def test_adjacency_extraction():
from wfc_tiles import make_tile_catalog
from wfc_patterns import make_pattern_catalog
import imageio
# TODO: generalize this to more than the four cardinal directions
direction_offsets = list(enumerate([(0, -1), (1, 0), (0, 1), (-1, 0)]))
filename = "../images/samples/Red Maze.png"
img = imageio.imread(filename)
tile_size = 1
pattern_width = 2
rotations = 0
tile_catalog, tile_grid, code_list, unique_tiles = make_tile_catalog(
img, tile_size)
pattern_catalog, pattern_weights, pattern_list, pattern_grid = make_pattern_catalog(
tile_grid, pattern_width, rotations)
adjacency_relations = adjacency_extraction(pattern_grid, pattern_catalog,
direction_offsets)
assert (((0, -1), -6150964001204120324, -4042134092912931260)
in adjacency_relations)
assert (((-1, 0), -4042134092912931260, 3069048847358774683)
in adjacency_relations)
assert (((1, 0), -3950451988873469076, -3950451988873469076)
in adjacency_relations)
assert (((-1, 0), -3950451988873469076, -3950451988873469076)
in adjacency_relations)
assert (((0, 1), -3950451988873469076, 3336256675067683735)
in adjacency_relations)
assert (not ((0, -1), -3950451988873469076, -3950451988873469076)
in adjacency_relations)
assert (not ((0, 1), -3950451988873469076, -3950451988873469076)
in adjacency_relations)
def test_recurse_vs_loop():
from wfc_tiles import make_tile_catalog
from wfc_patterns import make_pattern_catalog, pattern_grid_to_tiles
from wfc_adjacency import adjacency_extraction
from wfc_solver import run, makeWave, makeAdj, lexicalLocationHeuristic, lexicalPatternHeuristic
from wfc_visualize import figure_list_of_tiles, figure_false_color_tile_grid, figure_pattern_catalog, render_tiles_to_output, figure_adjacencies
import imageio
img = imageio.imread("../images/samples/Red Maze.png")
tile_size = 1
pattern_width = 2
rotations = 0
output_size = [84, 84]
ground = None
direction_offsets = list(enumerate([(0, -1), (1, 0), (0, 1), (-1, 0)]))
tile_catalog, tile_grid, code_list, unique_tiles = make_tile_catalog(
img, tile_size)
pattern_catalog, pattern_weights, pattern_list, pattern_grid = make_pattern_catalog(
tile_grid, pattern_width, rotations)
adjacency_relations = adjacency_extraction(pattern_grid, pattern_catalog,
direction_offsets)
number_of_patterns = len(pattern_weights)
encode_patterns = dict(enumerate(pattern_list))
decode_patterns = {x: i for i, x in enumerate(pattern_list)}
decode_directions = {j: i for i, j in direction_offsets}
adjacency_list = {}
for i, d in direction_offsets:
adjacency_list[d] = [set() for i in pattern_weights]
for i in adjacency_relations:
adjacency_list[i[0]][decode_patterns[i[1]]].add(decode_patterns[i[2]])
wave = makeWave(number_of_patterns, output_size[0], output_size[1])
adjacency_matrix = makeAdj(adjacency_list)
solution_loop = run(wave.copy(),
adjacency_matrix,
locationHeuristic=lexicalLocationHeuristic,
patternHeuristic=lexicalPatternHeuristic,
periodic=True,
backtracking=False,
onChoice=None,
onBacktrack=None)
solution_recurse = run_recurse(wave.copy(),
adjacency_matrix,
locationHeuristic=lexicalLocationHeuristic,
patternHeuristic=lexicalPatternHeuristic,
periodic=True,
backtracking=False,
onChoice=None,
onBacktrack=None)
assert (numpy.array_equiv(solution_loop, solution_recurse))
def test_pattern_to_tile():
from wfc_tiles import make_tile_catalog
import imageio
filename = "../images/samples/Red Maze.png"
img = imageio.imread(filename)
tile_size = 1
pattern_width = 2
rotations = 0
tile_catalog, tile_grid, code_list, unique_tiles = make_tile_catalog(
img, tile_size)
pattern_catalog, pattern_weights, pattern_list, pattern_grid = make_pattern_catalog(
tile_grid, pattern_width)
new_tile_grid = pattern_grid_to_tiles(pattern_grid, pattern_catalog)
assert (np.array_equal(tile_grid, new_tile_grid))
def test_unique_patterns_2d():
from wfc_tiles import make_tile_catalog
import imageio
filename = "../images/samples/Red Maze.png"
img = imageio.imread(filename)
tile_size = 1
pattern_width = 2
rotations = 0
tile_catalog, tile_grid, code_list, unique_tiles = make_tile_catalog(
img, tile_size)
patterns_in_grid, pattern_contents_list, patch_codes = unique_patterns_2d(
tile_grid, pattern_width, True)
assert (patch_codes[1][2] == 4867810695119132864)
assert (pattern_contents_list[7][1][1] == 8253868773529191888)
def test_make_pattern_catalog():
from wfc_tiles import make_tile_catalog
import imageio
filename = "../images/samples/Red Maze.png"
img = imageio.imread(filename)
tile_size = 1
pattern_width = 2
rotations = 0
tile_catalog, tile_grid, code_list, unique_tiles = make_tile_catalog(
img, tile_size)
pattern_catalog, pattern_weights, pattern_list, pattern_grid = make_pattern_catalog(
tile_grid, pattern_width)
assert (pattern_weights[-6150964001204120324] == 1)
assert (pattern_list[3] == 2800765426490226432)
assert (pattern_catalog[5177878755649963747][0][1] == -8754995591521426669)
wfc_ns_chess = find_pattern_center(wfc_ns_chess)
wfc_ns_chess = wfc_utilities.load_visualizer(wfc_ns_chess)
##
## Load image and make tile data structures
##
wfc_ns_chess.img = load_source_image(wfc_ns_chess.img_filename)
wfc_ns_chess.tiles = image_to_tiles(wfc_ns_chess.img, wfc_ns_chess.tile_size)
(
wfc_ns_chess.tile_catalog,
wfc_ns_chess.tile_grid,
wfc_ns_chess.code_list,
wfc_ns_chess.unique_tiles,
) = make_tile_catalog(wfc_ns_chess)
wfc_ns_chess.tile_ids = {
v: k
for k, v in dict(enumerate(wfc_ns_chess.unique_tiles[0])).items()
}
print(wfc_ns_chess.unique_tiles)
wfc_ns_chess.tile_weights = {
a: b
for a, b in zip(wfc_ns_chess.unique_tiles[0], wfc_ns_chess.unique_tiles[1])
}
print(wfc_ns_chess.tile_weights)
# print("wfc_ns_chess.tile_catalog")
# print(wfc_ns_chess.tile_catalog)
# print("wfc_ns_chess.tile_grid")
# print(wfc_ns_chess.tile_grid)
periodic_input=True,
periodic_output=True,
generated_size=(3, 3),
screenshots=1,
iteration_limit=0,
allowed_attempts=1,
)
test_ns = wfc_utilities.find_pattern_center(test_ns)
test_ns = wfc_utilities.load_visualizer(test_ns)
test_ns.img = wfc_tiles.load_source_image(test_ns.img_filename)
(
test_ns.tile_catalog,
test_ns.tile_grid,
test_ns.code_list,
test_ns.unique_tiles,
) = wfc_tiles.make_tile_catalog(test_ns)
test_ns.tile_ids = {
v: k
for k, v in dict(enumerate(test_ns.unique_tiles[0])).items()
}
test_ns.tile_weights = {
a: b
for a, b in zip(test_ns.unique_tiles[0], test_ns.unique_tiles[1])
}
(
test_ns.pattern_catalog,
test_ns.pattern_weights,
test_ns.patterns,
) = make_pattern_catalog_no_rotations(test_ns.tile_grid,
test_ns.pattern_width)
import doctest
generated_size = (16,8), #Size of the final image
screenshots = 1, # Number of times to run the algorithm, will produce this many distinct outputs
iteration_limit = 0, # After this many iterations, time out. 0 = never time out.
allowed_attempts = 1) # Give up after this many contradictions
wfc_ns_chess = find_pattern_center(wfc_ns_chess)
wfc_ns_chess = wfc_utilities.load_visualizer(wfc_ns_chess)
##
## Load image and make tile data structures
##
wfc_ns_chess.img = load_source_image(wfc_ns_chess.img_filename)
wfc_ns_chess.tiles = image_to_tiles(wfc_ns_chess.img, wfc_ns_chess.tile_size)
wfc_ns_chess.tile_catalog, wfc_ns_chess.tile_grid, wfc_ns_chess.code_list, wfc_ns_chess.unique_tiles = make_tile_catalog(wfc_ns_chess)
wfc_ns_chess.tile_ids = {v:k for k,v in dict(enumerate(wfc_ns_chess.unique_tiles[0])).items()}
print(wfc_ns_chess.unique_tiles)
wfc_ns_chess.tile_weights = {a:b for a,b in zip(wfc_ns_chess.unique_tiles[0], wfc_ns_chess.unique_tiles[1])}
print(wfc_ns_chess.tile_weights)
#print("wfc_ns_chess.tile_catalog")
#print(wfc_ns_chess.tile_catalog)
#print("wfc_ns_chess.tile_grid")
#print(wfc_ns_chess.tile_grid)
#print("wfc_ns_chess.code_list")
#print(wfc_ns_chess.code_list)
#print("wfc_ns_chess.unique_tiles")
#print(wfc_ns_chess.unique_tiles)
#assert False
s_columns = 24 // min(24, img_ns.pattern_width)
s_rows = 1 + (int(len(img_ns.pattern_catalog)) // s_columns)
fig = figure(figsize=(s_columns, s_rows * 1.5))
title('Extracted Patterns')
for i,tcode in img_ns.pattern_catalog.items():
pat_cat = img_ns.pattern_catalog[i]
ptr = render_pattern(pat_cat, img_ns).astype(np.uint8)
sp = subplot(s_rows, s_columns, i+1)
spi = sp.imshow(ptr)
spi.axes.xaxis.set_label_text(f'({img_ns.pattern_weights[i]})')
sp.set_title(i)
spi.axes.tick_params(labelleft=False,labelbottom=False, left=False, bottom=False)
spi.axes.grid(False)
plt.savefig(img_ns.output_filename + "_patterns.pdf", bbox_inches="tight")
plt.close()
if __name__ == "__main__":
import types
import wfc_tiles
test_ns = types.SimpleNamespace(img_filename = "red_maze.png", seed = 87386, tile_size = 1, pattern_width = 2, channels = 3, adjacency_directions = dict(enumerate([CoordXY(x=0,y=-1),CoordXY(x=1,y=0),CoordXY(x=0,y=1),CoordXY(x=-1,y=0)])), periodic_input = True, periodic_output = True, generated_size = (3,3), screenshots = 1, iteration_limit = 0, allowed_attempts = 1)
test_ns = wfc_utilities.find_pattern_center(test_ns)
test_ns = wfc_utilities.load_visualizer(test_ns)
test_ns.img = wfc_tiles.load_source_image(test_ns.img_filename)
test_ns.tile_catalog, test_ns.tile_grid, test_ns.code_list, test_ns.unique_tiles = wfc_tiles.make_tile_catalog(test_ns)
test_ns.tile_ids = {v:k for k,v in dict(enumerate(test_ns.unique_tiles[0])).items()}
test_ns.tile_weights = {a:b for a,b in zip(test_ns.unique_tiles[0], test_ns.unique_tiles[1])}
test_ns.pattern_catalog, test_ns.pattern_weights, test_ns.patterns = make_pattern_catalog_no_rotations(test_ns.tile_grid, test_ns.pattern_width)
import doctest
doctest.testmod()