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import cv2
import gradio as gr
import numpy as np
from human_pose_estimator import PoseEstimator
from pose_estimator import rect
pose_estimator = PoseEstimator("cpu")
def get_box(image):
image_box, _ = rect(pose_estimator, image)
return image_box
def predict(img: np.ndarray):
poses, _, _ = pose_estimator.get_poses(img, 512)
for pose in poses:
pose.draw(img)
cv2.rectangle(img, (pose.bbox[0], pose.bbox[1]),
(pose.bbox[0] + pose.bbox[2], pose.bbox[1] + pose.bbox[3]), (0, 255, 0))
return img
footer = r"""
<center>
<b>
Demo for <a href='https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch'>Lightweight OpenPose</a>
</b>
</center>
"""
with gr.Blocks(title="OpenPose") as app:
gr.HTML("<center><h1>Human Pose Estimation Pytorch</h1></center>")
gr.HTML("<center><h3>Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose</h3></center>")
with gr.Row(equal_height=False):
with gr.Column():
input_img = gr.Image(type="numpy", label="Input image")
# input_img = gr.Video(source="webcam")
run_btn = gr.Button(variant="primary")
with gr.Column():
output_img = gr.Image(type="pil", label="Output image")
gr.ClearButton(components=[input_img, output_img], variant="stop")
run_btn.click(predict, [input_img], [output_img])
with gr.Row():
blobs = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
examples = gr.Dataset(components=[input_img], samples=blobs)
examples.click(lambda x: x[0], [examples], [input_img])
with gr.Row():
gr.HTML(footer)
app.launch(share=False, debug=True, show_error=True)
app.queue()
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