. Run an example from command line
    ./example_dnn_classification googlenet
    For our image we get prediction of class space shuttle with more than 99% sureness.