speedy-vision
v0.9.0-wipTypeScript
GPU-accelerated Computer Vision for JavaScript
0/weekUpdated 3 years agoApache-2.0Unpacked: 17.8 MB
Published by Alexandre Martins
npm install speedy-visionspeedy-vision.js
Build real-time stuff with speedy-vision.js, a GPU-accelerated Computer Vision library for JavaScript.
Features
* Feature detection
* Harris corner detector
* FAST feature detector
* ORB feature descriptor
* Feature tracking
* KLT feature tracker
* LK optical flow
* Feature matching
* Soon
* Geometric transformations
* Homography matrix
* Affine transform
* Image processing
* Convert to greyscale
* Convolution
* Gaussian blur, box & median filters
* Contrast and brightness adjustment
* Image normalization & warping
* Image pyramids
* Linear Algebra
* Beautiful matrix algebra with a fluent interface
* Efficient computations with WebAssembly
* Systems of linear equations
* QR decomposition
... and more in development!
There are plenty of demos available!
Author
speedy-vision.js is developed by Alexandre Martins, a computer scientist from Brazil. It is released under the Apache-2.0 license.
If my work is of value to you, make a donation. Thank you.
For general enquiries, contact me at alemartf at gmail dot com.
-----
Table of contents
* Demos
* Installation
* Motivation
* The Pipeline
* API Reference
* Media routines
* Loading your media
* Media properties
* Playing with your media
* Pipeline
* Basic routines
* Basic properties
* Basic nodes
* Image processing
* Image basics
* Image filters
* General transformations
* Keypoints and descriptors
* Keypoint basics
* Keypoint detection
* Keypoint description
* Keypoint tracking (optical-flow)
* Keypoint matching
* Portals
* Image Portals
* Keypoint Portals
* Linear Algebra
* Creating new matrices
* Matrix properties
* Reading from the matrices
* Writing to the matrices
* Elementary operations
* Access by block
* Functional programming
* Systems of equations
* Matrix factorization
* Geometric transformations
* Homography
* Geometric Utilities
* 2D vectors
* 2D points
* 2D size
* Extras
* Promises
* Utilities
* Unit tests
Demos
Try the demos and take a look at their source code:
* Hello, world!
* Feature detection
* Feature detection in a webcam
* Feature detection in an image
* Feature detection in a video
* Find the best Harris corners
* ORB features
* Feature tracking
* Optical flow
* Image processing
* Image convolution
* Image warping
* Alpha blending
* Resize image
* Nightvision camera
* Convert image to greyscale
* Convert video to greyscale
* Blurring an image
* Blurring a video with a median filter
* Normalize camera stream
* Image Portals
* Linear Algebra
* System of equations
* QR decomposition
Installation
Download the latest release of speedy-vision.js and include it in the section of your HTML page:
``html`
Once you import the library, the Speedy object will be exposed. Check out the Hello World demo for a working example.
Motivation
Detecting features in an image is an important step of many computer vision algorithms. Traditionally, the computationally expensive nature of this process made it difficult to bring interactive Computer Vision applications to the web browser. The framerates were unsatisfactory for a compelling user experience. Speedy, a short name for speedy-vision.js, is a JavaScript library created to address this issue.
Speedy's real-time performance in the web browser is possible thanks to its efficient WebGL2 backend and to its GPU implementations of fast computer vision algorithms. With an easy-to-use API, Speedy is an excellent choice for real-time computer vision projects involving tasks such as: object detection in videos, pose estimation, Simultaneous Location and Mapping (SLAM), and others.
The Pipeline
The pipeline is a central concept in Speedy. It's a powerful structure that lets you organize the computations that take place in the GPU. It's a very flexible, yet conceptually simple, way of working with computer vision and image processing. Let's define a few things:
- A pipeline is a network of nodes in which data flows downstream from one or more sources to one or more sinks.
- Nodes have input and/or output ports. A node with no input ports is called a source. A node with no output ports is called a sink. A node with both input and output ports transforms the input data in some way and writes the results to its output port(s).
- A link connects an output port of a node to an input port of another node. Two nodes are said to be connected if there is a link connecting their ports. Data flows from one node to another by means of a link. An input port may only be connected to a single output port, but an output port may be connected to multiple input ports.
- Input ports expect data of a certain type (e.g., an image). Output ports hold data of a certain type. Two ports may only be connected if their types match.
- Ports may impose additional constraints on the data passing through them. For example, an input port may expect an image and also impose the constraint that this image must be greyscale.
- Different nodes may have different parameters. These parameters can be adjusted and are meant to modify the output of the nodes in some way.
- Nodes and their ports have names. An input port is typically called "in". An output port is typically called "out". These names can vary, e.g., if a node has more than one input / output port. Speedy automatically assigns names to the nodes, but you can assign your own names as well.
The picture below shows a visual representation of a pipeline that converts an image or video to greyscale. Data gets into the pipeline via the image source. It is then passed to the Convert to greyscale node. Finally, a greyscale image goes into the image sink, where it gets out of the pipeline.
!Convert to greyscale: a simple pipeline
Here's a little bit of code:
`js
// Load an image
const img = document.querySelector('img');
const media = await Speedy.load(img);
// Create the pipeline and the nodes
const pipeline = Speedy.Pipeline();
const source = Speedy.Image.Source();
const sink = Speedy.Image.Sink();
const greyscale = Speedy.Filter.Greyscale();
// Set the media source
source.media = media; // media is a SpeedyMedia object
// Connect the nodes
source.output().connectTo(greyscale.input());
greyscale.output().connectTo(sink.input());
// Specify the nodes to initialize the pipeline
pipeline.init(source, sink, greyscale);
// Run the pipeline
const { image } = await pipeline.run(); // image is a SpeedyMedia
// Create a
// Display the result
const ctx = canvas.getContext('2d');
ctx.drawImage(media.source, 0, 0);
`
Speedy provides many types of nodes. You can connect these nodes in a way that is suitable to your application, and Speedy will bring back the results you ask for.
API Reference
$3
A SpeedyMedia object encapsulates a media object: an image, a video, a canvas or a bitmap.
#### Loading your media
##### Speedy.load()
Speedy.load(source: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | ImageBitmap, options?: object): SpeedyPromise
Tells Speedy to load source. The source parameter may be an image, a video, a canvas or a bitmap.
###### Arguments
* source: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | ImageBitmap. The media source.options: object, optional
* . Additional options for advanced configuration. See SpeedyMedia.options for details.
###### Returns
A SpeedyPromise that resolves as soon as the media source is loaded.
###### Example
`js`
window.onload = async function() {
let image = document.getElementById('my-image'); //
let media = await Speedy.load(image);
}
##### Speedy.camera()
Speedy.camera(width?: number, height?: number): SpeedyPromise
Speedy.camera(constraints: MediaStreamConstraints): SpeedyPromise
Loads a camera stream into a new SpeedyMedia object. This is a wrapper around navigator.mediaDevices.getUserMedia(), provided for your convenience.
###### Arguments
* width: number, optional. The ideal width of the stream. The browser will use this value or a close match. Defaults to 640.height: number, optional
* . The ideal height of the stream. The browser will use this value or a close match. Defaults to 360.constraints: MediaStreamConstraints
* . A MediaStreamConstraints dictionary to be passed to getUserMedia() for complete customization.
###### Returns
A SpeedyPromise that resolves as soon as the media source is loaded with the camera stream.
###### Example
`js
// Display the contents of a webcam
window.onload = async function() {
const media = await Speedy.camera();
const canvas = createCanvas(media.width, media.height);
const ctx = canvas.getContext('2d');
function render()
{
ctx.drawImage(media.source, 0, 0);
requestAnimationFrame(render);
}
render();
}
function createCanvas(width, height)
{
const canvas = document.createElement('canvas');
canvas.width = width;
canvas.height = height;
document.body.appendChild(canvas);
return canvas;
}
`
##### SpeedyMedia.release()
SpeedyMedia.release(): null
Releases internal resources associated with this SpeedyMedia.
###### Returns
Returns null.
#### Media properties
##### SpeedyMedia.source
SpeedyMedia.source: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | ImageBitmap, read-only
The media source associated with the SpeedyMedia object.
##### SpeedyMedia.type
SpeedyMedia.type: string, read-only
The type of the media source. One of the following: "image", "video", "canvas", "bitmap".
##### SpeedyMedia.width
SpeedyMedia.width: number, read-only
The width of the media source, in pixels.
##### SpeedyMedia.height
SpeedyMedia.height: number, read-only
The height of the media source, in pixels.
##### SpeedyMedia.size
SpeedyMedia.size: SpeedySize, read-only
The size of the media, in pixels.
##### SpeedyMedia.options
SpeedyMedia.options: object, read-only
Read-only object defined when loading the media. Deprecated.
#### Playing with your media
##### SpeedyMedia.clone()
SpeedyMedia.clone(): SpeedyPromise
Clones the SpeedyMedia object.
###### Returns
A SpeedyPromise that resolves to a clone of the SpeedyMedia object.
###### Example
`js`
const clone = await media.clone();
##### SpeedyMedia.toBitmap()
SpeedyMedia.toBitmap(): SpeedyPromise
Converts the media to an ImageBitmap.
###### Returns
A SpeedyPromise that resolves to an ImageBitmap.
$3
#### Basic routines
##### Speedy.Pipeline.Pipeline()
Speedy.Pipeline.Pipeline(): SpeedyPipeline
Creates a new, empty pipeline.
###### Returns
A new SpeedyPipeline object.
##### SpeedyPipeline.init()
SpeedyPipeline.init(...nodes: ...SpeedyPipelineNode): SpeedyPipeline
Initializes a pipeline with the specified nodes.
###### Arguments
* ...nodes: ...SpeedyPipelineNode. The list of nodes that belong to the pipeline.
###### Returns
The pipeline itself.
###### Example
`js
const pipeline = Speedy.Pipeline(); // create the pipeline and the nodes
const source = Speedy.Image.Source();
const sink = Speedy.Image.Sink();
const greyscale = Speedy.Filter.Greyscale();
source.media = media; // set the media source
source.output().connectTo(greyscale.input()); // connect the nodes
greyscale.output().connectTo(sink.input());
pipeline.init(source, sink, greyscale); // add the nodes to the pipeline
`
##### SpeedyPipeline.release()
SpeedyPipeline.release(): null
Releases the resources associated with this pipeline.
###### Returns
Returns null.
##### SpeedyPipeline.run()
SpeedyPipeline.run(): SpeedyPromise
Runs this pipeline.
###### Returns
Returns a SpeedyPromise that resolves to an object whose keys are the names of the sinks of the pipeline and whose values are the data exported by those sinks.
###### Example
`js`
const { sink1, sink2 } = await pipeline.run();
##### SpeedyPipeline.node()
SpeedyPipeline.node(name: string): SpeedyPipelineNode | null
Finds a node by its name.
###### Arguments
* name: string. Name of the target node.
###### Returns
Returns a SpeedyPipelineNode that has the specified name and that belongs to this pipeline, or null if there is no such node.
##### SpeedyPipelineNode.input()
SpeedyPipelineNode.input(portName?: string): SpeedyPipelineNodePort
The input port of this node whose name is portName.
###### Arguments
* portName: string, optional. The name of the port you want to access. Defaults to "in".
###### Returns
The requested input port.
##### SpeedyPipelineNode.output()
SpeedyPipelineNode.output(portName?: string): SpeedyPipelineNodePort
The output port of this node whose name is portName.
###### Arguments
* portName: string, optional. The name of the port you want to access. Defaults to "out".
###### Returns
The requested output port.
##### SpeedyPipelineNodePort.connectTo()
SpeedyPipelineNodePort.connectTo(port: SpeedyPipelineNodePort): void
Creates a link connecting this port to another port.
#### Basic properties
##### SpeedyPipelineNode.name
SpeedyPipelineNode.name: string, read-only
The name of the node.
##### SpeedyPipelineNode.fullName
SpeedyPipelineNode.fullName: string, read-only
A string that exhibits the name and the type of the node.
##### SpeedyPipelineNodePort.name
SpeedyPipelineNodePort.name: string, read-only
The name of the port.
##### SpeedyPipelineNodePort.node
SpeedyPipelineNodePort.node: SpeedyPipelineNode, read-only
The node to which this port belongs.
#### Basic nodes
##### Speedy.Image.Source()
Speedy.Image.Source(name?: string): SpeedyPipelineNodeImageInput
Creates an image source with the specified name. If the name is not specified, Speedy will automatically generate a name for you.
###### Parameters
* media: SpeedyMedia. The media to be imported into the pipeline.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "out" | Image | An image corresponding to the media of this node. |
##### Speedy.Image.Sink()
Speedy.Image.Sink(name?: string): SpeedyPipelineNodeImageOutput
Creates an image sink with the specified name. If the name is not specified, Speedy will call this node "image". A SpeedyMedia object will be exported from the pipeline.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | An image to be exported from the pipeline. |
$3
#### Image basics
##### Speedy.Image.Pyramid()
Speedy.Image.Pyramid(name?: string): SpeedyPipelineNodeImagePyramid
Generate a Gaussian pyramid. A pyramid is a texture with mipmaps.
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image. |"out"
| | Image | Gaussian pyramid. |
##### Speedy.Image.Multiplexer()
Speedy.Image.Multiplexer(name?: string): SpeedyPipelineNodeImageMultiplexer
An image multiplexer receives two images as input and outputs one of the them.
###### Parameters
* port: number. Which input image should be redirected to the output: 0 or 1? Defaults to 0.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in0" | Image | First image. |"in1"
| | Image | Second image. |"out"
| | Image | Either the first or the second image, depending on the value of port. |
##### Speedy.Image.Buffer()
Speedy.Image.Buffer(name?: string): SpeedyPipelineNodeImageBuffer
An image buffer outputs at time t the input image received at time t-1. It's useful for tracking.
Note: an image buffer cannot be used to store a pyramid at this time.
###### Parameters
* frozen: boolean. A frozen buffer discards the input, effectively increasing the buffering time. Defaults to false.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image at time t. |"out"
| | Image | Output image: the input image at time t-1. |
##### Speedy.Image.Mixer()
Speedy.Image.Mixer(name?: string): SpeedyPipelineNodeImageMixer
An image mixer combines two images, image0 and image1, as follows:
output = alpha image0 + beta image1 + gamma
The above expression will be computed for each pixel of the resulting image and then clamped to the [0,1] interval. The dimensions of the resulting image will be the dimensions of the larger of the input images.
Note: Both input images must have the same format. If they're colored, the above expression will be evaluated in each color channel independently.
Tip: if you pick an alpha between 0 and 1, set beta to 1 - alpha and set gamma to 0, you'll get a nice alpha blending effect.
###### Parameters
* alpha: number. A scalar value. Defaults to 0.5.beta: number
* . A scalar value. Defaults to 0.5.gamma: number
* . A scalar value. Defaults to 0.0.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in0" | Image | Input image: the image0 above |"in1"
| | Image | Input image: the image1 above |"out"
| | Image | Output image |
#### Image filters
##### Speedy.Filter.Greyscale()
Speedy.Filter.Greyscale(name?: string): SpeedyPipelineNodeGreyscale
Convert an image to greyscale.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image. |"out"
| | Image | The input image converted to greyscale. |
##### Speedy.Filter.SimpleBlur()
Speedy.Filter.SimpleBlur(name?: string): SpeedyPipelineNodeSimpleBlur
Blur an image using a box filter.
###### Parameters
* kernelSize: SpeedySize. The size of the convolution kernel: from 3x3 to 15x15. Defaults to 5x5.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image. |"out"
| | Image | The input image, blurred. |
##### Speedy.Filter.GaussianBlur()
Speedy.Filter.SimpleBlur(name?: string): SpeedyPipelineNodeGaussianBlur
Blur an image using a Gaussian filter.
###### Parameters
* kernelSize: SpeedySize. The size of the convolution kernel: from 3x3 to 15x15. Defaults to 5x5.sigma: SpeedyVector2
* . The sigma of the Gaussian function in both x and y axes. If set to the zero vector, Speedy will automatically pick a sigma according to the selected kernelSize. Defaults to (0,0).
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image. |"out"
| | Image | The input image, blurred. |
##### Speedy.Filter.MedianBlur()
Speedy.Filter.MedianBlur(name?: string): SpeedyPipelineNodeMedianBlur
Median filter.
###### Parameters
* kernelSize: SpeedySize. One of the following: 3x3, 5x5 or 7x7. Defaults to 5x5.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | A greyscale image. |"out"
| | Image | The result of the median blur. |
###### Example
`js`
const median = Speedy.Filter.MedianBlur();
median.kernelSize = Speedy.Size(7,7);
##### Speedy.Filter.Convolution()
Speedy.Filter.Convolution(name?: string): SpeedyPipelineNodeConvolution
Compute the convolution of an image using a 2D kernel.
###### Parameters
* kernel: SpeedyMatrixExpr. A 3x3, 5x5 or 7x7 matrix.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image. |"out"
| | Image | The result of the convolution. |
###### Example
`js`
// Sharpening an image
const sharpen = Speedy.Filter.Convolution();
sharpen.kernel = Speedy.Matrix(3, 3, [
0,-1, 0,
-1, 5,-1,
0,-1, 0
]);
##### Speedy.Filter.Normalize()
Speedy.Filter.Normalize(name?: string): SpeedyPipelineNodeNormalize
Normalize the intensity values of the input image to the [minValue, maxValue] interval.
###### Parameters
* minValue: number. A value in [0,255].maxValue: number
* . A value in [0,255] greater than or equal to minValue.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Greyscale image. |"out"
| | Image | Normalized image. |
##### Speedy.Filter.Nightvision()
Speedy.Filter.Nightvision(name?: string): SpeedyPipelineNodeNightvision
Nightvision filter for local contrast stretching and brightness control.
###### Parameters
* gain: number. A value in [0,1]: the larger the number, the higher the contrast. Defaults to 0.5.offset: number
* . A value in [0,1] that controls the brightness. Defaults to 0.5.decay: number
* . A value in [0,1] specifying a contrast decay from the center of the image. Defaults to zero (no decay).quality: string
* . Quality level: "high", "medium" or "low". Defaults to "medium".
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image. |"out"
| | Image | Output image. |
#### General transformations
##### Speedy.Transform.Resize()
Speedy.Transform.Resize(name?: string): SpeedyPipelineNodeResize
Resize an image.
###### Parameters
* size: SpeedySize. The size of the output image, in pixels. If set to zero, scale will be used to determine the size of the output. Defaults to zero.scale: SpeedyVector2
* . The size of the output image relative to the size of the input image. This parameter is only applied if size is zero. Defaults to (1,1), meaning: keep the original size.method: string
* . Resize method. One of the following: "bilinear" (bilinear interpolation) or "nearest" (nearest neighbors). Defaults to "bilinear".
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image. |"out"
| | Image | Resized image. |
##### Speedy.Transform.PerspectiveWarp()
Speedy.Transform.PerspectiveWarp(name?: string): SpeedyPipelineNodePerspectiveWarp
Warp an image using a homography matrix.
###### Parameters
* transform: SpeedyMatrixExpr. A 3x3 perspective transformation. Defaults to the identity matrix.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Input image. |"out"
| | Image | Warped image. |
$3
A keypoint is a small patch in an image that is somehow distinctive. For example, a small patch with significant intensity changes in both x and y axes (i.e., a "corner") is distinctive. If we pick two "similar" images, we should be able to locate a set of keypoints in each of them and then match those keypoints based on their similarity.
A descriptor is a mathematical object that somehow describes a keypoint. Two keypoints are considered to be "similar" if their descriptors are "similar". Speedy works with binary descriptors, meaning that keypoints are described using bit vectors of fixed length.
There are different ways to detect and describe keypoints. For example, in order to detect a keypoint, you may take a look at the pixel intensities around a point or perhaps study the image derivatives. You may describe a keypoint by comparing the pixel intensities of the image patch in a special way. Additionally, it's possible to conceive a way to describe a keypoint in such a way that, if you rotate the patch, the descriptor stays roughly the same. This is called rotational invariance and is usually a desirable property for a descriptor.
Speedy offers different options for processing keypoints in multiple ways. A novelty of this work is that Speedy's implementations have been either adapted from the literature or conceived from scratch to work on the GPU. Therefore, keypoint processing is done in parallel and is often very fast.
#### Keypoint basics
##### SpeedyKeypoint
A SpeedyKeypoint object represents a keypoint.
###### SpeedyKeypoint.position
SpeedyKeypoint.position: SpeedyPoint2
The position of the keypoint in the image.
###### SpeedyKeypoint.x
SpeedyKeypoint.x: number
The x position of the keypoint in the image. A shortcut to position.x.
###### SpeedyKeypoint.y
SpeedyKeypoint.y: number
The y position of the keypoint in the image. A shortcut to position.y.
###### SpeedyKeypoint.lod
SpeedyKeypoint.lod: number
The level-of-detail (pyramid level) from which the keypoint was extracted, starting from zero. Defaults to 0.0.
###### SpeedyKeypoint.scale
SpeedyKeypoint.scale: number
The scale of the keypoint. This is equivalent to 2 ^ lod. Defaults to 1.0.
###### SpeedyKeypoint.rotation
SpeedyKeypoint.rotation: number
The orientation angle of the keypoint, in radians. Defaults to 0.0.
###### SpeedyKeypoint.score
SpeedyKeypoint.score: number
The score is a measure associated with the keypoint. Although different detection methods employ different measurement strategies, the larger the score, the "better" the keypoint is considered to be. The score is always a positive value.
###### SpeedyKeypoint.descriptor
SpeedyKeypoint.descriptor: SpeedyKeypointDescriptor | null, read-only
The descriptor associated with the keypoint, if it exists.
##### SpeedyKeypointDescriptor
A SpeedyKeypointDescriptor represents a keypoint descriptor.
###### SpeedyKeypointDescriptor.data
SpeedyKeypointDescriptor.data: Uint8Array, read-only
The bytes of the keypoint descriptor.
###### SpeedyKeypointDescriptor.size
SpeedyKeypointDescriptor.size: number, read-only
The size of the keypoint descriptor, in bytes.
###### SpeedyKeypointDescriptor.toString()
SpeedyKeypointDescriptor.toString(): string
Returns a string representation of the keypoint descriptor.
##### SpeedyTrackedKeypoint
A SpeedyTrackedKeypoint is a SpeedyKeypoint with the following additional properties:
###### SpeedyTrackerKeypoint.flow
SpeedyTrackedKeypoint.flow: SpeedyVector2
A displacement vector associated with the tracked keypoint.
##### Speedy.Keypoint.Source()
Speedy.Keypoint.Source(name?: string): SpeedyPipelineNodeKeypointSource
Creates a source of keypoints. Only the position, score and scale of the provided keypoints will be imported to the pipeline. Descriptors, if present, will be lost.
###### Parameters
* keypoints: SpeedyKeypoint[]. The keypoints you want to import.capacity: number
* . The maximum number of keypoints that can be imported to the GPU. If you have an idea of how many keypoints you expect (at most), use a tight bound to make processing more efficient. The default capacity is 2048. It can be no larger than 8192.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "out" | Keypoints | The imported set of keypoints. |
##### Speedy.Keypoint.Sink()
Speedy.Keypoint.Sink(name?: string): SpeedyPipelineNodeKeypointSink
Creates a sink of keypoints using the specified name. If the name is not specified, Speedy will call this node "keypoints". An array of SpeedyKeypoint objects will be exported from the pipeline.
###### Parameters
* turbo: boolean. Accelerate GPU-CPU transfers. You'll get the data from the previous frame. Defaults to false.includeDiscarded: boolean
* . Set discarded keypoints (e.g., by a tracker) to null in the exported set. Defaults to false, meaning that discarded keypoints will simply be dropped from the exported set rather than being set to null.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints to be exported from the pipeline. |
##### Speedy.Keypoint.SinkOfTrackedKeypoints()
Speedy.Keypoint.SinkOfTrackedKeypoints(name?: string): SpeedyPipelineNodeTrackedKeypointSink
Creates a sink of tracked keypoints using the specified name. If the name is not specified, Speedy will call this node "keypoints". An array of SpeedyTrackedKeypoint objects will be exported from the pipeline.
###### Parameters
The same as SpeedyPipelineNodeKeypointSink.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints to be exported from the pipeline. |"flow"
| | Vector2 | A set of displacement vectors associated with each keypoint. |
##### Speedy.Keypoint.Clipper()
Speedy.Keypoint.Clipper(name?: string): SpeedyPipelineNodeKeypointClipper
Clips a set of keypoints, so that it outputs no more than a fixed quantity of them. When generating the output, it will choose the "best" keypoints according to their score metric. The keypoint clipper is a very useful tool to reduce processing time, since it can discard "bad" keypoints regardless of the sensitivity of their detector. The clipping must be applied before computing any descriptors.
###### Parameters
* size: number. A positive integer. No more than this number of keypoints will be available in the output.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints. |"out"
| | Keypoints | A set of at most size keypoints. |
##### Speedy.Keypoint.BorderClipper()
Speedy.Keypoint.BorderClipper(name?: string): SpeedyPipelineNodeKeypointBorderClipper
Removes all keypoints within a specified border of the edge of an image. The border is specified in pixels as an ordered pair of integers: the first is the size of the horizontal border and the second is the size of the vertical border.
###### Parameters
* imageSize: SpeedySize. Image size, in pixels.borderSize: SpeedyVector2
* . Border size in both x and y axes. Defaults to zero, meaning that no clipping takes place.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints. |"out"
| | Keypoints | The clipped set of keypoints. |
##### Speedy.Keypoint.Mixer()
Speedy.Keypoint.Mixer(name?: string): SpeedyPipelineNodeKeypointMixer
Mixes (merges) two sets of keypoints.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in0" | Keypoints | A set of keypoints. |"in1"
| | Keypoints | Another set of keypoints. |"out"
| | Keypoints | The union of the two input sets. |
##### Speedy.Keypoint.Buffer()
Speedy.Keypoint.Buffer(name?: string): SpeedyPipelineNodeKeypointBuffer
A keypoint buffer outputs at time t the keypoints received at time t-1.
###### Parameters
* frozen: boolean. A frozen buffer discards the input, effectively increasing the buffering time. Defaults to false.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints at time t. |"out"
| | Keypoints | The set of keypoints received at time t-1. |
##### Speedy.Keypoint.Multiplexer()
Speedy.Keypoint.Multiplexer(name?: string): SpeedyPipelineNodeKeypointMultiplexer
A keypoint multiplexer receives two sets of keypoints as input and outputs one of the them.
###### Parameters
* port: number. Which input set of keypoints should be redirected to the output: 0 or 1? Defaults to 0.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in0" | Image | First set of keypoints. |"in1"
| | Image | Second set of keypoints. |"out"
| | Image | Either the first or the second set of keypoints, depending on the value of port. |
##### Speedy.Keypoint.Transformer()
Speedy.Keypoint.Transformer(name?: string): SpeedyPipelineNodeKeypointTransformer
Applies a transformation matrix to a set of keypoints.
###### Parameters
* transform: SpeedyMatrix. A 3x3 homography matrix. Defaults to the identity matrix.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints. |"out"
| | Keypoints | A transformed set of keypoints. |
##### Speedy.Keypoint.SubpixelRefiner()
Speedy.Keypoint.SubpixelRefiner(name?: string): SpeedyPipelineNodeKeypointSubpixelRefiner
Refines the position of a set of keypoints down to the subpixel level.
Note 1: filter the image to reduce the noise before working at the subpixel level.
Note 2: if there are keypoints in multiple scales, make sure to provide a pyramid as input.
Note 3: the position of the keypoints is stored as fixed-point. This representation may introduce a loss of accuracy (~0.1 pixel). This is probably enough already, but if you need higher accuracy, ignore the output keypoints and work with the displacement vectors instead. These are encoded as floating-point. In addition, use the upsampling methods.
###### Parameters
* method: string. The method to be used to compute the subpixel displacement. See the table below.maxIterations: number
* . The maximum number of iterations used by methods "bicubic-upsample" and "bilinear-upsample". Defaults to 6.epsilon: number
* . The threshold used to determine when the subpixel displacement has reached convergence. Used with methods "bicubic-upsample" and "bilinear-upsample". Defaults to 0.1 pixel.
Table of methods:
| Method | Description |
|-------------------|-------------|
| "quadratic1d" | Maximize a 1D parabola fit to a corner strength function. This is the default method. |"taylor2d"
| | Maximize a second-order 2D Taylor expansion of a corner strength function. Method "quadratic1d" seems to perform slightly better than this, but your mileage may vary."bicubic-upsample"
| | Iteratively upsample the image using bicubic interpolation in order to maximize a corner strength function. Repeat until convergence or until a maximum number of iterations is reached. |"bilinear-upsample"
| | Analogous to bicubic upsample, but this method uses bilinear interpolation instead. |
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "image" | Image | An image or pyramid from which you extracted the keypoints. |"keypoints"
| | Keypoints | Input set of keypoints. |"out"
| | Keypoints | Subpixel-refined output set of keypoints. |"displacements"
| | Vector2 | Displacement vectors (output). |
##### Speedy.Keypoint.DistanceFilter()
Speedy.Keypoint.DistanceFilter(name?: string): SpeedyPipelineNodeKeypointDistanceFilter
Given a set of pairs of keypoints, discard all pairs whose distance is above a user-defined threshold. This is useful for implementing bidirectional optical-flow.
The pairs of keypoints are provided as two separate sets, "in" and "reference". Keypoints that are kept will have their data extracted from the "in" set.
###### Parameters
* threshold: number. Distance threshold, given in pixels.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints. |"reference"
| | Keypoints | A reference set of keypoints. |"out"
| | Keypoints | Filtered set of keypoints. |
##### Speedy.Keypoint.HammingDistanceFilter()
Speedy.Keypoint.HammingDistanceFilter(name?: string): SpeedyPipelineNodeKeypointHammingDistanceFilter
Given a set of pairs of keypoints with descriptors, discard all pairs whose Hamming distance between their descriptors is above a user-defined threshold.
The pairs of keypoints are provided as two separate sets, "in" and "reference". Keypoints that are kept will have their data extracted from the "in" set.
###### Parameters
* threshold: number. Distance threshold, an integer.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints. |"reference"
| | Keypoints | A reference set of keypoints. |"out"
| | Keypoints | Filtered set of keypoints. |
##### Speedy.Keypoint.Shuffler()
Speedy.Keypoint.Shuffler(name?: string): SpeedyPipelineNodeKeypointShuffler
Shuffles the input keypoints, optionally clipping the output set.
###### Parameters
* maxKeypoints: number. Maximum number of keypoints of the output set. If unspecified, the number of keypoints of the output set will be the number of keypoints of the input set.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints. |"out"
| | Keypoints | The input set of keypoints, shuffled and possibly clipped. |
#### Keypoint detection
The following nodes expect greyscale images as input. They output a set of keypoints.
##### Speedy.Keypoint.Detector.FAST()
Speedy.Keypoint.Detector.FAST(name?: string): SpeedyPipelineNodeFASTKeypointDetector
FAST keypoint detector. Speedy implements the FAST-9,16 variant of the algorithm.
To use the multi-scale version of the algorithm, pass a pyramid as input, set the number of levels you want to scan and optionally set the scale factor. After scanning all levels and performing non-maximum suppression, the scale of the keypoints will be set by means of interpolation using the scale that maximizes a response measure and its adjacent scales.
###### Parameters
* threshold: number. An integer between 0 and 255, inclusive. The larger the number, the "stronger" your keypoints will be. The smaller the number, the more keypoint you will get. Numbers between 20 and 50 are usually meaningful.levels: number
* . The number of pyramid levels you want to use. Defaults to 1 (i.e., no pyramid is used). When using a pyramid, a value such as 7 is a reasonable choice.scaleFactor: number
* . The scale factor between two consecutive levels of the pyramid. This is a value between 1 (exclusive) and 2 (inclusive). Defaults to the square root of two. This is applicable only when using a pyramid.capacity: number
* . The maximum number of keypoints that can be detected by this node. The default capacity is 2048. It can be no larger than 8192.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Greyscale image or pyramid. |"out"
| | Keypoints | Detected keypoints. |
##### Speedy.Keypoint.Detector.Harris()
Speedy.Keypoint.Detector.Harris(name?: string): SpeedyPipelineNodeHarrisKeypointDetector
Harris corner detector. Speedy implements the Shi-Tomasi corner response for best results.
To use the multi-scale version of the algorithm, pass a pyramid as input, set the number of levels you want to scan and optionally set the scale factor. After scanning all levels and performing non-maximum suppression, the scale of the keypoints will be set by means of interpolation using the scale that maximizes a response measure and its adjacent scales.
###### Parameters
* quality: number. A value between 0 and 1 representing the minimum "quality" of the returned keypoints. Speedy will discard any keypoint whose score is lower than the specified percentage of the maximum keypoint score found in the image. A typical value for this parameter is 0.10 (10%).levels: number
* . The number of pyramid levels you want to use. Defaults to 1 (i.e., no pyramid is used). When using a pyramid, a value such as 7 is a reasonable choice.scaleFactor: number
* . The scale factor between two consecutive levels of the pyramid. This is a value between 1 (exclusive) and 2 (inclusive). Defaults to the square root of two. This is applicable only when using a pyramid.capacity: number
* . The maximum number of keypoints that can be detected by this node. The default capacity is 2048. It can be no larger than 8192.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | Greyscale image or pyramid. |"out"
| | Keypoints | Detected keypoints. |
#### Keypoint description
##### Speedy.Keypoint.Descriptor.ORB()
Speedy.Keypoint.Descriptor.ORB(name?: string): SpeedyPipelineNodeORBKeypointDescriptor
ORB descriptors. In order to improve robustness to noise, apply a Gaussian filter to the image before computing the descriptors.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "image" | Image | Input image. Must be greyscale. |"keypoints"
| | Keypoints | Input keypoints. |"out"
| | Keypoints | Keypoints with descriptors. |
###### Example
`js
/*
This is our pipeline:
Image ---> Convert to ---> Image ------> FAST corner -----> Keypoint ---> ORB ----------> Keypoint
Source greyscale Pyramid detector Clipper descriptors Sink
| ^
| |
+-------------------------> Gaussian ---------------------------+
Blur
*/
const pipeline = Speedy.Pipeline();
const source = Speedy.Image.Source();
const greyscale = Speedy.Filter.Greyscale();
const pyramid = Speedy.Image.Pyramid();
const fast = Speedy.Keypoint.Detector.FAST();
const blur = Speedy.Filter.GaussianBlur();
const clipper = Speedy.Keypoint.Clipper();
const descriptor = Speedy.Keypoint.Descriptor.ORB();
const sink = Speedy.Keypoint.Sink();
source.media = media;
blur.kernelSize = Speedy.Size(9, 9);
blur.sigma = Speedy.Vector2(2, 2);
fast.threshold = 50;
fast.levels = 8; // pyramid levels
fast.scaleFactor = 1.19; // approx. 2^0.25
clipper.size = 800; // up to how many features?
source.output().connectTo(greyscale.input());
greyscale.output().connectTo(pyramid.input());
pyramid.output().connectTo(fast.input());
fast.output().connectTo(clipper.input());
clipper.output().connectTo(descriptor.input('keypoints'));
greyscale.output().connectTo(blur.input());
blur.output().connectTo(descriptor.input('image'));
descriptor.output().connectTo(sink.input());
pipeline.init(source, greyscale, pyramid, blur, fast, clipper, descriptor, sink);
`
#### Keypoint tracking
Keypoint tracking is the process of tracking keypoints across a sequence of images. It allows you to get a sense of how keypoints are moving in time - i.e., how fast they are moving and where they are going.
Speedy uses sparse optical-flow algorithms to track keypoints in a video. Applications of optical-flow are numerous: you may get a sense of how objects are moving in a scene, estimate how the camera itself is moving, detect a transition in a film (a cut between two shots), and so on.
##### Speedy.Keypoint.Tracker.LK()
Speedy.Keypoint.Tracker.LK(name?: string): SpeedyPipelineNodeLKKeypointTracker
Pyramid-based LK optical-flow.
###### Parameters
* windowSize: SpeedySize. The size of the window to be used by the feature tracker. The algorithm will read neighbor pixels to determine the motion of a keypoint. You must specify a square window. Typical sizes include: 7x7, 11x11, 15x15 (use positive odd integers). Defaults to 11x11.levels: number
* . Specifies how many pyramid levels will be used in the computation. The more levels you use, the faster the motions you can capture. Defaults to 3.discardThreshold: number
* . A threshold used to discard keypoints that are not "good" candidates for tracking. The higher the value, the more keypoints will be discarded. Defaults to 0.0001.numberOfIterations: number
* . Maximum number of iterations for computing the local optical-flow on each level of the pyramid. Defaults to 30.epsilon: number
* . An accuracy threshold used to stop the computation of the local optical-flow of any level of the pyramid. The local optical-flow is computed iteratively and in small increments. If the length of an increment is too small, we discard it. This property defaults to 0.01.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "previousImage" | Image | Input image at time t-1. Must be greyscale. |"nextImage"
| | Image | Input image at time t. Must be greyscale. |"previousKeypoints"
| | Keypoints | Input keypoints at time t-1. |"out"
| | Keypoints | Output keypoints at time t. |"flow"
| | Vector2 | Flow vectors (output) at time t. |
Note: you need to provide pyramids as input if levels > 1.
#### Keypoint matching
Soon!
$3
Portals let you create loops within a pipeline. They also let you transfer data between different pipelines.
A portal is defined by a set of nodes: a portal sink and one or more portal sources. The portal sink receives data from a node of a pipeline, which is then read by the portal source(s). The portal source(s) feed(s) one or more pipelines. The portal nodes may or may not belong to the same pipeline.
#### Image Portals
##### Speedy.Image.Portal.Source()
Speedy.Image.Portal.Source(name?: string): SpeedyPipelineNodeImagePortalSource
Create a source of an Image Portal.
###### Parameters
* source: SpeedyPipelineNodeImagePortalSink. A sink of an Image Portal.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "out" | Image | An image. |
##### Speedy.Image.Portal.Sink()
Speedy.Image.Portal.Sink(name?: string): SpeedyPipelineNodeImagePortalSink
Create a sink of an Image Portal.
Note: pyramids can't travel through portals at this time.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Image | An image. |
#### Keypoint Portals
##### Speedy.Keypoint.Portal.Source()
Speedy.Keypoint.Portal.Source(name?: string): SpeedyPipelineNodeKeypointPortalSource
Create a source of a Keypoint Portal.
###### Parameters
* source: SpeedyPipelineNodeKeypointPortalSink. A sink of a Keypoint Portal.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "out" | Keypoints | A set of keypoints. |
##### Speedy.Keypoint.Portal.Sink()
Speedy.Keypoint.Portal.Sink(name?: string): SpeedyPipelineNodeKeypointPortalSink
Create a sink of a Keypoint Portal.
###### Ports
| Port name | Data type | Description |
|-----------|-----------|-------------|
| "in" | Keypoints | A set of keypoints. |
$3
Matrix computations play a crucial role in computer vision applications. Speedy includes its own implementation of numerical linear algebra algorithms.
Matrix operations are specified using a fluent interface that has been crafted to be easy to use and to mirror how we write matrix algebra using pen-and-paper.
Since numerical algorithms may be computationally demanding, Speedy uses WebAssembly for extra performance. Most matrix-related routines are written in C language. Matrices are stored in column-major format. Typed Arrays are used for storage.
There are two basic classes you need to be aware of: SpeedyMatrix and SpeedyMatrixExpr. The latter represents a symbolic expression, whereas the former represents an actual matrix with data. A SpeedyMatrix is a SpeedyMatrixExpr. A SpeedyMatrixExpr may be evaluated to a SpeedyMatrix.
#### Creating new matrices
##### Speedy.Matrix()
Speedy.Matrix(rows: number, columns: number, entries?: number[]): SpeedyMatrix
Speedy.Matrix(expr: SpeedyMatrixExpr): SpeedyMatrix
First form: create a new matrix with the specified size and entries.
Second form: synchronously evaluate a matrix expression and store the result in a new matrix.
###### Arguments
* rows: number. The number of rows of the matrix.columns: number, optional
* . The number of columns of the matrix. If not specified, it will be set to rows (i.e., you'll get a square matrix).entries: number[], optional
. The elements of the matrix in column-major format. The length of this array must be rows columns.expr: SpeedyMatrixExpr
* . The matrix expression to be evaluated.
###### Returns
A new SpeedyMatrix.
###### Example
`js
//
// We use the column-major format to specify
// the elements of the new matrix. For example,
// to create the 2x3 matrix (2 rows, 3 columns)
// below, we first specify the elements of the
// first column, then the elements of the second
// column, and finally the elements of the third
// column.
//
// M = [ 1 3 5 ]
// [ 2 4 6 ]
//
const mat = Speedy.Matrix(2, 3, [
1,
2,
3,
4,
5,
6
]);
// Alternatively, we may write the data in
// column-major format in a compact form:
const mat1 = Speedy.Matrix(2, 3, [
1, 2, // first column
3, 4, // second column
5, 6 // third column
]);
// Print the matrices to the console
console.log(mat.toString());
console.log(mat1.toString());
`
##### Speedy.Matrix.Zeros()
Speedy.Matrix.Zeros(rows: number, columns?: number): SpeedyMatrix
Create a new matrix filled with zeros.
###### Arguments
* rows: number. The number of rows of the matrix.columns: number, optional
* . The number of columns of the matrix. If not specified, it will be set to rows (square matrix).
###### Returns
A new rows x columns SpeedyMatrix filled with zeros.
###### Example
`js`
// A 3x3 matrix filled with zeros
const zeros = Speedy.Matrix.Zeros(3);
##### Speedy.Matrix.Ones()
Speedy.Matrix.Ones(rows: number, columns?: number): SpeedyMatrix
Create a new matrix filled with ones.
###### Arguments
* rows: number. The number of rows of the matrix.columns: number, optional
* . The number of columns of the matrix. If not specified, it will be set to rows (square matrix).
###### Returns
A new rows x columns SpeedyMatrix filled with ones.
##### Speedy.Matrix.Eye()
Speedy.Matrix.Eye(rows: number, columns?: number): SpeedyMatrix
Create a new matrix with ones on the main diagonal and zeros elsewhere.
###### Arguments
* rows: number. The number of rows of the matrix.columns: number, optional
* . The number of columns of the matrix. If not specified, it will be set to rows (identity matrix).
###### Returns
A new SpeedyMatrix with the specified configuration.
###### Example
`js`
// A 3x3 identity matrix
const eye = Speedy.Matrix.Eye(3);
#### Matrix properties
##### SpeedyMatrixExpr.rows
SpeedyMatrixExpr.rows: number, read-only
The number of rows of the matrix expression.
##### SpeedyMatrixExpr.columns
SpeedyMatrixExpr.columns: number, read-only
The number of columns of the matrix expression.
##### SpeedyMatrixExpr.dtype
SpeedyMatrixExpr.dtype: string, read-only
The constant "float32".
##### SpeedyMatrix.data
SpeedyMatrix.data: ArrayBufferView, read-only
Data storage.
##### SpeedyMatrix.step
SpeedyMatrix.step0: number, read-only
SpeedyMatrix.step1: number, read-only
Storage steps. The (i, j) entry of the matrix is stored at data[i step0 + j step1].
#### Reading from the matrices
##### SpeedyMatrix.read()
SpeedyMatrix.read(): number[]
Read the entries of the matrix.
###### Returns
An array containing the entries of the matrix in column-major format.
###### Example
`js
const mat = Speedy.Matrix(2, 2, [
1,
2,
3,
4
]);
const entries = mat.read();
console.log(entries); // [ 1, 2, 3, 4 ]
`
##### SpeedyMatrix.at()
SpeedyMatrix.at(row: number, column: number): number
Read a single entry of the matrix.
###### Arguments
* row: number. Index of the row of the desired element (0-based).column: number
* . Index of the column of the desired element (0-based).
###### Returns
The requested entry of the matrix, or a NaN if the entry is outside bounds.
###### Example
`js
const A = Speedy.Matrix(2, 2, [
1,
2,
3,
4
]);
const a00 = A.at(0, 0); // first row, first column
const a10 = A.at(1, 0); // second row, first column
const a01 = A.at(0, 1); // first row, second column
const a11 = A.at(1, 1); // second row, second column
console.log([ a00, a10, a01, a11 ]); // [ 1, 2, 3, 4 ]
`
##### SpeedyMatrixExpr.toString()
SpeedyMatrixExpr.toString(): string
Convert a matrix expression to a string. Entries will only be included if this expression is a SpeedyMatrix.
###### Returns
A string representation of the matrix expression.
#### Writing to the matrices
##### SpeedyMatrix.setTo()
SpeedyMatrix.setTo(expr: SpeedyMatrixExpr): SpeedyPromise
Evaluate a matrix expression and store the result in this matrix.
###### Arguments
* expr: SpeedyMatrixExpr. A matrix expression.
###### Returns
A SpeedyPromise that resolves to this matrix after evaluating expr.
###### Example
`js
//
// Let's add two matrices:
//
// A = [ 1 3 ] B = [ 4 2 ]
// [ 2 4 ] [ 3 1 ]
//
// We'll set C to the sum A + B
//
const matA = Speedy.Matrix(2, 2, [
1, 2,
3, 4
]);
const matB = Speedy.Matrix(2, 2, [
4, 3,
2, 1
]);
// Set C = A + B
const matC = Speedy.Matrix.Zeros(2, 2);
await matC.setTo(matA.plus(matB));
//
// Print the result:
//
// C = [ 5 5 ]
// [ 5 5 ]
//
console.log(matC.toString());
`
##### SpeedyMatrix.fill()
SpeedyMatrix.fill(value: number): SpeedyPromise
Fill this matrix with a scalar.
###### Arguments
* value: number. Scalar value.
###### Returns
A SpeedyPromise that resolves to this matrix.
###### Example
`js`
// Create a 5x5 matrix filled with twos
const twos = Speedy.Matrix.Zeros(5);
await twos.fill(2);
#### Synchronous writing
Speedy provides synchronous writing methods for convenience.
##### Speedy.Matrix.ready()
Speedy.Matrix.ready(): SpeedyPromise
This method lets you know that the matrix routines are initialized and ready to be used (the WebAssembly routines need to be loaded before usage). You should only use the synchronous writing methods when the matrix routines are ready.
###### Returns
A SpeedyPromise that resolves immediately if the matrix routines are already initialized, or as soon as they are initialized.
##### SpeedyMatrix.setToSync()
SpeedyMatrix.setToSync(expr: SpeedyMatrixExpr): SpeedyMatrix
Synchronously evaluate a matrix expression and store the result in this matrix.
###### Arguments
* expr: SpeedyMatrixExpr. A matrix expression.
###### Returns
Returns this matrix after setting it to the result of expr.
###### Example
`js
Speedy.Matrix.ready().then(() => {
const mat = Speedy.Matrix.Eye(3); // I := identity matrix
const pot = 3; // power-of-two
for(let i = 0; i < pot; i++)
mat.setToSync(mat.plus(mat)); // mat := mat + mat
console.log(mat.toString()); // mat will be (2^pot) * I
});
`
##### SpeedyMatrix.fillSync()
SpeedyMatrix.fillSync(value: number): SpeedyMatrix
Synchronously fill this matrix with a scalar.
###### Arguments
* value: number. Scalar value.
###### Returns
Returns this matrix after filling it with the provided value.
#### Access by block
Speedy lets you work with blocks of matrices. This is a very handy feature! Blocks share memory with the originating matrices. If you modify the entries of a block of a matrix M, you'll modify the corresponding entries of M. Columns and rows are examples of blocks.
##### SpeedyMatrix.block()
SpeedyMatrix.block(firstRow: number, lastRow: number, firstColumn: number, lastColumn: number): SpeedyMatrix
Extract a lastRow - firstRow + 1 x lastColumn - firstColumn + 1 block from the matrix. All indices are 0-based. They are all inclusive. The memory of the matrix is shared with the block.
###### Arguments
* firstRow: number. Index of the first row (0-based).lastRow: number
* . Index of the last row (0-based). Use lastRow >= firstRow.firstColumn: number
* . Index of the first column (0-based).lastColumn: number
* . Index of the last column (0-based). Use lastColumn >= firstColumn.
###### Returns
A new SpeedyMatrix representing the specified block.
###### Example
`js`
//
// We'll create the following 4x4 matrix:
// (a dot represents a zero)
//
// [ 5 5 5 . ]
// [ 5 5 5 . ]
// [ 5 5 5 . ]
// [ . . . . ]
//
const mat = Speedy.Matrix.Zeros(4);
await mat.block(0, 2, 0, 2).fill(5);
console.log(mat.toString());
##### SpeedyMatrix.column()
SpeedyMatrix.column(index: number): SpeedyMatrix
Extract a column of the matrix.
###### Arguments
* index: number. Index of the column (0-based).
###### Returns
A new SpeedyMatrix representing the specified column.
###### Example
`js
const mat = Speedy.Matrix(2, 3, [
1,
2,
3,
4,
5,
6
]);
const firstColumn = mat.column(0); // [1, 2]^T
const secondColumn = mat.column(1); // [3, 4]^T
const thirdColumn = mat.column(2); // [5, 6]^T
console.log(firstColumn.toString());
console.log(secondColumn.toString());
console.log(thirdColumn.toString());
`
##### SpeedyMatrix.row()
SpeedyMatrix.row(index: number): SpeedyMatrix
Extract a row of the matrix.
###### Arguments
* index: number. Index of the row (0-based).
###### Returns
A new SpeedyMatrix representing the specified row.
###### Example
`js`
//
// We'll create the following matrix:
// [ 0 0 0 0 ]
// [ 1 1 1 1 ]
// [ 2 2 2 2 ]
// [ 0 0 0 0 ]
//
const mat = Speedy.Matrix.Zeros(4);
await mat.row(1).fill(1);
await mat.row(2).fill(2);
console.log(mat.toString());
##### SpeedyMatrix.diagonal()
SpeedyMatrix.diagonal(): SpeedyMatrix
Extract the main diagonal of this matrix as a column vector.
###### Returns
A new SpeedyMatrix representing the main diagonal of this matrix.
###### Example
`js
//
// We'll create the following matrix:
// (a dot represents a zero)
//
// [ 5 . . . . ]
// [ . 5 . . . ]
// [ . . 5 . . ]
// [ . . . . . ]
// [ . . . . . ]
//
const mat = Speedy.Matrix.Zeros(5); // create a 5x5 matrix filled with zeros
const submat = mat.block(0, 2, 0, 2); // extract 3x3 submatrix at the "top-left"
const diag = submat.diagonal(); // extract the diagonal of the submatrix
await diag.fill(5); // fill the diagonal of the submatrix with a constant
console.log(mat.toString()); // print the entire matrix
// Alternatively, we may use this compact form:
await mat.block(0, 2, 0, 2).diagonal().fill(5);
`
#### Elementary operations
##### SpeedyMatrixExpr.transpose()
SpeedyMatrixExpr.transpose(): SpeedyMatrixExpr
Transpose this matrix expression.
###### Returns
A SpeedyMatrixExpr representing the tranpose of this matrix expression.
###### Example
`js
// Create a 2x3 matrix
const mat = Speedy.Matrix(2, 3, [
1, 2, // first column
3, 4, // second column
5, 6 // third column
]);
// We'll store the transpose of mat in matT
const matT = Speedy.Matrix.Zeros(mat.columns, mat.rows);
await matT.setTo(mat.transpose());
// Print the matrix and its transpose
console.log(mat.toString());
console.log(matT.toString());
`
##### SpeedyMatrixExpr.plus()
SpeedyMatrixExpr.plus(expr: SpeedyMatrixExpr): SpeedyMatrixExpr
Compute the sum between this matrix expression and expr. Both expressions must have the same shape.
###### Arguments
* expr: SpeedyMatrixExpr. Another matrix expression.
###### Returns
A SpeedyMatrixExpr representing the sum between this matrix expression and expr.
###### Example
`js
const matA = Speedy.Matrix(3, 3, [
1, 2, 3,
4, 5, 6,
7, 8, 9
]);
const ones = Speedy.Matrix.Ones(3);
// set B = A + 1
const matB = Speedy.Matrix.Zeros(3);
await matB.setTo(matA.plus(ones));
`
##### SpeedyMatrixExpr.minus()
SpeedyMatrixExpr.minus(expr: SpeedyMatrixExpr): SpeedyMatrixExpr
Compute the difference between this matrix expression and expr. Both expressions must have the same shape.
###### Arguments
* expr: SpeedyMatrixExpr. Another matrix expression.
###### Returns
A SpeedyMatrixExpr representing the difference between this matrix expression and expr.
##### SpeedyMatrixExpr.times()
SpeedyMatrixExpr.times(expr: SpeedyMatrixExpr): SpeedyMatrixExpr
SpeedyMatrixExpr.times(scalar: number): SpeedyMatrixExpr
Matrix multiplication.
In the first form, compute the matrix multiplication between this matrix expression and expr. The shape of expr must be compatible with the shape of this matrix expression.
In the second form, multiply this matrix expression by a scalar.
###### Arguments
* expr: SpeedyMatrixExpr. Matrix expression.scalar: number
* . A number.
###### Returns
A SpeedyMatrixExpr representing the result of the multiplication.
###### Example
`js
const col = Speedy.Matrix(3, 1, [0, 5, 2]);
const row = Speedy.Matrix(1, 3, [1, 2, 3]);
const dot = row.times(col); // 1x1 matrix expression: inner product
const out = col.times(row); // 3x3 matrix expression: outer product
const len = col.transpose().times(col); // 1x1 matrix expression: squared length of col
const mat = Speedy.Matrix.Zeros(1);
await mat.setTo(len); // evaluate len
console.log(mat.read()); // 29 = 00 + 55 + 2*2
`
##### SpeedyMatrixExpr.compMult()
SpeedyMatrixExpr.compMult(expr: SpeedyMatrixExpr): SpeedyMatrixExpr
Compute the component-wise multiplication between this matrix expression and expr. Both matrices must have the same shape.
###### Arguments
* expr: SpeedyMatrixExpr. Matrix expression.
###### Returns
A SpeedyMatrixExpr representing the component-wise multiplication.
##### SpeedyMatrixExpr.inverse()
SpeedyMatrixExpr.inverse(): SpeedyMatrixExpr
Compute the inverse of this matrix expression. Make sure it's square.
###### Returns
A SpeedyMatrixExpr representing the inverse of this matrix expression.
##### SpeedyMatrixExpr.ldiv()
SpeedyMatrixExpr.ldiv(expr: SpeedyMatrixExpr): SpeedyMatrixExpr
Left division this \ expr. This is equivalent to solving a system of linear equations Ax = b, where A is this and b is expr` (in