docs/html/group__cuda.html
| | Jetson Inference
DNN Vision Library |
CUDA Utilities Library (jetson-utils)
CUDA utilities and image processing kernels. More...
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| | Color Mapping |
| | Defines various colormaps and color mapping functions.
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| | Color Conversion |
| | Colorspace conversion functions for various YUV formats, RGB, BGR, Bayer, and grayscale.
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| | Cropping |
| | Crop an image to the specified region of interest (ROI).
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| | Drawing |
| | Drawing basic 2D shapes using CUDA.
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| | Error Checking |
| | Error checking and logging macros.
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| | Fonts |
| | TTF font rasterization and image overlay rendering using CUDA.
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| | Memory Management |
| | Allocation of CUDA mapped zero-copy memory.
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| | Normalization |
| | Normalize the pixel intensities of an image between two ranges.
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| | Overlay |
| | Overlay images and vector shapes onto other images.
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| | Pixel Filtering |
| | CUDA device functions for sampling pixels with bilinear filtering.
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| | Point Cloud |
| | 3D point cloud processing and visualization.
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| | Resize |
| | Rescale an image to a different resolution.
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| | Warping |
| | Various image warps and matrix transforms.
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| __device__ __host__ int | iDivUp (int a, int b) |
| | If a / b has a remainder, round up. More...
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CUDA utilities and image processing kernels.
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| __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.