Source code for MLV_toolbox.averageProperty

from .VecLD import VecLD
import numpy as np

def averageProperty(vecLD: VecLD, property: str) -> float:
    """
    Computes the average value of a property over the entire drawing vecLD.
    
    Args:
        vecLD (VecLD): A VecLD object containing contour data.
        property (str): The property to be averaged. Must be a property of the
            VecLD object. The following are valid options for property:
            'orientation': concatenate all straight line segments and return the
            orientation of the resulting vector, unit: degrees; 'length': Return the
            average of the lengths of the individual contours, unit: pixels;
            'curvature': compute the mean curvature over all line segments, weighted
            by the number of pixels in the segments, unit: degrees / pixel;
            'junctions': compute the number of juncions per 10,000 pixels, comptued
            as the sum over normJunctionTypeHistogram, unit: count per 10,000
            pixels; 'mirror','parallelism','separation': compute the average value
            over all contour pixels, unit: number between 0 and 1.
    
    Returns:
        meanProperty (float): the mean value of property based on the descriptions above.
    """
    
    property = property.lower()
    
    if property == 'orientation':
        # all line segments get concatenated
        totalVec = np.array([0, 0])
        
        for c in range(vecLD.numContours):
            theseVec = vecLD.contours[c][:, 2:4] - vecLD.contours[c][:, 0:2]
            
            """ 
            For orientation we need to count line segments irrespective
            of the direciton in which they were drawn. So all line segments with
            an orientation angle between 180 and 360 degrees get reversed before
            they are added to the total vector for the entire drawing. If we
            didn't do this, an alongated closed rectangle would have a totalVec
            of [0,0] - that's not what we mena by "average angle".
            """
            reverseIdx = vecLD.orientations[c] > 180
            theseVec[reverseIdx, :] = -theseVec[reverseIdx, :]
            totalVec = totalVec + np.sum(theseVec, axis=0)
        
        meanProperty = np.rad2deg(np.arctan2(-totalVec[1], totalVec[0])) % 180
        
    elif property == 'length':
        meanProperty = np.mean(vecLD.contourLengths)
        
    elif property == 'curvature':
        meanProperty = 0
        for c in range(vecLD.numContours):
            meanProperty = np.sum(np.array(vecLD.curvatures[c]) * np.array(vecLD.lengths[c]))
        meanProperty = meanProperty / np.sum(vecLD.contourLengths)

    elif property == 'junctions':
        meanProperty = np.sum(vecLD.normJunctionTypeHistogram)
        
    elif property == 'mirror':
        meanProperty = np.sum(vecLD.mirror_allScores)
        
    elif property == 'parallelism':
        meanProperty = np.sum(vecLD.parallelism_allScores)
        
    elif property == 'separation':
        meanProperty = np.sum(vecLD.separation_allScores)
        
    else:
        raise ValueError(f'Unknown property string: {property}')
    
    return meanProperty

setattr(VecLD, 'averageProperty', averageProperty)