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A python imlementation of the $P Point-Cloud Recognizer

Project description

dollarpy is a python implementation of the $P Point-Cloud Recognizer.

License

Python Compatibility PyPI Version Format

Build Status Coverage Status

From the website:

The $P Point-Cloud Recognizer is a 2-D gesture recognizer designed for rapid prototyping of gesture-based user interfaces. In machine learning terms, $P is an instance-based nearest-neighbor classifier with a Euclidean scoring function, i.e., a geometric template matcher.

$P is the latest in the dollar family of recognizers that includes $1 for unistrokes and $N for multistrokes. Although about half of $P’s code is from $1, unlike both $1 and $N, $P does not represent gestures as ordered series of points (i.e., strokes), but as unordered point-clouds. By representing gestures as point-clouds, $P can handle both unistrokes and multistrokes equivalently and without the combinatoric overhead of $N. When comparing two point-clouds, $P solves the classic assignment problem between two bipartite graphs using an approximation of the Hungarian algorithm.

Vatavu, R. D., Anthony, L., & Wobbrock, J. O., “Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes”, in: Proceedings of the 14th ACM International Conference on Multimodal Interaction (ICMI), Santa Monica, LA, USA, 2012, pp. 273-280.

Installation

dollarpy can be installed using pip:

pip install dollarpy

Usage

dollarpy is used in 3 steps:

from dollarpy import Recognizer, Template, Point

# Define 'Template' gestures, each consisting of a name and a list of 'Point' elements.
# These 'Point' elements have 'x' and 'y' coordinates and optionally the stroke index a point belongs to.
tmpl_1 = Template('X', [
    Point(0, 0, 1),
    Point(1, 1, 1),
    Point(0, 1, 2),
    Point(1, 0, 2)])
tmpl_2 = Template('line', [
    Point(0, 0),
    Point(1, 0)])

# Create a 'Recognizer' object and pass the created 'Template' objects as a list.
recognizer = Recognizer([tmpl_1, tmpl_2])

# Call 'recognize(...)' to match a list of 'Point' elements to the previously defined templates.
result = recognizer.recognize([
    Point( 31, 141, 1),
    Point(109, 222, 1),
    Point( 22, 219, 2),
    Point(113, 146, 2)])
print(result)  # Output: ('X', 0.733770116545184)

Project details


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