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import mediapipe as mp
import gradio as gr
import cv2
import torch
# Images
torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c789e54661bfb432c5522a36553f.jpeg', 'face1.jpg')
torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c86622e8cb58d490e35b01cb9996.jpeg', 'face2.jpg')
mp_face_mesh = mp.solutions.face_mesh
# Prepare DrawingSpec for drawing the face landmarks later.
mp_drawing = mp.solutions.drawing_utils
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
# Run MediaPipe Face Mesh.
def inference(image):
with mp_face_mesh.FaceMesh(
static_image_mode=True,
max_num_faces=2,
min_detection_confidence=0.5) as face_mesh:
# Convert the BGR image to RGB and process it with MediaPipe Face Mesh.
results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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=drawing_spec,
connection_drawing_spec=drawing_spec)
return annotated_image
title = "Face Mesh"
description = "Gradio demo for Face Mesh. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.06724'>Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs</a> | <a href='https://github.com/google/mediapipe'>Github Repo</a></p>"
gr.Interface(
inference,
[gr.inputs.Image(label="Input")],
gr.outputs.Image(type="pil", label="Output"),
title=title,
description=description,
article=article,
examples=[
["face1.jpg"],
["face2.jpg"]
]).launch() |