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#!/usr/bin/env python
from __future__ import annotations
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)"
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"]
)
def run(image: np.ndarray) -> 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].copy()}
)
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]
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=run,
)
run_button.click(
fn=run,
inputs=image,
outputs=[result, gallery],
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
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