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#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import os
import pathlib
import tarfile
import cv2
import gradio as gr
import huggingface_hub
import numpy as np
import onnxruntime as ort
TITLE = 'atksh/onnx-facial-lmk-detector'
DESCRIPTION = 'This is a demo for https://github.com/atksh/onnx-facial-lmk-detector.'
ARTICLE = None
TOKEN = os.environ['TOKEN']
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
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 load_sample_images() -> list[pathlib.Path]:
image_dir = pathlib.Path('images')
if not image_dir.exists():
image_dir.mkdir()
dataset_repo = 'hysts/input-images'
filenames = ['001.tar']
for name in filenames:
path = huggingface_hub.hf_hub_download(dataset_repo,
name,
repo_type='dataset',
use_auth_token=TOKEN)
with tarfile.open(path) as f:
f.extractall(image_dir.as_posix())
return sorted(image_dir.rglob('*.jpg'))
def run(image: np.ndarray, sess: ort.InferenceSession) -> np.ndarray:
# float32, int, int, uint8, int, float32
# (N,), (N, 4), (N, 5, 2), (N, 224, 224, 3), (N, 106, 2), (N, 2, 3)
scores, bboxes, keypoints, aligned_images, landmarks, affine_matrices = sess.run(
None, {'input': image[:, :, ::-1]})
res = image[:, :, ::-1].copy()
for box in bboxes:
cv2.rectangle(res, tuple(box[:2]), tuple(box[2:]), (0, 255, 0), 1)
for pts in landmarks:
for pt in pts:
cv2.circle(res, tuple(pt), 1, (255, 255, 0), cv2.FILLED)
return res[:, :, ::-1], [face[:, :, ::-1] for face in aligned_images]
def main():
gr.close_all()
args = parse_args()
options = ort.SessionOptions()
options.intra_op_num_threads = 8
options.inter_op_num_threads = 8
sess = ort.InferenceSession('onnx-facial-lmk-detector/model.onnx',
sess_options=options,
providers=['CPUExecutionProvider'])
func = functools.partial(run, sess=sess)
func = functools.update_wrapper(func, run)
image_paths = load_sample_images()
examples = ['onnx-facial-lmk-detector/input.jpg'
] + [[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'),
gr.outputs.Carousel(gr.outputs.Image(type='numpy'),
label='Aligned Faces'),
],
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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