Hi,
Am trying to deploy a trained Caffe model from DIGITS with OpenCV 3.1.0.
I have attempted the [sample code](https://github.com/opencv/opencv_contrib/blob/master/modules/dnn/samples/caffe_googlenet.cpp)
on both LeNet and AlexNet models without any problem.
However, for benchmarking purpose, I tried do run [SqueezeNet V1.1](https://github.com/DeepScale/SqueezeNet) on the same sample code but having an error at the **getMaxClass()** function where the OpenCV reshape function is called.
The full error message:
> OpenCV Error: Assertion failed (dims <= 2) in cv::Mat::reshape, file C:\opencv\sources\modules\core\src\matrix.cpp, line 982
Kindly advise on possible solutions.
For your reference, the full code I am running SqueezeNet on is as follows:
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#include
#include
#include
using namespace cv;
using namespace cv::dnn;
#include
#include
#include
using namespace std;
/* Find best class for the blob (i. e. class with maximal probability) */
void getMaxClass(dnn::Blob &probBlob, int *classId, double *classProb)
{
Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix
Point classNumber;
minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
*classId = classNumber.x;
}
std::vector readClassNames(const char *filename = "E:\\GitRepo\\tds-lane\\Release\\labels.txt")
{
std::vector classNames;
std::ifstream fp(filename);
if (!fp.is_open())
{
std::cerr << "File with classes labels not found: " << filename << std::endl;
exit(-1);
}
std::string name;
while (!fp.eof())
{
std::getline(fp, name);
if (name.length())
classNames.push_back( name.substr(name.find(' ')+1) );
}
fp.close();
return classNames;
}
int main(int argc, char **argv)
{
cv::dnn::initModule(); //Required if OpenCV is built as static libs
String modelTxt = "deploy.prototxt";
String modelBin = "snapshot_iter_37700.caffemodel";
String imageFile = (argc > 1) ? argv[1] : "Y:\\20160227_191832.511704_2.jpg";
//! [Read and initialize network]
Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
//! [Read and initialize network]
//! [Check that network was read successfully]
if (net.empty())
{
std::cerr << "Can't load network by using the following files: " << std::endl;
std::cerr << "prototxt: " << modelTxt << std::endl;
std::cerr << "caffemodel: " << modelBin << std::endl;
std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;
std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;
exit(-1);
}
//! [Check that network was read successfully]
//! [Prepare blob]
Mat img = imread(imageFile);
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
exit(-1);
}
//cvtColor(img,img,CV_BGR2GRAY);
resize(img, img, Size(227, 227)); //GoogLeNet accepts only 224x224 RGB-images
dnn::Blob inputBlob = dnn::Blob::fromImages(img); //Convert Mat to dnn::Blob batch of images
//! [Prepare blob]
//! [Set input blob]
net.setBlob(".data", inputBlob); //set the network input
//! [Set input blob]
//! [Make forward pass]
net.forward(); //compute output
//! [Make forward pass]
//! [Gather output]
dnn::Blob prob = net.getBlob("softmax"); //gather output of "prob" layer
int classId;
double classProb;
getMaxClass(prob, &classId, &classProb);//find the best class
//! [Gather output]
//! [Print results]
std::vector classNames = readClassNames();
std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
//! [Print results]
system("Pause");
return 0;
} //main
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