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copilot
564636a74e Tune RST profiles for high-res dense mosaics with full coverage. 
Raise master_max and piece counts, cache tile prep, and scale mittel/dicht
from the validated wenig density ladder so each profile can fill the canvas.

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-09-29 00:38:02 +02:00
copilot
1d90b820ee Add DESIGN.md for Python RST collage engine 
Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-09-24 17:04:33 +02:00
copilot
5bfeae238d Add Python fractal-mosaic CLI and unpack MATLAB reference 
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>
 2026-09-24 17:03:12 +02:00
21 changed files with 4155 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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# DESIGN — Fractal mosaic Python engine (Tower)
## Goal
Headless RST collage generator for the Tower mosaik inbox, adapted from Seth Benton’s Fractal-Mosaics (GPL).
## Pipeline
1. Load tile pool; cache mean RGB + HSV histogram (CBIR prefilter).
2. Resize master to profile `master_max`.
3. For each tile size (largest first), scan uncovered centers.
4. Rank tiles by histogram/mean distance (**selection only — no recolor**).
5. Estimate rotation/scale via log-polar phase correlation; snap to discrete rot/scale candidates.
6. Place with edge alpha blend if RMS and overlap pass thresholds.
7. Write PNG.
## Profiles
See `src/fractal_mosaic/profiles.py`: `wenig`, `mittel`, `dicht`.
## Coverage
Profiles target ~99% canvas coverage via denser RST passes + residual fill, while
keeping Fractal RST (rotation / scale / translate) — not a classic grid mosaic.
Pool images may be reused heavily when the library is small.
## Tile size (readability)
Piece sizes stay in a recognizable absolute range while `master_max` is high so
the canvas holds many pieces. Relative density ladder (largest tile / master):
wenig ≈1.75%, mittel ≈1.3%, dicht ≈1.0%. `max_placements` scales with
`(master_max / mean_tile)^2` so each profile can still hit ~99% coverage.
Scales never enlarge above 1.0.
## Non-goals (v1)
- Faithful bit-identical MATLAB port
- Color transfer / hue variants
- GPU acceleration

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GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
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)).
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’.
**Constraints for Tower v1:** geometric transforms + edge alpha only — **no tile recoloring**.
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 ;)
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)).
# Documentation
* [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)
| Path | Role |
|------|------|
| `reference/upstream-matlab/` | Unpacked `.m` files from upstream package |
| `reference/package/` | Original `FractalMosaics_package.tar.gz` |
| `src/fractal_mosaic/` | Python engine + CLI |
| docs site (`index.html`, …) | Upstream gh-pages documentation (preserved) |

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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
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
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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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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 Fractal RST collage — rebuild master from pool images, ~full coverage."""
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)
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,
*,
cache: dict[tuple[int, int, int, int], Image.Image] | None = None,
) -> Image.Image:
# Quantize rot for cache keys (profile steps are coarse anyway).
rot_q = int(round(rotation_deg))
sc_q = int(round(scale * 100))
key = (id(tile), size, rot_q, sc_q)
if cache is not None and key in cache:
return cache[key]
if tile.square_rgba is None:
with Image.open(tile.path) as im:
im = im.convert("RGBA")
side = min(im.size)
left = (im.size[0] - side) // 2
top = (im.size[1] - side) // 2
tile.square_rgba = im.crop((left, top, left + side, top + side))
im = tile.square_rgba
# Center-crop to square then resize — crop only, no recolor
target = max(8, int(round(size * scale)))
out = im.resize((target, target), Image.Resampling.BILINEAR)
if abs(rotation_deg) > 0.5:
out = out.rotate(rotation_deg, resample=Image.Resampling.BILINEAR, expand=True)
if cache is not None:
cache[key] = out
return out
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 piece 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 _reuse_limit(profile: Profile, library_n: int, placed: int) -> int:
"""Allow enough repeats that a small pool can still cover the canvas."""
# Rough budget: each piece covers ~tile²; need many repeats for full fill.
base = max(profile.reuse_soft_cap, (profile.max_placements // max(library_n, 1)) + 4)
# Late in the run, loosen further so residual fill isn't starved.
if placed > profile.max_placements // 2:
return base * 2
return base
def _accept_rms(tile_size: int, *, force: bool) -> float:
"""RMS gate vs master patch. `force` = residual fill must place something."""
if force:
return 1e9
# Looser than v1 so colour-diverse pools still rebuild structure.
return 95.0 + (220 - min(tile_size, 220)) * 0.12
def _pick_best_rst(
patch: NDArray[np.uint8],
library: list[TileRecord],
idxs: list[int],
tile_size: int,
profile: Profile,
reuse_counts: dict[int, int],
reuse_cap: int,
*,
prep_cache: dict[tuple[int, int, int, int], Image.Image] | None = None,
) -> tuple[float, Image.Image, int] | None:
# Small pieces: skip log-polar FFT (too costly at dense stride) and use a
# thinner discrete rot/scale grid so many tiny placements stay practical.
small = tile_size <= 120
best: tuple[float, Image.Image, int] | None = None
for ti in idxs:
if reuse_counts.get(ti, 0) >= reuse_cap:
continue
tile = library[ti]
if small:
rot_est, scale_est = 0.0, 1.0
else:
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
step = float(profile.rotation_step)
rot_list = [0.0, rot_est]
muls = (-1, 1) if small else (-2, -1, 1, 2)
for mul in muls:
rot_list.append(rot_est + mul * step)
rot_list = sorted({((r + 180) % 360) - 180 for r in rot_list})
scales = list(profile.scales)
if not small and 0.5 <= scale_est <= 2.0:
scales = sorted(set(scales + [float(np.clip(scale_est, 0.7, 1.0))]))
for rot in rot_list:
for sc in scales:
prepared = _prepare_tile(tile, tile_size, rot, sc, cache=prep_cache)
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)
return best
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] = {}
prep_cache: dict[tuple[int, int, int, int], Image.Image] = {}
for tile_size in profile.tile_sizes:
if placed >= profile.max_placements:
break
if float(coverage.mean()) >= profile.target_coverage:
break
stride = max(6, int(tile_size * profile.stride_frac))
centers: list[tuple[float, int, int]] = []
for y in range(tile_size // 2, max(tile_size // 2 + 1, H - tile_size // 2), stride):
for x in range(tile_size // 2, max(tile_size // 2 + 1, 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])
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 + 0.12:
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
reuse_cap = _reuse_limit(profile, len(library), placed)
idxs = rank_tiles(patch, library, top_k=profile.candidates)
best = _pick_best_rst(
patch,
library,
idxs,
tile_size,
profile,
reuse_counts,
reuse_cap,
prep_cache=prep_cache,
)
if best is None:
# All candidates hit reuse cap — try full library once
best = _pick_best_rst(
patch,
library,
list(range(len(library))),
tile_size,
profile,
reuse_counts,
reuse_cap * 3,
prep_cache=prep_cache,
)
if best is None:
continue
err, prepared, ti = best
if err > _accept_rms(tile_size, force=False):
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 % 25 == 0:
print(
f"placed {placed}/{profile.max_placements} "
f"(size≈{tile_size}px, coverage≈{float(coverage.mean()):.0%})",
flush=True,
)
# Residual Fractal fill until target coverage (still RST pieces, forced place).
fill_pass = 0
while (
float(coverage.mean()) < profile.target_coverage
and placed < profile.max_placements
and fill_pass < 400
):
fill_pass += 1
uncovered = coverage < 0.35
if not np.any(uncovered):
break
step = max(4, min(profile.tile_sizes) // 3)
ys_u, xs_u = np.where(uncovered[::step, ::step])
if len(ys_u) == 0:
break
order = rng.permutation(len(ys_u))
progressed = False
for oi in order[: max(40, len(order) // 4)]:
if placed >= profile.max_placements:
break
if float(coverage.mean()) >= profile.target_coverage:
break
cy, cx = int(ys_u[oi] * step), int(xs_u[oi] * step)
if not (0 <= cy < H and 0 <= cx < W):
continue
if coverage[cy, cx] >= 0.55:
continue
ts = profile.tile_sizes[min(fill_pass % len(profile.tile_sizes), len(profile.tile_sizes) - 1)]
# Prefer smaller pieces late
if fill_pass > 3:
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
reuse_cap = _reuse_limit(profile, len(library), placed) * 4
idxs = rank_tiles(patch, library, top_k=min(profile.candidates, len(library)))
best = _pick_best_rst(
patch,
library,
idxs,
ts,
profile,
reuse_counts,
reuse_cap,
prep_cache=prep_cache,
)
if best is None:
ti = int(idxs[0]) if idxs else int(rng.integers(0, len(library)))
prepared = _prepare_tile(
library[ti],
ts,
float(rng.uniform(-25, 25)),
float(rng.choice(profile.scales)),
cache=prep_cache,
)
err = 0.0
else:
err, prepared, ti = best
if err > _accept_rms(ts, force=True):
continue
_paste_rgba(
canvas,
coverage,
prepared,
cx,
cy,
profile.edge_blend,
min(1.0, profile.tile_alpha + 0.02),
)
reuse_counts[ti] = reuse_counts.get(ti, 0) + 1
placed += 1
progressed = True
if progress and fill_pass % 5 == 0:
print(
f"fill pass {fill_pass}: placed={placed}, coverage≈{float(coverage.mean()):.0%}",
flush=True,
)
if not progressed:
break
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

85
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

105
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
# RST piece sizes tried (largest first) — pool images, not a grid
tile_sizes: tuple[int, ...]
# Hard cap on placements (must be high enough for ~full coverage)
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 pool images required
min_tiles: int
# Grid stride for candidate centers as fraction of piece size
stride_frac: float
# Start from master (True) then bury it under RST pieces; False = black
use_master_bg: bool
# Scale factors relative to tile_size tried per candidate
scales: tuple[float, ...]
# Target mean coverage before stopping residual fill (0–1)
target_coverage: float
# Soft reuse cap per pool image (scaled up automatically if pool is small)
reuse_soft_cap: int
# Density ladder (relative to wenig baseline that looked good):
# - master_max ↑, largest tile / master ≈ 1.75% → 1.3% → 1.0%
# - max_placements ≈ k * (master_max / mean_tile)^2 so coverage can hit ~99%
PROFILES: Mapping[str, Profile] = {
"wenig": Profile(
name="wenig",
master_max=3200,
tile_sizes=(56, 40, 28, 20, 14),
max_placements=40000,
candidates=8,
rotation_step=25,
max_overlap=0.74,
edge_blend=0.18,
tile_alpha=0.98,
min_tiles=8,
stride_frac=0.34,
use_master_bg=True,
scales=(1.0, 0.75, 0.55),
target_coverage=0.99,
reuse_soft_cap=200,
),
"mittel": Profile(
name="mittel",
master_max=4000,
tile_sizes=(52, 38, 26, 18, 13),
max_placements=90000,
candidates=10,
rotation_step=20,
max_overlap=0.68,
edge_blend=0.16,
tile_alpha=0.97,
min_tiles=20,
stride_frac=0.32,
use_master_bg=True,
scales=(1.0, 0.75, 0.55),
target_coverage=0.99,
reuse_soft_cap=280,
),
"dicht": Profile(
name="dicht",
master_max=4800,
tile_sizes=(48, 34, 24, 16, 12),
max_placements=150000,
candidates=12,
rotation_step=15,
max_overlap=0.62,
edge_blend=0.14,
tile_alpha=0.96,
min_tiles=40,
stride_frac=0.28,
use_master_bg=True,
scales=(1.0, 0.75, 0.55),
target_coverage=0.99,
reuse_soft_cap=360,
),
}
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]

95
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]
# center-cropped square RGBA — loaded lazily for placement
square_rgba: Image.Image | None = None
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]]