app.py

import gradio as gr
import RetinaFace
import numpy as np

def RFace(img):
  faces = RetinaFace.extract_faces(img, align = True)
  last = np.zeros((20,20), np.uint8)
  for count, image in enumerate(faces):
    if count == 0:
      last = image
    else:
      h1, w1 = last.shape[:2]
      h2, w2 = image.shape[:2]
   #create empty matrix
      vis = np.zeros((max(h1, h2), w1+w2,3), np.uint8)
   #combine 2 images
      vis[:h1, :w1,:3] = last
      vis[:h2, w1:w1+w2,:3] = image
      last = vis
  return last
 


examples=[['Rdj.jpg'],['Rdj2.jpg'],['2.jpg'],['3.jpg'],['many.jpg']]
desc = "RetinaFace is a deep learning based cutting-edge facial detector for Python coming with facial landmarks. Its detection performance for faces is especially excellent for dense crowds."
gr.Interface(fn=RFace, inputs=gr.inputs.Image(type="filepath"), outputs="image", title="RetinaFace Face Detector and Extractor",examples=examples, description=desc).launch(inbrowser=True)