# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import os import cv2 import torch import numpy as np import sys import shutil from datetime import datetime import glob import gc import time from pathlib import Path from argparse import ArgumentParser from tqdm import tqdm from tqdm.contrib.concurrent import process_map sys.path.append("vggt/") from visual_util import predictions_to_glb from vggt.models.vggt import VGGT from vggt.utils.pose_enc import pose_encoding_to_extri_intri from vggt.utils.geometry import unproject_depth_map_to_point_map from rec_utils.datasets import ARKitDataset from PIL import Image from torchvision import transforms as TF val_split = ['47334096', '47895367', '41125696', '41125756', '45662926', '47429925', '42898581', '48018972', '48018387', '44358455', '45261150', '42898538', '47430490', '47334109', '45663114', '42897508', '47430475', '47332901', '42899461', '45662942', '47331964', '47204552', '45261144', '41069021', '42899736', '42899737', '47430026', '48018566', '48458489', '42444955', '42446536', '47895341', '47430034', '45663154', '47430489', '42444950', '42898862', '44358451', '47331069', '41254405', '42445028', '44358448', '48458481', '47895771', '47204566', '42898508', '47331990', '47332911', '48018358', '44358498', '41159519', '45260905', '42898854', '42446533', '47115548', '45261581', '45260899', '48018346', '47333940', '47332908', '48018386', '42897559', '42445022', '42897696', '42897541', '42446529', '47333927', '47331061', '45261190', '47331063', '41159558', '47429995', '47334110', '47333934', '47332905', '48018356', '42444953', '47334241', '47332895', '47895740', '47331333', '42446038', '42446156', '48458663', '48458657', '48458660', '47333924', '45260928', '47895536', '41125760', '42899691', '41254246', '42445991', '42445441', '45662987', '47334234', '47334367', '47430424', '44358442', '47430045', '45663105', '42897550', '47430005', '41254412', '44358532', '47331311', '42898816', '47895736', '47895738', '48458667', '47332893', '42899612', '47204605', '41142278', '42446517', '42446079', '41159553', '42899726', '42898574', '47115469', '47331963', '42899700', '47334237', '47430048', '48018957', '47334117', '42446540', '44358536', '42444954', '41125722', '41159504', '47430047', '41159566', '42897651', '47333456', '47331068', '42446519', '47333923', '47895739', '47430483', '45261142', '47430470', '45662970', '47334105', '47429922', '48018962', '41142281', '47895745', '42446546', '42897678', '47204554', '47331334', '42897667', '42897629', '42899720', '41159557', '47895556', '42897521', '42898486', '45663113', '47334093', '42899714', '45662944', '48458465', '42446137', '48458473', '45260898', '42445429', '47430036', '48458430', '47204559', '42898544', '47895353', '42899685', '44358505', '47430051', '45260900', '42899698', '47331316', '45260914', '48018572', '47333918', '47334238', '42899723', '44358513', '42899620', '47115460', '45261619', '47429912', '41159571', '47334362', '48458654', '42446163', '41254269', '45662975', '47331644', '41159530', '44358499', '47204609', '47333431', '41159555', '47429987', '42899688', '45662921', '47332890', '47895374', '47430001', '45261587', '45260856', '47430038', '42897599', '47332885', '42899679', '44358435', '42445966', '47895348', '48018353', '47895357', '47204573', '47333452', '45663115', '48458424', '42444976', '42444968', '42897564', '47331336', '42445448', '45260854', '42898527', '47334379', '45260925', '47430023', '47331662', '45662983', '42898826', '42899694', '42899617', '45662924', '42446049', '42899717', '48458650', '41069046', '42899699', '41254435', '47331972', '47895750', '47331339', '42446165', '41159525', '47895547', '47332899', '47895541', '42445031', '47895365', '42446535', '42899739', '45261631', '47333925', '47895554', '47430485', '47115463', '42897695', '47430468', '47333916', '47895776', '42899471', '44358446', '47334360', '47334381', '42897552', '42898868', '47333436', '48018562', '42898519', '42899680', '41254402', '47334256', '42897692', '42899725', '47331653', '41254400', '42445026', '45261588', '42899734', '45662943', '47334120', '47331314', '48018737', '48458472', '47331971', '45261193', '42446016', '45260920', '48018571', '42446056', '47333443', '41069025', '42897549', '44358515', '47115526', '42897688', '48458417', '47115474', '47430024', '47332916', '42898554', '48018732', '48018375', '47331989', '47115452', '45261615', '47334103', '41159572', '41159508', '42446541', '47115529', '44358440', '47115550', '45663165', '47895779', '47334240', '47331646', '48018970', '47430002', '42446527', '47334102', '47332000', '47895783', '47895542', '48458747', '42898570', '47331337', '42899613', '48018345', '48458665', '42446083', '41254382', '41125731', '48458732', '44358518', '42899696', '42897504', '41069051', '48018368', '48018741', '47429971', '47331266', '42897528', '42445981', '45663107', '42897501', '47895534', '42445029', '47430471', '47333440', '42445988', '45260903', '41159540', '42897566', '48458456', '47331651', '47332910', '47333904', '42445021', '45261575', '47895355', '45261140', '47331654', '47333920', '47895743', '45261143', '42898822', '47430479', '42446167', '47334361', '47334380', '45662981', '48018966', '44358436', '47334252', '41254432', '48458647', '48018560', '47334107', '47895549', '45261632', '45261128', '47895350', '44358538', '41159534', '42899611', '42898521', '47331988', '42899729', '48458656', '47115525', '42897538', '42897545', '47331970', '42897647', '42897554', '47430003', '47332904', '41159541', '48018379', '42897526', '41069043', '47331319', '47895371', '42446104', '41159538', '42898818', '48018956', '42899619', '48018381', '41069042', '48458735', '45261182', '42446151', '42898869', '47334368', '47333899', '47430033', '41125718', '47331645', '44358584', '48018739', '45261179', '47333931', '47333898', '42898817', '47332918', '45261121', '42446522', '45261637', '48018559', '45663164', '47332005', '41254386', '47331265', '45663175', '42898497', '48018367', '47429904', '41254262', '47115543', '41254425', '48458652', '42445984', '41069050', '48018960', '42898811', '41069048', '47895364', '48018382', '42446103', '48458427', '45260857', '42899731', '47895782', '47430419', '42446093', '47429913', '47332915', '44358452', '47333457', '47334091', '45261133', '42446532', '47895735', '47204607', '47204556', '47334115', '41254441', '42897561', '48458484', '47429998', '42446116', '47331071', '45261594', '47333937', '47204575', '47333932', '47331661', '47895732', '47332004', '42445998', '47429914', '44358582', '48018361', '47204563', '41125700', '42899690', '41159529', '41125763', '47115473', '48458415', '47204578', '47331668', '45261185', '47430043', '42446114', '47430422', '47331324', '42444949', '47334372', '45663150', '42444966', '42444946', '41125709', '48018360', '47429975', '42898867', '45261129', '47333435', '42899712', '48018730', '47429992', '42897542', '48018372', '41254398', '47429906', '41159503', '47332886', '42897672', '47331064', '47334239', '47333441', '45261181', '48018347', '45662979', '47895777', '45663149', '47895552', '47331974', '47331322', '47334254', '48458428', '42898849', '41142280', '44358583', '45261620', '47429977', '47430007', '42899459', '42446100', '45663099', '47331262', '47331331'] def load_and_preprocess_images(image_list, mode="crop"): """ A quick start function to load and preprocess images for model input. This assumes the images should have the same shape for easier batching, but our model can also work well with different shapes. Args: image_path_list (list): List of paths to image files mode (str, optional): Preprocessing mode, either "crop" or "pad". - "crop" (default): Sets width to 518px and center crops height if needed. - "pad": Preserves all pixels by making the largest dimension 518px and padding the smaller dimension to reach a square shape. Returns: torch.Tensor: Batched tensor of preprocessed images with shape (N, 3, H, W) Raises: ValueError: If the input list is empty or if mode is invalid Notes: - Images with different dimensions will be padded with white (value=1.0) - A warning is printed when images have different shapes - When mode="crop": The function ensures width=518px while maintaining aspect ratio and height is center-cropped if larger than 518px - When mode="pad": The function ensures the largest dimension is 518px while maintaining aspect ratio and the smaller dimension is padded to reach a square shape (518x518) - Dimensions are adjusted to be divisible by 14 for compatibility with model requirements """ # Check for empty list if len(image_list) == 0: raise ValueError("At least 1 image is required") # Validate mode if mode not in ["crop", "pad"]: raise ValueError("Mode must be either 'crop' or 'pad'") images = [] shapes = set() to_tensor = TF.ToTensor() target_size = 518 # First process all images and collect their shapes for image in image_list: # Open image img = Image.fromarray(image) # If there's an alpha channel, blend onto white background: if img.mode == "RGBA": # Create white background background = Image.new("RGBA", img.size, (255, 255, 255, 255)) # Alpha composite onto the white background img = Image.alpha_composite(background, img) # Now convert to "RGB" (this step assigns white for transparent areas) img = img.convert("RGB") width, height = img.size if mode == "pad": # Make the largest dimension 518px while maintaining aspect ratio if width >= height: new_width = target_size new_height = round(height * (new_width / width) / 14) * 14 # Make divisible by 14 else: new_height = target_size new_width = round(width * (new_height / height) / 14) * 14 # Make divisible by 14 else: # mode == "crop" # Original behavior: set width to 518px new_width = target_size # Calculate height maintaining aspect ratio, divisible by 14 new_height = round(height * (new_width / width) / 14) * 14 # Resize with new dimensions (width, height) img = img.resize((new_width, new_height), Image.Resampling.BICUBIC) img = to_tensor(img) # Convert to tensor (0, 1) # Center crop height if it's larger than 518 (only in crop mode) # if mode == "crop" and new_height > target_size: # start_y = (new_height - target_size) // 2 # img = img[:, start_y : start_y + target_size, :] # For pad mode, pad to make a square of target_size x target_size if mode == "pad": h_padding = target_size - img.shape[1] w_padding = target_size - img.shape[2] if h_padding > 0 or w_padding > 0: pad_top = h_padding // 2 pad_bottom = h_padding - pad_top pad_left = w_padding // 2 pad_right = w_padding - pad_left # Pad with white (value=1.0) img = torch.nn.functional.pad( img, (pad_left, pad_right, pad_top, pad_bottom), mode="constant", value=1.0 ) shapes.add((img.shape[1], img.shape[2])) images.append(img) # Check if we have different shapes # In theory our model can also work well with different shapes if len(shapes) > 1: print(f"Warning: Found images with different shapes: {shapes}") # Find maximum dimensions max_height = max(shape[0] for shape in shapes) max_width = max(shape[1] for shape in shapes) # Pad images if necessary padded_images = [] for img in images: h_padding = max_height - img.shape[1] w_padding = max_width - img.shape[2] if h_padding > 0 or w_padding > 0: pad_top = h_padding // 2 pad_bottom = h_padding - pad_top pad_left = w_padding // 2 pad_right = w_padding - pad_left img = torch.nn.functional.pad( img, (pad_left, pad_right, pad_top, pad_bottom), mode="constant", value=1.0 ) padded_images.append(img) images = padded_images images = torch.stack(images) # concatenate images # Ensure correct shape when single image if len(image_list) == 1: # Verify shape is (1, C, H, W) if images.dim() == 3: images = images.unsqueeze(0) return images # ------------------------------------------------------------------------- # 1) Core model inference # ------------------------------------------------------------------------- def run_model(model, scene, device, max_images) -> dict: """ Run the VGGT model on images in the 'target_dir/images' folder and return predictions. """ if not torch.cuda.is_available(): raise ValueError("CUDA is not available. Check your environment.") scene.filter_valid_poses() print(f"Found {len(scene.images)} images") frames = scene.frames if len(scene.images) == 0: raise ValueError(f"No images found at {scene.id}. Check your upload.") if len(scene) > max_images: print(f"Downsampling {len(scene)} images to {max_images} images") frames = [scene.frames[i] for i in np.linspace(0, len(scene) - 1, max_images).round().astype(int)] images = load_and_preprocess_images([frame.image for frame in frames]).to(device) print(f"Preprocessed images shape: {images.shape}") # Run inference print("Running inference...") dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16 with torch.no_grad(): with torch.cuda.amp.autocast(dtype=dtype): predictions = model(images) # Convert pose encoding to extrinsic and intrinsic matrices print("Converting pose encoding to extrinsic and intrinsic matrices...") extrinsic, intrinsic = pose_encoding_to_extri_intri(predictions["pose_enc"], images.shape[-2:]) predictions["poses"] = extrinsic predictions["Ks"] = intrinsic # Convert tensors to numpy for key in predictions.keys(): if isinstance(predictions[key], torch.Tensor): predictions[key] = predictions[key].cpu().numpy().squeeze(0) # remove batch dimension # Generate world points from depth map # print("Computing world points from depth map...") # depth_map = predictions["depth"] # (S, H, W, 1) # world_points = unproject_depth_map_to_point_map(depth_map, predictions["poses"], predictions["Ks"]) # predictions["world_points_from_depth"] = world_points # Clean up torch.cuda.empty_cache() predictions["image_names"] = [frame.image_path for frame in frames] return predictions def process_scene( model, scene_name, scene, output_dir, device, max_images=10000, force=False ): """ Perform reconstruction using the already-created target_dir/images. """ if not force and (output_dir / "predictions.npz").exists(): print(f"Skipping scene {scene_name} because it already exists") return start_time = time.time() gc.collect() torch.cuda.empty_cache() print("Running run_model...") with torch.no_grad(): predictions = run_model(model, scene, device, max_images) # Save predictions del predictions["images"] np.savez(output_dir / "predictions.npz", **predictions) del predictions gc.collect() torch.cuda.empty_cache() end_time = time.time() import pickle val_path = Path("../") / "Indoor/OKNO/data/arkitscenes/arkitscenes_offline_infos_train.pkl" out_dir = Path("data/arkit_gt/processed") with open(val_path, "rb") as f: data = pickle.load(f) data_list = data["data_list"] val_split = [scene["lidar_points"]["lidar_path"] for scene in data_list][:2500] val_split = [a.split("_")[0] for a in val_split] print(val_split) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--scene_names", nargs="+", default=val_split) parser.add_argument("--input_dir", type=str, default='/workspace-SR006.nfs2/datasets/arkitscenes/offline_prepared_data/posed_images/') parser.add_argument("--output_dir", type=str, default='output/arkit_new') parser.add_argument("--max_images", type=int, default=100) parser.add_argument("--conf_thres", type=float, default=3.0) parser.add_argument("--job_num", "-n", type=int, default=1) parser.add_argument("--job_id", "-i", type=int, default=0) parser.add_argument("--device", type=str, default="2") parser.add_argument("--force", action="store_true") args = parser.parse_args() model = VGGT() _URL = "https://huggingface.co/facebook/VGGT-1B/resolve/main/model.pt" model.load_state_dict(torch.hub.load_state_dict_from_url(_URL)) model.eval() scene_names = args.scene_names[args.job_id::args.job_num] # scene_names = ['47334096'] device = f"cuda:{args.device}" if torch.cuda.is_available() else "cpu" model = model.to(device) from datetime import datetime errors_path = Path(f"logs/errors_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt") dataset = ARKitDataset(args.input_dir) for scene_name in tqdm(scene_names): print(f"Processing scene {scene_name}") try: scene = dataset[scene_name] output_dir = Path(args.output_dir) / scene_name output_dir.mkdir(parents=True, exist_ok=True) process_scene(model, scene_name, scene, output_dir, device=device, max_images=args.max_images, force=args.force) except Exception as e: print(f"Error processing scene {scene_name}: {e}") errors_path.parent.mkdir(parents=True, exist_ok=True) with open(errors_path, "a") as f: f.write(f"{scene_name}\n")