File size: 1,829 Bytes
9a02a1c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
import PIL.Image
import PIL.ImageOps
import gradio as gr
import numpy as np
import tensorflow as tf
from poser import draw_bones, movenet
def predict(image: PIL.Image):
input_size = 256
size = (1280, 1280)
image = PIL.ImageOps.fit(image, size, PIL.Image.LANCZOS)
image_tf = tf.keras.preprocessing.image.img_to_array(image)
# Resize and pad the image to keep the aspect ratio and fit the expected size.
input_image = tf.expand_dims(image_tf, axis=0)
input_image = tf.image.resize_with_pad(input_image, input_size, input_size)
keypoints = movenet(input_image)
keypoints = np.array(keypoints)
image = tf.keras.preprocessing.image.array_to_img(image_tf)
draw_bones(image, keypoints)
return image
footer = r"""
<center>
<b>
Demo for <a href='https://www.tensorflow.org/hub/tutorials/movenet'>MoveNet</a>
</b>
</center>
"""
with gr.Blocks(title="MoveNet") as app:
gr.HTML("<center><h1>Human Pose Estimation with MoveNet</h1></center>")
gr.HTML("<center><h3>MoveNet: Ultra fast and accurate pose detection model</h3></center>")
with gr.Row().style(equal_height=False):
with gr.Column():
input_img = gr.Image(type="pil", label="Input image")
run_btn = gr.Button(variant="primary")
with gr.Column():
output_img = gr.Image(type="numpy", 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()
|