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#!/usr/bin/env python
from __future__ import annotations
import functools
import os
import cv2
import gradio as gr
import numpy as np
import onnxruntime as ort
DESCRIPTION = '# [atksh/onnx-facial-lmk-detector](https://github.com/atksh/onnx-facial-lmk-detector)'
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]
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'])
fn = functools.partial(run, sess=sess)
examples = [['onnx-facial-lmk-detector/input.jpg'],
['images/pexels-ksenia-chernaya-8535230.jpg']]
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
image = gr.Image(label='Input', type='numpy')
run_button = gr.Button()
with gr.Column():
result = gr.Image(label='Output')
gallery = gr.Gallery(label='Aligned Faces')
gr.Examples(examples=examples,
inputs=image,
outputs=[result, gallery],
fn=fn,
cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
run_button.click(fn=fn,
inputs=image,
outputs=[result, gallery],
api_name='run')
demo.queue(max_size=10).launch()
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