Celebrity Recognition

Model to recognize celebrities using a face matching algorithm.

Refer this for detailed documentation.

You can also read my article on medium here.

Basic working of the algorithm includes the following:

Face detection is done using MTCNN face detection model.
Face encodings are created using VGGFace model in keras.
Face matching is done using annoy library (spotify).

Installing dependencies

Run pip install -r requirements.txt to install all the dependencies (preferably in a virtual environment).

PyPI package

Installation

To ensure you have all the required additional packages, run pip install -r requirements.txt first.

To install pip package, run:

# pip release version
pip install celeb-detector
# also install additional dependencies with this (if not installed via requirements.txt file)
pip install annoy keras-vggface keras-applications
# Directly from repo
pip install git+https://github.com/shobhit9618/celeb_recognition.git

If you are using conda on linux or ubuntu, you can use the following commands to create and use a new environment called celeb-detector:
```
conda env create shobhit9618/celeb-detector
conda activate celeb-detector
```
This will install all the required dependencies. To ensure you are using the latest version of the package, also run (inside the environment):
```
pip install --upgrade celeb-detector
```

Using pip pakcage

For using my model for predictions, use the following lines of code after installation:

import celeb_detector # on running for the first time, this will download vggface model
img_path = 'sample_image.jpg' # this supports both local path and web url like https://sample/sample_image_url.jpg
celeb_detector.celeb_recognition(img_path) # on running for the first time, 2 files (celeb_mapping.json and celeb_index_60.ann) will downloaded to your home directory

This returns a list of dictionaries, each dictionary contains bbox coordinates, celeb name and confidence for each face detected in the image (celeb name will be unknown if no matching face detected).

For using your own custom model, also provide path to json and ann files as shown below:

import celeb_detector
img_path = 'sample_image.jpg'
ann_path = 'sample_index.ann'
celeb_map = 'sample_mapping.json'
celeb_detector.celeb_recognition(img_path, ann_path, celeb_map)

For creating your own model (refer this for more details on usage) and run as follows:

import celeb_detector
folder_path = 'celeb_images'
celeb_detector.create_celeb_model(folder_path)

Create your own celeb model

Create a dataset of celebs in the following directory structure:

celeb_images/
    celeb-a/
        celeb-a_1.jpg
        celeb-a_2.jpg
        ...
    celeb-b/
        celeb-b_1.jpg
        celeb-b_1.jpg
        ...
    ...

Each folder name will be considered as the corresponding celeb name for the model (WARNING: Do not provide any special characters or spaces in the names).
Make sure each image has only 1 face (of the desired celebrity), if there are multiple faces, only the first detected face will be considered.
Provide path to the dataset folder (for example, celeb_images folder) in the create_celeb_model.py file.
Run create_celeb_model.py file.
Upon successful completion of the code, we get celeb_mapping.json (for storing indexes vs celeb names), celeb_index.ann (ann file for searching encodings) and celeb_name_encoding.pkl files (for storing encodings vs indexes for each celeb). (WARNING: You need to provide paths for storing each of these files, default is to store in the current directory)

Model predictions in jupyter

Provide paths to celeb_mapping.json and celeb_index.ann files in celeb_recognition.ipynb file. If you want to try my model, ignore this step.
Run all the cells in the celeb_recognition.ipynb file, the final cell will provide widgets for uploading images and making predictions (this will also download the necessary model files).
NOTE: celeb_recognition.ipynb is a standalone file and does not require any other files from the repo for running.

Model predictions in python

Provide paths to celeb_mapping.json and celeb_index.ann files in celeb_recognition.py and celeb_utils.py files. If you want to try my model, ignore this step.
Run celeb_recognition.py file, provide path to image in the file.
Output includes a list of the identified faces, bounding boxes and the predicted celeb name (unknown if not found).
It also displays the output with bounding boxes.

Sample image output

Binder

You can run a binder application by clicking the following link:

You can also launch a voila binder application (which only has widgets for image upload and celeb prediction) by clicking here.

Google Colab

To open and run celeb_recognition.ipynb file in google colab, click the following link:

shobhit9618 / celeb_recognition

readme