WaveFunctionCollapse | tilemap generation from a single example | Learning library

In a nutshell:

My implementation of the Wave Collapse Function algorithm in Python 2.7 is flawed but I'm unable to identify where the problem is located. I would need help to find out what I'm possibly missing or doing wrong.

What is the Wave Collapse Function algorithm ?

It is an algorithm written in 2016 by Maxim Gumin that can generate procedural patterns from a sample image. You can see it in action here (2D overlapping model) and here (3D tile model).

Goal of this implementation:

To boil down the algorithm (2D overlapping model) to its essence and avoid the redondancies and clumsiness of the original C# script (surprisingly long and difficult to read). This is an attempt to make a shorter, clearer and pythonic version of this algorithm.

Characteristics of this implementation:

I'm using Processing (Python mode), a software for visual design that makes image manipulation easier (no PIL, no Matplotlib, ...). The main drawbacks are that I'm limited to Python 2.7 and can NOT import numpy.

Unlike the original version, this implementation:

• is not object oriented (in its current state), making it easier to understand / closer to pseudo-code
• is using 1D arrays instead of 2D arrays
• is using array slicing for matrix manipulation

The Algorithm (as I understand it)

1/ Read the input bitmap, store every NxN patterns and count their occurences. (optional: Augment pattern data with rotations and reflections.)

For example, when N = 3:

2/ Precompute and store every possible adjacency relations between patterns. In the example below, patterns 207, 242, 182 and 125 can overlap the right side of pattern 246

3/ Create an array with the dimensions of the output (called W for wave). Each element of this array is an array holding the state (True of False) of each pattern.

For example, let's say we count 326 unique patterns in input and we want our output to be of dimensions 20 by 20 (400 cells). Then the "Wave" array will contain 400 (20x20) arrays, each of them containing 326 boolan values.

At start, all booleans are set to True because every pattern is allowed at any position of the Wave.