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# Last modified: 2024-04-15
import io
import os
import random
import tarfile
from enum import Enum
import numpy as np
import cv2
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision.transforms import InterpolationMode, Resize
class DatasetMode(Enum):
RGB_ONLY = "rgb_only"
EVAL = "evaluate"
TRAIN = "train"
def read_image_from_tar(tar_obj, img_rel_path):
image = tar_obj.extractfile("./" + img_rel_path)
image = image.read()
image = Image.open(io.BytesIO(image))
class BaseDepthDataset(Dataset):
def __init__(
self,
mode: DatasetMode,
filename_ls_path: str,
dataset_dir: str,
disp_name: str,
min_depth,
max_depth,
has_filled_depth,
name_mode,
depth_transform=None,
augmentation_args: dict = None,
resize_to_hw=None,
move_invalid_to_far_plane: bool = True,
rgb_transform=lambda x: x / 255.0 * 2 - 1, # [0, 255] -> [-1, 1],
**kwargs,
) -> None:
super().__init__()
self.mode = mode
# dataset info
self.filename_ls_path = filename_ls_path
self.dataset_dir = dataset_dir
self.disp_name = disp_name
self.has_filled_depth = has_filled_depth
self.name_mode: DepthFileNameMode = name_mode
self.min_depth = min_depth
self.max_depth = max_depth
# training arguments
self.depth_transform = depth_transform
self.augm_args = augmentation_args
self.resize_to_hw = resize_to_hw
self.rgb_transform = rgb_transform
self.move_invalid_to_far_plane = move_invalid_to_far_plane
# Load filenames
with open(self.filename_ls_path, "r") as f:
self.filenames = [
s.split() for s in f.readlines()
] # [['rgb.png', 'depth.tif'], [], ...]
# Tar dataset
self.tar_obj = None
self.is_tar = (
True
if os.path.isfile(dataset_dir) and tarfile.is_tarfile(dataset_dir)
else False
)
def __len__(self):
return len(self.filenames)
def __getitem__(self, index):
rasters, other = self._get_data_item(index)
if DatasetMode.TRAIN == self.mode:
rasters = self._training_preprocess(rasters)
# merge
outputs = rasters
outputs.update(other)
return outputs
def _get_data_item(self, index):
rgb_rel_path, depth_rel_path, filled_rel_path = self._get_data_path(index=index)
rasters = {}
# RGB data
rasters.update(self._load_rgb_data(rgb_rel_path=rgb_rel_path))
# Depth data
if DatasetMode.RGB_ONLY != self.mode:
# load data
depth_data = self._load_depth_data(
depth_rel_path=depth_rel_path, filled_rel_path=filled_rel_path
)
rasters.update(depth_data)
# valid mask
rasters["valid_mask_raw"] = self._get_valid_mask(
rasters["depth_raw_linear"]
).clone()
rasters["valid_mask_filled"] = self._get_valid_mask(
rasters["depth_filled_linear"]
).clone()
other = {"index": index, "rgb_relative_path": rgb_rel_path}
return rasters, other
def _load_rgb_data(self, rgb_rel_path):
# Read RGB data
rgb = self._read_rgb_file(rgb_rel_path)
outputs = {
"rgb_int": torch.from_numpy(rgb).int(),
}
return outputs
def _load_depth_data(self, depth_rel_path, filled_rel_path):
# Read depth data
outputs = {}
depth_raw = self._read_depth_file(depth_rel_path).squeeze()
depth_raw_linear = torch.from_numpy(depth_raw).float().unsqueeze(0) # [1, H, W]
outputs["depth_raw_linear"] = depth_raw_linear.clone()
if self.has_filled_depth:
depth_filled = self._read_depth_file(filled_rel_path).squeeze()
depth_filled_linear = torch.from_numpy(depth_filled).float().unsqueeze(0)
outputs["depth_filled_linear"] = depth_filled_linear
else:
outputs["depth_filled_linear"] = depth_raw_linear.clone()
return outputs
def _get_data_path(self, index):
filename_line = self.filenames[index]
# Get data path
rgb_rel_path = filename_line[0]
depth_rel_path, filled_rel_path = None, None
if DatasetMode.RGB_ONLY != self.mode:
depth_rel_path = filename_line[1]
if self.has_filled_depth:
filled_rel_path = filename_line[2]
return rgb_rel_path, depth_rel_path, filled_rel_path
def _read_image(self, img_rel_path) -> np.ndarray:
if self.is_tar:
if self.tar_obj is None:
self.tar_obj = tarfile.open(self.dataset_dir)
image = self.tar_obj.extractfile("./" + img_rel_path)
image = image.read()
image = Image.open(io.BytesIO(image)) # [H, W, rgb]
else:
img_path = os.path.join(self.dataset_dir, img_rel_path)
image = Image.open(img_path).convert('RGB')
image = np.asarray(image)
return image
def _read_depth_cv2(self, img_rel_path) -> np.ndarray:
depth_path = os.path.join(self.dataset_dir, img_rel_path)
depth_in = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED)
if depth_in.shape[2] == 3: # If image has 3 channels
depth_in = depth_in[..., 0] # PANO
depth_in = depth_in.astype(np.float32)
return depth_in
def _read_rgb_file(self, rel_path) -> np.ndarray:
rgb = self._read_image(rel_path)
# Handle RGBA images by converting to RGB
if rgb.shape[2] == 4: # If image has 4 channels (RGBA)
rgb = rgb[:, :, :3] # Take only the RGB channels
rgb = np.transpose(rgb, (2, 0, 1)).astype(int) # [rgb, H, W]
return rgb
def _read_depth_file(self, rel_path):
depth_in = self._read_image(rel_path)
# Replace code below to decode depth according to dataset definition
depth_decoded = depth_in
return depth_decoded
def _get_valid_mask(self, depth: torch.Tensor):
valid_mask = torch.logical_and(
(depth > self.min_depth), (depth < self.max_depth)
).bool()
return valid_mask
def _training_preprocess(self, rasters):
# Augmentation
if self.augm_args is not None:
rasters = self._augment_data(rasters)
# Normalization
rasters["depth_raw_norm"] = self.depth_transform(
rasters["depth_raw_linear"], rasters["valid_mask_raw"]
).clone()
rasters["depth_filled_norm"] = self.depth_transform(
rasters["depth_filled_linear"], rasters["valid_mask_filled"]
).clone()
# Set invalid pixel to far plane
if self.move_invalid_to_far_plane:
if self.depth_transform.far_plane_at_max:
rasters["depth_filled_norm"][~rasters["valid_mask_filled"]] = (
self.depth_transform.norm_max
)
else:
rasters["depth_filled_norm"][~rasters["valid_mask_filled"]] = (
self.depth_transform.norm_min
)
# Resize
if self.resize_to_hw is not None:
resize_transform = Resize(
size=self.resize_to_hw, interpolation=InterpolationMode.NEAREST_EXACT
)
rasters = {k: resize_transform(v) for k, v in rasters.items()}
return rasters
def _augment_data(self, rasters_dict):
# lr flipping
lr_flip_p = self.augm_args.lr_flip_p
if random.random() < lr_flip_p:
rasters_dict = {k: v.flip(-1) for k, v in rasters_dict.items()}
return rasters_dict
def __del__(self):
if self.tar_obj is not None:
self.tar_obj.close()
self.tar_obj = None
# Prediction file naming modes
class DepthFileNameMode(Enum):
id = 1 # id.png
rgb_id = 2 # rgb_id.png
i_d_rgb = 3 # i_d_1_rgb.png
rgb_i_d = 4
def get_pred_name(rgb_basename, name_mode, suffix=".png"):
if DepthFileNameMode.rgb_id == name_mode:
pred_basename = "pred_" + rgb_basename.split("_")[1]
elif DepthFileNameMode.i_d_rgb == name_mode:
pred_basename = rgb_basename.replace("_rgb.", "_pred.")
elif DepthFileNameMode.id == name_mode:
pred_basename = "pred_" + rgb_basename
elif DepthFileNameMode.rgb_i_d == name_mode:
pred_basename = "pred_" + "_".join(rgb_basename.split("_")[1:])
else:
raise NotImplementedError
# change suffix
pred_basename = os.path.splitext(pred_basename)[0] + suffix
return pred_basename
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