CUDA

CUDA utilities and image processing kernels. More...

Modules
	Color Mapping
	Defines various colormaps and color mapping functions.
	Color Conversion
	Colorspace conversion functions for various YUV formats, RGB, BGR, Bayer, and grayscale.
	Cropping
	Crop an image to the specified region of interest (ROI).
	Drawing
	Drawing basic 2D shapes using CUDA.
	Error Checking
	Error checking and logging macros.
	Fonts
	TTF font rasterization and image overlay rendering using CUDA.
	Memory Management
	Allocation of CUDA mapped zero-copy memory.
	Normalization
	Normalize the pixel intensities of an image between two ranges.
	Overlay
	Overlay images and vector shapes onto other images.
	Pixel Filtering
	CUDA device functions for sampling pixels with bilinear filtering.
	Point Cloud
	3D point cloud processing and visualization.
	Resize
	Rescale an image to a different resolution.
	Warping
	Various image warps and matrix transforms.

Functions
__device__ __host__ int	iDivUp (int a, int b)
	If a / b has a remainder, round up. More...

Detailed Description

CUDA utilities and image processing kernels.

Function Documentation

iDivUp()

__device__ __host__ int iDivUp ( int  a, int  b  )

inline

If a / b has a remainder, round up.

This function is commonly using when launching CUDA kernels, to compute a grid size inclusive of the entire dataset if it's dimensions aren't evenly divisible by the block size.

For example:

const dim3 blockDim(8,8); const dim3 gridDim(iDivUp(imgWidth,blockDim.x), iDivUp(imgHeight,blockDim.y));

Then inside the CUDA kernel, there is typically a check that thread index is in-bounds.

Without the use of iDivUp(), if the data dimensions weren't evenly divisible by the block size, parts of the data wouldn't be covered by the grid and not processed.