Model to recognize celebrities using a face matching algorithm
Project description
Celebrity Recognition
Model to recognize celebrities using a face matching algorithm.
Model is based on a dataset of around 6000 images of 60 celebrities (100 each).
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.py
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.py
first. - To install pip package, run:
# pip release version (on test PyPI only as of now) pip3 install --user -i https://test.pypi.org/simple/ celeb-detector==0.0.14 # Directly from repo pip3 install git+https://github.com/shobhit9618/celeb_recognition.git
Using pip pakcage
-
For using my model for predictions, use the following lines of code after installation:
import celeb_detector img_path = 'sample_image.jpg' celeb_detector.celeb_recognition(img_path)
-
For using you 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, use as follows:
import celeb_detector folder_path = 'sample_folder' celeb_detector.create_celeb_model(folder_path)
-
NOTE: pip package is unstable as of now, it is recommended to use python files from the repo for creating your model and making predictions. Details for the same are provided below.
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) andceleb_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
andceleb_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
andceleb_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:
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