Averaging a list of intersecting rectangles I have a list of rectangles as tuples (x, y, w, h).  I need to find which ones intersect and if they do, average all the intersecting rectangles into a new rectangle. I also track the resultant averaged rectangle "weight" (the number of intersecting rectangles) as an addition member in the new tuple. (x, y, w, h, weight)

The final return is resultant list of averaged sorted by weight.

I am looking for feedback on how to improve performance and make it even more pythonic.  Or, as I have discovered many times in python, there may a completely different and better way to approach the problem (such as with NumPy).

I pass a list of rect tuples to ordered_average_intersecting_rects:

import numpy as np

def rects_intersect(r1, r2):
    hoverlaps = (r1[0] &lt;= r2[0] + r2[2]) and (r1[0] + r1[2] &gt;= r2[0])
    voverlaps = (r1[1] &lt;= r2[1] + r2[3]) and (r1[1] + r1[3] &gt;= r2[1])

    return hoverlaps and voverlaps


def average_rects(rects):
    x = np.average([coord[0] for coord in rects])
    y = np.average([coord[1] for coord in rects])
    w = np.average([coord[2] for coord in rects])
    h = np.average([coord[3] for coord in rects])

    return x, y, w, h, len(rects)

def ordered_average_intersecting_rects(rects):
    intersecting_rect_clusters = []

    while rects:  # modified from `while len(rect) &gt; 0:` (janos feedback)
        r1 = rects[0]
        rect_cluster = [r1]

        for r2 in rects[1:]:
            if rects_intersect(r1, r2):
                rect_cluster.append(r2)
                rects.remove(r2)

        rects.remove(r1)
        intersecting_rect_clusters.append(rect_cluster)

    averaged_rects = [average_rects(cluster) for cluster in intersecting_rect_clusters]
    averaged_rects.sort(key=lambda w: w[4], reverse=True)

    return averaged_rects


Here is my unit test to help understand how the code is being used:

def test_ordered_average_intersecting_rects(self):

    # Arrange
    rects = [(150, 55, 40, 20, 0),
             (160, 40, 20, 20, 0),
             (100, 120, 20, 20, 0),
             (45, 10, 15, 30, 0),
             (50, 25, 25, 20, 0),
             (35, 35, 20, 30, 0)]

    # Act
    with test_helpers.Timer('average_intersecting_rects') as t:
        result = ordered_average_intersecting_rects(rects)

    # Assert
    assert len(result) == 3

    cluster1 = result[0]
    assert cluster1[4] == 3
    assert int(cluster1[0]) == 43
    assert int(cluster1[1]) == 23
    assert int(cluster1[2]) == 20
    assert int(cluster1[3]) == 26

    cluster2 = result[1]
    assert cluster2[4] == 2
    assert cluster2[0] == 155
    assert cluster2[1] == 47.5
    assert cluster2[2] == 30
    assert cluster2[3] == 20

    cluster3 = result[2]
    assert cluster3[4] == 1
    assert cluster3[0] == 100
    assert cluster3[1] == 120
    assert cluster3[2] == 20
    assert cluster3[3] == 20
To improve performance, wait for another review with some clever numpy trick.

To make it more Pythonic, it would be better to represent a rectangle with a Rectangle class instead of a simple tuple, for example:

class Rectangle(object):
    def __init__(self, x, y, width, height):
        self.x = x
        self.y = y
        self.width = width
        self.height = height

    def intersect(self, other):
        hoverlaps = (self.x &lt;= other.x + other.width) and (self.x + self.width &gt;= other.x)
        voverlaps = (self.y &lt;= other.y + other.height) and (self.y + self.height &gt;= other.y)

        return hoverlaps and voverlaps


The intersect method fits naturally into it, and now the code is easier to read.

The averaging method becomes:

def average_rects(rects):
    x = np.average([rect.x for rect in rects])
    y = np.average([rect.y for rect in rects])
    w = np.average([rect.width for rect in rects])
    h = np.average([rect.height for rect in rects])

    return x, y, w, h, len(rects)


The main part of ordered_average_intersecting_rects becomes:

while rects:
    r1 = rects[0]

    rect_cluster = [r1]

    for r2 in rects[1:]:
        if r1.intersect(r2):
            rect_cluster.append(r2)
            rects.remove(r2)


And in your testing method you can create rects with:

rects = [Rectangle(150, 55, 40, 20),
         Rectangle(160, 40, 20, 20),
         Rectangle(100, 120, 20, 20),
         Rectangle(45, 10, 15, 30),
         Rectangle(50, 25, 25, 20),
         Rectangle(35, 35, 20, 30)]


And of course you can (and should) go further:


average_rects could return a tuple: Rectangle(x, y, w, h), len(rects)
The assertions could use cluster1.x, .y, .width, instead of cryptic [0], [1], ...




You can replace while len(rects) &gt; 0: with simply while rects:, because in Python empty sequences are false. This is recommended on PEP8 too.