app.py

import gradio as gr
import cv2
import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_face_mesh = mp.solutions.face_mesh

drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)

def face_mesh( image ):

  with mp_face_mesh.FaceMesh( max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5 ) as face_mesh:
    # Convert the BGR image to RGB before processing.
    results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    # Print and draw face mesh landmarks on the image.

    if results.multi_face_landmarks:
      annotated_image = image.copy()

      for face_landmarks in results.multi_face_landmarks:

        mp_drawing.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_TESSELATION,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp_drawing_styles
            .get_default_face_mesh_tesselation_style())

        mp_drawing.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_CONTOURS,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp_drawing_styles
            .get_default_face_mesh_contours_style())

        mp_drawing.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_IRISES,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp_drawing_styles
            .get_default_face_mesh_iris_connections_style()) 
           
  return annotated_image


with gr.Blocks(title="Face Mesh | Data Science Dojo", css="footer {display:none !important} .output-markdown{display:none !important}") as demo:
    with gr.Row():
      with gr.Column():
        input = gr.Webcam(streaming=True)
      with gr.Column():
        output = gr.outputs.Image()
        
    input.stream(fn=face_mesh,
        inputs = input,
        outputs = output)

demo.launch(debug=True)