caire README

The project's README from github.com/esimov/caire. As one markdown file: README.md.

Caire Logo

build Go Reference license release homebrew caire

Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper.

How does it work

The process illustrated:

Original image Energy map Seams applied
original sobel debug

Features

Key features which differentiates this library from the other existing open source solutions:

Install

First, install Go, set your GOPATH, and make sure $GOPATH/bin is on your PATH.

$ go install github.com/esimov/caire/cmd/caire@latest

MacOS (Brew) install

The library can also be installed via Homebrew.

$ brew install caire

Usage

$ caire -in input.jpg -out output.jpg

Supported commands:

$ caire --help

The following flags are supported:

Flag Default Description
in - Input file
out - Output file
width n/a New width
height n/a New height
preview true Show GUI window
perc false Reduce image by percentage
square false Reduce image to square dimensions
blur 4 Blur radius
sobel 2 Sobel filter threshold
debug false Use debugger
face false Use face detection
angle float Plane rotated faces angle
mask string Mask file path
rmask string Remove mask file path
color string Seam color (default #ff0000)
shape string Shape type used for debugging: circle,line (default circle)

Face detection

The library is capable of detecting human faces prior resizing the images by using the lightweight Pigo (https://github.com/esimov/pigo) face detection library.

The image below illustrates the application capabilities for human face detection prior resizing. It's clearly visible that with face detection activated the algorithm will avoid cropping pixels inside the detected faces, retaining the face zone unaltered.

Original image With face detection Without face detection
Original With Face Detection Without Face Detection

Sample image source

GUI progress indicator

GUI preview

A GUI preview mode is also incorporated into the library for in time process visualization. The Gio GUI library has been used because of its robustness and modern architecture. Prior running it please make sure that you have installed all the required dependencies noted in the installation section (https://gioui.org/#installation) .

The preview window is activated by default but you can deactivate it any time by setting the -preview flag to false. When the images are processed concurrently from a directory the preview mode is deactivated.

Face detection to avoid face deformation

In order to detect faces prior rescaling, use the -face flag. There is no need to provide a face classification file, since it's already embedded into the generated binary file. The sample code below will resize the provided image with 20%, but checks for human faces in order tot avoid face deformations.

For face detection related settings please check the Pigo documentation.

$ caire -in input.jpg -out output.jpg -face=1 -perc=1 -width=20

Support for stdin and stdout pipe commands

You can also use stdin and stdout with -:

$ cat input/source.jpg | caire -in - -out - >out.jpg

in and out default to - so you can also use:

$ cat input/source.jpg | caire >out.jpg
$ caire -out out.jpg < input/source.jpg

You can provide also an image URL for the -in flag or even use curl or wget as a pipe command in which case there is no need to use the -in flag.

$ caire -in <image_url> -out <output-folder>
$ curl -s <image_url> | caire > out.jpg

Process multiple images from a directory concurrently

The library can also process multiple images from a directory concurrently. You have to provide only the source and the destination folder and the new width or height in this case.

$ caire -in <input_folder> -out <output-folder>

Support for multiple output image type

There is no need to define the output file type, just use the correct extension and the library will encode the image to that specific type.

Other options

In case you wish to scale down the image by a specific percentage, it can be used the -perc boolean flag. In this case the values provided for the width and height are expressed in percentage and not pixel values. For example to reduce the image dimension by 20% both horizontally and vertically you can use the following command:

$ caire -in input/source.jpg -out ./out.jpg -perc=1 -width=20 -height=20 -debug=false

Also the library supports the -square option. When this option is used the image will be resized to a square, based on the shortest edge.

When an image is resized on both the X and Y axis, the algorithm will first try to rescale it prior resizing, but also will preserve the image aspect ratio. The seam carving algorithm is applied only to the remaining points. Ex. : given an image of dimensions 2048x1536 if we want to resize to the 1024x500, the tool first rescale the image to 1024x768 and then will remove only the remaining 268px.

Masks support:

Mask Mask removal

Caire integrations

snapcraft caire

Results

Shrunk images

Original Shrunk
broadway_tower_edit broadway_tower_edit
waterfall waterfall
dubai dubai
boat boat

Enlarged images

Original Extended
gasadalur gasadalur
dubai dubai

Useful resources

Author

License

Copyright © 2018 Endre Simo

This project is under the MIT License. See the LICENSE file for the full license text.

caire (esimov/caire)

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10.7K ·GitHub
What is this?Caire is a content aware image resize library based on the Seam Carving for Content-Aware Image Resizing paper. It ships as both a Go library and a command-line tool that can shrink or enlarge images vertically and horizontally.
How is that different from just squashing an image to make it smaller?Normal resizing scales every pixel; Caire generates an energy map via edge detection, then uses dynamic programming to find low-energy seams of the least important pixels and removes them one by one, inserting new seams when enlarging. Important areas carry a high energy cost, so they keep their proportions.
How do I get it running?Install Go, set your GOPATH and make sure $GOPATH/bin is on your PATH, then build caire from source. On macOS you can install it via Homebrew instead.
Anything I should watch out for before running it on my photos?Face detection is off by default — without the face flag, seams can cut straight through people. A GUI preview window also pops up on every run unless you turn the preview flag off.
Do I need to know Go to actually use it?No — the command-line interface is driven by plain flags like in, out, width and height, so anyone comfortable with a terminal can use it. Writing Go only matters if you want to import caire as a library in your own programs.
How does it keep faces from getting distorted?Before resizing, caire runs face detection with the lightweight Pigo library; pixels inside detected faces are excluded from seam removal, so the face zone stays unaltered. The angle flag adjusts the detection plane for rotated faces.
Can I tell it which parts of the picture to protect or force it to delete?Yes — a protective mask shields a region from being carved away, and a removal mask marks areas that should be discarded first.
What can it do besides shrinking photos?It enlarges images too, and can square a picture or scale it proportionally with a single flag. It writes jpg, jpeg, png and bmp files, supports stdin and stdout pipes, and processes whole directories recursively and concurrently.
Can I wire it into scripts or a serverless pipeline?Yes — pipes let it chain into shell workflows, it runs as a serverless function via OpenFaaS, and it is also distributed as a snap ($ snap run caire).
What are similar projects?usql, veet, hexyl, fx, spotify-tui, qrcp, noai-watermark, homebrew-cask
Creator
esimov
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90.2 MB
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