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Implement selection of tile from multiple best fitting ones #22

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Implement selection of tile from multiple best fitting ones #22

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e637a39
Add random selection of candidate tiles
kpj May 5, 2024
82caae3
Fix config comments
kpj May 5, 2024
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25 changes: 19 additions & 6 deletions mosaic.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,17 @@
import sys
import random
import os, os.path
from PIL import Image, ImageOps
from multiprocessing import Process, Queue, cpu_count

# Change these 3 config parameters to suit your needs...
TILE_SIZE = 50 # height/width of mosaic tiles in pixels
TILE_MATCH_RES = 5 # tile matching resolution (higher values give better fit but require more processing)
ENLARGEMENT = 8 # the mosaic image will be this many times wider and taller than the original
# Change these 5 config parameters to suit your needs...
TILE_SIZE = 50 # height/width of mosaic tiles in pixels
TILE_MATCH_RES = 5 # tile matching resolution (higher values give better fit but require more processing)
ENLARGEMENT = 8 # the mosaic image will be this many times wider and taller than the original
SELECTION_THRESHOLD = 50_000 # consider all images with difference smaller than this to top candidate when selecting tile
DISABLE_DIFF_BAILOUT = True # disable pre-termination of image difference computation (makes diff threshold work properly)

Image.MAX_IMAGE_PIXELS = sys.maxsize

TILE_BLOCK_SIZE = TILE_SIZE / max(min(TILE_MATCH_RES, TILE_SIZE), 1)
WORKER_COUNT = max(cpu_count() - 1, 1)
Expand Down Expand Up @@ -92,7 +97,7 @@ def __get_tile_diff(self, t1, t2, bail_out_value):
for i in range(len(t1)):
#diff += (abs(t1[i][0] - t2[i][0]) + abs(t1[i][1] - t2[i][1]) + abs(t1[i][2] - t2[i][2]))
diff += ((t1[i][0] - t2[i][0])**2 + (t1[i][1] - t2[i][1])**2 + (t1[i][2] - t2[i][2])**2)
if diff > bail_out_value:
if not DISABLE_DIFF_BAILOUT and diff > bail_out_value:
# we know already that this isn't going to be the best fit, so no point continuing with this tile
return diff
return diff
Expand All @@ -103,14 +108,22 @@ def get_best_fit_tile(self, img_data):
tile_index = 0

# go through each tile in turn looking for the best match for the part of the image represented by 'img_data'
scores = []
for tile_data in self.tiles_data:
diff = self.__get_tile_diff(img_data, tile_data, min_diff)
scores.append((tile_index, diff))
if diff < min_diff:
min_diff = diff
best_fit_tile_index = tile_index
tile_index += 1

return best_fit_tile_index
assert min(scores, key=lambda x: x[1])[0] == best_fit_tile_index
scores = sorted(scores, key=lambda x: x[1])

best_match = scores[0]
candidates = [idx for idx, val in scores if abs(best_match[1] - val) < SELECTION_THRESHOLD]

return random.choice(candidates)

def fit_tiles(work_queue, result_queue, tiles_data):
# this function gets run by the worker processes, one on each CPU core
Expand Down