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

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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

ResizeCropConvertReformat

Performs fused Resize-Crop-Convert-Reformat sequence with optional channel reordering.

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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