face detection system example.md

Before you start

Make sure you have python, OpenFace and dlib installed. You can either install them manually or use a preconfigured docker image that has everying already installed:

docker pull bamos/openface
docker run -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface

Pro-tip: If you are using Docker on OSX, you can make your OSX /Users/ folder visible inside a docker image like this:

docker run -v /Users:/host/Users -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface

Then you can access all your OSX files inside of the docker image at /host/Users/...

ls /host/Users/

Step 1

Make a folder called ./training-images/ inside the openface folder.

mkdir training-images

Step 2

Make a subfolder for each person you want to recognize. For example:

mkdir ./training-images/will-ferrell/
mkdir ./training-images/chad-smith/
mkdir ./training-images/jimmy-fallon/

Step 3

Copy all your images of each person into the correct sub-folders. Make sure only one face appears in each image. There's no need to crop the image around the face. OpenFace will do that automatically.

Step 4

Run the openface scripts from inside the openface root directory:

First, do pose detection and alignment:

./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96

This will create a new ./aligned-images/ subfolder with a cropped and aligned version of each of your test images.

Second, generate the representations from the aligned images:

./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/

After you run this, the ./generated-embeddings/ sub-folder will contain a csv file with the embeddings for each image.

Third, train your face detection model:

./demos/classifier.py train ./generated-embeddings/

This will generate a new file called ./generated-embeddings/classifier.pkl. This file has the SVM model you'll use to recognize new faces.

At this point, you should have a working face recognizer!

Step 5: Recognize faces!

Get a new picture with an unknown face. Pass it to the classifier script like this:

./demos/classifier.py infer ./generated-embeddings/classifier.pkl your_test_image.jpg

You should get a prediction that looks like this:

=== /test-images/will-ferrel-1.jpg ===
Predict will-ferrell with 0.73 confidence.

From here it's up to you to adapt the ./demos/classifier.py python script to work however you want.

Important notes:

• If you get bad results, try adding a few more pictures of each person in Step 3 (especially picures in different poses).
• This script will always make a prediction even if the face isn't one it knows. In a real application, you would look at the confidence score and throw away predictions with a low confidence since they are most likely wrong.

Hello,

I am running code from this example . Everything works perfectly when I launch align of ./util/align-dlib.py from command line. However, when I am debugging "util/align-dlib" from Spyder I have:
it falls on line
import openface
With error
'No module named openface'

Is there some way to hardcode the reference to openface?

Thanks!

Hi! I did everything according to the instructions, up to step 5 everything works. But when I go to step 5, I get an error:

Traceback (most recent call last):
File "./demos/classifier.py", line 298, in <module> infer(args, args.multi)
  File "./demos/classifier.py", line 196, in infer
      person = le.inverse_transform(maxI)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/label.py", line 273, in inverse_transform
    y = column_or_1d(y, warn=True)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 797, in column_or_1d
    raise ValueError("bad input shape {0}".format(shape))

ValueError: bad input shape ()

Solved by cmusatyalab/openface#393 (comment)

When executing lua batch-represent/main.lua -outDir generated-embeddings -data aligned-images I got :
table: 0x56399ed5d410
aligned-images
cache lotation: /mnt/c/Users/aleksandra/Desktop/Diplomski/openface/aligned-images/cache.t7
Creating metadata for cache.
table: 0x56399ee143d0
running "find" on each class directory, and concatenate all those filenames into a single file containing all image paths for a given class
now combine all the files to a single large file
load the large concatenated list of sample paths to self.imagePath
Segmentation fault
Did someone have this problem? Please, help