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Add Python fractal-mosaic CLI and unpack MATLAB reference

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Fork of s-ben/Fractal-Mosaics for Tower mosaik inbox: GPL license,
upstream .m sources under reference/, RST placement with CBIR +
log-polar hints, edge alpha, profiles wenig/mittel/dicht, no recolor.

Co-authored-by: Cursor <cursoragent@cursor.com>
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copilot 2026-09-24 17:03:12 +02:00
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commit 5bfeae238d
20 changed files with 3981 additions and 32 deletions
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# Upstream docs / package archives are large; keep Matlab sources + Python engine tracked.
reference/package/*.tar.gz
reference/package/*.zip
__pycache__/
*.py[cod]
.venv/
dist/
*.egg-info/
.pytest_cache/

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You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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Fractal-Mosaics # Fractal Mosaics (Tower fork)
===============
Fractal Mosaics is a rotation, scale, and translation invariant photomosaic algorithm (i.e., the images can be placed at arbitrary locations, can be scaled to any size, and can be rotated by any angle). Fork of [s-ben/Fractal-Mosaics](https://github.com/s-ben/Fractal-Mosaics) adapted for the Tower **mosaik** drop-inbox.
The figure below shows a Fractal Mosaic and traditional photomosaic made using the same target image and image set. Upstream implements a rotation / scale / translation invariant photomosaic algorithm (log-polar registration + CBIR prefilter + large-first placement). Original code is MATLAB (GPL-3.0). This repository keeps the unpacked MATLAB sources under `reference/upstream-matlab/` and ships a **Python CLI** for headless use on Arch/Linux.
![Photomosaic New Algorithm Fractal Eye](https://raw.github.com/s-ben/Fractal-Mosaics/gh-pages/images/fractal_trad_compare.png) ## Credit
For a detailed description of the algorithm, see my adademic-style "[paper](http://s-ben.github.io/Fractal-Mosaics/)" on it and documentation below. - Original author: Seth Benton — https://github.com/s-ben/Fractal-Mosaics
- Algorithm write-up: https://s-ben.github.io/Fractal-Mosaics/
- License: GNU GPL v3 (see `LICENSE` and `reference/upstream-matlab/gpl_license.txt`)
# Prerequisites ## Python CLI (Tower)
Fractal Mosaics is implemented in Matlab. You’ll need a copy of Matlab with the Signal Processing toolbox - most versions should have the Signal Processing toolbox. ```bash
python3 -m venv .venv && source .venv/bin/activate
pip install -e '.[cli]'
fractal-mosaic \
--master path/to/target.jpg \
--tiles path/to/tile_pool/ \
--output out.png \
--profile mittel
```
# Getting Started Profiles: `wenig` (fast, larger tiles), `mittel`, `dicht` (more placements, slower).
1. Clone the Fractal-Mosaics repository (or, if you don't have git, just download the [zip file](https://github.com/s-ben/Fractal-Mosaics/archive/gh-pages.zip)). **Constraints for Tower v1:** geometric transforms + edge alpha only — **no tile recoloring**.
2. Navigate to the /code folder and unpack the 'FractalMosaics_package' zip or tar.gz file. This contains the demo example.
3. Download the [example image set](https://docs.google.com/file/d/0B_2ApIVBvXm1Y0VMVTVqNUhCaVk/edit?usp=sharing) (9.1 GB database of greyscale images of graffiti).
4. Unpack the zip file containing the example image set and copy its contents into the /library_images folder in the demo example.
5. Navigate to the /m-files directory in the demo example.
6. Run ‘fractal_mosaic.m’.
Your mosaic is now rendering. ## Layout
WARNING: it takes a long time to render a mosaic (~10.5 hrs on my macbook pro). If you want to optimize it, please do ;) | Path | Role |
|------|------|
The demo example creates a black and white mosaic. This is because color takes ~3X as long. To create a color mosaic, download the color image set and set Fractal Mosaics to color mode (see the [Variables](https://github.com/s-ben/Fractal-Mosaics/wiki/Variables) page for how to do this). Due to file size limits in Google Drive, the color image set is divided into two zip files ([image set 1](https://docs.google.com/file/d/0B3M2Bu_2k-QSSnhSVzNWRERtenc/edit?usp=sharing), [image set 2](https://docs.google.com/file/d/0B3M2Bu_2k-QSTkxxbEVGay1Gcjg/edit?usp=sharing)). | `reference/upstream-matlab/` | Unpacked `.m` files from upstream package |
| `reference/package/` | Original `FractalMosaics_package.tar.gz` |
# Documentation | `src/fractal_mosaic/` | Python engine + CLI |
| docs site (`index.html`, …) | Upstream gh-pages documentation (preserved) |
* [Fractal Mosaic “paper”](http://s-ben.github.io/Fractal-Mosaics/): academic-style paper explaining how the algorithm works.
* [Wiki](https://github.com/s-ben/Fractal-Mosaics/wiki): details on how to create your own mosaic.
* [Gallery](http://www.flickr.com/photos/travelingseth/sets/72157623789223762/): Flickr page showing finished mosaics.
# Licencing
Fractal Mosaics is licensed under the GNU General Public License ([GPL](http://www.gnu.org/licenses/gpl.txt)). Some functions used in Fractal Mosaics are from the Matlab File Exchange and use the [BSD license](http://opensource.org/licenses/bsd-license.php). This license is "permissive" (can be used for any purpose, commercial or non). If you wish to use Fractal Mosaics for proprietary software, let's talk! (seth.benton@gmail.com)
[![githalytics.com alpha](https://cruel-carlota.pagodabox.com/80d211511c492cf9dce9dd9841acf603 "githalytics.com")](http://githalytics.com/s-ben/Fractal-Mosaics)

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[build-system]
requires = ["setuptools>=68", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "fractal-mosaic"
version = "0.1.0"
description = "RST collage / fractal photomosaic engine (fork of s-ben/Fractal-Mosaics) for Tower inbox"
readme = "README.md"
license = { text = "GPL-3.0-or-later" }
requires-python = ">=3.10"
authors = [{ name = "Tower / copilot" }]
dependencies = [
"numpy>=1.24",
"Pillow>=10.0",
"scipy>=1.10",
]
[project.optional-dependencies]
cli = []
[project.scripts]
fractal-mosaic = "fractal_mosaic.cli:main"
[tool.setuptools.packages.find]
where = ["src"]

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clear all
close all
color = 0;
% ----------------------------- get jpeg file paths --------------------
'getting image paths...'
if color ==0
path = ['../library_images/graffiti_256_black_white/'];
else
path = ['C:\Americano\library_images\graffiti_256_color\'];
end
% files = dir('C:\Image_registration\flickr_spider\graffiti_library\*.jpg'); % get file names
directories = dir(path); % get directory names
num_files = 0;
file_count = 0;
dir_temp = directories(3).name; % get first directory name
files = dir([path,dir_temp,'/*.jpg']); % get file names for first directory
file_count = length(files);
% add directory path to jpeg files name
for j=1:length(files)
files(j).name = [path,dir_temp,'/',files(j).name];
end
for i=2:(length(directories)-2) % offset by 2 because first two entries are '.' and '..'
dir_temp = directories(i+2).name; % get next directory name
files_temp = dir([path,dir_temp,'/*.jpg']); % get file names for given directory
% add directory path to jpeg files name
for j=1:length(files_temp)
files_temp(j).name = [path,dir_temp,'/',files_temp(j).name];
end
% concatenate file paths from next directory to existing
files((file_count+1):(file_count+length(files_temp))) = files_temp;
file_count = file_count+length(files_temp);
% pause
end
DC_v = zeros(length(files),1);
for z=1:length(files)
% z
Im_lib = imread([files(z).name]); % read in library image
DC_component = sum(sum(Im_lib))/(256*256);
DC_v(z) = DC_component;
end
save DC_vals_graffiti DC_v

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clear all
load Fractal_Mosaic % load Matlab workspace with mosaic parameters
black_bg = 0; % determines if a black background is used or the original target image. '1' = use target image as background
color_render = 1; % If color_render = '1', use color images, even if matching was done in black and white mode. If color ='0', use black and white images
perc_sloped = 0.1;
I1 = imread(target_path);
size_r = size(Render_target);
I1 = imresize(I1, [size_r(1) size_r(2)]);
% I1 = imresize(I1, [8192 8192]);
Render_target = I1;
clear I1
pat = '\';
regexprep(path, pat, '/')
if black_bg == 1
'overlaying on target image'
else
Render_target(:,:,:) = 0;
end
%---------- calculate distance windows -----------
for i=1:length(level_v)
% i
perc_sloped_fixed = 0.01;%.1; % perc sloped for fixed window (one that's used to cut off clipping)
fd = zeros(lib_im_v(i));
fd_fixed = zeros(lib_im_v(i));
y_d = ones(1,lib_im_v(i)/2);
y_d_fixed = ones(1,lib_im_v(i)/2);
x_ind = linspace(0,lib_im_v(i)/2,lib_im_v(i)/2);
for u=1:(perc_sloped*(lib_im_v(i)/2))
y_d(u) = (1/(perc_sloped*(lib_im_v(i)/2)))*x_ind(u);
end
for u=1:(perc_sloped_fixed*(lib_im_v(i)/2))
y_d_fixed(u) = (1/(perc_sloped_fixed*(lib_im_v(i)/2)))*x_ind(u);
end
%
% x_ind = linspace(0,lib_im_v(i)/2,lib_im_v(i)/2); %triangular window.
% y_d = (-1/(lib_im_v(i)/2))*x_ind + 1;
fd(1:lib_im_v(i)/2) = y_d;
fd(lib_im_v(i)/2+1:lib_im_v(i)) = fliplr(y_d);
fd_2 = fd*fd';
window(i).win = fd_2;
fd_fixed(1:lib_im_v(i)/2) = y_d_fixed;
fd_fixed(lib_im_v(i)/2+1:lib_im_v(i)) = fliplr(y_d_fixed);
fd_2_fixed = fd_fixed*fd_fixed';
window(i).win_fixed = fd_2_fixed;
% figure(2),mesh(fd_2_fixed)
% pause
end
for i=1:num_cand_pixels_final
if color_render == 0
color_path = '../library_images/graffiti_256_black_white';
else
color_path = '../library_images/graffiti_256_color';
end
image_path = cand_pixels_final(i).file_path; % read in file path
% if file path uses '\' in path (default for PC), replace
% with '/' (works on both PC and mac)
for j=1:length(image_path)
if (image_path(j) == '\')
image_path(j) = '/';
end
end
path = [color_path image_path];
% % -------- (for mac) convert '\' to '/' ------
% pat = '\';
% path = regexprep(path, pat, '/');
% ---------- read in candidate library image ------------------
Im_lib = imread(path); % read in library image
DC_adjust = cand_pixels_final(i).DC;
i_r = cand_pixels_final(i).level;
j_r = cand_pixels_final(i).res;
x_r = cand_pixels_final(i).x_cand;
y_r = cand_pixels_final(i).y_cand;
scale_est_r = cand_pixels_final(i).scale;
Theta_r = cand_pixels_final(i).rot;
lib_im_num_r = cand_pixels_final(i).lib_im;
cand_pixels_final(i).valid_pix;
% x_admin = cand_pixels_final(i).x
% y_admin = cand_pixels_final(i).y
perc_DC = 1; % percentage DC adjusted
% DC_adjust = (DC_v(nearest_neighbor_int_index(z))-DC_component)*perc_DC;
Im_lib = Im_lib-DC_adjust;
% ---------- scale cand pixel position according to scale and res ---------------------
x_r = round(x_r*2^(scaling_constant_v(rendering_level)-(i_r+j_r)));
y_r = round(y_r*2^(scaling_constant_v(rendering_level)-(i_r+j_r)));
% radius_cand =
% ((lib_im_v(i)/2)/scale_est)*(1-perc_sloped);
% ---------- read in candidate library image ------------------
% Im_lib = imread([files(lib_im_num_r).name]); % read in library image
% -------resize library image for level -------------
Im_lib_tmp = imresize(Im_lib, [lib_im_v(i_r) lib_im_v(i_r)]);
% ------- crop analysis window -------------------------
xmin = x_r - lib_im_v(i_r)/2;
ymin = y_r - lib_im_v(i_r)/2;
width = lib_im_v(i_r)-1;
height = lib_im_v(i_r)-1;
rect = [xmin ymin width height];
Im_target_tile = imcrop(Render_target,rect);
% Im_target_tile_masked = zeros([lib_im_v(i_r) lib_im_v(i_r)]);
mask = zeros([lib_im_v(i_r) lib_im_v(i_r)]);
% ---------- matrix for backing up Render target pixels -----------------
Render_target_backup = zeros([lib_im_v(i_r) lib_im_v(i_r) 3]);
% --------- scale image ----------------
I_sc_tmp = imresize(Im_lib_tmp, 1/scale_est_r);
I_sc_tmp_size = size(I_sc_tmp);
tmp_height = I_sc_tmp_size(1);
tmp_width = I_sc_tmp_size(2);
radius = ((lib_im_v(i_r)/2)/scale_est_r)*(1-perc_sloped);
fd_2 = window(i_r).win;
fd_2 = imresize(fd_2,[I_sc_tmp_size(1) I_sc_tmp_size(2)]);
mask_tmp = ones([I_sc_tmp_size(1) I_sc_tmp_size(2)]);
I_sc = zeros([lib_im_v(i_r) lib_im_v(i_r) 3]);
% I_comb = zeros([lib_im_v(i_r) lib_im_v(i_r) 3]);
I_sc(round(lib_im_v(i_r)/2 - tmp_height/2)+1:round(lib_im_v(i_r)/2 - tmp_height/2)+tmp_height,round(lib_im_v(i_r)/2 - tmp_width/2)+1:round(lib_im_v(i_r)/2 - tmp_width/2)+tmp_width, :) = I_sc_tmp;
% mask(round(lib_im_v(i)/2 - tmp_height/2)+1:round(lib_im_v(i)/2 - tmp_height/2)+tmp_height,round(lib_im_v(i)/2 - tmp_width/2)+1:round(lib_im_v(i)/2 - tmp_width/2)+tmp_width) = mask_tmp;
mask(round(lib_im_v(i_r)/2 - tmp_height/2)+1:round(lib_im_v(i_r)/2 - tmp_height/2)+tmp_height,round(lib_im_v(i_r)/2 - tmp_width/2)+1:round(lib_im_v(i_r)/2 - tmp_width/2)+tmp_width) = fd_2;
% -------Rotate image back by theta estimate --------
R1 = imrotate(I_sc, -Theta_r, 'bilinear', 'crop');
mask = imrotate(mask, -Theta_r, 'bilinear', 'crop');
fd_2_tmp = window(i_r).win_fixed;
mask = mask.*fd_2_tmp;
% pause
% window(i).win_fixed
% mask_t = mask*window(i).win;
%
% alpha = 0.5;
% I_comb = (alpha)*double(Im_target_tile) + (1-alpha)*R1;
%
% ----------------- Render match ---------------------
for u= 1:lib_im_v(i_r)%ymin:(ymin+height)
for v = 1:lib_im_v(i_r)%xmin:(xmin+width)
Render_target_backup(u,v) = Render_target(ymin+u-1,xmin+v-1);
if (mask(u,v) > .01)
Render_target(ymin+u-1,xmin+v-1,1) = uint8(R1(u,v,1)*mask(u,v) + (1-mask(u,v))*Render_target(ymin+u-1,xmin+v-1,1));
Render_target(ymin+u-1,xmin+v-1,2) = uint8(R1(u,v,2)*mask(u,v) + (1-mask(u,v))*Render_target(ymin+u-1,xmin+v-1,2));
Render_target(ymin+u-1,xmin+v-1,3) = uint8(R1(u,v,3)*mask(u,v) + (1-mask(u,v))*Render_target(ymin+u-1,xmin+v-1,3));
end
end
end
% figure(2),subplot(1,3,1),imshow(uint8(Render_target_backup))
% subplot(1,3,2),imshow(Render_target(max(y-sq_center_disp(1),1):min(y+sq_center_disp(1),size_render_target(1)), max(x-sq_center_disp(2),1):min(x+sq_center_disp(2),size_render_target(2))));
% subplot(1,3,3),imshow(Render_target)
% figure(2),imshow(Render_target)
%
figure(2),imshow(Render_target)
drawnow
% pause
end
imwrite(Render_target,'fractal_mosaic_graffiti_color','jpg','quality',100);

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clear all
close all
color = 0;
% ----------------------------- get jpeg file paths --------------------
'getting image paths...'
if color ==0
path = ['../library_images/graffiti_256_black_white/'];
else
path = ['C:\Americano\library_images\graffiti_256_color\'];
end
% files = dir('C:\Image_registration\flickr_spider\graffiti_library\*.jpg'); % get file names
directories = dir(path); % get directory names
num_files = 0;
file_count = 0;
dir_temp = directories(3).name; % get first directory name
files = dir([path,dir_temp,'/*.jpg']); % get file names for first directory
file_count = length(files);
% add directory path to jpeg files name
for j=1:length(files)
files(j).name = [path,dir_temp,'/',files(j).name];
end
for i=2:(length(directories)-2) % offset by 2 because first two entries are '.' and '..'
dir_temp = directories(i+2).name; % get next directory name
files_temp = dir([path,dir_temp,'/*.jpg']); % get file names for given directory
% add directory path to jpeg files name
for j=1:length(files_temp)
files_temp(j).name = [path,dir_temp,'/',files_temp(j).name];
end
% concatenate file paths from next directory to existing
files((file_count+1):(file_count+length(files_temp))) = files_temp;
file_count = file_count+length(files_temp);
% pause
end
% bins = 16;
% hist_x = [(255/bins)/2:255/bins:(255-(255/bins)/2)];
% hist_matrix = zeros(length(files),bins);
radial_freq = 6;
angular_freq = 6;
vector_len = radial_freq*angular_freq;
gfd_matrix = zeros(length(files),vector_len);
% gfd_matrix = zeros(8000,vector_len);
size_v = [128 128];
avg_gfd = zeros(radial_freq,angular_freq);
for i=1:length(files)
i
Im_lib = imread([files(i).name]); % read in library image
Im_lib = imresize(Im_lib,size_v); % resize image to 128
% size_Im_lib = size(Im_lib);
%
% % ------ reshape Im_lib into 1D array ------
% Im_lib_array = reshape(Im_lib,size_Im_lib(1)*size_Im_lib(2),1);
%
% histo = hist(double(Im_lib_array),hist_x);
Im_lib_polar = polar_conversion(Im_lib);
Im_lib_polar_FFT = abs(fftshift(fft2(Im_lib_polar)));
DC = Im_lib_polar_FFT(size_v(1)/2+1,size_v(2)/2+1);
% energy_norm = sum(sum(Im_lib_polar_FFT));
% --------------- normalize coefs ------------------------
% Im_lib_polar_FFT(size_v(1)/2+1,size_v(2)/2+1) = Im_lib_polar_FFT(size_v(1)/2+1,size_v(2)/2+1)/(size_v(1)*size_v(2));
Im_lib_polar_FFT = Im_lib_polar_FFT./DC;
% Im_lib_polar_FFT = Im_lib_polar_FFT./energy_norm;
% Im_lib_polar_FFT(size_v(1)/2+1,size_v(2)/2+1) = DC;
center_gfd = [size_v(1)/2+1 size_v(2)/2+1];
gfd = Im_lib_polar_FFT(center_gfd(1):(center_gfd(1)+angular_freq-1),center_gfd(2):(center_gfd(2)+radial_freq-1));
avg_gfd = avg_gfd + gfd;
% figure(1),subplot(1,2,1),imshow(uint8(Im_lib))
% subplot(1,2,2),mesh(gfd)
%
% pause
% I1_gfd = fourier_mellin(Im_lib);
% hu_moment = humoments(Im_lib);
gfd_matrix(i,:) = gfd(:);
% figure(1),subplot(2,1,1),imshow(Im_lib)
% subplot(2,1,2),imshow(Im_lib)
% subplot(3,1,3),mesh(I1_fm)
% figure(1),subplot(1,2,1),imshow(Im_lib)
% subplot(1,2,2),mesh(I1_fm);
% pause
% subplot(1,2,1),imshow(Im_lib)
% subplot(1,2,2),plot(histo)
end
% pause
gfd_matrix = single(gfd_matrix);
save('gfd_matrix_graffiti_128', 'gfd_matrix')
% save('gfd_matrix_SF_128', 'gfd_matrix')
% save('avg_gfd_3045', 'avg_gfd')
% avg_gfd = gfd_matrix(1,:);
%
% for i=2:4846
% avg_gfd = avg_gfd+ gfd_matrix(i,:);
%
% end
% for i=1:1000%length(files)
% i
%
% fm = fm_matrix(i,:);
%
% % for j=1:1000
%
%
% [nearest_neighbor_fm neighbor_distances_fm] = kNearestNeighbors(fm_matrix(1:1000,:), fm, 1000);
% min_fm_dist = neighbor_distances_fm(2);
% max_fm_dist = neighbor_distances_fm(end);
%
% % end
%
% end
% fm_matrix = single(fm_matrix);
% save('gfd_matrix_graffiti_128', 'gfd_matrix')
% load('hist_matrix_graffiti');
% [index_vals,vector_vals,final_nodes] = kd_knn(tree,point,k,plot_stuff,node_number)

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GNU GENERAL PUBLIC LICENSE
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Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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<http://www.gnu.org/philosophy/why-not-lgpl.html>.

85
reference/upstream-matlab/histogram_calc.m Normal file
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@ -0,0 +1,85 @@
clear all
close all
% ----------------------------- get jpeg file paths --------------------
'getting image paths...'
path = ['../library_images/graffiti_256_black_white/']; % path to library images
% files = dir('C:\Image_registration\flickr_spider\graffiti_library\*.jpg'); % get file names
directories = dir(path); % get directory names
num_files = 0;
file_count = 0;
dir_temp = directories(3).name; % get first directory name
files = dir([path,dir_temp,'/*.jpg']); % get file names for first directory
file_count = length(files);
% add directory path to jpeg files name
for j=1:length(files)
files(j).name = [path,dir_temp,'/',files(j).name];
end
for i=2:(length(directories)-2) % offset by 2 because first two entries are '.' and '..'
dir_temp = directories(i+2).name; % get next directory name
files_temp = dir([path,dir_temp,'/*.jpg']); % get file names for given directory
% add directory path to jpeg files name
for j=1:length(files_temp)
files_temp(j).name = [path,dir_temp,'/',files_temp(j).name];
end
% concatenate file paths from next directory to existing
files((file_count+1):(file_count+length(files_temp))) = files_temp;
file_count = file_count+length(files_temp);
% pause
end
bins = 16;
hist_x = [(255/bins)/2:255/bins:(255-(255/bins)/2)];
hist_matrix = zeros(length(files),bins);
for i=1:length(files)
i
Im_lib = imread([files(i).name]); % read in library image
Im_lib = imresize(Im_lib,[128 128]); % resize image to 128
size_Im_lib = size(Im_lib);
% ------ reshape Im_lib into 1D array ------
Im_lib_array = reshape(Im_lib,size_Im_lib(1)*size_Im_lib(2),1);
histo = hist(double(Im_lib_array),hist_x);
hist_matrix(i,:) = histo;
% subplot(1,2,1),imshow(Im_lib)
% subplot(1,2,2),plot(histo)
end
save('hist_matrix_graffiti_128', 'hist_matrix')
% load('hist_matrix_graffiti');

41
reference/upstream-matlab/kNearestNeighbors_no_sort.m Executable file
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@ -0,0 +1,41 @@
function [neighborDistances] = kNearestNeighbors_no_sort(dataMatrix, queryMatrix, k)
%--------------------------------------------------------------------------
% Program to find the k - nearest neighbors (kNN) within a set of points.
% Distance metric used: Euclidean distance
%
% Usage:
% [neighbors distances] = kNearestNeighbors(dataMatrix, queryMatrix, k);
% dataMatrix (N x D) - N vectors with dimensionality D (within which we search for the nearest neighbors)
% queryMatrix (M x D) - M query vectors with dimensionality D
% k (1 x 1) - Number of nearest neighbors desired
%
% Example:
% a = [1 1; 2 2; 3 2; 4 4; 5 6];
% b = [1 1; 2 1; 6 2];
% [neighbors distances] = kNearestNeighbors(a,b,2);
%
% Output:
% neighbors =
% 1 2
% 1 2
% 4 3
%
% distances =
% 0 1.4142
% 1.0000 1.0000
% 2.8284 3.0000
%--------------------------------------------------------------------------
% neighborIds = zeros(size(queryMatrix,1),k);
% neighborDistances = neighborIds;
neighborDistances= zeros(size(queryMatrix,1),k);
numDataVectors = size(dataMatrix,1);
numQueryVectors = size(queryMatrix,1);
for i=1:numQueryVectors,
neighborDistances = sum((repmat(queryMatrix(i,:),numDataVectors,1)-dataMatrix).^2,2);
% [sortval sortpos] = sort(dist,'ascend');
% neighborIds(i,:) = sortpos(1:k);
% neighborDistances(i,:) = sqrt(sortval(1:k));
end

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reference/upstream-matlab/polar_conversion.m Executable file
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function polar = polar_conversion(A)
A_dim = size(A); % get dimensions of A
center = A_dim/2; % get center of image coordinates
% ---------- calculate polar coordinates -------------
theta = linspace(0,2*pi,A_dim(1)+1); theta(end) = [];
% d = min([A_dim(1)-center(1) center(1)-1 A_dim(2)-center(2) center(2)-1]);
rho = linspace(0, center(1)-1,A_dim(2))';
% convert polar coordinates to cartesian coordinates and center
xx = rho*cos(theta) + center(1);
yy = rho*sin(theta) + center(2);
xx = double(xx);
yy = double(yy);
polar=interp2(double(A),xx,yy);

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reference/upstream-matlab/std_calc.m Normal file
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clear all
close all
color = 0;
% ----------------------------- get jpeg file paths --------------------
'getting image paths...'
if color ==0
path = ['../library_images/graffiti_256_black_white/'];
else
path = ['C:\Americano\library_images\graffiti_256_color\'];
end
% files = dir('C:\Image_registration\flickr_spider\graffiti_library\*.jpg'); % get file names
directories = dir(path); % get directory names
num_files = 0;
file_count = 0;
dir_temp = directories(3).name; % get first directory name
files = dir([path,dir_temp,'/*.jpg']); % get file names for first directory
file_count = length(files);
% add directory path to jpeg files name
for j=1:length(files)
files(j).name = [path,dir_temp,'/',files(j).name];
end
for i=2:(length(directories)-2) % offset by 2 because first two entries are '.' and '..'
dir_temp = directories(i+2).name; % get next directory name
files_temp = dir([path,dir_temp,'/*.jpg']); % get file names for given directory
% add directory path to jpeg files name
for j=1:length(files_temp)
files_temp(j).name = [path,dir_temp,'/',files_temp(j).name];
end
% concatenate file paths from next directory to existing
files((file_count+1):(file_count+length(files_temp))) = files_temp;
file_count = file_count+length(files_temp);
% pause
end
% bins = 16;
% hist_x = [(255/bins)/2:255/bins:(255-(255/bins)/2)];
% hist_matrix = zeros(length(files),bins);
% radial_freq = 6;
% angular_freq = 6;
% vector_len = radial_freq*angular_freq;
std_matrix = zeros(length(files),1);
% gfd_matrix = zeros(8000,vector_len);
size_v = [128 128];
% avg_gfd = zeros(radial_freq,angular_freq);
for i=1:length(files)
i
Im_lib = imread([files(i).name]); % read in library image
Im_lib = imresize(Im_lib,size_v); % resize image to 128
std_lib = std2(Im_lib);
std_matrix(i) = std_lib;
% figure(1),subplot(2,1,1),imshow(Im_lib)
% subplot(2,1,2),imshow(Im_lib)
% subplot(3,1,3),mesh(I1_fm)
% figure(1),subplot(1,2,1),imshow(Im_lib)
% subplot(1,2,2),mesh(I1_fm);
% pause
% subplot(1,2,1),imshow(Im_lib)
% subplot(1,2,2),plot(histo)
end
% pause
% std_matrix = single(gfd_matrix);
save('std_matrix_graffiti_128', 'std_matrix')
% save('std_matrix_SF_128', 'std_matrix')

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reference/upstream-matlab/transformImage_log.m Executable file
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function [r,g,b] = transformImage_log(A, Ar, Ac, Nrho, Ntheta, Method, Center, Shape)
% Inputs: A the input image
% Nrho the desired number of rows of transformed image
% Ntheta the desired number of columns of transformed image
% Method interpolation method (nearest,bilinear,bicubic)
% Center origin of input image
% Shape output size (full,valid)
% Class storage class of A
global rho;
theta = linspace(0,2*pi,Ntheta+1); theta(end) = [];
switch Shape
case 'full'
corners = [1 1;Ar 1;Ar Ac;1 Ac];
d = max(sqrt(sum((repmat(Center(:)',4,1)-corners).^2,2)));
case 'valid'
d = min([Ac-Center(1) Center(1)-1 Ar-Center(2) Center(2)-1]);
end
% minScale = .5;
% rho = logspace(log10(minScale),log10(d),Nrho)'; % default 'base 10' logspace - play with d to change the scale of the log axis
% rad_lin = linspace(logb(1,1.044), logb(256,1.044),256)';
% rho = 0.5.*(1.044.^rad_lin);
minScale = 1;
% maxScale = 127;
% maxScale = 2;
maxScale = Ar/2-1;
% rad_lin = linspace(log(minScale), log(maxScale),256)';
rad_lin = linspace(log(minScale), log(maxScale),Ar)';
% rad_lin = linspace(logb(1,1.044), logb(127,1.044),256)';
rho = exp(rad_lin);
% rho = (1.044.^rad_lin);
% convert polar coordinates to cartesian coordinates and center
xx = rho*cos(theta) + Center(1);
yy = rho*sin(theta) + Center(2);
if nargout==3
if strcmp(Method,'nearest'), % Nearest neighbor interpolation
r=interp2(A(:,:,1),xx,yy,'nearest');
g=interp2(A(:,:,2),xx,yy,'nearest');
b=interp2(A(:,:,3),xx,yy,'nearest');
elseif strcmp(Method,'bilinear'), % Linear interpolation
r=interp2(A(:,:,1),xx,yy,'linear');
g=interp2(A(:,:,2),xx,yy,'linear');
b=interp2(A(:,:,3),xx,yy,'linear');
elseif strcmp(Method,'bicubic'), % Cubic interpolation
r=interp2(A(:,:,1),xx,yy,'cubic');
g=interp2(A(:,:,2),xx,yy,'cubic');
b=interp2(A(:,:,3),xx,yy,'cubic');
else
error(['Unknown interpolation method: ',method]);
end
% any pixels outside , pad with black
mask= (xx>Ac) | (xx<1) | (yy>Ar) | (yy<1);
r(mask)=0;
g(mask)=0;
b(mask)=0;
else
if strcmp(Method,'nearest'), % Nearest neighbor interpolation
r=interp2(A,xx,yy,'nearest');
elseif strcmp(Method,'bilinear'), % Linear interpolation
r=interp2(double(A),xx,yy,'linear');
elseif strcmp(Method,'bicubic'), % Cubic interpolation
r=interp2(double(A),xx,yy,'cubic');
else
error(['Unknown interpolation method: ',method]);
end
% any pixels outside warp, pad with black
mask= (xx>Ac) | (xx<1) | (yy>Ar) | (yy<1);
r(mask)=0;
end

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src/fractal_mosaic/__init__.py Normal file
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"""Fractal mosaic engine — RST placement inspired by s-ben/Fractal-Mosaics."""
from __future__ import annotations
from fractal_mosaic.engine import generate_mosaic
from fractal_mosaic.profiles import PROFILES, get_profile
__all__ = ["generate_mosaic", "PROFILES", "get_profile"]
__version__ = "0.1.0"

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src/fractal_mosaic/cli.py Normal file
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"""CLI: fractal-mosaic --master X --tiles DIR --output Y --profile mittel"""
from __future__ import annotations
import argparse
from pathlib import Path
from fractal_mosaic.engine import generate_mosaic
from fractal_mosaic.profiles import PROFILES, get_profile
def main(argv: list[str] | None = None) -> None:
p = argparse.ArgumentParser(
prog="fractal-mosaic",
description=(
"RST collage photomosaic (Fractal-Mosaics inspired). "
"Tiles are selected/placed with rotation, scale, overlap and edge alpha — "
"never recolored."
),
)
p.add_argument("--master", required=True, type=Path, help="Target / master image")
p.add_argument("--tiles", required=True, type=Path, help="Directory of tile images (pool)")
p.add_argument("--output", "-o", required=True, type=Path, help="Output image path")
p.add_argument(
"--profile",
default="mittel",
choices=sorted(PROFILES.keys()),
help="Density / quality profile",
)
p.add_argument("--quiet", action="store_true", help="Less progress output")
args = p.parse_args(argv)
if not args.master.is_file():
raise SystemExit(f"master not found: {args.master}")
if not args.tiles.is_dir():
raise SystemExit(f"tiles dir not found: {args.tiles}")
profile = get_profile(args.profile)
generate_mosaic(
args.master,
args.tiles,
args.output,
profile,
progress=not args.quiet,
)
if __name__ == "__main__":
main()

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src/fractal_mosaic/engine.py Normal file
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"""Large-first collage placement with edge alpha — no tile recoloring."""
from __future__ import annotations
from pathlib import Path
import numpy as np
from numpy.typing import NDArray
from PIL import Image
from fractal_mosaic.logpolar import estimate_rotation_scale, to_gray_f32
from fractal_mosaic.profiles import Profile
from fractal_mosaic.retrieval import TileRecord, load_tile_library, rank_tiles
def _resize_master(path: Path, max_edge: int) -> Image.Image:
im = Image.open(path).convert("RGB")
w, h = im.size
long_edge = max(w, h)
if long_edge > max_edge:
scale = max_edge / float(long_edge)
im = im.resize(
(max(1, int(w * scale)), max(1, int(h * scale))),
Image.Resampling.LANCZOS,
)
return im
def _edge_alpha_mask(w: int, h: int, blend: float) -> NDArray[np.float32]:
"""Soft rectangular falloff (upstream perc_sloped window idea)."""
blend = float(np.clip(blend, 0.02, 0.49))
ys = np.linspace(-1.0, 1.0, h, dtype=np.float32)
xs = np.linspace(-1.0, 1.0, w, dtype=np.float32)
xv, yv = np.meshgrid(xs, ys)
# Distance from edge in each axis
ax = np.clip((1.0 - np.abs(xv)) / blend, 0.0, 1.0)
ay = np.clip((1.0 - np.abs(yv)) / blend, 0.0, 1.0)
return (ax * ay).astype(np.float32)
def _prepare_tile(
tile: TileRecord,
size: int,
rotation_deg: float,
scale: float,
) -> Image.Image:
with Image.open(tile.path) as im:
im = im.convert("RGBA")
# Center-crop to square then resize — crop only, no recolor
side = min(im.size)
left = (im.size[0] - side) // 2
top = (im.size[1] - side) // 2
im = im.crop((left, top, left + side, top + side))
target = max(8, int(round(size * scale)))
im = im.resize((target, target), Image.Resampling.LANCZOS)
if abs(rotation_deg) > 0.5:
im = im.rotate(rotation_deg, resample=Image.Resampling.BICUBIC, expand=True)
return im
def _tile_geometry(
tile_rgba: Image.Image,
cx: int,
cy: int,
canvas_shape: tuple[int, int, int],
edge_blend: float,
tile_alpha: float,
) -> tuple[slice, slice, NDArray[np.float32], NDArray[np.float32]] | None:
"""Return (ys, xs, rgb, alpha) clipped to canvas, or None if empty."""
tw, th = tile_rgba.size
x0 = cx - tw // 2
y0 = cy - th // 2
x1 = x0 + tw
y1 = y0 + th
H, W, _ = canvas_shape
sx0 = max(0, -x0)
sy0 = max(0, -y0)
sx1 = tw - max(0, x1 - W)
sy1 = th - max(0, y1 - H)
if sx1 <= sx0 or sy1 <= sy0:
return None
dx0, dy0 = x0 + sx0, y0 + sy0
dx1, dy1 = x0 + sx1, y0 + sy1
patch = np.asarray(tile_rgba.crop((sx0, sy0, sx1, sy1)), dtype=np.float32) / 255.0
ph, pw = patch.shape[:2]
mask = _edge_alpha_mask(pw, ph, edge_blend)
a = (patch[..., 3] * mask * tile_alpha).astype(np.float32)
return slice(dy0, dy1), slice(dx0, dx1), patch[..., :3], a
def _overlap_fraction(
coverage: NDArray[np.float32],
ys: slice,
xs: slice,
alpha: NDArray[np.float32],
) -> float:
solid = alpha > 0.05
if not np.any(solid):
return 1.0
return float(coverage[ys, xs][solid].mean())
def _paste_rgba(
canvas: NDArray[np.float32],
coverage: NDArray[np.float32],
tile_rgba: Image.Image,
cx: int,
cy: int,
edge_blend: float,
tile_alpha: float,
) -> float:
"""Paste tile centered at (cx,cy). Returns overlap fraction before paste."""
geo = _tile_geometry(tile_rgba, cx, cy, canvas.shape, edge_blend, tile_alpha)
if geo is None:
return 1.0
ys, xs, rgb, a = geo
overlap = _overlap_fraction(coverage, ys, xs, a)
dest = canvas[ys, xs]
aa = a[..., None]
dest[:] = dest * (1.0 - aa) + rgb * aa
coverage[ys, xs] = np.maximum(coverage[ys, xs], a)
return overlap
def _rms(patch: NDArray[np.uint8], tile_rgb: NDArray[np.uint8]) -> float:
a = patch.astype(np.float32)
b = np.asarray(
Image.fromarray(tile_rgb).resize((patch.shape[1], patch.shape[0]), Image.Resampling.BILINEAR)
).astype(np.float32)
return float(np.sqrt(np.mean((a - b) ** 2)))
def generate_mosaic(
master_path: Path,
tiles_dir: Path,
output_path: Path,
profile: Profile,
*,
progress: bool = True,
) -> Path:
master_path = Path(master_path)
tiles_dir = Path(tiles_dir)
output_path = Path(output_path)
library = load_tile_library(tiles_dir)
if len(library) < profile.min_tiles:
raise SystemExit(
f"tile pool has {len(library)} images; profile {profile.name!r} needs ≥ {profile.min_tiles}"
)
master_im = _resize_master(master_path, profile.master_max)
master = np.asarray(master_im, dtype=np.uint8)
H, W = master.shape[:2]
if profile.use_master_bg:
canvas = master.astype(np.float32) / 255.0
else:
canvas = np.zeros((H, W, 3), dtype=np.float32)
coverage = np.zeros((H, W), dtype=np.float32)
rng = np.random.default_rng(42)
placed = 0
reuse_counts: dict[int, int] = {}
for tile_size in profile.tile_sizes:
if placed >= profile.max_placements:
break
stride = max(8, int(tile_size * profile.stride_frac))
# Prefer uncovered regions: sample grid, sort by low coverage
centers: list[tuple[float, int, int]] = []
for y in range(tile_size // 2, H - tile_size // 2, stride):
for x in range(tile_size // 2, W - tile_size // 2, stride):
y0, x0 = y - tile_size // 2, x - tile_size // 2
y1, x1 = y0 + tile_size, x0 + tile_size
cov = float(coverage[y0:y1, x0:x1].mean())
centers.append((cov, x, y))
centers.sort(key=lambda t: t[0])
# shuffle lightly among similar coverage
if len(centers) > 4:
head = centers[: max(1, len(centers) // 3)]
rng.shuffle(head)
centers = head + centers[len(head) :]
for cov0, cx, cy in centers:
if placed >= profile.max_placements:
break
if cov0 > profile.max_overlap + 0.15:
continue
y0, x0 = cy - tile_size // 2, cx - tile_size // 2
y1, x1 = y0 + tile_size, x0 + tile_size
patch = master[y0:y1, x0:x1]
if patch.size == 0:
continue
idxs = rank_tiles(patch, library, top_k=profile.candidates)
best: tuple[float, Image.Image, int] | None = None
for ti in idxs:
# Soft reuse limit: denser profiles allow more repeats
limit = 3 if profile.name == "wenig" else (6 if profile.name == "mittel" else 12)
if reuse_counts.get(ti, 0) >= limit:
continue
tile = library[ti]
# Log-polar hint for rot/scale, then snap to discrete search
try:
rot_est, scale_est = estimate_rotation_scale(
to_gray_f32(patch),
to_gray_f32(tile.thumb),
n_rho=48,
n_theta=96,
)
except Exception:
rot_est, scale_est = 0.0, 1.0
rot_list = [0.0]
step = float(profile.rotation_step)
for mul in (-2, -1, 1, 2):
rot_list.append(rot_est + mul * step)
rot_list.append(rot_est)
# Normalize to [-180,180) and unique
rot_list = sorted({((r + 180) % 360) - 180 for r in rot_list})
scales = list(profile.scales)
if 0.5 <= scale_est <= 2.0:
scales = sorted(set(scales + [float(np.clip(scale_est, 0.7, 1.3))]))
for rot in rot_list:
for sc in scales:
prepared = _prepare_tile(tile, tile_size, rot, sc)
# Compare center crop of prepared RGB to patch (no recolor)
rgb = prepared.convert("RGB")
sample = np.asarray(
rgb.resize((patch.shape[1], patch.shape[0]), Image.Resampling.BILINEAR),
dtype=np.uint8,
)
err = _rms(patch, sample)
if best is None or err < best[0]:
best = (err, prepared, ti)
if best is None:
continue
err, prepared, ti = best
# Accept threshold scaled by tile size (coarser allowed for large tiles)
thresh = 55.0 + (220 - min(tile_size, 220)) * 0.08
if err > thresh:
continue
geo = _tile_geometry(
prepared, cx, cy, canvas.shape, profile.edge_blend, profile.tile_alpha
)
if geo is None:
continue
ys, xs, _rgb, a = geo
overlap = _overlap_fraction(coverage, ys, xs, a)
if overlap > profile.max_overlap:
continue
_paste_rgba(
canvas,
coverage,
prepared,
cx,
cy,
profile.edge_blend,
profile.tile_alpha,
)
reuse_counts[ti] = reuse_counts.get(ti, 0) + 1
placed += 1
if progress and placed % 10 == 0:
print(f"placed {placed}/{profile.max_placements} (tile≈{tile_size}px)", flush=True)
# Mild residual fill: boost uncovered with smaller opaque tiles
uncovered = coverage < 0.15
if np.any(uncovered) and placed < profile.max_placements:
ys, xs = np.where(uncovered[::8, ::8])
for yi, xi in zip(ys[:40], xs[:40], strict=False):
if placed >= profile.max_placements:
break
cy, cx = int(yi * 8), int(xi * 8)
if not (0 <= cy < H and 0 <= cx < W):
continue
ts = profile.tile_sizes[-1]
y0, x0 = max(0, cy - ts // 2), max(0, cx - ts // 2)
y1, x1 = min(H, y0 + ts), min(W, x0 + ts)
patch = master[y0:y1, x0:x1]
if patch.size < 16:
continue
idxs = rank_tiles(patch, library, top_k=3)
if not idxs:
continue
ti = idxs[0]
prepared = _prepare_tile(library[ti], ts, float(rng.uniform(-20, 20)), 1.0)
_paste_rgba(
canvas,
coverage,
prepared,
cx,
cy,
profile.edge_blend,
min(1.0, profile.tile_alpha + 0.05),
)
placed += 1
out = (np.clip(canvas, 0.0, 1.0) * 255.0).astype(np.uint8)
output_path.parent.mkdir(parents=True, exist_ok=True)
Image.fromarray(out, mode="RGB").save(output_path, optimize=True)
if progress:
fill = float(coverage.mean())
print(
f"done: {placed} placements, coverage≈{fill:.0%}, wrote {output_path}",
flush=True,
)
return output_path

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src/fractal_mosaic/logpolar.py Normal file
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"""Log-polar transform + FFT phase correlation for rotation/scale (Fractal-Mosaics core idea)."""
from __future__ import annotations
import numpy as np
from numpy.typing import NDArray
from scipy import ndimage
def to_gray_f32(rgb: NDArray[np.floating] | NDArray[np.integer]) -> NDArray[np.float32]:
arr = np.asarray(rgb, dtype=np.float32)
if arr.ndim == 2:
return arr
if arr.shape[2] >= 3:
r, g, b = arr[..., 0], arr[..., 1], arr[..., 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
return arr[..., 0]
def log_polar(
image: NDArray[np.floating],
*,
n_rho: int | None = None,
n_theta: int | None = None,
) -> NDArray[np.float32]:
"""Map Cartesian image to log-polar (rows=rho, cols=theta), matching upstream intent."""
gray = to_gray_f32(image)
h, w = gray.shape
cy, cx = (h - 1) / 2.0, (w - 1) / 2.0
n_rho = n_rho or max(32, min(h, w) // 2)
n_theta = n_theta or max(64, min(h, w))
max_rho = max(1.0, min(cx, cy, w - 1 - cx, h - 1 - cy))
# log spacing like transformImage_log.m (exp of linspace log)
rad = np.exp(np.linspace(np.log(1.0), np.log(max_rho), n_rho, dtype=np.float64))
theta = np.linspace(0.0, 2.0 * np.pi, n_theta, endpoint=False, dtype=np.float64)
tt, rr = np.meshgrid(theta, rad)
xx = rr * np.cos(tt) + cx
yy = rr * np.sin(tt) + cy
sampled = ndimage.map_coordinates(
gray, [yy, xx], order=1, mode="constant", cval=0.0
)
return sampled.astype(np.float32)
def estimate_rotation_scale(
reference: NDArray[np.floating],
moving: NDArray[np.floating],
*,
n_rho: int = 64,
n_theta: int = 128,
) -> tuple[float, float]:
"""
Estimate rotation (degrees) and scale (moving→reference) via log-polar phase correlation.
Returns (rotation_deg, scale). Scale ~1 means same size.
"""
a = log_polar(reference, n_rho=n_rho, n_theta=n_theta)
b = log_polar(moving, n_rho=n_rho, n_theta=n_theta)
# Normalize
a = a - a.mean()
b = b - b.mean()
fa = np.fft.fft2(a)
fb = np.fft.fft2(b)
cross = fa * np.conj(fb)
denom = np.abs(cross)
denom[denom < 1e-8] = 1e-8
cross /= denom
corr = np.fft.ifft2(cross).real
peak = np.unravel_index(int(np.argmax(corr)), corr.shape)
d_rho, d_theta = int(peak[0]), int(peak[1])
# Wrap shifts
if d_theta > n_theta // 2:
d_theta -= n_theta
if d_rho > n_rho // 2:
d_rho -= n_rho
rotation_deg = (d_theta / float(n_theta)) * 360.0
# Scale from rho shift: log-polar row index ≈ log(rho)
# Approximation: scale = exp(d_rho * log(max/min) / n_rho)
# With rho from 1..max_rho: delta_log ≈ d_rho * (log(max)-log(1))/(n_rho-1)
max_rho = float(min(reference.shape[:2]) / 2.0 - 1.0)
max_rho = max(max_rho, 2.0)
delta_log = d_rho * (np.log(max_rho) - np.log(1.0)) / max(n_rho - 1, 1)
scale = float(np.exp(delta_log))
# Clamp absurd scales from weak correlations
scale = float(np.clip(scale, 0.4, 2.5))
return float(rotation_deg), scale

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src/fractal_mosaic/profiles.py Normal file
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"""Named profiles mapped from upstream Fractal-Mosaics knobs (coarse for inbox)."""
from __future__ import annotations
from dataclasses import dataclass
from typing import Mapping
@dataclass(frozen=True)
class Profile:
name: str
# Master long-edge cap (pixels)
master_max: int
# Tile base sizes tried (largest first)
tile_sizes: tuple[int, ...]
# How many placements to attempt (approx)
max_placements: int
# CBIR: how many library candidates per site
candidates: int
# Rotation search step in degrees (smaller = slower/better)
rotation_step: int
# Max fractional overlap of coverage map before rejecting a site
max_overlap: float
# Edge fade as fraction of half-width (upstream perc_sloped ~0.1–0.25)
edge_blend: float
# Global multiply on tile alpha (1.0 = full)
tile_alpha: float
# Minimum tiles required in pool
min_tiles: int
# Grid stride for candidate centers as fraction of tile size
stride_frac: float
# Whether to composite over master (True) or black (False)
use_master_bg: bool
# Scale factors relative to tile_size tried per candidate
scales: tuple[float, ...]
PROFILES: Mapping[str, Profile] = {
"wenig": Profile(
name="wenig",
master_max=960,
tile_sizes=(220, 160, 110),
max_placements=80,
candidates=8,
rotation_step=30,
max_overlap=0.45,
edge_blend=0.22,
tile_alpha=0.92,
min_tiles=8,
stride_frac=0.55,
use_master_bg=True,
scales=(1.0, 0.85),
),
"mittel": Profile(
name="mittel",
master_max=1280,
tile_sizes=(200, 140, 100, 72),
max_placements=220,
candidates=12,
rotation_step=20,
max_overlap=0.35,
edge_blend=0.18,
tile_alpha=0.90,
min_tiles=20,
stride_frac=0.45,
use_master_bg=True,
scales=(1.1, 1.0, 0.85),
),
"dicht": Profile(
name="dicht",
master_max=1600,
tile_sizes=(180, 130, 90, 64, 48),
max_placements=480,
candidates=16,
rotation_step=15,
max_overlap=0.28,
edge_blend=0.14,
tile_alpha=0.88,
min_tiles=40,
stride_frac=0.35,
use_master_bg=True,
scales=(1.15, 1.0, 0.85, 0.7),
),
}
def get_profile(name: str) -> Profile:
key = name.strip().lower()
if key not in PROFILES:
known = ", ".join(sorted(PROFILES))
raise SystemExit(f"unknown profile {name!r} (expected one of: {known})")
return PROFILES[key]

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src/fractal_mosaic/retrieval.py Normal file
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"""CBIR-style prefilter (HSV / mean colour) — selection only, never recolors tiles."""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
import numpy as np
from numpy.typing import NDArray
from PIL import Image
IMAGE_SUFFIXES = {".png", ".jpg", ".jpeg", ".webp", ".bmp", ".tif", ".tiff"}
@dataclass
class TileRecord:
path: Path
mean_rgb: NDArray[np.float32]
hist: NDArray[np.float32]
# cached thumb for matching (RGB uint8)
thumb: NDArray[np.uint8]
def _hist_hsv(rgb: NDArray[np.uint8], bins: int = 8) -> NDArray[np.float32]:
"""Flattened 3D HSV histogram (normalized)."""
arr = rgb.astype(np.float32) / 255.0
r, g, b = arr[..., 0], arr[..., 1], arr[..., 2]
mx = np.maximum(np.maximum(r, g), b)
mn = np.minimum(np.minimum(r, g), b)
diff = mx - mn
s = np.where(mx > 1e-6, diff / mx, 0.0)
v = mx
h = np.zeros_like(mx)
mask = diff > 1e-6
# Hue in [0,1)
rc = ((g - b) / (diff + 1e-8)) % 6
gc = (b - r) / (diff + 1e-8) + 2
bc = (r - g) / (diff + 1e-8) + 4
h = np.where(mask & (mx == r), rc, h)
h = np.where(mask & (mx == g), gc, h)
h = np.where(mask & (mx == b), bc, h)
h = (h / 6.0) % 1.0
hist, _ = np.histogramdd(
np.stack([h.ravel(), s.ravel(), v.ravel()], axis=1),
bins=bins,
range=((0, 1), (0, 1), (0, 1)),
)
hist = hist.astype(np.float32)
hist /= max(float(hist.sum()), 1e-6)
return hist.ravel()
def load_tile_library(tile_dir: Path, thumb_size: int = 64) -> list[TileRecord]:
paths = sorted(
p
for p in tile_dir.rglob("*")
if p.is_file() and p.suffix.lower() in IMAGE_SUFFIXES and not p.name.startswith(".")
)
out: list[TileRecord] = []
for p in paths:
try:
with Image.open(p) as im:
im = im.convert("RGB")
thumb_im = im.copy()
thumb_im.thumbnail((thumb_size, thumb_size), Image.Resampling.LANCZOS)
thumb = np.asarray(thumb_im, dtype=np.uint8)
except OSError:
continue
mean = thumb.reshape(-1, 3).mean(axis=0).astype(np.float32)
hist = _hist_hsv(thumb)
out.append(TileRecord(path=p, mean_rgb=mean, hist=hist, thumb=thumb))
return out
def rank_tiles(
patch_rgb: NDArray[np.uint8],
library: list[TileRecord],
*,
top_k: int,
) -> list[int]:
"""Return indices of best matching tiles (histogram L2 + mean colour)."""
if not library:
return []
ph = _hist_hsv(patch_rgb)
pm = patch_rgb.reshape(-1, 3).mean(axis=0).astype(np.float32)
scores = []
for i, t in enumerate(library):
d_h = float(np.linalg.norm(ph - t.hist))
d_m = float(np.linalg.norm(pm - t.mean_rgb) / 255.0)
scores.append((d_h + 0.35 * d_m, i))
scores.sort(key=lambda x: x[0])
return [i for _, i in scores[:top_k]]