Я работаю над Google Colab и хочу использовать библиотеку libsvm в своем проекте. Я скачал libsvm и установил его. Теперь, когда я использую команду! Nv cc -o и запускаю код, используя CUDA, я получаю ошибки вроде:
undefined reference to `svm_get_nr_class
undefined reference to 'svm_predict_probability'
undefined reference to `svm_free_and_destroy_model
Полагаю, проблема в том, что libsvm неправильно связан, так как я использую -l с соответствующими флагами для компиляции с nv cc, но я не знаю, что использовать с -l чтобы правильно связать libsvm и использовать его.
я скачал libsvm, используя
!git clone https://github.com/cjlin1/libsvm
%cd libsvm/
!make && make install
%cd /content/libsvm/python/
!make
import sys
sys.path.append('/content/libsvm/python')
%cd /content
теперь, когда я запускаю эту программу
%%cuda --name Blind_Deblurring_Cuda.cu
#include <iostream>
#include <fstream>
#include <iostream>
#include <fstream>
#include "/content/brisque.h"
#include "/content/libsvm/svm.h"
#include <vector>
#include <stdio.h>
#include "fstream"
#include "iostream"
#include <algorithm>
#include <iterator>
#include <cmath>
#include<stdlib.h>
#include <math.h>
#include <curand.h>
#include <opencv2/core/cuda.hpp>
#include <opencv2/core.hpp>
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include <opencv2/core/core.hpp>
#include <iostream>
#include "opencv2/highgui.hpp"
#include <opencv2/core/utility.hpp>
//rescaling based on training data i libsvm
float rescale_vector[36][2];
using namespace std;
using namespace cv;
float computescore(string imagename);
void ComputeBrisqueFeature(Mat& orig, vector<double>& featurevector);
int read_range_file() {
//check if file exists
char buff[100];
int i;
string range_fname = "allrange";
FILE* range_file = fopen(range_fname.c_str(), "r");
if(range_file == NULL) return 1;
//assume standard file format for this program
fgets(buff, 100, range_file);
fgets(buff, 100, range_file);
//now we can fill the array
for(i = 0; i < 36; ++i) {
float a, b, c;
fscanf(range_file, "%f %f %f", &a, &b, &c);
rescale_vector[i][0] = b;
rescale_vector[i][1] = c;
}
return 0;
}
int main(int argc, char** argv)
{
if(argc < 2) {
cout << "Input Image argument not given." << endl;
return -1;
}
//read in the allrange file to setup internal scaling array
if(read_range_file()) {
cerr<<"unable to open allrange file"<<endl;
return -1;
}
float qualityscore;
qualityscore = computescore(argv[1]);
cout << "Quality Score: " << qualityscore << endl;
}
float computescore(string imagename) {
// pre-loaded vectors from allrange file
float min_[36] = {0.336999 ,0.019667 ,0.230000 ,-0.125959 ,0.000167 ,0.000616 ,0.231000 ,-0.125873 ,0.000165 ,0.000600 ,0.241000 ,-0.128814 ,0.000179 ,0.000386 ,0.243000 ,-0.133080 ,0.000182 ,0.000421 ,0.436998 ,0.016929 ,0.247000 ,-0.200231 ,0.000104 ,0.000834 ,0.257000 ,-0.200017 ,0.000112 ,0.000876 ,0.257000 ,-0.155072 ,0.000112 ,0.000356 ,0.258000 ,-0.154374 ,0.000117 ,0.000351};
float max_[36] = {9.999411, 0.807472, 1.644021, 0.202917, 0.712384, 0.468672, 1.644021, 0.169548, 0.713132, 0.467896, 1.553016, 0.101368, 0.687324, 0.533087, 1.554016, 0.101000, 0.689177, 0.533133, 3.639918, 0.800955, 1.096995, 0.175286, 0.755547, 0.399270, 1.095995, 0.155928, 0.751488, 0.402398, 1.041992, 0.093209, 0.623516, 0.532925, 1.042992, 0.093714, 0.621958, 0.534484};
double qualityscore;
int i;
struct svm_model* model; // create svm model object
Mat orig = imread(imagename, 1); // read image (color mode)
vector<double> brisqueFeatures; // feature vector initialization
ComputeBrisqueFeature(orig, brisqueFeatures); // compute brisque features
// use the pre-trained allmodel file
string modelfile = "allmodel";
//if((model=svm_load_model(modelfile.c_str()))==0) {
//fprintf(stderr,"can't open model file allmodel\n");
// exit(1);
//}
// float min_[37];
// float max_[37];
struct svm_node x[37];
// rescale the brisqueFeatures vector from -1 to 1
// also convert vector to svm node array object
for(i = 0; i < 36; ++i) {
float min = min_[i];
float max = max_[i];
x[i].value = -1 + (2.0/(max - min) * (brisqueFeatures[i] - min));
x[i].index = i + 1;
}
x[36].index = -1;
int nr_class=svm_get_nr_class(model);
double *prob_estimates = (double *) malloc(nr_class*sizeof(double));
// predict quality score using libsvm class
qualityscore = svm_predict_probability(model,x,prob_estimates);
free(prob_estimates);
svm_free_and_destroy_model(&model);
return qualityscore;
}
void ComputeBrisqueFeature(Mat& orig, vector<double>& featurevector)
{
Mat orig_bw_int(orig.size(), CV_64F, 1);
// convert to grayscale
cvtColor(orig, orig_bw_int, COLOR_BGR2GRAY);
// create a copy of original image
Mat orig_bw(orig_bw_int.size(), CV_64FC1, 1);
orig_bw_int.convertTo(orig_bw, 1.0/255);
orig_bw_int.release();
// orig_bw now contains the grayscale image normalized to the range 0,1
int scalenum = 2; // number of times to scale the image
for (int itr_scale = 1; itr_scale<=scalenum; itr_scale++)
{
// resize image
Size dst_size(orig_bw.cols/cv::pow((double)2, itr_scale-1), orig_bw.rows/pow((double)2, itr_scale-1));
Mat imdist_scaled;
resize(orig_bw, imdist_scaled, dst_size, 0, 0, INTER_CUBIC); // INTER_CUBIC
imdist_scaled.convertTo(imdist_scaled, CV_64FC1, 1.0/255.0);
// calculating MSCN coefficients
// compute mu (local mean)
Mat mu(imdist_scaled.size(), CV_64FC1, 1);
GaussianBlur(imdist_scaled, mu, Size(7, 7), 1.166);
Mat mu_sq;
cv::pow(mu, double(2.0), mu_sq);
//compute sigma (local sigma)
Mat sigma(imdist_scaled.size(), CV_64FC1, 1);
cv::multiply(imdist_scaled, imdist_scaled, sigma);
GaussianBlur(sigma, sigma, Size(7, 7), 1.166);
cv::subtract(sigma, mu_sq, sigma);
cv::pow(sigma, double(0.5), sigma);
add(sigma, Scalar(1.0/255), sigma); // to avoid DivideByZero Error
Mat structdis(imdist_scaled.size(), CV_64FC1, 1);
subtract(imdist_scaled, mu, structdis);
divide(structdis, sigma, structdis); // structdis is MSCN image
// Compute AGGD fit to MSCN image
double lsigma_best, rsigma_best, gamma_best;
structdis = AGGDfit(structdis, lsigma_best, rsigma_best, gamma_best);
featurevector.push_back(gamma_best);
featurevector.push_back((lsigma_best*lsigma_best + rsigma_best*rsigma_best)/2);
// Compute paired product images
// indices for orientations (H, V, D1, D2)
int shifts[4][2]={{0,1},{1,0},{1,1},{-1,1}};
for(int itr_shift=1; itr_shift<=4; itr_shift++)
{
// select the shifting index from the 2D array
int* reqshift = shifts[itr_shift-1];
// declare shifted_structdis as pairwise image
Mat shifted_structdis(imdist_scaled.size(), CV_64F, 1);
// create copies of the images using BwImage constructor
// utility constructor for better subscript access (for pixels)
BwImage OrigArr(structdis);
BwImage ShiftArr(shifted_structdis);
// create pair-wise product for the given orientation (reqshift)
for(int i=0; i<structdis.rows; i++)
{
for(int j=0; j<structdis.cols; j++)
{
if(i+reqshift[0]>=0 && i+reqshift[0]<structdis.rows && j+reqshift[1]>=0 && j+reqshift[1]<structdis.cols)
{
ShiftArr[i][j]=OrigArr[i + reqshift[0]][j + reqshift[1]];
}
else
{
ShiftArr[i][j]=0;
}
}
}
// Mat structdis_pairwise;
shifted_structdis = ShiftArr.equate(shifted_structdis);
// calculate the products of the pairs
multiply(structdis, shifted_structdis, shifted_structdis);
// fit the pairwise product to AGGD
shifted_structdis = AGGDfit(shifted_structdis, lsigma_best, rsigma_best, gamma_best);
double constant = sqrt(tgamma(1/gamma_best))/sqrt(tgamma(3/gamma_best));
double meanparam = (rsigma_best-lsigma_best)*(tgamma(2/gamma_best)/tgamma(1/gamma_best))*constant;
// push the calculated parameters from AGGD fit to pair-wise products
featurevector.push_back(gamma_best);
featurevector.push_back(meanparam);
featurevector.push_back(cv::pow(lsigma_best,2));
featurevector.push_back(cv::pow(rsigma_best,2));
}
}
}
// function to compute best fit parameters from AGGDfit
Mat AGGDfit(Mat structdis, double& lsigma_best, double& rsigma_best, double& gamma_best)
{
// create a copy of an image using BwImage constructor (brisque.h - more info)
BwImage ImArr(structdis);
long int poscount=0, negcount=0;
double possqsum=0, negsqsum=0, abssum=0;
for(int i=0;i<structdis.rows;i++)
{
for (int j =0; j<structdis.cols; j++)
{
double pt = ImArr[i][j]; // BwImage provides [][] access
if(pt>0)
{
poscount++;
possqsum += pt*pt;
abssum += pt;
}
else if(pt<0)
{
negcount++;
negsqsum += pt*pt;
abssum -= pt;
}
}
}
lsigma_best = cv::pow(negsqsum/negcount, 0.5);
rsigma_best = cv::pow(possqsum/poscount, 0.5);
double gammahat = lsigma_best/rsigma_best;
long int totalcount = (structdis.cols)*(structdis.rows);
double rhat = cv::pow(abssum/totalcount, static_cast<double>(2))/((negsqsum + possqsum)/totalcount);
double rhatnorm = rhat*(cv::pow(gammahat,3) +1)*(gammahat+1)/pow(pow(gammahat,2)+1,2);
double prevgamma = 0;
double prevdiff = 1e10;
float sampling = 0.001;
for (float gam=0.2; gam<10; gam+=sampling) //possible to coarsen sampling to quicken the code, with some loss of accuracy
{
double r_gam = tgamma(2/gam)*tgamma(2/gam)/(tgamma(1/gam)*tgamma(3/gam));
double diff = abs(r_gam-rhatnorm);
if(diff> prevdiff) break;
prevdiff = diff;
prevgamma = gam;
}
gamma_best = prevgamma;
return structdis.clone();
}
А затем попробуйте скомпилировать, используя
!nvcc -o /content/src/Blind_Deblurring_Cuda /content/src/Blind_Deblurring_Cuda.cu -lopencv_core -lopencv_imgcodecs -lopencv_imgproc -lopencv_highgui -lopencv_ml
выдает следующую ошибку
/tmp/tmpxft_00003d8d_00000000-10_Blind_Deblurring_Cuda.o: In function `computescore(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)':
tmpxft_00003d8d_00000000-5_Blind_Deblurring_Cuda.cudafe1.cpp:(.text+0x9bc): undefined reference to `svm_get_nr_class'
tmpxft_00003d8d_00000000-5_Blind_Deblurring_Cuda.cudafe1.cpp:(.text+0x9fd): undefined reference to `svm_predict_probability'
tmpxft_00003d8d_00000000-5_Blind_Deblurring_Cuda.cudafe1.cpp:(.text+0xa27): undefined reference to `svm_free_and_destroy_model'
collect2: error: ld returned 1 exit status