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face-detect-annotator

face-detect-annotator is a tool to detect faces from Image file by AWS Rekognition, Google Vision, Azure Computer Vision, Face++, OpenCV, Dlib, Pigo, TensorFlow

What's for?

Amazon advertise like "Our product is awesome!".

Google advocates like "Our AI is brilliant!!".

ML engineer says like "My top-noch model reach beyond the existing world!!!".

You may think, "Who is the winner?". Okay, let's decide the No.1.

Quick Usage

At first, install golang. And install dependensies.

• OpenCV: https://gocv.io/getting-started/
• Dlib: https://github.com/Kagami/go-face
• TensorFlow: https://www.tensorflow.org/install/lang_go

Then, create binary file.

# Basic engines [pigo,azure,google,rekognition] (you can use these engines without OS dependencies or libraries)
$ go build -o face-detect-annotator ./cmd/face-detect-annotator-basic

# All engines
# $ go build -o face-detect-annotator ./cmd/face-detect-annotator-all

$ ./face-detect-annotator -h

Commands:

  help       show help
  detect     Detect faces from image file or csv list
  annotate   Annotate faces of image from --input TSV file

Subcommands

detect

detect command detecting faces from --input image file or csv list file.

$ ./face-detect-annotator detect -h

Detect faces from image file or csv list

Options:

  -h, --help                              display help information
  -i, --input                            *image dir path --input='/path/to/image_dir'
  -o, --output[=./output.tsv]            *output TSV file path --output='./output.tsv'
  -a, --all                               use all engines
  -e, --engine[=opencv,dlib,tensorflow]   comma separate Face Detect Engines --engine='opencv,dlib,pigo,tensorflow,rekognition,google,azure,face++'

For example, if you want to detect faces of images from the CSV file,

cat ./input,csv

path,count
myimages/foobar/001.jpg,1
myimages/foobar/002.jpg,1
myimages/foobar/003.jpg,0

With all of the engines,

$ FDA_AZURE_REGION=westus \
FDA_AZURE_SUBSCRIPTION_KEY=ZZZ \
GOOGLE_APPLICATION_CREDENTIALS=$HOME/google/service_account.json \
AWS_ACCESS_KEY_ID=XXX \
AWS_SECRET_ACCESS_KEY=ZZZ \
FACEPP_API_KEY=XXX \
FACEPP_API_SECRET=ZZZ \
./face-detect-annotator detector -i ./input.csv -o ./output.tsv  -e="rekognition,google,azure,face++,tensorflow,opencv,dlib,pigo"

[INFO] Use rekognition
[INFO] Use google
[INFO] Use azure
[INFO] Use face++
[INFO] Use tensorflow
[INFO] Use opencv
[INFO] Use dlib
[INFO] Use pigo
exec #: [2]
exec #: [1]
exec #: [0]

After a while, output.tsv will be created.

annotate

annotate command draw rectangle lines around faces from TSV file, generated from detect command.

$ ./face-detect-annotator annotate -h

Annotate faces of image from --input TSV file

Options:

  -h, --help    display help information
  -i, --input  *detector's output tsv file --input='/path/to/output.tsv'

$ ./face-detect-annotator annotate -i ./output.tsv

After a while, _annotated_ prefixed files will be created in the same directory of the given images.

$ tree

└── myimages
    └── foobar
        ├── _annotated_001.jpg
        ├── _annotated_002.jpg
        ├── _annotated_003.jpg
        ├── 001.jpg
        ├── 002.jpg
        └── 003.jpg

Example output

Environment variables

Credit

This library depends on these awesome libraries,

• Dlib: github.com/Kagami/go-face by Kagami
• OpenCV: gocv.io/x/gocv
• Pigo: github.com/esimov/pigo
• TensorFlow: github.com/tensorflow/tensorflow
• Azure Computer Vision API: github.com/Azure/azure-sdk-for-go
• Google Vision API: github.com/googleapis/google-api-go-client
• AWS Rekognition: github.com/aws/aws-sdk-go
• Face++: evalphobia/go-face-plusplus