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import gradio as gr
import cv2
import numpy as np
import os
from PIL import Image
import spaces
import torch
css = """
#img-display-container {
max-height: 100vh;
}
#img-display-input {
max-height: 80vh;
}
#img-display-output {
max-height: 80vh;
}
"""
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
title = "# Stereo Anything"
description = """Official demo for **Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data**.
Please refer to our [paper](https://arxiv.org/abs/2411.14053), [github](https://github.com/XiandaGuo/OpenStereo/) for more details."""
@spaces.GPU
@torch.no_grad()
def predict_depth(model, image):
return model(image)
with gr.Blocks(css=css) as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown("### Depth Prediction demo")
gr.Markdown("You can slide the output to compare the depth prediction with input image")
with gr.Row():
left_image = gr.Image(label="Left Image", type='numpy', elem_id='img-display-input')
right_image = gr.Image(label="Right Image", type='numpy', elem_id='img-display-input')
depth_image = gr.Image(label="Depth Image", type='numpy', elem_id='img-display-input')
# raw_file = gr.File(label="16-bit raw depth (can be considered as disparity)")
submit = gr.Button("Submit")
def on_submit(left_image,right_image):
sample = {
'left': left_image,
'right': right_image,
}
sample['left'] = sample['left'].unsqueeze(0)
sample['right'] = sample['right'].unsqueeze(0)
# model.eval()
for k, v in sample.items():
sample[k] = v.to(0) if torch.is_tensor(v) else v
# model_pred = model(sample)
model_pred = None
return [model_pred]
submit.click(on_submit, inputs=[left_image,right_image], outputs=[depth_image])
if __name__ == '__main__':
demo.queue().launch() |