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import gradio as gr
import numpy as np
from PIFuHD.data import EvalWMetaDataset
from PIFuHD.data.ImageBundle import ImageBundle
from PIFuHD.options import BaseOptions
from PIFuHD.recontructor import Reconstructor
from huggingface_hub import hf_hub_download
from human_pose_estimator import PoseEstimator
from estimator import rect
REPO_ID = "cxeep/PIFuHD"
pose_estimator = PoseEstimator("cpu")
checkpoint_path = hf_hub_download(repo_id=REPO_ID, filename="pifuhd.pt")
cmd = [
'--dataroot', './data',
'--results_path', './results',
'--loadSize', '1024',
'--resolution', '256',
'--load_netMR_checkpoint_path', checkpoint_path,
'--start_id', '-1',
'--end_id', '-1'
]
parser = BaseOptions()
opts = parser.parse(cmd)
reconstructor = Reconstructor(opts)
def make_bundle(image, name):
image, rects = rect(pose_estimator, image)
return ImageBundle(img=image, name=name, meta=rects)
def predict(img: np.ndarray):
bundle = make_bundle(img, "Model3D")
dataset = EvalWMetaDataset(opts, [bundle])
return reconstructor.evaluate(dataset)
footer = r"""
<center>
<b>
Demo for <a href='https://github.com/facebookresearch/pifuhd'>PIFuHD</a>
</b>
</center>
"""
with gr.Blocks(title="PIFuHD") as app:
gr.HTML("<center><h1>3D Human Digitization</h1></center>")
gr.HTML("<center><h3>PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization (CVPR 2020)</h3></center>")
with gr.Row(equal_height=False):
with gr.Column():
input_img = gr.Image(type="numpy", label="Input image")
run_btn = gr.Button(variant="primary")
with gr.Column():
output_obj = gr.Model3D(label="Output model")
output_img = gr.Image(type="filepath", label="Output image")
gr.ClearButton(components=[input_img, output_img, output_obj], variant="stop")
run_btn.click(predict, [input_img], [output_img, output_obj])
with gr.Row():
blobs = [[f"examples/{x:02d}.png"] 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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