This code finds bounding box of a single human mouth.

Related tags

Deep LearningSwabAI
Overview

Swab AI

Travis (.com) AGPL License GitHub release (latest by date) Lines of code GitHub repo size GitHub last commit

This code finds bounding box of a single human mouth. In comparison to other face segmentation methods, it is relatively insusceptible to open mouth conditions, e.g., yawning, surgical robots, etc. The mouth coordinates are found in a more certified way using two independent algorithms. Therefore, the algorithm can be used in more sensitive applications.

Sample Output

image Selected images from YawDD and CelebAMask dataset.

Example Code

only for debugging purposes cv2.imwrite("output.jpg", cv2.rectangle(img, points[0], points[1], (0, 0, 255), 2)) print("output.jpg is written.") "> 
import swab_ai
import cv2

img = cv2.imread("input.jpg")  # img is numpy array

points = swab_ai.Get_BoundingBox(img)  # returns bounding box coordinates of img

if points: # bounding box is found
    # save output file -> only for debugging purposes
    cv2.imwrite("output.jpg", cv2.rectangle(img, points[0], points[1], (0, 0, 255), 2))
    print("output.jpg is written.")

Python API

swab_ai.py

It loads required libraries and models. This module contains three functions:

  • GetFace(input_image, show=False, save=False, live=False): calls FaceInference.py and returns coordinates of face bounding box and two landmarks of mouth.
  • GetMouth(face_image, show=False, save=False, live=False): return mouth mask.
  • Get_BoundingBox(input_image, show=False, save=False, live=False): calls GetFace and GetMouth functions and checks if the bounding box could be certified by checking for sufficient overlap of two independent algorithms. This function returns the mouth bounding box and also prints the FPS in the output. In case mouth bounding box could not be found Frame Droppe is printed in the console.

live_test.py

For debugging purposes: The algorithm is run on live webcam video. The result of face detection and segmentation algorithms is visualized in real-time.

Dockerfile

We have used the latest version of pytorch image pytorch:1.9.0-cuda11.1-cudnn8-runtime as of now.

To build the docker image and run the container in one step:

 docker build -t swabai .
 docker run --runtime=nvidia swabai

Note that --runtime=nvidia is necessary to enable GPU.

If you see the message output.jpg is written, it means that everything is working correctly. To see the output image use this command to mount local working directory:

 docker run -v $PWD:/usr/src/app/ --runtime=nvidia swabai

Model

The first time that the code is run, model is downloaded automatically. Every time that the code restarts, it checks for model updates.

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