import json from dataclasses import dataclass from functools import cached_property from io import BytesIO from pathlib import Path import random from typing import Literal import os import cv2 import numpy as np import torch import torchvision.transforms as tf from einops import rearrange, repeat from jaxtyping import Float, UInt8 from PIL import Image import torchvision from torch import Tensor from torch.utils.data import Dataset from concurrent.futures import ThreadPoolExecutor, as_completed import copy from .shims.geometry_shim import depthmap_to_absolute_camera_coordinates from .shims.load_shim import imread_cv2 from ..geometry.projection import get_fov from .dataset import DatasetCfgCommon from .shims.augmentation_shim import apply_augmentation_shim from .shims.crop_shim import apply_crop_shim from .types import Stage from .view_sampler import ViewSampler from ..misc.cam_utils import camera_normalization @dataclass class DatasetScannetppCfg(DatasetCfgCommon): name: str roots: list[Path] baseline_min: float baseline_max: float max_fov: float make_baseline_1: bool augment: bool relative_pose: bool skip_bad_shape: bool metric_thre: float intr_augment: bool make_baseline_1: bool rescale_to_1cube: bool normalize_by_pts3d: bool @dataclass class DatasetScannetppCfgWrapper: scannetpp: DatasetScannetppCfg class DatasetScannetpp(Dataset): cfg: DatasetScannetppCfgWrapper stage: Stage view_sampler: ViewSampler to_tensor: tf.ToTensor chunks: list[Path] near: float = 0.1 far: float = 100.0 def __init__( self, cfg: DatasetScannetppCfgWrapper, stage: Stage, view_sampler: ViewSampler, ) -> None: super().__init__() self.cfg = cfg self.stage = stage self.view_sampler = view_sampler self.to_tensor = tf.ToTensor() # load data self.data_root = cfg.roots[0] self.data_list = [] # we use dslr rather than iphone data_index = os.listdir(f"{self.data_root}") # we train all the scenes if self.stage != "train": with open(f"{self.data_root}/valid.json", "r") as file: data_index = json.load(file)[:10] data_index = data_index * 100 random.shuffle(data_index) else: with open(f"{self.data_root}/valid.json", "r") as file: data_index = json.load(file)[10:] self.data_list = [ os.path.join(self.data_root, item) for item in data_index ] self.scene_ids = {} self.scenes = {} index = 0 with ThreadPoolExecutor(max_workers=32) as executor: futures = [executor.submit(self.load_metadata, scene_path) for scene_path in self.data_list] for future in as_completed(futures): scene_frames, scene_id = future.result() self.scenes[scene_id] = scene_frames self.scene_ids[index] = scene_id index += 1 # if self.stage != "train": # self.scene_ids = self.scene_ids # random.shuffle(self.scene_ids) print(f"Scannetpp: {self.stage}: loaded {len(self.scene_ids)} scenes") def shuffle(self, lst: list) -> list: indices = torch.randperm(len(lst)) return [lst[x] for x in indices] def load_metadata(self, scene_path): metadata_path = os.path.join(scene_path, "scene_metadata.npz") metadata = np.load(metadata_path, allow_pickle=True) intrinsics = metadata["intrinsics"] trajectories = metadata["trajectories"] images = metadata["images"] scene_id = scene_path.split("/")[-1].split(".")[0] scene_frames = [ { "file_path": os.path.join(scene_path, "images", images[i].split(".")[0] + ".jpg"), "depth_path": os.path.join(scene_path, "depth", images[i].split(".")[0] + ".png"), "intrinsics": self.convert_intrinsics(intrinsics[i]), "extrinsics": trajectories[i], } for i in range(len(images)) ] scene_frames.sort(key=lambda x: x["file_path"]) # sort by file path to ensure correct order return scene_frames, scene_id def convert_intrinsics(self, intrinsics): w = intrinsics[0, 2] * 2 h = intrinsics[1, 2] * 2 intrinsics[0, 0] = intrinsics[0, 0] / w intrinsics[1, 1] = intrinsics[1, 1] / h intrinsics[0, 2] = intrinsics[0, 2] / w intrinsics[1, 2] = intrinsics[1, 2] / h return intrinsics def blender2opencv_c2w(self, pose): blender2opencv = np.array( [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]] ) opencv_c2w = np.array(pose) @ blender2opencv return opencv_c2w.tolist() def load_frames(self, frames): with ThreadPoolExecutor(max_workers=1) as executor: # Create a list to store futures with their original indices futures_with_idx = [] for idx, file_path in enumerate(frames): file_path = file_path["file_path"] futures_with_idx.append( ( idx, executor.submit( lambda p: self.to_tensor(Image.open(p).convert("RGB")), file_path, ), ) ) # Pre-allocate list with correct size to maintain order torch_images = [None] * len(frames) for idx, future in futures_with_idx: torch_images[idx] = future.result() # Check if all images have the same size sizes = set(img.shape for img in torch_images) if len(sizes) == 1: torch_images = torch.stack(torch_images) # Return as list if images have different sizes return torch_images def load_depths(self, frames): torch_depths = [] for idx, frame in enumerate(frames): depthmap = imread_cv2(frame["depth_path"], cv2.IMREAD_UNCHANGED) depthmap = depthmap.astype(np.float32) / 1000 depthmap[~np.isfinite(depthmap)] = 0 torch_depths.append(torch.from_numpy(depthmap)) return torch.stack(torch_depths) # [N, H, W] def getitem(self, index: int, num_context_views: int, patchsize: tuple) -> dict: # import time # start_time = time.time() scene = self.scene_ids[index] example = self.scenes[scene] # load poses extrinsics = [] intrinsics = [] for frame in example: extrinsic = frame["extrinsics"] intrinsic = frame["intrinsics"] extrinsics.append(extrinsic) intrinsics.append(intrinsic) extrinsics = np.array(extrinsics) intrinsics = np.array(intrinsics) extrinsics = torch.tensor(extrinsics, dtype=torch.float32) intrinsics = torch.tensor(intrinsics, dtype=torch.float32) try: context_indices, target_indices, overlap = self.view_sampler.sample( "scannetpp_"+scene, num_context_views, extrinsics, intrinsics, ) except ValueError: # Skip because the example doesn't have enough frames. raise Exception("Not enough frames") # Skip the example if the field of view is too wide. if (get_fov(intrinsics).rad2deg() > self.cfg.max_fov).any(): raise Exception("Field of view too wide") # Load the images. input_frames = [example[i] for i in context_indices] target_frame = [example[i] for i in target_indices] context_images = self.load_frames(input_frames) target_images = self.load_frames(target_frame) context_depths = self.load_depths(input_frames) target_depths = self.load_depths(target_frame) # Skip the example if the images don't have the right shape. context_image_invalid = context_images.shape[1:] != (3, *self.cfg.original_image_shape) target_image_invalid = target_images.shape[1:] != (3, *self.cfg.original_image_shape) if self.cfg.skip_bad_shape and (context_image_invalid or target_image_invalid): print( f"Skipped bad example {example['key']}. Context shape was " f"{context_images.shape} and target shape was " f"{target_images.shape}." ) raise Exception("Bad example image shape") context_extrinsics = extrinsics[context_indices] if self.cfg.make_baseline_1: a, b = context_extrinsics[0, :3, 3], context_extrinsics[-1, :3, 3] scale = (a - b).norm() if scale < self.cfg.baseline_min or scale > self.cfg.baseline_max: print( f"Skipped {scene} because of baseline out of range: " f"{scale:.6f}" ) raise Exception("baseline out of range") extrinsics[:, :3, 3] /= scale else: scale = 1 if self.cfg.relative_pose: extrinsics = camera_normalization(extrinsics[context_indices][0:1], extrinsics) if self.cfg.rescale_to_1cube: scene_scale = torch.max(torch.abs(extrinsics[context_indices][:, :3, 3])) # target pose is not included rescale_factor = 1 * scene_scale extrinsics[:, :3, 3] /= rescale_factor if torch.isnan(extrinsics).any() or torch.isinf(extrinsics).any(): raise Exception("encounter nan or inf in input poses") example = { "context": { "extrinsics": extrinsics[context_indices], "intrinsics": intrinsics[context_indices], "image": context_images, "depth": context_depths, "near": self.get_bound("near", len(context_indices)) / scale, "far": self.get_bound("far", len(context_indices)) / scale, "index": context_indices, "overlap": overlap, }, "target": { "extrinsics": extrinsics[target_indices], "intrinsics": intrinsics[target_indices], "image": target_images, "depth": target_depths, "near": self.get_bound("near", len(target_indices)) / scale, "far": self.get_bound("far", len(target_indices)) / scale, "index": target_indices, }, "scene": f"Scannetpp {scene}", } if self.stage == "train" and self.cfg.augment: example = apply_augmentation_shim(example) if self.stage == "train" and self.cfg.intr_augment: intr_aug = True else: intr_aug = False example = apply_crop_shim(example, (patchsize[0] * 14, patchsize[1] * 14), intr_aug=intr_aug) # world pts image_size = example["context"]["image"].shape[2:] context_intrinsics = example["context"]["intrinsics"].clone().detach().numpy() context_intrinsics[:, 0] = context_intrinsics[:, 0] * image_size[1] context_intrinsics[:, 1] = context_intrinsics[:, 1] * image_size[0] target_intrinsics = example["target"]["intrinsics"].clone().detach().numpy() target_intrinsics[:, 0] = target_intrinsics[:, 0] * image_size[1] target_intrinsics[:, 1] = target_intrinsics[:, 1] * image_size[0] context_pts3d_list, context_valid_mask_list = [], [] target_pts3d_list, target_valid_mask_list = [], [] for i in range(len(example["context"]["depth"])): context_pts3d, context_valid_mask = depthmap_to_absolute_camera_coordinates(example["context"]["depth"][i].numpy(), context_intrinsics[i], example["context"]["extrinsics"][i].numpy()) context_pts3d_list.append(torch.from_numpy(context_pts3d).to(torch.float32)) context_valid_mask_list.append(torch.from_numpy(context_valid_mask)) context_pts3d = torch.stack(context_pts3d_list, dim=0) context_valid_mask = torch.stack(context_valid_mask_list, dim=0) for i in range(len(example["target"]["depth"])): target_pts3d, target_valid_mask = depthmap_to_absolute_camera_coordinates(example["target"]["depth"][i].numpy(), target_intrinsics[i], example["target"]["extrinsics"][i].numpy()) target_pts3d_list.append(torch.from_numpy(target_pts3d).to(torch.float32)) target_valid_mask_list.append(torch.from_numpy(target_valid_mask)) target_pts3d = torch.stack(target_pts3d_list, dim=0) target_valid_mask = torch.stack(target_valid_mask_list, dim=0) # normalize by context pts3d if self.cfg.normalize_by_pts3d: transformed_pts3d = context_pts3d[context_valid_mask] scene_factor = transformed_pts3d.norm(dim=-1).mean().clip(min=1e-8) context_pts3d /= scene_factor example["context"]["depth"] /= scene_factor example["context"]["extrinsics"][:, :3, 3] /= scene_factor target_pts3d /= scene_factor example["target"]["depth"] /= scene_factor example["target"]["extrinsics"][:, :3, 3] /= scene_factor example["context"]["pts3d"] = context_pts3d example["target"]["pts3d"] = target_pts3d example["context"]["valid_mask"] = context_valid_mask example["target"]["valid_mask"] = target_valid_mask if torch.isnan(example["context"]["depth"]).any() or torch.isinf(example["context"]["depth"]).any() or \ torch.isnan(example["context"]["extrinsics"]).any() or torch.isinf(example["context"]["extrinsics"]).any() or \ torch.isnan(example["context"]["intrinsics"]).any() or torch.isinf(example["context"]["intrinsics"]).any() or \ torch.isnan(example["target"]["depth"]).any() or torch.isinf(example["target"]["depth"]).any() or \ torch.isnan(example["target"]["extrinsics"]).any() or torch.isinf(example["target"]["extrinsics"]).any() or \ torch.isnan(example["target"]["intrinsics"]).any() or torch.isinf(example["target"]["intrinsics"]).any(): raise Exception("encounter nan or inf in context depth") for key in ["context", "target"]: example[key]["valid_mask"] = (torch.ones_like(example[key]["valid_mask"]) * -1).type(torch.int32) return example def __getitem__(self, index_tuple: tuple) -> dict: index, num_context_views, patchsize_h = index_tuple # generate a random patch size patchsize_w = (self.cfg.input_image_shape[1] // 14) try: return self.getitem(index, num_context_views, (patchsize_h, patchsize_w)) except Exception as e: print(f"Error: {e}") index = np.random.randint(len(self)) return self.__getitem__((index, num_context_views, patchsize_h)) def convert_poses( self, poses: Float[Tensor, "batch 18"], ) -> tuple[ Float[Tensor, "batch 4 4"], # extrinsics Float[Tensor, "batch 3 3"], # intrinsics ]: b, _ = poses.shape # Convert the intrinsics to a 3x3 normalized K matrix. intrinsics = torch.eye(3, dtype=torch.float32) intrinsics = repeat(intrinsics, "h w -> b h w", b=b).clone() fx, fy, cx, cy = poses[:, :4].T intrinsics[:, 0, 0] = fx intrinsics[:, 1, 1] = fy intrinsics[:, 0, 2] = cx intrinsics[:, 1, 2] = cy # Convert the extrinsics to a 4x4 OpenCV-style W2C matrix. w2c = repeat(torch.eye(4, dtype=torch.float32), "h w -> b h w", b=b).clone() w2c[:, :3] = rearrange(poses[:, 6:], "b (h w) -> b h w", h=3, w=4) return w2c.inverse(), intrinsics def convert_images( self, images: list[UInt8[Tensor, "..."]], ) -> Float[Tensor, "batch 3 height width"]: torch_images = [] for image in images: image = Image.open(BytesIO(image.numpy().tobytes())) torch_images.append(self.to_tensor(image)) return torch.stack(torch_images) def get_bound( self, bound: Literal["near", "far"], num_views: int, ) -> Float[Tensor, " view"]: value = torch.tensor(getattr(self, bound), dtype=torch.float32) return repeat(value, "-> v", v=num_views) @property def data_stage(self) -> Stage: if self.cfg.overfit_to_scene is not None: return "test" if self.stage == "val": return "test" return self.stage @cached_property def index(self) -> dict[str, Path]: merged_index = {} data_stages = [self.data_stage] if self.cfg.overfit_to_scene is not None: data_stages = ("test", "train") for data_stage in data_stages: for root in self.cfg.roots: # Load the root's index. with (root / data_stage / "index.json").open("r") as f: index = json.load(f) index = {k: Path(root / data_stage / v) for k, v in index.items()} # The constituent datasets should have unique keys. assert not (set(merged_index.keys()) & set(index.keys())) # Merge the root's index into the main index. merged_index = {**merged_index, **index} return merged_index def __len__(self) -> int: return len(self.data_list)