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私のプログラムを実行中にこのエラーが発生しました。OpenCVとC、cvPreprocessCategoricalResponsesの不正な引数（応答＃0は整数ではありません）
私は
void calcolaSubpixel(IplImage *GT, costi ** totalCostMatrix, float ** subpixelM, int h, int w){
int modulo;
int subpixel;
int i,j;
for (i = 0; i < h; i++) {
for (j = 0; j < w; j++) {
//TODO i valori ottneuti vanno 0-1, io li voglio da -0.5,0.5
int modulo = (CV_IMAGE_ELEM(GT, ushort, i, j))%256;
float subpixel01 = modulo/256.0f;
subpixelM[i][j] = subpixel01;
}
}
}
この機能を使用して値のfloatマトリックスを作成し、その後、私は
void fillLabels(IplImage *GT, float** subpixelM, float * labels){
int h = GT->height, w = GT->width;
int i,j,L = 0;
for (i = 0; i < h; i++) {
for (j = 0; j < w; j++) {
//se il pixel in esame della ground truth non ha intensita 0 lo considero,
//altrimenti lo inserisco nella label:
if (CV_IMAGE_ELEM(GT, ushort, i, j) != 0) {
labels[L] = subpixelM[i][j];
L++;
}
}
}
}
この機能でフロートの配列に入れとMat labelsMat(nRighe, 1, CV_32FC1, labels);場所のために、この配列のラベルを使用nRigheはSVMを教える前に計算された値です
CvSVM SVM;
SVM.train(trainingDatasMat, labelsMat, Mat(), Mat(), params);
trainingDatasMatは私に何かトラブルを与えませんが、ラベルはそのように構築されました。返信
OpenCV Error: Bad argument (response #0 is not integral) in cvPreprocessCategoricalResponses, file /home/vision/opencv-2.4.11/modules/ml/src/inner_functions.cpp, line 715 terminate called after throwing an instance of 'cv::Exception' what(): /home/vision/opencv-2.4.11/modules/ml/src/inner_functions.cpp:715: error: (-5) response #0 is not integral in function cvPreprocessCategoricalResponses
この問題を克服するための提案はありますか？これはSVMのためにこれらの設定では、メイン
#include "cost.h"
#include "disparity.h"
#include "fixed_window.h"
#include "confidence.h"
#include "utils.h"
#include "stereoPipeline.h"
#include "interpolation.h"
#include "SGM.h"
#include "learning.h"
#include<iostream>
#include<iomanip>
#include<cv.h>
using namespace cv;
using namespace std;
int main(int argc, char *argv[]) {
int dMax=15;
if (argc != 4)
{
printf("Usage: %s <image_id> <gaps> < <algo>\n", argv[0]);
return -1;
}
int id=atoi(argv[1]);
//sistema l'id per evitare problemi con il numero di cifre
char q[10];
if(id < 10){sprintf(q, "00%d", id); }
if(id >= 10 && id <= 99){sprintf(q, "0%d", id); }
if(id >= 100){sprintf(q, "%d", id); }
int gaps=atoi(argv[2]);
int algo=atoi(argv[3]);
IplImage *L, *R, *GT;
// ***********************************************************
// ***********************************************************
// ****** STEREO ALGORITHM ******
// ***********************************************************
// ***********************************************************
char filenameL[100];
char filenameR[100];
char filenameGT[100];
//carico il nome delle immagini
sprintf(filenameL,"/KITTI/image_0/000%s_10.png",q);
sprintf(filenameR,"/KITTI/image_1/000%s_10.png",q);
sprintf(filenameGT,"/KITTI/disp_occ/000%s_10.png",q);
// load grayscale images
L = cvLoadImage(filenameL, CV_LOAD_IMAGE_GRAYSCALE);
R = cvLoadImage(filenameR, CV_LOAD_IMAGE_GRAYSCALE);
GT= cvLoadImage(filenameGT, CV_LOAD_IMAGE_UNCHANGED);
dMax=kitti_dMax(GT);
int h = L->height, w = L->width;
IplImage* DisparityL = cvCreateImage(cvGetSize(L),8,1);
IplImage* DisparityR = cvCreateImage(cvGetSize(R),8,1);
t_DSI* DSI=create_DSI(w,h,dMax);
t_DSI *boxFilteredDSI=create_DSI(w,h,dMax);
t_DSI* outDSI=create_DSI(w,h,dMax);
// point-wise cost (Absolute Difference or Hamming distance on Census transforms)
if (algo == 0)
AbsoluteDifferenceCost(L, R, dMax, DSI);
else
HammingDistanceCost(L, R, dMax, 5, DSI);
// TAD aggregation
BoxFiltering(DSI, boxFilteredDSI, 5, 100);
disparity_map(boxFilteredDSI,DisparityL, true);
disparity_map_R(boxFilteredDSI,DisparityR, true);
// show results
/*cvShowImage("Left",L);
cvShowImage("FW Left",DisparityL);
cvShowImage("FW Right",DisparityR);
cvWaitKey(0);*/
//matrice disparità
int **disparityMatrix;
disparityMatrix= (int **) calloc(h,sizeof(int *));
int z=0;
for (z;z<h;z++){
disparityMatrix[z]=(int *) calloc(w,sizeof (int));
}
//matrice dei costi
costi ** totalCostMatrix;
totalCostMatrix= (costi **) calloc(h,sizeof(costi *));
for (z=0;z<h;z++){
totalCostMatrix[z]=(costi *) calloc(w,sizeof (costi));
}
SGM(boxFilteredDSI, outDSI, dMax, 30, 300, 255);
disparity_map(outDSI,DisparityL, true);
disparity_map_R(outDSI,DisparityR, true);
//findtotalcost riempie la matrice ---->> test con outdSI usata per SGM
findTotalCost(outDSI,totalCostMatrix);
int nRighe = linesLength(GT);
printf ("\n\n%d\n", nRighe);
float * labels;
labels = (float *) calloc(nRighe, sizeof(float));
float ** trainingDatas;
trainingDatas = (float **) calloc(nRighe, sizeof(float *));
for (z=0;z<nRighe;z++){
trainingDatas[z] = (float *) calloc(3, sizeof(float));
}
//matricisubpixels
float ** subpixelM;
subpixelM= (float **) calloc(h,sizeof(float *));
for (z=0;z<h;z++){
subpixelM[z]=(float *) calloc(w,sizeof (float));
}
//riempio i vettori labels e traingDatas
//labels -> valori della GT
//traingDatas -> i costi
calcolaSubpixel(GT, totalCostMatrix, subpixelM, h, w);
fillLabels(GT, subpixelM, labels);
fillTrainingdatas(GT, totalCostMatrix, trainingDatas);
//test
labels[5] = 1.0;
//trainingDatas[0][0] = 1;
//trainingDatas[0][1] = 1;
//trainingDatas[0][2] = 1;
//imposto le label e i dati di training per effettuare il learning
Mat labelsMat(nRighe, 1, CV_32FC1, labels);
Mat trainingDatasMat(nRighe, 3, CV_32FC1, trainingDatas);
// Set up SVM's parameters
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
// Train the SVM
CvSVM SVM;
SVM.train(trainingDatasMat, labelsMat, Mat(), Mat(), params);
paramsを設定するコードを投稿できますか？ –
答えはありませんが、 'sprintf（q、"％03d "....' '' –
に感謝していますが、上記の部分を見つけてください// SGMあたりのテストアウトアウトうまく動作するファイル – heartlex