- Object Detection Using opencv I - Integral Histogram for fast Calculation of HOG Features
- Object Detection using opencv II - Calculation of Hog Features
- Object Detection using opencv III - Training an svm for the extracted hog features
Start of borrowed stuff
Histograms of Oriented Gradients or HOG features in combination with a support vector machine have been successfully used for object Detection (most popularly pedestrian detection). An Integral Histogram representation can be used for fast calculation of Histograms of Oriented Gradients over arbitrary rectangular regions of the image. The idea of an integral histogram is analogous to that of an integral image, used by viola and jones for fast calculation of haar features for face detection. Mathematically,
/*
Function to calculate the integral histogram
*/
IplImage **calculateIntegralHOG(IplImage * in)
{
/*
Convert the input image to grayscale
*/
IplImage *img_gray = cvCreateImage(cvGetSize(in), IPL_DEPTH_8U, 1);
cvCvtColor(in, img_gray, CV_BGR2GRAY);
cvEqualizeHist(img_gray, img_gray);
/*
Calculate the derivates of the grayscale image in the x and y directions using a
sobel operator and obtain 2 gradient images for the x and y directions
*/
IplImage *xsobel, *ysobel;
xsobel = doSobel(img_gray, 1, 0, 3);
ysobel = doSobel(img_gray, 0, 1, 3);
cvReleaseImage(&img_gray);
/*
Create an array of 9 images (9 because I assume bin size 20 degrees and unsigned gradient
( 180/20 = 9), one for each bin which will have zeroes for all pixels, except for the
pixels in the original image for which the gradient values correspond to the particular
bin. These will be referred to as bin images. These bin images will be then used to
calculate the integral histogram, which will quicken the calculation of HOG descriptors
*/
IplImage **bins = (IplImage **) malloc(9 * sizeof(IplImage *));
for (int i = 0; i < 9; i++) {
bins[i] = cvCreateImage(cvGetSize(in), IPL_DEPTH_32F, 1);
cvSetZero(bins[i]);
}
/*
Create an array of 9 images ( note the dimensions of the image, the cvIntegral() function
requires the size to be that), to store the integral images calculated from the above bin
images. These 9 integral images together constitute the integral histogram
*/
IplImage **integrals = (IplImage **) malloc(9 * sizeof(IplImage *));
for (int i = 0; i < 9; i++) {
integrals[i] =
cvCreateImage(cvSize(in->width + 1, in->height + 1),
IPL_DEPTH_64F, 1);
}
/*
Calculate the bin images. The magnitude and orientation of the gradient at each pixel
is calculated using the xsobel and ysobel images.
{Magnitude = sqrt(sq(xsobel) + sq(ysobel) ), gradient = itan (ysobel/xsobel) }.
Then according to the orientation of the gradient, the value of the corresponding pixel
in the corresponding image is set
*/
int x, y;
float temp_gradient, temp_magnitude;
for (y = 0; y < in->height; y++) {
/*
ptr1 and ptr2 point to beginning of the current row in the xsobel and ysobel images
respectively. ptrs[i] point to the beginning of the current rows in the bin images
*/
float *ptr1 =
(float *)(xsobel->imageData + y * (xsobel->widthStep));
float *ptr2 =
(float *)(ysobel->imageData + y * (ysobel->widthStep));
float **ptrs = (float **)malloc(9 * sizeof(float *));
for (int i = 0; i < 9; i++) {
ptrs[i] =
(float *)(bins[i]->imageData +
y * (bins[i]->widthStep));
}
/*
For every pixel in a row gradient orientation and magnitude are calculated
and corresponding values set for the bin images.
*/
for (x = 0; x < in->width; x++) {
/*
If the xsobel derivative is zero for a pixel, a small value is added to it, to avoid
division by zero. atan returns values in radians, which on being converted to degrees,
correspond to values between -90 and 90 degrees. 90 is added to each orientation, to
shift the orientation values range from {-90-90} to {0-180}. This is just a matter of
convention. {-90-90} values can also be used for the calculation.
*/
if (ptr1[x] == 0) {
temp_gradient =
((atan(ptr2[x] / (ptr1[x] + 0.00001))) *
(180 / PI)) + 90;
} else {
temp_gradient =
((atan(ptr2[x] / ptr1[x])) * (180 / PI)) +
90;
}
temp_magnitude =
sqrt((ptr1[x] * ptr1[x]) + (ptr2[x] * ptr2[x]));
/*
The bin image is selected according to the gradient values. The corresponding pixel
value is made equal to the gradient magnitude at that pixel in the corresponding bin image
*/
if (temp_gradient <= 20) {
ptrs[0][x] = temp_magnitude;
} else if (temp_gradient <= 40) {
ptrs[1][x] = temp_magnitude;
} else if (temp_gradient <= 60) {
ptrs[2][x] = temp_magnitude;
} else if (temp_gradient <= 80) {
ptrs[3][x] = temp_magnitude;
} else if (temp_gradient <= 100) {
ptrs[4][x] = temp_magnitude;
} else if (temp_gradient <= 120) {
ptrs[5][x] = temp_magnitude;
} else if (temp_gradient <= 140) {
ptrs[6][x] = temp_magnitude;
} else if (temp_gradient <= 160) {
ptrs[7][x] = temp_magnitude;
} else {
ptrs[8][x] = temp_magnitude;
}
}
}
cvReleaseImage(&xsobel);
cvReleaseImage(&ysobel);
/*Integral images for each of the bin images are calculated*/
for (int i = 0; i < 9; i++) {
cvIntegral(bins[i], integrals[i]);
}
for (int i = 0; i < 9; i++) {
cvReleaseImage(&bins[i]);
}
/*
The function returns an array of 9 images which consitute the integral histogram
*/
return (integrals);
}
The following demonstrates how the integral histogram calculated using the above function can be used to calculate the histogram of oriented gradients for any rectangular region in the image: /*
The following function takes as input the rectangular cell for which the histogram of
oriented gradients has to be calculated, a matrix hog_cell of dimensions 1x9 to store
the bin values for the histogram, the integral histogram, and the normalization scheme
to be used. No normalization is done if normalization = -1
*/
void calculateHOG_rect(CvRect cell, CvMat * hog_cell,
IplImage ** integrals, int normalization)
{
/* Calculate the bin values for each of the bin of the histogram one by one */
for (int i = 0; i < 9; i++) {
float a = ((double *)(integrals[i]->imageData + (cell.y)
* (integrals[i]->widthStep)))[cell.x];
float b =
((double *)(integrals[i]->imageData + (cell.y + cell.height)
* (integrals[i]->widthStep)))[cell.x +
cell.width];
float c = ((double *)(integrals[i]->imageData + (cell.y)
* (integrals[i]->widthStep)))[cell.x +
cell.width];
float d =
((double *)(integrals[i]->imageData + (cell.y + cell.height)
* (integrals[i]->widthStep)))[cell.x];
((float *)hog_cell->data.fl)[i] = (a + b) - (c + d);
}
/*
Normalize the matrix
*/
if (normalization != -1) {
cvNormalize(hog_cell, hog_cell, 1, 0, normalization);
}
}
End of borrowed stuff
I will describe how the HOG features for pedestrian detection can be obtained using the above framework and how an svm can be trained for such features for pedestrian detection in a later post.
thanks a lot for explaning the calculation of hog's with a code! i was having a few problems understanding the practicals, but you saved my day!
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