The onnx model exported by exportONNXNetwork() is not the same as the result of running in opencv and Matlab?
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For example, I use the pre-training model googlenet to classify images, use the official example to test in OpenCV4.1, and identify "peppers.png", the recognition result is not bell pepper.No matter how I set the input image mean, normalization, etc., it always fails.
My matlab program is:
net = googlenet;
exportONNXNetwork(net,'mygoogleNet.onnx','OpsetVersion',9); // or 6,7,8
My OpenCV program is as follows,"synset_words.txt" is in the attachment:
void main()
{
Mat img = imread("C:\\Program Files\\MATLAB\\R2019a\\examples\\deeplearning_shared\\peppers.png");
String onnx_path = "mygoogleNet.onnx"; // this is matlab googlenet export onnx file;
std::string file = "synset_words.txt";
vector<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);
}
// read net
Net net = readNetFromONNX(onnx_path);
if (net.empty())
{
cout << "net is empty!" << endl;
}
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(DNN_TARGET_CPU);
int net_size = 224;// googlenet net input size
img = img(Rect(0, 0, net_size, net_size)); // keep the same image in matlab
while (true)
{
Mat image = img.clone();
Mat blob;
blobFromImage(image, blob, 1.0/255, Size(net_size, net_size), Scalar(122.6789, 116.6686, 104.0069),true); // set params
//! [Set input blob]
net.setInput(blob);
Mat prob = net.forward();
Point classIdPoint;
double confidence;
minMaxLoc(prob.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
int classId = classIdPoint.x;
//! show result
resize(image, image, Size(500, 500));
// 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(image, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
// Print predicted class.
label = format("%s: %.4f", (classes.empty() ? format("Class #%d", classId).c_str() :
classes[classId].c_str()),
confidence);
putText(image, label, Point(0, 40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
imshow("", image);
waitKey(1);
}
}
result :
why is not correct? anyone know?
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Réponses (3)
Don Mathis
le 29 Mai 2019
Modifié(e) : Don Mathis
le 29 Mai 2019
Could it be that you're multiplying the test image by 1.0/255 before passing it to your imported network? Notice in the MATLAB example that the network was passed an image with pixels in the range [0 255]. It looks like you're normalizing it to [0 1]?
Also, does openCV import images as BGR? If so, you'll need to change the image to RGB because the network expects that.Maybe both of these problems are occurring?
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David
le 26 Avr 2021
re: image normalization
When executing an exported ONNX model in say python, it is unclear to me if we're supposed to leave the image in the raw 0-255 range or do some normalization. I have yet to get the same answer in Matlab (classifier accuracy great) and ONNXRuntime in python. Having a hard time finding the right combination of reshaping and image processing in python. What I see on the webs are people doing a sort of mean subtraction for each color plane, but the Matlab code isn't doing any of that, except for imresize.
Any examples would be greatly appreciated.
KAAN AYKUT KABAKÇI
le 6 Août 2020
Hello,
in my environment the problem was totally about OpenCV version. When i use OpenCV 4.2.0, i was getting different results between MATLAB and Python. After downgrade the OpenCV version to 4.0.0, the problem disappeared. I am using following blobFromImage configuration:
blob = cv2.dnn.blobFromImage(input_image, 1, (512,512), (0,0,0), True, False)
SwapRB=True.
Crop=False.
Shape of my images is (512,512,3)
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