I want to use OpenCv's dnn::Net class inside of the openFrameworks.
I checked the version of the openCV in openFrameworks by printing out CV_VERSION and etc, which was 3.1.0.
I looked up the openCV documentation of 3.1.0 and found out that 3.1.0 supports dnn::Net class.
However, when I tried to use cv::dnn::Net in openFrameworks, it says cv has no module called dnn.
Any kind of advise or insights would be really appreciated.
cout << "OpenCV version : " << CV_VERSION << endl;
cout << "Major version : " << CV_MAJOR_VERSION << endl;
cout << "Minor version : " << CV_MINOR_VERSION << endl;
cout << "Subminor version : " << CV_SUBMINOR_VERSION << endl;
cv::dnn::Net net
Early opencv_dnn was not in the mainOpenCV, it was in opencv_contrib modules. But in all cases I recommend to use dnn from lates OpenCV version: it fixed a lot of errors and added new NN architectures.
Related
Data transfer between LibTorch C++ and Eigen (Questions and Help)
Hello all,
I'm developing a Data Transfer Tools for C++ Linear Algebra Libraries, as you can see here:
https://github.com/andrewssobral/dtt
(considering bi-dimensional arrays or matrices)
and I'm wondering if you can help me on the following code for data transfer between LibTorch and Eigen:
std::cout << "Testing LibTorch to Eigen:" << std::endl;
// LibTorch
torch::Device device(torch::cuda::is_available() ? torch::kCUDA : torch::kCPU);
torch::Tensor T = torch::rand({3, 3});
std::cout << "LibTorch:" << std::endl;
std::cout << T << std::endl;
// Eigen
float* data = T.data_ptr<float>();
Eigen::Map<Eigen::MatrixXf> E(data, T.size(0), T.size(1));
std::cout << "EigenMat:\n" << E << std::endl;
// re-check after changes
E(0,0) = 0;
std::cout << "EigenMat:\n" << E << std::endl;
std::cout << "LibTorch:" << std::endl;
std::cout << T << std::endl;
This is the output of the code:
--------------------------------------------------
Testing LibTorch to Eigen:
LibTorch:
0.6232 0.5574 0.6925
0.7996 0.9860 0.1471
0.4431 0.5914 0.8361
[ Variable[CPUFloatType]{3,3} ]
EigenMat (after data transfer):
0.6232 0.7996 0.4431
0.5574 0.986 0.5914
0.6925 0.1471 0.8361
# Modifying EigenMat, set element at (0,0) = 0
EigenMat:
0 0.7996 0.4431
0.5574 0.986 0.5914
0.6925 0.1471 0.8361
# Now, the LibTorch matrix was also modified (OK), but the rows and columns were switched.
LibTorch:
0.0000 0.5574 0.6925
0.7996 0.9860 0.1471
0.4431 0.5914 0.8361
[ Variable[CPUFloatType]{3,3} ]
Do someone knows what's happening ?
There's a better way to do that?
I need also to do the same for Armadillo, ArrayFire and OpenCV (cv::Mat).
Thanks in advance!
The reason for the switched rows and columns is that LibTorch (apparently) uses row-major storage, while Eigen by default uses column-major storage. I don't know if you can change the behavior of LibTorch, but with Eigen you can also use row-major storage, like so:
typedef Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> MatrixXf_rm; // same as MatrixXf, but with row-major memory layout
and then use it like this:
Eigen::Map<MatrixXf_rm> E(data, T.size(0), T.size(1));
I am implementing the Bag of words Model using SURF and SIFT features and SVM Classifier. I want to train(80% of 2876 images) and test(20% of 2876 images) it. I have kept dictionarySize set to 1000. My Computer configuration is intel Xeon(2 processors)/ 32GB RAM/ 500GB HDD. Here, images are read whenever necessary instead of storing them.
like,
std::ifstream file("C:\\testFiles\\caltech4\\train0.csv", ifstream::in);
if (!file)
{
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
printf("\nReading Training images................\n");
while (getline(file, line))
{
stringstream liness(line);
getline(liness, path, separator);
getline(liness,classlabel);
if(!path.empty())
{
Mat image = imread(path, 0);
cout << " " << path << "\n";
detector.detect(image, keypoints1);
detector.compute(image, keypoints1,descriptor1);
featuresUnclustered.push_back(descriptor1);
}
}
Here, the train0.csv contains the paths to the images with the labels. It stops from this loop while reading the images, computing the descriptor and adding it to the features to be clustered. Following error apprears on the console:
Here, in the code, I re-sized images being read to the dimension 256*256; the requirement of the memory is reduced. Ergo, the error disappeared.
Mat image = imread(path, 0);
resize(image,image,Size(256,256));
cout << " " << path << "\n";
detector.detect(image, keypoints1);
detector.compute(image, keypoints1,descriptor1);
featuresUnclustered.push_back(descriptor1);
But, it might appear with bigger dataset.
I am trying to learn how to use the GPU programs in OpenCV. I have built everything with CUDA and if I run
cout << " Number of devices " << cv::gpu::getCudaEnabledDeviceCount() << endl;
I get the answer 1 device so at least something seems to work. However, I try the following peace of code, it just prints out the message and then nothing happens. It gets stuck on
cv::gpu::cvtColor(input_gpu, output_gpu, CV_BGR2GRAY);
Here is the code
#include<iostream>
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/gpu/gpu.hpp>
#include <opencv2/opencv.hpp>
using std::cout;
using std::endl;
int main(void){
cv::Mat input = cv::imread("image.jpg");
if (input.empty()){
cout << "Image Not Found" << endl;
return -1;
}
cv::Mat output;
// Declare the input and output GpuMat
cv::gpu::GpuMat input_gpu;
cv::gpu::GpuMat output_gpu;
cout << "Number of devices: " << cv::gpu::getCudaEnabledDeviceCount() << endl;
// Copy the input cv::Mat to device.
// Device memory will be allocated automatically according to the parameters of input image
input_gpu.upload(input);
// Convert the input image to grayScale on GPU
cv::gpu::cvtColor(input_gpu, output_gpu, CV_BGR2GRAY);
//// Copy the result from GPU back to host
output_gpu.download(output);
cv::imshow("Input", input);
cv::imshow("Output", output);
cv::waitKey(0);
return 0;
}
I just found this issue and it seems to be a problem with the Maxwell architecture, but that post is over a year old. Has anybody else experienced the same problem? I am using windows 7, Visual Studio 2013 and an Nvidia Geforce GTX 770.
/ Erik
Ok, I do not really know what the problem was, but in CMake, I changed the CUDA_GENERATION to Kepler, which is the micro architecture of my GPU, then I recompiled it, and now the code works as it should.
Interestingly, there were only Fermi and Kepler to choose, so I do not know if one will get problem with Maxwell.
/ Erik
I just installed opencv. I use VisualStudio 2013 the free version.
Whenever I use cout to display Mat object I get this:
Unhandled exception at 0x734ADE19 (msvcp100.dll) in myOpenCVStudy.exe: 0xC0000005: Access violation reading location 0x00000000.
My code is simply:
Mat M;
M.create(4, 4, CV_8UC(2));
cout << "M = " << endl << " " << M << endl << endl;
Any ideas?
Thanks
I am experimenting with the following code on the raspberry pi:
https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/cpp/camshiftdemo.cpp?rev=4234
and I would like to do the recommendations from this thread:
Getting better performance using OpenCV?
Specifically, how do I perform #1 - #4 in the link above?
Looking at this site:
http://opencv.willowgarage.com/documentation/cpp/reading_and_writing_images_and_video.html#cv-videocapture-set
I have attempted to do the following:
cap.set(CV_CAP_PROP_FRAME_WIDTH, 320);
cap.set(CV_CAP_PROP_FRAME_WIDTH, 240);
cout << "width = " << cap.get(CV_CAP_PROP_FRAME_WIDTH) << endl;
cout << "height = " << cap.get(CV_CAP_PROP_FRAME_HEIGHT) << endl;
However the output always says 640 and 480 for width and height. Am I missing something big here?
I am using OpenCV 2.3.1