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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_kitti.ckpt", | |
| dataset="kitti", | |
| n_classes=20, | |
| feature = 64, | |
| project_scale = 2, | |
| full_scene_size = (256, 256, 32), | |
| ) | |
| img_W, img_H = 1220, 370 | |
| def predict(img): | |
| 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() | |
| # print(pred.shape) | |
| # pred = majority_pooling(pred, k_size=2) | |
| fig = draw(pred, batch['fov_mask_1']) | |
| return fig | |
| # The output is <b>downsampled by 2</b> to be able to be rendered in browsers. | |
| 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 | |
| <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://cv-rits.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> | |
| <img src='https://visitor-badge.glitch.me/badge?page_id=anhquancao.MonoScene&left_color=darkmagenta&right_color=purple' alt='visitor badge'> | |
| </center> | |
| """ | |
| examples = [ | |
| 'images/08/000010.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/08/002505.jpg', | |
| 'images/08/004059.jpg', | |
| 'images/08/003149.jpg', | |
| 'images/08/001446.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, | |
| examples=examples, | |
| description=description) | |
| demo.launch(enable_queue=True, debug=True) |