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#!/usr/bin/env python
from __future__ import annotations
import argparse
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
ORIGINAL_REPO_URL = 'https://github.com/1adrianb/face-alignment'
TITLE = '1adrianb/face-alignment'
DESCRIPTION = f'A demo for {ORIGINAL_REPO_URL}'
ARTICLE = ''
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--theme', type=str)
parser.add_argument('--live', action='store_true')
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
parser.add_argument('--allow-flagging', type=str, default='never')
parser.add_argument('--allow-screenshot', action='store_true')
return parser.parse_args()
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
def main():
gr.close_all()
args = parse_args()
device = torch.device(args.device)
detector = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D,
device=device.type)
func = functools.partial(detect, detector=detector, device=device)
func = functools.update_wrapper(func, detect)
image_paths = sorted(pathlib.Path('images').glob('*.jpg'))
examples = [[path.as_posix()] for path in image_paths]
gr.Interface(
func,
gr.inputs.Image(type='numpy', label='Input'),
gr.outputs.Image(type='numpy', label='Output'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
article=ARTICLE,
theme=args.theme,
allow_screenshot=args.allow_screenshot,
allow_flagging=args.allow_flagging,
live=args.live,
).launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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