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Zero
File size: 7,813 Bytes
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import os
import pickle
import time
from pathlib import Path
from typing import Optional
import cv2
import numpy as np
import torch
from torch.utils.data import Dataset
from .re10k_util import get_target_size_and_crop, process_flow, process_img, process_proj
class RealEstate10kDataset(Dataset):
NAME = "Re10K"
def __init__(
self,
data_path: str,
split_path: Optional[str],
image_size: Optional[tuple] = None,
frame_count: int = 4,
keyframe_offset: int = 0,
dilation: int = 3,
return_depth: bool = False,
full_size_depth: bool = False,
return_flow: bool = False,
preprocessed_path: Optional[str] = None,
index_selector=None,
sequence_sampler=None,
):
self.data_path = os.path.dirname(data_path)
self.split = os.path.basename(data_path).split(".")[0]
self.split_path = split_path
self.image_size = image_size
self.return_depth = return_depth
self.full_size_depth = full_size_depth
self.return_flow = return_flow
self.preprocessed_path = preprocessed_path
self.frame_count = frame_count
self.keyframe_offset = keyframe_offset
self.dilation = dilation
self._left_offset = (
(self.frame_count - 1) // 2 + self.keyframe_offset
) * self.dilation
self._seq_data = self._get_sequences(data_path, self.data_path, self.split, has_split=split_path is not None)
self._seq_keys = list(self._seq_data.keys())
if self.split_path is not None:
self._datapoints = self._load_split(self.split_path)
else:
self._left_offset = 0
self._datapoints = self._full_split(self._seq_data, self._left_offset, (self.frame_count - 1) * dilation, sequence_sampler)
self.index_selector = index_selector
self.length = len(self._datapoints)
self._skip = 0
@staticmethod
def _get_sequences(data_path: str, data_root: str, split: str, has_split: bool = False):
with open(data_path, "rb") as f:
seq_data = pickle.load(f)
seq_data = {k: v for k, v in seq_data.items() if os.path.exists(os.path.join(data_root, "frames_720", split, k))}
if not has_split:
for k in seq_data.keys():
seq_data[k]["timestamps"] = seq_data[k]["timestamps"][::10]
seq_data[k]["poses"] = seq_data[k]["poses"][::10]
seq_data[k]["intrinsics"] = seq_data[k]["intrinsics"][::10]
return seq_data
@staticmethod
def _full_split(seq_data, left_offset: int = 0, sub_seq_len: int = 2, sequence_sampler=None):
datapoints = []
for k in seq_data.keys():
seq_len = len(seq_data[k]["timestamps"])
if sequence_sampler is not None:
datapoints.extend(sequence_sampler(k, seq_len, left_offset, sub_seq_len))
else:
if seq_len < sub_seq_len:
continue
for i in range(seq_len - 1): # -1 because we need at least two frames
datapoints.append((k, i))
return datapoints
def _get_id_from_timestamp(self, seq, timestamp):
data = self._seq_data[seq]
id = int(np.where(((data["timestamps"] / 1000).astype(np.int64)==int(timestamp)) | ((data["timestamps"]).astype(np.int64)==int(timestamp)))[0])
return id
def _load_split(self, split_path: str):
def get_key_id(s):
parts = s.split(" ")
key = parts[0]
t0 = parts[1]
t1 = parts[2]
id0 = self._get_id_from_timestamp(key, t0)
id1 = self._get_id_from_timestamp(key, t1)
return key, (id0, id1)
with open(split_path, "r") as f:
lines = f.readlines()
datapoints = list(map(get_key_id, lines))
return datapoints
def __len__(self) -> int:
return self.length
def load_images(self, seq: str, ids: list):
imgs = []
for id in ids:
timestamp = int(self._seq_data[seq]["timestamps"][id] / 1000)
img = cv2.cvtColor(cv2.imread(os.path.join(self.data_path, "frames_720", self.split, seq, f"{timestamp}.jpg")), cv2.COLOR_BGR2RGB).astype(np.float32) / 255
imgs += [img]
return imgs
@staticmethod
def process_pose(pose):
pose = np.concatenate((pose.astype(np.float32), np.array([[0, 0, 0, 1]], dtype=np.float32)), axis=0)
pose = np.linalg.inv(pose)
return pose
@staticmethod
def scale_projs(proj, original_size):
K = np.eye(3, dtype=np.float32)
K[0, 0] = proj[0] * original_size[1]
K[1, 1] = proj[1] * original_size[0]
K[0, 2] = proj[2] * original_size[1]
K[1, 2] = proj[3] * original_size[0]
return K
def _index_to_seq_ids(self, index):
if index >= self.length:
raise IndexError()
sequence, id = self._datapoints[index]
seq_len = len(self._seq_data[sequence]["timestamps"])
if type(id) != int:
ids = id
else:
if self.index_selector is not None:
ids = self.index_selector(id, self.frame_count, self.dilation, self._left_offset)
else:
ids = [id] + [i
for i in range(
id - self._left_offset,
id - self._left_offset + self.frame_count * self.dilation,
self.dilation,
)
if i != id
]
ids = [max(min(i, seq_len - 1), 0) for i in ids]
return sequence, ids
def __getitem__(self, index: int):
sequence, ids = self._index_to_seq_ids(index)
imgs = self.load_images(sequence, ids)
original_size = imgs[0].shape[:2]
target_size, crop = get_target_size_and_crop(self.image_size, original_size)
if self.return_flow:
raise ValueError("Flow not implemented.") # flows_fwd, flows_bwd = self.load_flows(sequence, ids)
else:
flows_fwd = None
flows_bwd = None
imgs = [process_img(img, target_size, crop) * 2.0 - 1.0 for img in imgs]
if self.return_flow:
flows_fwd = np.stack([process_flow(flow, target_size, crop) for flow in flows_fwd])
flows_bwd = np.stack([process_flow(flow, target_size, crop) for flow in flows_bwd])
# These poses are camera to world !!
poses = [self.process_pose(self._seq_data[sequence]["poses"][i, :, :]) for i in ids]
projs = [process_proj(self.scale_projs(self._seq_data[sequence]["intrinsics"][i, :], original_size), original_size, target_size, crop) for i in ids]
depth = np.ones_like(imgs[0][:1, :, :])
# print(projs[0])
# print(poses[0])
data = {
"imgs": imgs,
"projs": projs,
"poses": poses,
"ids": np.array(ids, dtype=np.int64),
"index": np.array([index]),
}
if self.return_depth:
data["depths"] = depth[None, ...]
if self.return_flow:
data["flows_fwd"] = flows_fwd
data["flows_bwd"] = flows_bwd
return data
def get_img_paths(self, index):
sequence, ids = self._index_to_seq_ids(index)
img_paths = [
os.path.join(self.data_path, "frames_720", self.split, sequence, f"{self._seq_data[sequence]['timestamps'][id]}.jpg")
for id in ids
]
return img_paths
def get_sequence(self, index: int):
sequence, _ = self._index_to_seq_ids(index)
return sequence
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