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import gradio as gr
import numpy as np
from torchvision import transforms
import torch
from helpers import *
import sys
import csv
from monoscene.monoscene import MonoScene
csv.field_size_limit(sys.maxsize)
torch.set_grad_enabled(False)
# pipeline = pipeline(model="anhquancao/monoscene_kitti")
# model = AutoModel.from_pretrained(
# "anhquancao/monoscene_kitti", trust_remote_code=True, revision='bf033f87c2a86b60903ab811b790a1532c1ae313'
# )#.cuda()
model = MonoScene.load_from_checkpoint(
"monoscene_nyu.ckpt",
dataset="NYU",
feature=200,
project_scale=1,
full_scene_size=(60, 36, 60),
)
def get_projections(img_W, img_H):
scale_3ds = [1, 2]
data = {}
for scale_3d in scale_3ds:
scene_size = (4.8, 4.8, 2.88)
vox_origin = np.array([-1.54591799, 0.8907361 , -0.05 ])
voxel_size = 0.08
cam_k = np.array([[518.8579, 0, 320], [0, 518.8579, 240], [0, 0, 1]])
cam_pose = np.asarray([[ 9.6699458e-01, 4.2662762e-02, 2.5120059e-01, 0.0000000e+00],
[-2.5147417e-01, 1.0867463e-03, 9.6786356e-01, 0.0000000e+00],
[ 4.1018680e-02, -9.9908894e-01, 1.1779292e-02, 1.1794727e+00],
[ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.0000000e+00]])
T_velo_2_cam = np.linalg.inv(cam_pose)
# compute the 3D-2D mapping
projected_pix, fov_mask, pix_z = vox2pix(
T_velo_2_cam,
cam_k,
vox_origin,
voxel_size * scale_3d,
img_W,
img_H,
scene_size,
)
data["projected_pix_{}".format(scale_3d)] = projected_pix
data["pix_z_{}".format(scale_3d)] = pix_z
data["fov_mask_{}".format(scale_3d)] = fov_mask
return data
def predict(img):
img_W, img_H = 640, 480
img = np.array(img, dtype=np.float32, copy=False) / 255.0
normalize_rgb = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
img = normalize_rgb(img)
batch = get_projections(img_W, img_H)
batch["img"] = img
for k in batch:
batch[k] = batch[k].unsqueeze(0) # .cuda()
pred = model(batch).squeeze()
y_pred = torch.softmax(pred["ssc_logit"], dim=1).detach().cpu().numpy()
cam_pose = np.asarray([[ 9.6699458e-01, 4.2662762e-02, 2.5120059e-01, 0.0000000e+00],
[-2.5147417e-01, 1.0867463e-03, 9.6786356e-01, 0.0000000e+00],
[ 4.1018680e-02, -9.9908894e-01, 1.1779292e-02, 1.1794727e+00],
[ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.0000000e+00]])
vox_origin = np.array([-1.54591799, 0.8907361 , -0.05 ])
fig = draw(y_pred.squeeze(),cam_pose, vox_origin)
return fig
description = """
MonoScene Demo on SemanticKITTI Validation Set (Sequence 08), which uses the <b>camera parameters of Sequence 08</b>.
Due to the <b>CPU-only</b> inference, it might take up to 20s to predict a scene. \n
The output is <b>downsampled by 2</b> for faster rendering. <b>Darker</b> colors represent the <b>scenery outside the Field of View</b>, i.e. not visible on the image.
<center>
<a href="https://astra-vision.github.io/MonoScene/">
<img style="display:inline" alt="Project page" src="https://img.shields.io/badge/Project%20Page-MonoScene-red">
</a>
<a href="https://arxiv.org/abs/2112.00726"><img style="display:inline" src="https://img.shields.io/badge/arXiv%20%2B%20supp-2112.00726-purple"></a>
<a href="https://github.com/cv-rits/MonoScene"><img style="display:inline" src="https://img.shields.io/github/stars/cv-rits/MonoScene?style=social"></a>
</center>
"""
title = "MonoScene: Monocular 3D Semantic Scene Completion"
article = """
<center>
We also released a <b>smaller</b> MonoScene model (Half resolution - w/o 3D CRP) at: <a href="https://huggingface.co/spaces/CVPR/monoscene_lite">https://huggingface.co/spaces/CVPR/monoscene_lite</a>
<img src='https://visitor-badge.glitch.me/badge?page_id=anhquancao.MonoScene&left_color=darkmagenta&right_color=purple' alt='visitor badge'>
</center>
"""
examples = [
'images/08/3-1.jpg',
'images/08/001385.jpg',
'images/08/000295.jpg',
'images/08/002505.jpg',
'images/08/000085.jpg',
'images/08/000290.jpg',
'images/08/000465.jpg',
'images/08/000790.jpg',
'images/08/001005.jpg',
'images/08/001380.jpg',
'images/08/001530.jpg',
'images/08/002360.jpg',
'images/08/004059.jpg',
'images/08/003149.jpg',
'images/08/001446.jpg',
'images/08/000010.jpg',
'images/08/001122.jpg',
'images/08/003533.jpg',
'images/08/003365.jpg',
'images/08/002944.jpg',
'images/08/000822.jpg',
'images/08/000103.jpg',
'images/08/002716.jpg',
'images/08/000187.jpg',
'images/08/002128.jpg',
'images/08/000511.jpg',
'images/08/000618.jpg',
'images/08/002010.jpg',
'images/08/000234.jpg',
'images/08/001842.jpg',
'images/08/001687.jpg',
'images/08/003929.jpg',
'images/08/002272.jpg',
]
demo = gr.Interface(
predict,
gr.Image(shape=(1220, 370)),
gr.Plot(),
article=article,
title=title,
enable_queue=True,
cache_examples=False,
live=False,
examples=examples,
description=description)
demo.launch(enable_queue=True, debug=False)
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