# Copyright (C) 2024-present Naver Corporation. All rights reserved. # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). # # -------------------------------------------------------- # gradio demo # -------------------------------------------------------- import argparse import math import gradio import os import torch import numpy as np import tempfile import functools import copy from tqdm import tqdm import cv2 from PIL import Image import os.path as path import sys import tempfile from dust3r.inference import inference from dust3r.model import AsymmetricCroCo3DStereo from dust3r.image_pairs import make_pairs from dust3r.utils.image_pose import load_images, rgb, enlarge_seg_masks, resize_numpy_image from dust3r.utils.device import to_numpy from dust3r.cloud_opt_flow import global_aligner, GlobalAlignerMode import matplotlib.pyplot as pl from transformers import pipeline from dust3r.utils.viz_demo import convert_scene_output_to_glb import depth_pro import spaces from huggingface_hub import hf_hub_download pl.ion() HERE_PATH = path.normpath(path.dirname(__file__)) # noqa sys.path.insert(0, HERE_PATH) # noqa # for gpu >= Ampere and pytorch >= 1.12 torch.backends.cuda.matmul.allow_tf32 = True batch_size = 1 tmpdirname = tempfile.mkdtemp(suffix='_align3r_gradio_demo') image_size = 512 silent = False gradio_delete_cache = 7200 print(f'{HERE_PATH}/third_party/ml-depth-pro/checkpoints/') hf_hub_download(repo_id="apple/DepthPro", filename='depth_pro.pt', local_dir=f'{HERE_PATH}/third_party/ml-depth-pro/checkpoints/') directory = f'{HERE_PATH}/third_party/ml-depth-pro/checkpoints/' items = os.listdir(directory) # 过滤出文件 files = [item for item in items if os.path.isfile(os.path.join(directory, item))] # 打印文件列表 print(files) class FileState: def __init__(self, outfile_name=None): self.outfile_name = outfile_name def __del__(self): if self.outfile_name is not None and os.path.isfile(self.outfile_name): os.remove(self.outfile_name) self.outfile_name = None def get_3D_model_from_scene(outdir, silent, scene, min_conf_thr=3, as_pointcloud=False, mask_sky=False, clean_depth=False, transparent_cams=False, cam_size=0.05, show_cam=True, save_name=None, thr_for_init_conf=True): """ extract 3D_model (glb file) from a reconstructed scene """ if scene is None: return None # post processes if clean_depth: scene = scene.clean_pointcloud() if mask_sky: scene = scene.mask_sky() # get optimized values from scene rgbimg = scene.imgs focals = scene.get_focals().cpu() cams2world = scene.get_im_poses().cpu() # 3D pointcloud from depthmap, poses and intrinsics pts3d = to_numpy(scene.get_pts3d(raw_pts=True)) scene.min_conf_thr = min_conf_thr scene.thr_for_init_conf = thr_for_init_conf msk = to_numpy(scene.get_masks()) cmap = pl.get_cmap('viridis') cam_color = [cmap(i/len(rgbimg))[:3] for i in range(len(rgbimg))] cam_color = [(255*c[0], 255*c[1], 255*c[2]) for c in cam_color] return convert_scene_output_to_glb(outdir, rgbimg, pts3d, msk, focals, cams2world, as_pointcloud=as_pointcloud, transparent_cams=transparent_cams, cam_size=cam_size, show_cam=show_cam, silent=silent, save_name=save_name, cam_color=cam_color) # @spaces.GPU(duration=180) def generate_monocular_depth_maps(img_list, depth_prior_name): depth_list = [] focallength_px_list = [] if depth_prior_name=='Depth Pro': DEFAULT_MONODEPTH_CONFIG_DICT = depth_pro.depth_pro.DepthProConfig( patch_encoder_preset="dinov2l16_384", image_encoder_preset="dinov2l16_384", checkpoint_uri=f'{HERE_PATH}/third_party/ml-depth-pro/checkpoints/depth_pro.pt', decoder_features=256, use_fov_head=True, fov_encoder_preset='dinov2l16_384', ) model, transform = depth_pro.create_model_and_transforms(config=DEFAULT_MONODEPTH_CONFIG_DICT, device='cuda') model.eval() for image_path in tqdm(img_list): #path_depthpro = image_path.replace('.png','_pred_depth_depthpro.npz').replace('.jpg','_pred_depth_depthpro.npz') image, _, f_px = depth_pro.load_rgb(image_path) image = transform(image) # Run inference. prediction = model.infer(image, f_px=f_px) depth = prediction["depth"].cpu().numpy() # Depth in [m]. focallength_px=prediction["focallength_px"].cpu() #depth = cv2.resize(depth[0], image.size, interpolation=cv2.INTER_CUBIC) depth_list.append(depth) focallength_px_list.append(focallength_px) #np.savez_compressed(path_depthpro, depth=depth, focallength_px=prediction["focallength_px"].cpu()) elif depth_prior_name=='Depth Anything V2': pipe = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Large-hf",device='cuda') for image_path in tqdm(img_list): #path_depthanything = image_path.replace('.png','_pred_depth_depthanything.npz').replace('.jpg','_pred_depth_depthanything.npz') image = Image.open(image_path) #print(image.size) depth = pipe(image)["predicted_depth"].numpy() #print(depth.max(),depth.min()) #depth = cv2.resize(depth[0], image.size, interpolation=cv2.INTER_CUBIC) focallength_px = 200 depth_list.append(depth) focallength_px_list.append(focallength_px) #np.savez_compressed(path_depthanything, depth=depth) return depth_list, focallength_px_list @spaces.GPU(duration=180) def local_get_reconstructed_scene(filelist, min_conf_thr, as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size, depth_prior_name, **kw): depth_list, focallength_px_list = generate_monocular_depth_maps(filelist, depth_prior_name) imgs = load_images(filelist, depth_list, focallength_px_list, size=image_size, verbose=not silent,traj_format='custom', depth_prior_name=depth_prior_name) # pairs = [] # pairs.append((imgs[0], imgs[1])) # pairs.append((imgs[1], imgs[0])) scenegraph_type = 'swinstride-5-noncyclic' pairs = make_pairs(imgs, scene_graph=scenegraph_type, prefilter=None, symmetrize=True) if depth_prior_name == "Depth Pro": weights_path = "cyun9286/Align3R_DepthPro_ViTLarge_BaseDecoder_512_dpt" else: weights_path = "cyun9286/Align3R_DepthAnythingV2_ViTLarge_BaseDecoder_512_dpt" device = 'cuda' if torch.cuda.is_available() else 'cpu' model = AsymmetricCroCo3DStereo.from_pretrained(weights_path).to(device) output = inference(pairs, model, device, batch_size=batch_size, verbose=not silent) mode = GlobalAlignerMode.PointCloudOptimizer scene = global_aligner(output, device=device, mode=mode, verbose=not silent, shared_focal = True, temporal_smoothing_weight=0.01, translation_weight=1.0, flow_loss_weight=0.01, flow_loss_start_epoch=0.1, flow_loss_thre=25, use_self_mask=True, num_total_iter=300, empty_cache= len(filelist) > 72) lr = 0.01 if mode == GlobalAlignerMode.PointCloudOptimizer: loss = scene.compute_global_alignment(init='mst', niter=300, schedule='linear', lr=lr) # mode = GlobalAlignerMode.PairViewer # scene = global_aligner(output, device=device, mode=mode, verbose=not silent) outfile = get_3D_model_from_scene(tmpdirname, silent, scene, min_conf_thr, as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size) return outfile def run_example(snapshot, min_conf_thr, as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size, depth_prior_name, inputfiles, **kw): return local_get_reconstructed_scene(inputfiles, min_conf_thr, as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size, depth_prior_name, **kw) css = """.gradio-container {margin: 0 !important; min-width: 100%};""" title = "Align3R Demo" with gradio.Blocks(css=css, title=title, delete_cache=(gradio_delete_cache, gradio_delete_cache)) as demo: filestate = gradio.State(None) gradio.HTML('

3D Reconstruction with Align3R

') gradio.HTML('

Upload two images (wait for them to be fully uploaded before hitting the run button). ' 'If you want to try larger image collections, you can find the more complete version of this demo that you can run locally ' 'and more details about the method at github.com/jiah-cloud/Align3R. ' 'The checkpoint used in this demo is available at Align3R (Depth Anything V2) and Align3R (Depth Pro).

') with gradio.Column(): inputfiles = gradio.File(file_count="multiple") snapshot = gradio.Image(None, visible=False) with gradio.Row(): # adjust the camera size in the output pointcloud cam_size = gradio.Slider(label="cam_size", value=0.02, minimum=0.001, maximum=1.0, step=0.001) depth_prior_name = gradio.Dropdown( ["Depth Pro", "Depth Anything V2"], label="monocular depth estimation model", info="Select the monocular depth estimation model.") min_conf_thr = gradio.Slider(label="min_conf_thr", value=2, minimum=0.0, maximum=20, step=0.01) with gradio.Row(): as_pointcloud = gradio.Checkbox(value=True, label="As pointcloud") mask_sky = gradio.Checkbox(value=True, label="Mask sky") clean_depth = gradio.Checkbox(value=True, label="Clean-up depthmaps") transparent_cams = gradio.Checkbox(value=False, label="Transparent cameras") # not to show camera show_cam = gradio.Checkbox(value=True, label="Show Camera") run_btn = gradio.Button("Run") outmodel = gradio.Model3D() examples = gradio.Examples( examples=[ [ os.path.join(HERE_PATH, 'example/bear/00000.jpg'), 2, True, True, True, False, 0.02, "Depth Pro", [os.path.join(HERE_PATH, 'example/bear/00000.jpg'), os.path.join(HERE_PATH, 'example/bear/00001.jpg'), os.path.join(HERE_PATH, 'example/bear/00002.jpg'), os.path.join(HERE_PATH, 'example/bear/00003.jpg'), os.path.join(HERE_PATH, 'example/bear/00004.jpg'), os.path.join(HERE_PATH, 'example/bear/00005.jpg'), os.path.join(HERE_PATH, 'example/bear/00006.jpg'), os.path.join(HERE_PATH, 'example/bear/00007.jpg'), os.path.join(HERE_PATH, 'example/bear/00008.jpg'), os.path.join(HERE_PATH, 'example/bear/00009.jpg'), ] ], [ os.path.join(HERE_PATH, 'example/breakdance/00000.jpg'), 2, True, True, True, False, 0.02, "Depth Anything V2", [os.path.join(HERE_PATH, 'example/breakdance/00000.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00001.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00002.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00003.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00004.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00005.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00006.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00007.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00008.jpg'), os.path.join(HERE_PATH, 'example/breakdance/00009.jpg'), ] ], [ os.path.join(HERE_PATH, 'example/tennis/00000.jpg'), 2, True, True, True, False, 0.02, "Depth Anything V2", [os.path.join(HERE_PATH, 'example/tennis/00000.jpg'), os.path.join(HERE_PATH, 'example/tennis/00001.jpg'), os.path.join(HERE_PATH, 'example/tennis/00002.jpg'), os.path.join(HERE_PATH, 'example/tennis/00003.jpg'), os.path.join(HERE_PATH, 'example/tennis/00004.jpg'), os.path.join(HERE_PATH, 'example/tennis/00005.jpg'), os.path.join(HERE_PATH, 'example/tennis/00006.jpg'), os.path.join(HERE_PATH, 'example/tennis/00007.jpg'), os.path.join(HERE_PATH, 'example/tennis/00008.jpg'), os.path.join(HERE_PATH, 'example/tennis/00009.jpg'), ] ], [ os.path.join(HERE_PATH, 'example/camel/00000.jpg'), 2, True, True, True, False, 0.02, "Depth Anything V2", [os.path.join(HERE_PATH, 'example/camel/00000.jpg'), os.path.join(HERE_PATH, 'example/camel/00001.jpg'), os.path.join(HERE_PATH, 'example/camel/00002.jpg'), os.path.join(HERE_PATH, 'example/camel/00003.jpg'), os.path.join(HERE_PATH, 'example/camel/00004.jpg'), os.path.join(HERE_PATH, 'example/camel/00005.jpg'), os.path.join(HERE_PATH, 'example/camel/00006.jpg'), os.path.join(HERE_PATH, 'example/camel/00007.jpg'), os.path.join(HERE_PATH, 'example/camel/00008.jpg'), os.path.join(HERE_PATH, 'example/camel/00009.jpg'), ] ], ], inputs=[snapshot, min_conf_thr, as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size, depth_prior_name, inputfiles], outputs=[outmodel], fn=run_example, cache_examples="lazy", ) # events run_btn.click(fn=local_get_reconstructed_scene, inputs=[inputfiles, min_conf_thr, as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size, depth_prior_name], outputs=[outmodel]) demo.launch(show_error=True, share=None, server_name=None, server_port=None) shutil.rmtree(tmpdirname)