Detector Methods

constructor(haardata, Parallel)

new detector(haardata, Parallel);

Explanation of parameters

haardata : The actual haardata (as generated by haartojs tool), this is specific per feature, openCV haar data can be used.
Parallel : Optional, this is the Parallel object, as returned by the parallel.js script (included). It enables HAAR.js to run parallel computations both in browser and node (can be much faster)

dispose()

detector.dispose();

Disposes the detector and clears space of data cached

clearCache()

detector.clearCache();

Clear any cached image data and haardata in case space is an issue. Use image method and cascade method (see below) to re-set image and haar data

cascade(haardata)

detector.cascade(haardata);

Allow to use same detector (with its cached image data), to detect different feature on same image, by using another cascade. This way any image pre-processing is done only once

Explanation of parameters

haardata : The actual haardata (as generated by haartojs tool), this is specific per feature, openCV haar data can be used.

parallel(Parallel)

detector.paralell(Parallel | false);

Enable/disable parallel processing (passing the Parallel Object or null/false)

Explanation of parameters

Parallel : The actual Parallel object used in parallel.js (included)

image(ImageOrVideoOrCanvas, scale, CanvasClass)

detector.image(ImageOrVideoOrCanvas, scale, CanvasClass);

Explanation of parameters

ImageOrVideoOrCanvas : an actual Image or Video element or Canvas Object (in this case they are equivalent).
scale : The percent of scaling from the original image, so detection proceeds faster on a smaller image (default 1.0 ). NOTE scaling might alter the detection results sometimes, if having problems opt towards 1 (slower)
CanvasClass : This is optional and used only when running in nodejs (passing an alternative Canvas object for nodejs).

interval(detectionInterval)

detector.interval(detectionInterval);

Explanation of parameters

detectionInterval : interval to run the detection asynchronously (if not parallel) in microseconds (default 30).

cannyThreshold({low: lowThreshold, high: highThreshold})

detector.cannyThreshold({low: lowThreshold, high: highThreshold});

Set the thresholds when Canny Pruning is used, for extra fine-tuning. Canny Pruning detects the number/density of edges in a given region. A region with too few or too many edges is unlikely to be a feature. Default values work fine in most cases, however depending on image size and the specific feature, some fine tuning could be needed

Explanation of parameters

low : (Optional) The low threshold (default 20 ).
high : (Optional) The high threshold (default 100 ).

select|selection('auto'|object|feature|x [,y, width, height])

detector.selection('auto'|object|feature|x [,y, width, height]);

Allow to set a custom region in the image to confine the detection process only in that region (eg detect nose while face already detected)

Explanation of parameters

1st parameter : This can be the string 'auto' which sets the whole image as the selection, or an object ie: {x:10, y:'auto', width:100, height:'auto'} (every param set as 'auto' will take the default image value) or a detection rectangle/feature, or a x coordinate (along with rest coordinates).
y : (Optional) the selection start y coordinate, can be an actual value or 'auto' (y=0)
width : (Optional) the selection width, can be an actual value or 'auto' (width=image.width)
height : (Optional) the selection height, can be an actual value or 'auto' (height=image.height)

The actual selection rectangle/feature is available as this.Selection or detector.Selection

complete(callback)

detector.complete(callback);

Set the callback handler when detection completes (for parallel and asynchronous detection)

Explanation of parameters

callback : The user-defined callback function (will be called within the detectors scope, the value of 'this' will be the detector instance).

detect(baseScale, scale_inc, increment, min_neighbors, epsilon, doCannyPruning)

detector.detect(baseScale, scale_inc, increment, min_neighbors, epsilon, doCannyPruning);

Explanation of parameters (JViolaJones Parameters)

baseScale : The initial ratio between the window size and the Haar classifier size (default 1 ).
scale_inc : The scale increment of the window size, at each step (default 1.25 ).
increment : The shift of the window at each sub-step, in terms of percentage of the window size (default 0.5 ).
min_neighbors : The minimum numbers of similar rectangles needed for the region to be considered as a feature (avoid noise) (default 1 )
epsilon : Epsilon value that determines similarity between detected rectangles. 0 means identical (default 0.2 )
doCannyPruning : enable Canny Pruning to pre-detect regions unlikely to contain features, in order to speed up the execution (optional default false ).

detectSync(baseScale, scale_inc, increment, min_neighbors, doCannyPruning)

var features = detector.detectSync(baseScale, scale_inc, increment, min_neighbors, doCannyPruning);

Run detector synchronously in one step, instead of parallel or asynchronously. Can be useful when immediate results are needed. Due to massive amount of processing the UI thread may be blocked.

Explanation of parameters (similar to detect method)

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