caire

README

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

How does it work

• An energy map (edge detection) is generated from the provided image.
• The algorithm tries to find the least important parts of the image taking into account the lowest energy values.
• Using a dynamic programming approach the algorithm will generate individual seams across the image from top to down, or from left to right (depending on the horizontal or vertical resizing) and will allocate for each seam a custom value, the least important pixels having the lowest energy cost and the most important ones having the highest cost.
• We traverse the image from the second row to the last row and compute the cumulative minimum energy for all possible connected seams for each entry.
• The minimum energy level is calculated by summing up the current pixel value with the lowest value of the neighboring pixels obtained from the previous row.
• We traverse the image from top to bottom and compute the minimum energy level. For each pixel in a row we compute the energy of the current pixel plus the energy of one of the three possible pixels above it.
• Find the lowest cost seam from the energy matrix starting from the last row and remove it.
• Repeat the process.

The process illustrated:

Original image	Energy map	Seams applied

Features

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

• GUI progress indicator
• Customizable command line support
• Support for both shrinking or enlarging the image
• Resize image both vertically and horizontally
• Face detection to avoid face deformation
• Support for multiple output image type (jpg, jpeg, png, bmp, gif)
• Support for stdin and stdout pipe commands
• Can process whole directories recursively and concurrently
• Use of sobel threshold for fine tuning
• Use of blur filter for increased edge detection
• Support for squaring the image with a single command
• Support for proportional scaling
• Support for protective mask
• Support for removal mask

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 library does not require to already have OpenCV installed.

The image below illustrates the application capabilities for human face detection prior resizing. It's clearly visible from the image 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

Sample image source

Install

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

$ export GOPATH="$HOME/go"
$ export PATH="$PATH:$GOPATH/bin"

Next download the project and build the binary file.

$ go get -u -f github.com/esimov/caire/cmd/caire
$ go install

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	1	Blur radius
sobel	10	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

GUI progress indicator

A GUI preview window is also supported for showing the resizing process. For the GUI part I've opted of using the Gio library for its robustness and modern architecture. But in order to use it you have to install all of its dependencies. So please check the installation section here: https://gioui.org/#installation.

The preview window is activated by default but you can disable it with by setting the -preview flag to false. When the images are processed concurrently from a directory the preview mode is disabled.

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 cascade file, since it's already embedded into the generated binary file. The sample code below will rescale the provided image with 20%, but will run the face detection prior rescaling 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 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 only need to provide the source and the destination folder by using the -in and -out flags.

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

Support for multiple output image type

There is no need to define the output file type. The library is capable of detecting the output image type directly by the file extension and encodes the image to that specific type. You can export the resized image even to a Gif file, in which case the generated file shows the resizing process interactively.

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 first try to rescale it prior resizing, but also preserves the image aspect ratio. Afterwards 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.

Caire integrations

• Caire can be used as a serverless function via OpenFaaS: https://github.com/esimov/caire-openfaas
• Caire can also be used as a snap function (https://snapcraft.io/caire): $ snap run caire --h

Results

Shrunk images

Original	Shrunk

Enlarged images

Original	Extended

Useful resources

• https://en.wikipedia.org/wiki/Seam_carving
• https://inst.eecs.berkeley.edu/~cs194-26/fa16/hw/proj4-seamcarving/imret.pdf
• http://pages.cs.wisc.edu/~moayad/cs766/download_files/alnammi_cs_766_final_report.pdf
• https://stacks.stanford.edu/file/druid:my512gb2187/Zargham_Nassirpour_Content_aware_image_resizing.pdf

Author

• Endre Simo (@simo_endre)

License

Copyright © 2018 Endre Simo

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

Documentation

Overview

Package caire is a content aware image resize library, which can rescale the source image seamlessly both vertically and horizontally by eliminating the less important parts of the image.

The package provides a command line interface, supporting various flags for different types of rescaling operations. To check the supported commands type:

$ caire --help

In case you wish to integrate the API in a self constructed environment here is a simple example:

package main

import (
	"fmt"
	"github.com/esimov/caire"
)

func main() {
	p := &caire.Processor{
		// Initialize struct variables
	}

	if err := p.Process(in, out); err != nil {
		fmt.Printf("Error rescaling image: %s", err.Error())
	}
}

Index

• type ActiveSeam
• type Carver
  • func NewCarver(width, height int) *Carver
  • func (c *Carver) AddSeam(img *image.NRGBA, seams []Seam, debug bool) *image.NRGBA
  • func (c *Carver) ComputeSeams(p *Processor, img *image.NRGBA) error
  • func (c *Carver) FindLowestEnergySeams() []Seam
  • func (c *Carver) RemoveSeam(img *image.NRGBA, seams []Seam, debug bool) *image.NRGBA
  • func (c *Carver) RotateImage90(src *image.NRGBA) *image.NRGBA
  • func (c *Carver) RotateImage270(src *image.NRGBA) *image.NRGBA
  • func (c *Carver) SobelDetector(img *image.NRGBA, threshold float64) *image.NRGBA
  • func (c *Carver) StackBlur(img *image.NRGBA, radius uint32) *image.NRGBA
• type Gui
  • func (g *Gui) DrawSeam(shape string, x, y, s float64)
  • func (g *Gui) EncodeSeamToImg()
  • func (g *Gui) Run() error
• type Processor
  • func (p *Processor) Grayscale(src *image.NRGBA) *image.NRGBA
  • func (p *Processor) Process(r io.Reader, w io.Writer) error
  • func (p *Processor) Resize(img *image.NRGBA) (image.Image, error)
• type Seam
• type SeamCarver
• type UsedSeams

Types

type ActiveSeam

type ActiveSeam struct {
	Seam
	Pix color.Color
}

ActiveSeam contains the current seam position and color.

type Carver

type Carver struct {
	Width  int
	Height int
	Points []float64
	Seams  []Seam
}

Carver is the main entry struct having as parameters the newly generated image width, height and seam points.

func NewCarver

func NewCarver(width, height int) *Carver

NewCarver returns an initialized Carver structure.

func (*Carver) AddSeam

func (c *Carver) AddSeam(img *image.NRGBA, seams []Seam, debug bool) *image.NRGBA

AddSeam add a new seam.

func (*Carver) ComputeSeams

func (c *Carver) ComputeSeams(p *Processor, img *image.NRGBA) error

ComputeSeams compute the minimum energy level based on the following logic:

• traverse the image from the second row to the last row and compute the cumulative minimum energy M for all possible connected seams for each entry (i, j).

• the minimum energy level is calculated by summing up the current pixel value with the minimum pixel value of the neighboring pixels from the previous row.

func (*Carver) FindLowestEnergySeams

func (c *Carver) FindLowestEnergySeams() []Seam

FindLowestEnergySeams find the lowest vertical energy seam.

func (*Carver) RemoveSeam

func (c *Carver) RemoveSeam(img *image.NRGBA, seams []Seam, debug bool) *image.NRGBA

RemoveSeam remove the least important columns based on the stored energy (seams) level.

func (*Carver) RotateImage90

func (c *Carver) RotateImage90(src *image.NRGBA) *image.NRGBA

RotateImage90 rotate the image by 90 degree counter clockwise.

func (*Carver) RotateImage270

func (c *Carver) RotateImage270(src *image.NRGBA) *image.NRGBA

RotateImage270 rotate the image by 270 degree counter clockwise.

func (*Carver) SobelDetector

func (c *Carver) SobelDetector(img *image.NRGBA, threshold float64) *image.NRGBA

SobelDetector uses the sobel filter operator for detecting image edges. See https://en.wikipedia.org/wiki/Sobel_operator

func (*Carver) StackBlur

func (c *Carver) StackBlur(img *image.NRGBA, radius uint32) *image.NRGBA

StackBlur applies a blur filter to the provided image. The radius defines the bluring average.

type Gui

type Gui struct {
	// contains filtered or unexported fields
}

Gui is the basic struct containing all of the information needed for the UI operation. It receives the resized image transferred through a channel which is called in a separate goroutine.

func (*Gui) DrawSeam

func (g *Gui) DrawSeam(shape string, x, y, s float64)

DrawSeam visualizes the seam carver in action when the preview mode is activated. It receives as parameters the shape type, the seam (x,y) coordinate and a size.

func (*Gui) EncodeSeamToImg

func (g *Gui) EncodeSeamToImg()

EncodeSeamToImg draws the seams into an image widget.

func (*Gui) Run

func (g *Gui) Run() error

Run is the core method of the Gio GUI application. This updates the window with the resized image obtained from a channel and terminates when the image resizing operation completes.

type Processor

type Processor struct {
	SobelThreshold   int
	BlurRadius       int
	NewWidth         int
	NewHeight        int
	Percentage       bool
	Square           bool
	Debug            bool
	Preview          bool
	FaceDetect       bool
	ShapeType        string
	SeamColor        string
	MaskPath         string
	RMaskPath        string
	Mask             image.Image
	RMask            image.Image
	FaceAngle        float64
	PigoFaceDetector *pigo.Pigo
	Spinner          *utils.Spinner
	// contains filtered or unexported fields
}

Processor options

func (*Processor) Grayscale

func (p *Processor) Grayscale(src *image.NRGBA) *image.NRGBA

Grayscale converts the image to grayscale mode.

func (*Processor) Process

func (p *Processor) Process(r io.Reader, w io.Writer) error

Process encodes the resized image into an io.Writer interface. We are using the io package, since we can provide different input and output types, as long as they implement the io.Reader and io.Writer interface.

func (*Processor) Resize

func (p *Processor) Resize(img *image.NRGBA) (image.Image, error)

Resize is the main entry point for the image resize operation. The new image can be resized either horizontally or vertically (or both). Depending on the provided options the image can be either reduced or enlarged.

type Seam

type Seam struct {
	X int
	Y int
}

Seam struct contains the seam pixel coordinates.

type SeamCarver

type SeamCarver interface {
	Resize(*image.NRGBA) (image.Image, error)
}

SeamCarver interface defines the Resize method. This needs to be implemented by every struct which declares a Resize method.

type UsedSeams

type UsedSeams struct {
	ActiveSeam []ActiveSeam
}

UsedSeams contains the already generated seams.