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#!/usr/bin/env python
from __future__ import annotations
import functools
import os
import pathlib
import subprocess
if os.environ.get('SYSTEM') == 'spaces':
subprocess.call('pip uninstall -y opencv-python'.split())
subprocess.call('pip uninstall -y opencv-python-headless'.split())
subprocess.call('pip install opencv-python-headless==4.5.5.62'.split())
import cv2
import face_alignment
import gradio as gr
import numpy as np
import torch
TITLE = 'face-alignment'
DESCRIPTION = 'This is an unofficial demo for https://github.com/1adrianb/face-alignment.'
def detect(
image: np.ndarray,
detector,
device: torch.device,
) -> np.ndarray:
preds = detector.get_landmarks(image)
if len(preds) == 0:
raise RuntimeError('No face was found')
res = image.copy()
for pts in preds:
tl = pts.min(axis=0)
br = pts.max(axis=0)
size = (br - tl).max()
radius = max(2, int(3 * size / 256))
for pt in np.round(pts).astype(int):
cv2.circle(res, tuple(pt), radius, (0, 255, 0), cv2.FILLED)
return res
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
detector = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D,
device=device.type)
func = functools.partial(detect, detector=detector, device=device)
image_paths = sorted(pathlib.Path('images').glob('*.jpg'))
examples = [[path.as_posix()] for path in image_paths]
gr.Interface(
fn=func,
inputs=gr.Image(label='Input', type='numpy'),
outputs=gr.Image(label='Output', type='numpy'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
).launch(show_api=False)
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