本文整理汇总了C++中CFloatImage::ClearPixels方法的典型用法代码示例。如果您正苦于以下问题：C++ CFloatImage::ClearPixels方法的具体用法？C++ CFloatImage::ClearPixels怎么用？C++ CFloatImage::ClearPixels使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类CFloatImage的用法示例。
在下文中一共展示了CFloatImage::ClearPixels方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1:
void
SupportVectorMachine::predictSlidingWindow(const Feature &feat, CFloatImage &response) const
{
response.ReAllocate(CShape(feat.Shape().width, feat.Shape().height, 1));
response.ClearPixels();
/******** BEGIN TODO ********/
// Sliding window prediction.
//
// In this project we are using a linear SVM. This means that
// it's classification function is very simple, consisting of a
// dot product of the feature vector with a set of weights learned
// during training, followed by a subtraction of a bias term
//
// pred <- dot(feat, weights) - bias term
//
// Now this is very simple to compute when we are dealing with
// cropped images, our computed features have the same dimensions
// as the SVM weights. Things get a little more tricky when you
// want to evaluate this function over all possible subwindows of
// a larger feature, one that we would get by running our feature
// extraction on an entire image.
//
// Here you will evaluate the above expression by breaking
// the dot product into a series of convolutions (remember that
// a convolution can be though of as a point wise dot product with
// the convolution kernel), each one with a different band.
//
// Convolve each band of the SVM weights with the corresponding
// band in feat, and add the resulting score image. The final
// step is to subtract the SVM bias term given by this->getBiasTerm().
//
// Hint: you might need to set the origin for the convolution kernel
// in order to get the result from convoltion to be correctly centered
//
// Useful functions:
// Convolve, BandSelect, this->getWeights(), this->getBiasTerm()
Feature weights = this->getWeights();
int nWtBands = weights.Shape().nBands;
// Set the center of the window as the origin for the conv. kernel
for (int band = 0; band < nWtBands; band++)
{
// Select a band
CFloatImage featBand;
CFloatImage weightBand;
BandSelect(feat, featBand, band, 0);
BandSelect(weights, weightBand, band, 0);
// Set the origin of the kernel
weightBand.origin[0] = weights.Shape().width / 2;
weightBand.origin[1] = weights.Shape().height / 2;
// Compute the dot product
CFloatImage dotproduct;
dotproduct.ClearPixels();
Convolve(featBand, dotproduct, weightBand);
// Add the resulting score image
for (int y = 0; y < feat.Shape().height; y++)
{
for (int x = 0; x < feat.Shape().width; x++)
{
response.Pixel(x, y, 0) += dotproduct.Pixel(x, y, 0);
}
// End of x loop
}
// End of y loop
}
// End of band loop
// Substract the SVM bias term
for (int y = 0; y < feat.Shape().height; y++)
{
for (int x = 0; x < feat.Shape().width; x++)
{
response.Pixel(x, y, 0) -= this->getBiasTerm();
}
// End of x loop
}
// End of y loop
/******** END TODO ********/
}