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Running
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Zero
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import io
import os
from time import time
from typing import Any, Dict, List, Tuple
import numpy as np
import tables
import torch
import torchvision
import torchvision.transforms.v2.functional as TF
from PIL import Image
from unik3d.datasets.base_dataset import BaseDataset
from unik3d.utils import is_main_process
from unik3d.utils.camera import BatchCamera, Pinhole
"""
Awful class for legacy reasons, we assume only pinhole cameras
And we "fake" sequences by setting sequence_fields to [(0, 0)] and cam2w as eye(4)
"""
class ImageDataset(BaseDataset):
def __init__(
self,
image_shape: Tuple[int, int],
split_file: str,
test_mode: bool,
normalize: bool,
augmentations_db: Dict[str, Any],
shape_constraints: Dict[str, Any],
resize_method: str,
mini: float,
benchmark: bool = False,
**kwargs,
) -> None:
super().__init__(
image_shape=image_shape,
split_file=split_file,
test_mode=test_mode,
benchmark=benchmark,
normalize=normalize,
augmentations_db=augmentations_db,
shape_constraints=shape_constraints,
resize_method=resize_method,
mini=mini,
**kwargs,
)
self.mapper = self.get_mapper()
def get_single_item(self, idx, sample=None, mapper=None):
sample = self.dataset[idx] if sample is None else sample
mapper = self.mapper if mapper is None else mapper
results = {
(0, 0): dict(
gt_fields=set(),
image_fields=set(),
mask_fields=set(),
camera_fields=set(),
)
}
results = self.pre_pipeline(results)
results["sequence_fields"] = [(0, 0)]
chunk_idx = (
int(sample[self.mapper["chunk_idx"]]) if "chunk_idx" in self.mapper else 0
)
h5_path = os.path.join(self.data_root, self.hdf5_paths[chunk_idx])
with tables.File(
h5_path,
mode="r",
libver="latest",
swmr=True,
) as h5file_chunk:
for key_mapper, idx_mapper in mapper.items():
if "image" not in key_mapper and "depth" not in key_mapper:
continue
value = sample[idx_mapper]
results[(0, 0)][key_mapper] = value
name = key_mapper.replace("_filename", "")
value_root = "/" + value
if "image" in key_mapper:
results[(0, 0)]["filename"] = value
file = h5file_chunk.get_node(value_root).read()
image = (
torchvision.io.decode_image(torch.from_numpy(file))
.to(torch.uint8)
.squeeze()
)
results[(0, 0)]["image_fields"].add(name)
results[(0, 0)][f"image_ori_shape"] = image.shape[-2:]
results[(0, 0)][name] = image[None, ...]
# collect camera information for the given image
name = name.replace("image_", "")
results[(0, 0)]["camera_fields"].update({"camera", "cam2w"})
K = self.get_intrinsics(idx, value)
if K is None:
K = torch.eye(3)
K[0, 0] = K[1, 1] = 0.7 * self.image_shape[1]
K[0, 2] = 0.5 * self.image_shape[1]
K[1, 2] = 0.5 * self.image_shape[0]
camera = Pinhole(K=K[None, ...].clone())
results[(0, 0)]["camera"] = BatchCamera.from_camera(camera)
results[(0, 0)]["cam2w"] = self.get_extrinsics(idx, value)[
None, ...
]
elif "depth" in key_mapper:
# start = time()
file = h5file_chunk.get_node(value_root).read()
depth = Image.open(io.BytesIO(file))
depth = TF.pil_to_tensor(depth).squeeze().to(torch.float32)
if depth.ndim == 3:
depth = depth[2] + depth[1] * 255 + depth[0] * 255 * 255
results[(0, 0)]["gt_fields"].add(name)
results[(0, 0)][f"depth_ori_shape"] = depth.shape
depth = (
depth.view(1, 1, *depth.shape).contiguous() / self.depth_scale
)
results[(0, 0)][name] = depth
results = self.preprocess(results)
if not self.test_mode:
results = self.augment(results)
results = self.postprocess(results)
return results
def preprocess(self, results):
results = self.replicate(results)
for i, seq in enumerate(results["sequence_fields"]):
self.resizer.ctx = None
results[seq] = self.resizer(results[seq])
num_pts = torch.count_nonzero(results[seq]["depth"] > 0)
if num_pts < 50:
raise IndexError(f"Too few points in depth map ({num_pts})")
for key in results[seq].get("image_fields", ["image"]):
results[seq][key] = results[seq][key].to(torch.float32) / 255
# update fields common in sequence
for key in ["image_fields", "gt_fields", "mask_fields", "camera_fields"]:
if key in results[(0, 0)]:
results[key] = results[(0, 0)][key]
results = self.pack_batch(results)
return results
def postprocess(self, results):
# normalize after because color aug requires [0,255]?
for key in results.get("image_fields", ["image"]):
results[key] = TF.normalize(results[key], **self.normalization_stats)
results = self.filler(results)
results = self.unpack_batch(results)
results = self.masker(results)
results = self.collecter(results)
return results
def __getitem__(self, idx):
try:
if isinstance(idx, (list, tuple)):
results = [self.get_single_item(i) for i in idx]
else:
results = self.get_single_item(idx)
except Exception as e:
print(f"Error loading sequence {idx} for {self.__class__.__name__}: {e}")
idx = np.random.randint(0, len(self.dataset))
results = self[idx]
return results
def get_intrinsics(self, idx, image_name):
idx_sample = self.mapper.get("K", 1000)
sample = self.dataset[idx]
if idx_sample >= len(sample):
return None
return sample[idx_sample]
def get_extrinsics(self, idx, image_name):
idx_sample = self.mapper.get("cam2w", 1000)
sample = self.dataset[idx]
if idx_sample >= len(sample):
return torch.eye(4)
return sample[idx_sample]
def get_mapper(self):
return {
"image_filename": 0,
"depth_filename": 1,
"K": 2,
}
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