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Build Status GoDoc license release snapcraft

Pigo is a pure Go face detection, pupil/eyes localization and facial landmark points detection library based on Pixel Intensity Comparison-based Object detection paper (https://arxiv.org/pdf/1305.4537.pdf).

Rectangle face marker Circle face marker
rectangle circle

Motivation

I've intended to implement this face detection method because all of the existing solutions for face detection in the Go ecosystem are only bindings to some C/C++ libraries like OpenCV, but installing OpenCV on various platforms is cumbersome.

The library does not require any third party modules or applications to be installed. However in case you wish to try the real time, webcam based face detection you might need to have Python2 and OpenCV installed, but the core API does not require any third party module or external dependency.

Key features

  • Does not require OpenCV or any 3rd party modules to be installed
  • High processing speed
  • There is no need for image preprocessing prior detection
  • There is no need for the computation of integral images, image pyramid, HOG pyramid or any other similar data structure
  • The face detection is based on pixel intensity comparison encoded in the binary file tree structure
  • Fast detection of in-plane rotated faces
  • The library can detect even faces with eyeglasses
  • Pupils/eyes localization
  • Facial landmark points detection
  • Webassembly support 🎉

Todo

  • Features detection and description

The library can also detect in plane rotated faces. For this reason a new -angle parameter have been included into the command line utility. The command below will generate the following result (see the table below for all the supported options).

$ pigo -in input.jpg -out output.jpg -cf cascade/facefinder -angle=0.8 -iou=0.01
Input file Output file
input output

Note: In case of in plane rotated faces the angle value should be adapted to the provided image.

Pupils / eyes localization

Starting from v1.2.0 Pigo offer pupils/eyes localization capabilites. The implementation is based on Eye pupil localization with an ensemble of randomized trees.

Check out this example for a realtime demo: https://github.com/esimov/pigo/tree/master/examples/puploc

puploc

Facial landmark points detection

v1.3.0 marks a new milestone in the library evolution, Pigo being able for facial landmark points detection. The implementation is based on Fast Localization of Facial Landmark Points.

Check out this example for a realtime demo: https://github.com/esimov/pigo/tree/master/examples/facial_landmark

flp_example

Install

Important note: for the Webassembly demo at least Go 1.13 is required!

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/pigo/cmd/pigo
$ go install

Binary releases

In case you do not have installed or do not wish to install Go, you can obtain the binary file from the releases folder.

The library can be accessed as a snapcraft function too.

snapcraft pigo

API

Below is a minimal example of using the face detection API.

First you need to load and parse the binary classifier, then convert the image to grayscale mode, and finally to run the cascade function which returns a slice containing the row, column, scale and the detection score.

cascadeFile, err := ioutil.ReadFile("/path/to/cascade/file")
if err != nil {
	log.Fatalf("Error reading the cascade file: %v", err)
}

src, err := pigo.GetImage("/path/to/image")
if err != nil {
	log.Fatalf("Cannot open the image file: %v", err)
}

pixels := pigo.RgbToGrayscale(src)
cols, rows := src.Bounds().Max.X, src.Bounds().Max.Y

cParams := pigo.CascadeParams{
	MinSize:     20,
	MaxSize:     1000,
	ShiftFactor: 0.1,
	ScaleFactor: 1.1,

	ImageParams: pigo.ImageParams{
		Pixels: pixels,
		Rows:   rows,
		Cols:   cols,
		Dim:    cols,
	},
}

pigo := pigo.NewPigo()
// Unpack the binary file. This will return the number of cascade trees,
// the tree depth, the threshold and the prediction from tree's leaf nodes.
classifier, err := pigo.Unpack(cascadeFile)
if err != nil {
	log.Fatalf("Error reading the cascade file: %s", err)
}

angle := 0.0 // cascade rotation angle. 0.0 is 0 radians and 1.0 is 2*pi radians

// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
dets := classifier.RunCascade(cParams, angle)

// Calculate the intersection over union (IoU) of two clusters.
dets = classifier.ClusterDetections(dets, 0.2)

A note about imports: in order to decode the image you will need to import image/jpeg or image/png (depending on the provided image type) and the Pigo library as well, otherwise you will get a "Image: Unkown format" error. See the following example:

import (
    _ "image/jpeg"
    pigo "github.com/esimov/pigo/core"
)

Usage

A command line utility is bundled into the library to detect faces in static images.

$ pigo -in input.jpg -out out.jpg -cf cascade/facefinder

Supported flags:

$ pigo --help

┌─┐┬┌─┐┌─┐
├─┘││ ┬│ │
┴  ┴└─┘└─┘

Go (Golang) Face detection library.
    Version: 1.4.2

  -angle float
    	0.0 is 0 radians and 1.0 is 2*pi radians
  -cf string
    	Cascade binary file
  -flpc string
    	Facial landmark points cascade directory
  -in string
    	Source image (default "-")
  -iou float
    	Intersection over union (IoU) threshold (default 0.2)
  -json string
    	Output the detection points into a json file
  -mark
    	Mark detected eyes (default true)
  -marker string
    	Detection marker: rect|circle|ellipse (default "rect")
  -max int
    	Maximum size of face (default 1000)
  -min int
    	Minimum size of face (default 20)
  -out string
    	Destination image (default "-")
  -plc string
    	Pupils/eyes localization cascade file
  -scale float
    	Scale detection window by percentage (default 1.1)
  -shift float
    	Shift detection window by percentage (default 0.1)

Important notice: In case the plc flag is not empty and the provided path is a valid file it will run the pupil/eyes detection method. The same is true for the flpc flag, only that in this case you need to provide the directory to the landmark point cascades found under cascades/lps.

CLI command examples

You can also use the stdin and stdout pipe commands:

$ cat input/source.jpg | pigo > -in - -out - >out.jpg -cf=/path/to/cascade

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

$ cat input/source.jpg | pigo >out.jpg -cf=/path/to/cascade
$ pigo -out out.jpg < input/source.jpg -cf=/path/to/cascade

Using the empty string as value for the -out flag will skip the image generation part. This, combined with the -json flag will encode the detection results into the specified json file. You can also use the pipe - value for the -json flag to output the detection coordinates to the standard (stdout) output.

Real time face detection (running as a shared object)

In case you wish to test the library real time face detection capabilities using a webcam, the examples folder contains a web and a few Python examples. Prior running it you need to have Python2 and OpenCV2 installed.

Select one of the few Python files provided in the examples folder and simply run them. Each of them will execute the exported Go binary file as a shared library. This is also a proof of concept how Pigo can be integrated into different programming languages. I have provided examples only for Python, since this was the only viable way to access the webcam, the Go ecosystem suffering badly from a comprehensive, cross platform and widely available library for accessing the webcam.

WASM (Webassembly) support 🎉

Starting from version v1.4.0 the library has been ported to WASM. This gives the library a huge performance gain in terms of real time face detection capabilities.

WASM demo

To run the wasm demo select the wasm folder and type make.

For more details check the subpage description: https://github.com/esimov/pigo/tree/master/wasm.

Benchmark results

Below are the benchmark results obtained running Pigo against GoCV using the same conditions.

    BenchmarkGoCV-4   	       3	 414122553 ns/op	     704 B/op	       1 allocs/op
    BenchmarkPIGO-4   	      10	 173664832 ns/op	       0 B/op	       0 allocs/op
    PASS
    ok  	github.com/esimov/gocv-test	4.530s

The code used for the above test can be found under the following link: https://github.com/esimov/pigo-gocv-benchmark

Author

License

Copyright © 2019 Endre Simo

This software is distributed under the MIT license. See the LICENSE file for the full license text.

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