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import spaces |
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import os |
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import sys |
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import os.path as path |
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import torch |
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import tempfile |
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import gradio |
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import shutil |
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import math |
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HERE_PATH = path.normpath(path.dirname(__file__)) |
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MASt3R_REPO_PATH = path.normpath(path.join(HERE_PATH, './mast3r')) |
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sys.path.insert(0, MASt3R_REPO_PATH) |
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from mast3r.demo import get_reconstructed_scene |
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from mast3r.model import AsymmetricMASt3R |
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from mast3r.utils.misc import hash_md5 |
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import mast3r.utils.path_to_dust3r |
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from dust3r.demo import set_print_with_timestamp |
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import matplotlib.pyplot as pl |
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pl.ion() |
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torch.backends.cuda.matmul.allow_tf32 = True |
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batch_size = 1 |
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set_print_with_timestamp() |
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weights_path = "naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric" |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model = AsymmetricMASt3R.from_pretrained(weights_path).to(device) |
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chkpt_tag = hash_md5(weights_path) |
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tmpdirname = tempfile.mkdtemp(suffix='_mast3r_gradio_demo') |
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image_size = 512 |
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silent = True |
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gradio_delete_cache = 7200 |
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class FileState: |
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def __init__(self, outfile_name=None): |
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self.outfile_name = outfile_name |
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def __del__(self): |
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if self.outfile_name is not None and os.path.isfile(self.outfile_name): |
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os.remove(self.outfile_name) |
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self.outfile_name = None |
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@spaces.GPU(duration=180) |
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def local_get_reconstructed_scene(filelist, min_conf_thr, matching_conf_thr, |
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as_pointcloud, cam_size, |
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shared_intrinsics, **kw): |
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lr1 = 0.07 |
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niter1 = 500 |
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lr2 = 0.014 |
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niter2 = 200 |
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optim_level = 'refine' |
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mask_sky, clean_depth, transparent_cams = False, True, False |
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if len(filelist) < 5: |
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scenegraph_type = 'complete' |
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winsize = 1 |
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else: |
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scenegraph_type = 'logwin' |
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half_size = math.ceil((len(filelist) - 1) / 2) |
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max_winsize = max(1, math.ceil(math.log(half_size, 2))) |
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winsize = min(5, max_winsize) |
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refid = 0 |
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win_cyclic = False |
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scene_state, outfile = get_reconstructed_scene(tmpdirname, gradio_delete_cache, model, device, silent, image_size, None, |
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filelist, optim_level, lr1, niter1, lr2, niter2, min_conf_thr, matching_conf_thr, |
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as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size, scenegraph_type, winsize, |
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win_cyclic, refid, TSDF_thresh=0, shared_intrinsics=shared_intrinsics, **kw) |
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filestate = FileState(scene_state.outfile_name) |
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scene_state.outfile_name = None |
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del scene_state |
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return filestate, outfile |
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def run_example(snapshot, min_conf_thr, matching_conf_thr,as_pointcloud, cam_size, shared_intrinsics, filelist, **kw): |
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return local_get_reconstructed_scene(filelist, min_conf_thr, matching_conf_thr, as_pointcloud, cam_size, shared_intrinsics, **kw) |
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css = """.gradio-container {margin: 0 !important; min-width: 100%};""" |
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title = "MASt3R Demo" |
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with gradio.Blocks(css=css, title=title, delete_cache=(gradio_delete_cache, gradio_delete_cache)) as demo: |
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filestate = gradio.State(None) |
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gradio.HTML('<h2 style="text-align: center;">3D Reconstruction with MASt3R</h2>') |
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gradio.HTML('<p>Upload one or multiple images (wait for them to be fully uploaded before hitting the run button). ' |
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'We tested with up to 18 images before running into the allocation timeout - set at 3 minutes but your mileage may vary. ' |
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'At the very bottom of this page, you will find an example. If you click of it, it will pull the 3D reconstruction from 7 images of the small Naver Labs Europe tower from cache. ' |
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'If you want to try larger image collections, you can find the more complete version of this demo that you can run locally ' |
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'and more details about the method at <a href="https://github.com/naver/mast3r">github.com/naver/mast3r</a>. ' |
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'The checkpoint used in this demo is available at <a href="https://huggingface.co./naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric">huggingface.co/naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric</a>.</p>') |
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with gradio.Column(): |
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inputfiles = gradio.File(file_count="multiple") |
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snapshot = gradio.Image(None, visible=False) |
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with gradio.Row(): |
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matching_conf_thr = gradio.Slider(label="Matching Confidence Thr", value=2., |
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minimum=0., maximum=30., step=0.1, |
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info="Before Fallback to Regr3D!") |
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min_conf_thr = gradio.Slider(label="min_conf_thr", value=1.5, minimum=0.0, maximum=10, step=0.1) |
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cam_size = gradio.Slider(label="cam_size", value=0.2, minimum=0.001, maximum=1.0, step=0.001) |
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with gradio.Row(): |
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as_pointcloud = gradio.Checkbox(value=True, label="As pointcloud") |
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shared_intrinsics = gradio.Checkbox(value=False, label="Shared intrinsics", |
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info="Only optimize one set of intrinsics for all views") |
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run_btn = gradio.Button("Run") |
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outmodel = gradio.Model3D() |
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examples = gradio.Examples( |
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examples=[ |
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[ |
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os.path.join(HERE_PATH, 'mast3r/assets/NLE_tower/FF5599FD-768B-431A-AB83-BDA5FB44CB9D-83120-000041DADDE35483.jpg'), |
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1.5, 0.0, True, 0.2, False, |
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[os.path.join(HERE_PATH, 'mast3r/assets/NLE_tower/01D90321-69C8-439F-B0B0-E87E7634741C-83120-000041DAE419D7AE.jpg'), |
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os.path.join( |
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HERE_PATH, 'mast3r/assets/NLE_tower/1AD85EF5-B651-4291-A5C0-7BDB7D966384-83120-000041DADF639E09.jpg'), |
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os.path.join( |
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HERE_PATH, 'mast3r/assets/NLE_tower/28EDBB63-B9F9-42FB-AC86-4852A33ED71B-83120-000041DAF22407A1.jpg'), |
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os.path.join( |
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HERE_PATH, 'mast3r/assets/NLE_tower/91E9B685-7A7D-42D7-B933-23A800EE4129-83120-000041DAE12C8176.jpg'), |
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os.path.join( |
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HERE_PATH, 'mast3r/assets/NLE_tower/2679C386-1DC0-4443-81B5-93D7EDE4AB37-83120-000041DADB2EA917.jpg'), |
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os.path.join( |
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HERE_PATH, 'mast3r/assets/NLE_tower/CDBBD885-54C3-4EB4-9181-226059A60EE0-83120-000041DAE0C3D612.jpg'), |
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os.path.join(HERE_PATH, 'mast3r/assets/NLE_tower/FF5599FD-768B-431A-AB83-BDA5FB44CB9D-83120-000041DADDE35483.jpg')] |
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] |
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], |
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inputs=[snapshot, min_conf_thr, matching_conf_thr, as_pointcloud, cam_size, shared_intrinsics, inputfiles], |
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outputs=[filestate, outmodel], |
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fn=run_example, |
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cache_examples="lazy", |
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) |
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run_btn.click(fn=local_get_reconstructed_scene, |
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inputs=[inputfiles, min_conf_thr, matching_conf_thr, |
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as_pointcloud, |
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cam_size, shared_intrinsics], |
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outputs=[filestate, outmodel]) |
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demo.launch(show_error=True, share=None, server_name=None, server_port=None) |
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shutil.rmtree(tmpdirname) |
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