celeb_detector

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 face_recognition module.

• Face encodings are created using VGGFace model (converted to pytorch here).

• Face matching is done using annoy library.

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
  # Directly from repo
  pip install -e .
  

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' # this supports both local path and web url like https://sample/sample_image_url.jpg
  celeb_detector.celeb_recognition(img_path)
  

  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_prediction_main.py. If you want to try my model, ignore this step.
• Run celeb_prediction_main.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.