| Tiles | Photomosaic (Fair tile usage) |
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| Tiles Sorted by RGB sum | Photomosaic (Best-fit) |
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- Distinguishing Features of this Photomosaic Maker
- Getting Started
- Command line usage
- Utility Script: download profile pictures of your WeChat friends
- Credits (Names in alphabetical order)
A number of photomosaic makers already exist (like mosaic and Photomosaic-generator), but this photomosaic maker has the following unique features
- Can trade off between the fairness of the tiles and quality of the constructed photomosaic
- Can ensure each tile is used exactly N times if desired (N is customizable)
- Supports non square tile size
- Supports photomosaic videos
- Supports maintaining transparency
- Supports dithering
- Supports saliency detection
- Has a graphical user interface
- Optional GPU acceleration
You can either use our pre-built binaries from release or directly run our python script.
If you need GPU acceleration or need to make photomosaic videos, please refer to the Command line usage
Binaries can be downloaded from release.
On Windows and MacOS, my program may be blocked because it is not signed (signing costs money!). Don't worry as there is no security risk. On MacOS or Linux, after downloading the binary, you may need to add executing permission. Open your terminal, go to the file's directory and type
chmod +x ./photomosaic-maker-5.1-macos-x64Then you can run from terminal as
./photomosaic-maker-5.1-macos-x64First, you need Python >= 3.7 with pip. You can install dependencies by running
pip install -r requirements.txtNote: if have problems installing the packages due to missing dependencies, you can use this command (on Linux) to install them one by one.
cat requirements.txt | xargs -n 1 pip installIf you want GPU acceleration, you need to install cupy. Please consult the cupy documentation.
Then, you can either use the GUI by running or refer to the commandline usage.
python gui.pyIf you see errors like No module named '_tkinter', you may need to install tkinter in your system like this (command will vary depending on the OS)
sudo apt-get install python3-tkFor command line usage and documentation regarding different photmosaic options, please refer to the section below.
If you do not wish to use the GUI, a command line interface is also available. Make sure that you've installed dependencies in the section above.
python make_img.py --path img/zhou --sort bgr_sum --size 50 --out examples/sort-bgr.png--size takes one or two arguments. If only one is specified, it is interpreted as the tile width and tile height will be inferred from the aspect ratios of the tiles provided (this corresponds to the infer height option in the GUI). If two are specified, they are interpreted as width and height.
Use --ratio w h to change the aspect ratio, whose default is 16:9. E.g. --ratio 21 9 specifies the aspect ratio to be 21:9.
Note: when the tiles are a bit short to completely fill the grid, white tiles will be added.
Result:
To make a photomosaic, specify the path to the destination image using --dest_img
This fitting option ensures that each tile is used for the same amount of times, but is the most computationally and memory intensive option.
a few tiles might be used one more time than others. This may happen when the number of tiles is not an integer multiple of the blocks of the destination image.
python make_img.py --path img/zhou --dest_img examples/dest.jpg --size 25 --dup 6 --out examples/fair-dup-10.png--dup 6 specifies that each tile needs to be used 6 times (i.e. duplicates the set of tiles 6 times). Increase that number if you don't have enough source tiles or you want a better fitting result. This can be a non integer too. For example, --dup 0.5 means only 50% of the tiles will be used, and --dup 2.5 means all tiles on average will be used 2.5 times (half of the tiles will be used 2 times and the other half will be used 3 times).
To make sure the computation completes within a reasonable amount of time, it is recommended that you use less than 6000 tiles after duplication. Tile number larger than 6000 will probably takes longer than a minute to compute. Note that this recommended limit does not apply for the best fit option (see section below).
| Original | Fitting Result |
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This fitting option just selects the best subset of tiles you provided to approximate your destination tiles. Each tile in that subset will be used for an arbitrary number of times.
Add --unfair flag to enable this option. You can also specify --max_width to change the width of the grid. The height will be automatically calculated based on the max_width provided. Generally, a larger grid will give a better result. The default value is 80.
python make_img.py --path img/zhou --dest_img examples/dest.jpg --size 25 --unfair --max_width 56 --out examples/best-fit.png| Original | Fitting Result |
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Optionally, you can specify the --freq_mul (frequency multiplier) parameter that trade offs between the fairness of the tiles and quality of the mosaic.
python3 make_img.py --path img --out best-fit.png --dest_img img/1.png --size 25 --unfair --freq_mul 1.0The larger the freq_mul, more tiles will be used to construct the photomosaic, but the quality will deteriorate. The results under different freq_mul are shown below. Note that if you need a large freq_mul, you will better off by going for the fair tile usage (see section above) instead.
This option makes photomosaic only for the salient part of the destination image. Rest of the area will be transparent.
Add --salient flag to enable this option. You can still specify whether each tile is used for the same amount of times with the --unfair flag.
Use --lower_thresh to specify the threshold for object detection. The threshold ranges from 0.0 to 1.0; a higher threshold would lead to less object area. The default threshold is 0.5.
python make_img.py --path img/zhou --dest_img examples/messi.jpg --size 25 --salient --lower_thresh 0.15 --dup 5 --out examples/messi-fair.pngpython make_img.py --path img/zhou --dest_img examples/messi.jpg --size 25 --salient --lower_thresh 0.15 --unfair --max_width 115 --out examples/messi-unfair.png| Original | Unfair-Fitting Result | Fair-Fitting Result |
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If your destination image has transparent regions, you can add --transparent flag to only put tiles for the non transparent part. In this way, the transparent regions are maintained in the resulting photomosaic. Note that this option is not compatible with --dithering and --salient.
| Original | Unfair-Fitting Result (freq_mul=0.5) |
Fair-Fitting Result |
|---|---|---|
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To enhance the effect of the photomosaic, you can enable alpha or brightness blending. Use the --blending option to select the types of blending and --blending_level to change the level of blending.
# alpha blending
python make_img.py --path img/zhou --dest_img examples/dest.jpg --size 25 --dup 8 --blending alpha --blending_level 0.25 --out examples/blend-alpha-0.25.png
# brightness blending
python make_img.py --path img/zhou --dest_img examples/dest.jpg --size 25 --dup 8 --blending brightness --blending_level 0.25 --out examples/blend-brightness-0.25.png| Fair tile usage, no blending | Alpha blending (25%) | Brightness blending (25%) |
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See https://en.wikipedia.org/wiki/Dither for a detailed explanation of dithering
Dithering can be used to reduce color banding when there exists a color gradient. To enable dithering, add --dither flag. My implementation uses Floyd–Steinberg dithering. Note that this option is incompatible with --transparent and --salient.
python make_img.py --path img/zhou --dest_img examples/dest2.jpg --size 10 --unfair --max_width 200 --freq_mul 0.0 --dither --out examples/dither.png| Original image | Best fit, no dither | Best fit, dither |
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While dithering works the best when freq_mul is set to zero, it can still work and provide some visual differences when freq_mul > 0.
# dither when freq_mul is 0.1
python make_img.py --path img/zhou --dest_img examples/dest2.jpg --size 10 --unfair --max_width 200 --freq_mul 0.1 --dither --deterministic --out examples/f-dither.png| Original image | freq_mul = 0.1, no dither |
freq_mul = 0.1, dither |
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Note that dithering is not supported in fair mode, when randomization is enabled or when saliency is enabled. Also, dithering is not recommended to use with --gpu, or you may experience slow computation compared to CPU processing.
It is possible to make a photomosaic video simply by repeating the methods listed in Option 2 to every certain frame (specified by --skip_frame) of the video. You can pass the path of the video with --dest_img and add the --video flag to tell the program it is a video. This is much faster than processing the video manually frame by frame (e.g. pass different dest_img each time), because a lot of information is cached and can be reused between frames. Example:
python make_img.py --path img/catsdogs --dest_img img/2out.mp4 --size 20 --unfair --max_width 100 --freq_mul 2 --out tests/video/frame.png --gpu --video --skip_frame 2Since photomosaic version 5.2, the output will be uncompressed individual frames rather than a video. This allows users to use custom video encoding format.
Do note that some options are not supported, and some options are slower than other. Generally, saliency is not recommended to use on videos due to its long computational time and difficulty to tune. Each frame might need its own threshold.
| Saliency/Fairness | Fair | Unfair |
|---|---|---|
| saliency enabled | Unsupported | Slow |
| saliency disabled | Very slow | Fast |
Different photomosaic making options have different computational complexity. The following table shows the time and space complexity of different cases. Here, n is the number of tiles (after duplication in fair mode), m is the number of pixels in the destination image, and k is the number of tiles used in the unfair mode (this is equal to your specified max_width multiplied by the aspect ratio of your destination image).
| Type of photomosaic | Time complexity | Space complexity | GPU acceleration |
|---|---|---|---|
| Fair | O(nm + n^3) |
O(nm) |
partial (~10x speed up for the nm part only) |
| Unfair, freq_mul > 0 | O(nm + nk log n) |
O(m + n) |
full (~5-10x speed up) |
| Unfair, freq_mul = 0 | O(nm + nk) |
O(m + n) |
full (~5-10x speed up) |
Takeaway 1:
The high (cubic) computational complexity of the fair mode means that the computation time grows much faster with respect to the number of tiles. It typically takes 30 seconds for 5000 tiles and 5 minutes for 10000 tiles. For large tile count, unless you need strict fair tile usage, you should go for the unfair mode and set freq_mul appropriately.
Takeaway 2:
Notice the role of m in the complexity. If you have a high-definition destination image (e.g. 8000x6000) and notice the computation time is long, you can first downsample it so the number of pixels (m) will be lower. Do note that over downsampling will reduce the quality of the photomosaic.
The --num_process option specifies the number of processes (cpu cores) to use. This defaults to half of you available logical CPUs. However, this only applies to the reading tiles phase and photomosaic video processing. For photomosaic video, if you have a small number of tiles or a large number of available CPU cores, using multiprocessing may be faster than enabling GPU acceleration.
For command line, GPU acceleration can be enabled with the --gpu flag. For GUI (gui.py), it will be automatically enabled if you have cupy installed. However, note it can only provide the listed speedup if nm is large, typically >= 10^10. Another way to judge whether GPU acceleration could be useful is observe the Distance matrix size message from the log. Typically, Distance matrix size >= 100MB work great on GPU.
python make_img.py -h will give you all the available command line options.
$ python make_img.py --help
usage: make_img.py [-h] [--path PATH] [--recursive] [--num_process NUM_PROCESS] [--out OUT] [--size SIZE [SIZE ...]]
[--quiet] [--auto_rotate {-1,0,1}] [--resize_opt {center,stretch,fit}] [--gpu]
[--mem_limit MEM_LIMIT] [--tile_info_out TILE_INFO_OUT] [--ratio RATIO RATIO]
[--sort {none,bgr_sum,av_hue,av_sat,av_lum,rand}] [--rev_row] [--rev_sort] [--dest_img DEST_IMG]
[--colorspace {hsv,hsl,bgr,lab,luv}] [--metric {euclidean,cityblock,chebyshev,cosine}]
[--transparent] [--unfair] [--max_width MAX_WIDTH] [--freq_mul FREQ_MUL] [--dither]
[--deterministic] [--dup DUP] [--salient] [--lower_thresh LOWER_THRESH]
[--blending {alpha,brightness}] [--blending_level BLENDING_LEVEL] [--video]
[--skip_frame SKIP_FRAME] [--exp]
optional arguments:
-h, --help show this help message and exit
--path PATH Path to the tiles (default: None)
--recursive Whether to read the sub-folders for the specified path (default: False)
--num_process NUM_PROCESS
Number of processes to use for parallelizable operations (default: 8)
--out OUT The filename of the output collage/photomosaic (default: result.png)
--size SIZE [SIZE ...]
Width and height of each tile in pixels in the resulting collage/photomosaic. If two numbers
are specified, they are treated as width and height. If one number is specified, the number
is treated as the width and the height is inferred from the aspect ratios of the images
provided. (default: (50,))
--quiet Do not print progress message to console (default: False)
--auto_rotate {-1,0,1}
Options to auto rotate tiles to best match the specified tile size. 0: do not auto rotate. 1:
attempt to rotate counterclockwise by 90 degrees. -1: attempt to rotate clockwise by 90
degrees (default: 0)
--resize_opt {center,stretch,fit}
How to resize each tile so they have the desired aspect ratio and size, which can be
specified fully or partially by --size. Center: crop the largest rectangle from the center.
Stretch: stretch the tile. Fit: pad the tiles with white background (default: center)
--gpu Use GPU acceleration. Requires cupy to be installed and a capable GPU. Note that USUALLY this
is useful when you: 1. have a lot of tiles (typically > 10000), and2. are using the unfair
mode, and3. (for photomosaic videos only) only have few cpu coresAlso note: enabling GPU
acceleration will disable multiprocessing on CPU for videos (default: False)
--mem_limit MEM_LIMIT
The APPROXIMATE memory limit in MB when computing a photomosaic in unfair mode. Applicable
both CPU and GPU computing. If you run into memory issues when using GPU, try reduce this
memory limit (default: 4096)
--tile_info_out TILE_INFO_OUT
Path to save the list of tile filenames for the collage/photomosaic. If empty, it will not be
saved. (default: )
--ratio RATIO RATIO Aspect ratio of the output image (default: (16, 9))
--sort {none,bgr_sum,av_hue,av_sat,av_lum,rand}
Sort method to use (default: bgr_sum)
--rev_row Whether to use the S-shaped alignment. (default: False)
--rev_sort Sort in the reverse direction. (default: False)
--dest_img DEST_IMG The path to the destination image that you want to build a photomosaic for (default: )
--colorspace {hsv,hsl,bgr,lab,luv}
The colorspace used to calculate the metric (default: lab)
--metric {euclidean,cityblock,chebyshev,cosine}
Distance metric used when evaluating the distance between two color vectors (default:
euclidean)
--transparent Enable transparency masking. The transparent regions of the destination image will be
maintained in the photomosaicCannot be used together with --salient (default: False)
--unfair Whether to allow each tile to be used different amount of times (unfair tile usage).
(default: False)
--max_width MAX_WIDTH
Maximum width of the collage. This option is only valid if unfair option is enabled (default:
80)
--freq_mul FREQ_MUL Frequency multiplier to balance tile fairless and mosaic quality. Minimum: 0. More weight
will be put on tile fairness when this number increases. (default: 0.0)
--dither Whether to enabled dithering. You must also specify --deterministic if enabled. (default:
False)
--deterministic Do not randomize the tiles. This option is only valid if unfair option is enabled (default:
False)
--dup DUP If a positive integer: duplicate the set of tiles by how many times. Can be a fraction
(default: 1)
--salient Make photomosaic for salient objects only (default: False)
--lower_thresh LOWER_THRESH
The threshold for saliency detection, between 0.0 (no object area = blank) and 1.0 (maximum
object area = original image) (default: 0.5)
--blending {alpha,brightness}
The types of blending used. alpha: alpha (transparency) blending. Brightness: blending of
brightness (lightness) channel in the HSL colorspace (default: alpha)
--blending_level BLENDING_LEVEL
Level of blending, between 0.0 (no blending) and 1.0 (maximum blending). Default is no
blending (default: 0.0)
--video Make a photomosaic video from dest_img which is assumed to be a video (default: False)
--skip_frame SKIP_FRAME
Make a photomosaic every this number of frames (default: 1)
--exp Do experiments (for testing only) (default: False)
If you have a WeChat account, an utility script extract_img.py is provided to download your friends' profile pictures so you can make a photomosaic using them. To use this script, you need to have itchat-uos installed
pip install itchat-uosThen, use --dir to specify the directory to store the profile pictures of your WeChat friends.
python extract_img.py --dir imgYou can also download the group members' profiles images from a group chat
python extract_img.py --dir img --type groupchat --name "groupchatname"You can download members' profile pictures from all your groupchats if you omit the --name argument
python extract_img.py --dir img --type groupchatYou can download profile pictures from both your friends and members from all your groupchats by specifying --type all.
python extract_img.py --dir img --type all- Due to unknown issues, sometimes some profile pictures are not available, so they will be blank and unusable. The photomosaic maker will automatically ignore them when loading images.
- When you download a large amount of profile pictures at once, WeChat may block you from downloading more. This will appear as
timeout downloading pics, retrying.... attempt xin terminal. When this happens, you can terminate the program and run it again a day after. Already downloaded profile pictures will not be downloaded again.
Hanzhi Zhou (hanzhi713): Main algorithm and GUI implementation
Kaiying Shan (kaiyingshan): Saliency idea and implementation
Xinyue Lin: Idea for the "Best-fit"
Yufeng Chi (T-K) : Initial Idea, crawler