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Set stricter warning rules for gcc

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This commit is contained in:
Andrey Kamaev
2012-06-07 17:21:29 +00:00
parent 0395f7c63f
commit 49a1ba6038
241 changed files with 9054 additions and 8947 deletions
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108
modules/objdetect/src/featurepyramid.cpp
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@@ -30,12 +30,12 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
int height, width, numChannels;
int i, j, kk, c, ii, jj, d;
float * datadx, * datady;
//<2F><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD>
int ch;
int ch;
//<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
float magnitude, x, y, tx, ty;
IplImage * dx, * dy;
int *nearest;
float *w, a_x, b_x;
@@ -51,7 +51,7 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
// <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
int * alfa;
// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
float boundary_x[NUM_SECTOR + 1];
float boundary_y[NUM_SECTOR + 1];
@@ -63,9 +63,9 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
numChannels = image->nChannels;
dx = cvCreateImage(cvSize(image->width, image->height),
dx = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
dy = cvCreateImage(cvSize(image->width, image->height),
dy = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
sizeX = width / k;
@@ -77,7 +77,7 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));
float arg_vector;
for(i = 0; i <= NUM_SECTOR; i++)
{
@@ -113,20 +113,20 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
y = ty;
}
}/*for(ch = 1; ch < numChannels; ch++)*/
max = boundary_x[0] * x + boundary_y[0] * y;
maxi = 0;
for (kk = 0; kk < NUM_SECTOR; kk++)
for (kk = 0; kk < NUM_SECTOR; kk++)
{
dotProd = boundary_x[kk] * x + boundary_y[kk] * y;
if (dotProd > max)
if (dotProd > max)
{
max = dotProd;
maxi = kk;
}
else
else
{
if (-dotProd > max)
if (-dotProd > max)
{
max = -dotProd;
maxi = kk + NUM_SECTOR;
@@ -134,14 +134,14 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
}
}
alfa[j * width * 2 + i * 2 ] = maxi % NUM_SECTOR;
alfa[j * width * 2 + i * 2 + 1] = maxi;
alfa[j * width * 2 + i * 2 + 1] = maxi;
}/*for(i = 0; i < width; i++)*/
}/*for(j = 0; j < height; j++)*/
//<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
nearest = (int *)malloc(sizeof(int ) * k);
w = (float*)malloc(sizeof(float) * (k * 2));
for(i = 0; i < k / 2; i++)
{
nearest[i] = -1;
@@ -155,15 +155,15 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
{
b_x = k / 2 + j + 0.5f;
a_x = k / 2 - j - 0.5f;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = 0; j < k / 2; j++)*/
for(j = k / 2; j < k; j++)
{
a_x = j - k / 2 + 0.5f;
b_x =-j + k / 2 - 0.5f + k;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = k / 2; j < k; j++)*/
@@ -176,40 +176,40 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
{
for(jj = 0; jj < k; jj++)
{
if ((i * k + ii > 0) &&
(i * k + ii < height - 1) &&
(j * k + jj > 0) &&
if ((i * k + ii > 0) &&
(i * k + ii < height - 1) &&
(j * k + jj > 0) &&
(j * k + jj < width - 1))
{
d = (k * i + ii) * width + (j * k + jj);
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] +=
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] +=
r[d] * w[ii * 2] * w[jj * 2];
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2] * w[jj * 2];
if ((i + nearest[ii] >= 0) &&
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1))
{
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] +=
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
}
if ((j + nearest[jj] >= 0) &&
if ((j + nearest[jj] >= 0) &&
(j + nearest[jj] <= sizeX - 1))
{
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2] * w[jj * 2 + 1];
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2] * w[jj * 2 + 1];
}
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1) &&
(j + nearest[jj] >= 0) &&
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1) &&
(j + nearest[jj] >= 0) &&
(j + nearest[jj] <= sizeX - 1))
{
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
}
}
@@ -217,14 +217,14 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
}/*for(ii = 0; ii < k; ii++)*/
}/*for(j = 1; j < sizeX - 1; j++)*/
}/*for(i = 1; i < sizeY - 1; i++)*/
cvReleaseImage(&dx);
cvReleaseImage(&dy);
free(w);
free(nearest);
free(r);
free(alfa);
@@ -232,7 +232,7 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
}
/*
// Feature map Normalization and Truncation
// Feature map Normalization and Truncation
//
// API
// int normalizeAndTruncate(featureMap *map, const float alfa);
@@ -270,7 +270,7 @@ int normalizeAndTruncate(CvLSVMFeatureMap *map, const float alfa)
}/*for(j = 0; j < p; j++)*/
partOfNorm[i] = valOfNorm;
}/*for(i = 0; i < sizeX * sizeY; i++)*/
sizeX -= 2;
sizeY -= 2;
@@ -369,13 +369,13 @@ int normalizeAndTruncate(CvLSVMFeatureMap *map, const float alfa)
// Error status
*/
int PCAFeatureMaps(CvLSVMFeatureMap *map)
{
{
int i,j, ii, jj, k;
int sizeX, sizeY, p, pp, xp, yp, pos1, pos2;
float * newData;
float val;
float nx, ny;
sizeX = map->sizeX;
sizeY = map->sizeY;
p = map->numFeatures;
@@ -424,7 +424,7 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map)
}/*for(jj = 0; jj < xp; jj++)*/
newData[pos2 + k] = val * nx;
k++;
} /*for(ii = 0; ii < yp; ii++)*/
} /*for(ii = 0; ii < yp; ii++)*/
}/*for(j = 0; j < sizeX; j++)*/
}/*for(i = 0; i < sizeY; i++)*/
//swop data
@@ -439,7 +439,7 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map)
}
int getPathOfFeaturePyramid(IplImage * image,
static int getPathOfFeaturePyramid(IplImage * image,
float step, int numStep, int startIndex,
int sideLength, CvLSVMFeaturePyramid **maps)
{
@@ -447,7 +447,7 @@ int getPathOfFeaturePyramid(IplImage * image,
IplImage *scaleTmp;
float scale;
int i, err;
for(i = 0; i < numStep; i++)
{
scale = 1.0f / powf(step, (float)i);
@@ -462,13 +462,13 @@ int getPathOfFeaturePyramid(IplImage * image,
}
/*
// Getting feature pyramid
// Getting feature pyramid
//
// API
// int getFeaturePyramid(IplImage * image, const filterObject **all_F,
// int getFeaturePyramid(IplImage * image, const filterObject **all_F,
const int n_f,
const int lambda, const int k,
const int startX, const int startY,
const int lambda, const int k,
const int startX, const int startY,
const int W, const int H, featurePyramid **maps);
// INPUT
// image - image
@@ -484,7 +484,7 @@ int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps)
int numStep;
int maxNumCells;
int W, H;
if(image->depth == IPL_DEPTH_32F)
{
imgResize = image;
@@ -493,9 +493,9 @@ int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps)
{
imgResize = cvCreateImage(cvSize(image->width , image->height) ,
IPL_DEPTH_32F , 3);
cvConvert(image, imgResize);
cvConvert(image, imgResize);
}
W = imgResize->width;
H = imgResize->height;
@@ -506,14 +506,14 @@ int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps)
maxNumCells = H / SIDE_LENGTH;
}
numStep = (int)(logf((float) maxNumCells / (5.0f)) / logf( step )) + 1;
allocFeaturePyramidObject(maps, numStep + LAMBDA);
getPathOfFeaturePyramid(imgResize, step , LAMBDA, 0,
getPathOfFeaturePyramid(imgResize, step , LAMBDA, 0,
SIDE_LENGTH / 2, maps);
getPathOfFeaturePyramid(imgResize, step, numStep, LAMBDA,
getPathOfFeaturePyramid(imgResize, step, numStep, LAMBDA,
SIDE_LENGTH , maps);
if(image->depth != IPL_DEPTH_32F)
{
cvReleaseImage(&imgResize);