CV-CUDA™ is an open-source, graphics processing unit (GPU)-accelerated library for cloud-scale image processing and computer vision developed jointly by NVIDIA and the ByteDance Applied Machine Learning teams. CV-CUDA helps developers build highly efficient pre- and post-processing pipelines that 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: object classification and image segmentation
| 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 a 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 |
| Find Contours | Extract closed contours from an input binary image |
| FindHomography | Calculates a perspective transform from four pairs of the corresponding points |
| 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 |
| 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 a morphological operation that involves dilation followed by erosion on an image |
| Morphology (open) | Performs a morphological operation that involves erosion followed by dilation on an image |
| Non-max 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 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 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. |
| Stack | Concatenates two input tensors into a single output tensor |
| 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 |
An awesome product requires excellent support. CV-CUDA release notes can be found here
File requests for enhancements and bug reports here.
We are providing limited, direct, support to select enterprises using CV-CUDA. To apply for direct enterprise developer engagement from NVIDIA , please fill out the early access developer application here.
NVIDIA offers a number of computer vision products
In addition to cloud-scale computer vision and image processing, NVIDIA offers:
- 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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