CV-CUDA

NVIDIA CV-CUDA™ is an open-source project for building cloud-scale Artificial Intelligence (AI) imaging and Computer Vision (CV) applications. It uses graphics processing unit (GPU) acceleration to help developers build highly efficient pre- and post-processing pipelines. It can improve throughput by more than 10x while lowering cloud computing costs.

CV-CUDA includes:

A unified, specialized set of high-performance CV and image processing kernels
C, C++, and Python APIs
Batching support, with variable shape images
Zero-copy interfaces to PyTorch
Sample applications: classification, object detection, and image segmentation

CV-CUDA Pre- and Post-Processing Operators

CV-CUDA offers a comprehensive collection of Computer Vision and Image Processing operators, listed below.

List of operators
Pre/Post-Processing Operators	Definition
Adaptive Thresholding	Chooses threshold based on smaller regions in the neighborhood of each pixel.
Advanced Color Format Conversions	Performs color conversion from interleaved RGB/BGR <-> YUV/YVU and semi planar. Supported standards: BT.601. BT.709. BT.2020
AverageBlur	Reduces image noise using an average filter
BilateralFilter	Reduces image noise while preserving strong edges
Bounding Box	Draws an rectangular border using the X-Y coordinates and dimensions typically to define the location and size of an object in an image
Box Blurring	Overlays a blurred rectangle using the X-Y coordinates and dimensions that define the location and size of an object in an image
Brightness_Contrast	Adjusts brightness and contrast of an image
CenterCrop	Crops an image at its center
ChannelReorder	Shuffles the order of image channels
Color_Twist	Adjusts the hue saturation brightness and contrast of an image
Composite	Composites two images together
Conv2D	Convolves an image with a provided kernel
CopyMakeBorder	Creates a border around an image
CustomCrop	Crops an image with a given region-of-interest
CvtColor	Converts an image from one color space to another
DataTypeConvert	Converts an image’s data type with optional scaling
Erase	Erases image regions
Flip	Flips a 2D image around its axis
GammaContrast	Adjusts image contrast
Gaussian	Applies a gaussian blur filter to the image
Gaussian Noise	Generates a statistical noise with a normal (Gaussian) distribution
Histogram	Provides a grayscale value distribution showing the frequency of occurrence of each gray value.
Histogram Equalizer	Allows effective spreading out the intensity range of the image typically used to improve contrast
HqResize	Performs advanced resizing supporting 2D and 3D data, tensors, tensor batches, and varshape image batches (2D only). Supports nearest neighbor, linear, cubic, Gaussian and Lanczos interpolation, with optional antialiasing when down-sampling.
Inpainting	Performs inpainting by replacing a pixel by normalized weighted sum of all the known pixels in the neighborhood
Joint Bilateral Filter	Reduces image noise while preserving strong edges based on a guidance image
Label	Labels connected regions in an image using 4-way connectivity for foreground and 8-way for background pixels
Laplacian	Applies a Laplace transform to an image
MedianBlur	Reduces an image’s salt-and-pepper noise
MinArea Rect	Finds the minimum area rotated rectangle typically used to draw bounding rectangle with minimum area
MinMaxLoc	Finds the maximum and minimum values in a given array
Morphology	Performs morphological erode and dilate transformations
Morphology (close)	Performs morphological operation that involves dilation followed by erosion on an image
Morphology (open)	Performs morphological operation that involves erosion followed by dilation on an image
Non-Maximum Suppression	Enables selecting a single entity out of many overlapping ones typically used for selecting from multiple bounding boxes during object detection
Normalize	Normalizes an image pixel’s range
OSD (Polyline Line Text Rotated Rect Segmented Mask)	Displays an overlay on the image of of different forms including polyline line text rotated rectangle segmented mask
PadStack	Stacks several images into a tensor with border extension
PairwiseMatcher	Matches features computed separately (e.g. via the SIFT operator) in two images, e.g. using the brute force method
PillowResize	Changes the size and scale of an image using python-pillow algorithm
RandomResizedCrop	Crops a random portion of an image and resizes it to a specified size.
Reformat	Converts a planar image into non-planar and vice versa
Remap	Maps pixels in an image with one projection to another projection in a new image.
Resize	Changes the size and scale of an image
Rotate	Rotates a 2D array in multiples of 90 degrees
SIFT	Identifies and matches features in images that are invariant to scale rotation and affine distortion.
Thresholding	Chooses a global threshold value that is the same for all pixels across the image.
WarpAffine	Applies an affine transformation to an image
WarpPerspective	Applies a perspective transformation to an image

Where Are the Release Notes?

CV-CUDA release notes can be found here.

Where Can I Get Help?

An awesome product requires excellent support. File requests for enhancements and bug reports here.

What Other Computer Vision Products Does NVIDIA Offer?

NVIDIA offers a number of products for accelerating computer vision and image processing applications. In addition to CV-CUDA, some of the others include:

DALI (Data Loading Library), a portable, holistic framework for accelerated data loading and augmentation in deep learning workflows involving images, videos, and audio data.
VPI (Vision Programming Interface), an accelerated computer vision and image processing software library primarily for embedded/edge applications.
cuCIM (Compute Unified Device Architecture Clara Image), an open source, accelerated computer vision and image processing library for multidimensional images in biomedical, geospatial, material life science, and remote sensing use cases.
NPP (NVIDIA Performance Primitives), an image, signal, and video processing library that accelerates and performs domain-specific functions.

If you want to learn more about what computer vision solutions are available, review the computer vision solutions landing page.

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