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"""
ETH3D multi-view benchmark, used for line matching evaluation.
"""
import logging
import os
import shutil
import zipfile
from pathlib import Path
import cv2
import numpy as np
import torch
from ..geometry.wrappers import Camera, Pose
from ..settings import DATA_PATH
from ..utils.image import ImagePreprocessor, load_image
from .base_dataset import BaseDataset
from .utils import scale_intrinsics
logger = logging.getLogger(__name__)
def read_cameras(camera_file, scale_factor=None):
"""Read the camera intrinsics from a file in COLMAP format."""
with open(camera_file, "r") as f:
raw_cameras = f.read().rstrip().split("\n")
raw_cameras = raw_cameras[3:]
cameras = []
for c in raw_cameras:
data = c.split(" ")
fx, fy, cx, cy = np.array(list(map(float, data[4:])))
K = np.array([[fx, 0.0, cx], [0.0, fy, cy], [0.0, 0.0, 1.0]], dtype=np.float32)
if scale_factor is not None:
K = scale_intrinsics(K, np.array([scale_factor, scale_factor]))
cameras.append(Camera.from_calibration_matrix(K).float())
return cameras
def qvec2rotmat(qvec):
"""Convert from quaternions to rotation matrix."""
return np.array(
[
[
1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2,
2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2],
],
[
2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2,
2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1],
],
[
2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2,
],
]
)
class ETH3DDataset(BaseDataset):
default_conf = {
"data_dir": "ETH3D_undistorted",
"grayscale": True,
"downsize_factor": 8,
"min_covisibility": 500,
"batch_size": 1,
"two_view": True,
"min_overlap": 0.5,
"max_overlap": 1.0,
"sort_by_overlap": False,
"seed": 0,
}
def _init(self, conf):
self.grayscale = conf.grayscale
self.downsize_factor = conf.downsize_factor
# Set random seeds
np.random.seed(conf.seed)
torch.manual_seed(conf.seed)
# Auto-download the dataset
if not (DATA_PATH / conf.data_dir).exists():
logger.info("Downloading the ETH3D dataset...")
self.download_eth3d()
# Form pairs of images from the multiview dataset
self.img_dir = DATA_PATH / conf.data_dir
self.data = []
for folder in self.img_dir.iterdir():
img_folder = Path(folder, "images", "dslr_images_undistorted")
depth_folder = Path(folder, "ground_truth_depth/undistorted_depth")
depth_ext = ".png"
names = [img.name for img in img_folder.iterdir()]
names.sort()
# Read intrinsics and extrinsics data
cameras = read_cameras(
str(Path(folder, "dslr_calibration_undistorted", "cameras.txt")),
1 / self.downsize_factor,
)
name_to_cam_idx = {name: {} for name in names}
with open(
str(Path(folder, "dslr_calibration_jpg", "images.txt")), "r"
) as f:
raw_data = f.read().rstrip().split("\n")[4::2]
for raw_line in raw_data:
line = raw_line.split(" ")
img_name = os.path.basename(line[-1])
name_to_cam_idx[img_name]["dist_camera_idx"] = int(line[-2])
T_world_to_camera = {}
image_visible_points3D = {}
with open(
str(Path(folder, "dslr_calibration_undistorted", "images.txt")), "r"
) as f:
lines = f.readlines()[4:] # Skip the header
raw_poses = [line.strip("\n").split(" ") for line in lines[::2]]
raw_points = [line.strip("\n").split(" ") for line in lines[1::2]]
for raw_pose, raw_pts in zip(raw_poses, raw_points):
img_name = os.path.basename(raw_pose[-1])
# Extract the transform from world to camera
target_extrinsics = list(map(float, raw_pose[1:8]))
pose = np.eye(4, dtype=np.float32)
pose[:3, :3] = qvec2rotmat(target_extrinsics[:4])
pose[:3, 3] = target_extrinsics[4:]
T_world_to_camera[img_name] = pose
name_to_cam_idx[img_name]["undist_camera_idx"] = int(raw_pose[-2])
# Extract the visible 3D points
point3D_ids = [id for id in map(int, raw_pts[2::3]) if id != -1]
image_visible_points3D[img_name] = set(point3D_ids)
# Extract the covisibility of each image
num_imgs = len(names)
n_covisible_points = np.zeros((num_imgs, num_imgs))
for i in range(num_imgs - 1):
for j in range(i + 1, num_imgs):
visible_points3D1 = image_visible_points3D[names[i]]
visible_points3D2 = image_visible_points3D[names[j]]
n_covisible_points[i, j] = len(
visible_points3D1 & visible_points3D2
)
# Keep only the pairs with enough covisibility
valid_pairs = np.where(n_covisible_points >= conf.min_covisibility)
valid_pairs = np.stack(valid_pairs, axis=1)
self.data += [
{
"view0": {
"name": names[i][:-4],
"img_path": str(Path(img_folder, names[i])),
"depth_path": str(Path(depth_folder, names[i][:-4]))
+ depth_ext,
"camera": cameras[name_to_cam_idx[names[i]]["dist_camera_idx"]],
"T_w2cam": Pose.from_4x4mat(T_world_to_camera[names[i]]),
},
"view1": {
"name": names[j][:-4],
"img_path": str(Path(img_folder, names[j])),
"depth_path": str(Path(depth_folder, names[j][:-4]))
+ depth_ext,
"camera": cameras[name_to_cam_idx[names[j]]["dist_camera_idx"]],
"T_w2cam": Pose.from_4x4mat(T_world_to_camera[names[j]]),
},
"T_world_to_ref": Pose.from_4x4mat(T_world_to_camera[names[i]]),
"T_world_to_target": Pose.from_4x4mat(T_world_to_camera[names[j]]),
"T_0to1": Pose.from_4x4mat(
np.float32(
T_world_to_camera[names[j]]
@ np.linalg.inv(T_world_to_camera[names[i]])
)
),
"T_1to0": Pose.from_4x4mat(
np.float32(
T_world_to_camera[names[i]]
@ np.linalg.inv(T_world_to_camera[names[j]])
)
),
"n_covisible_points": n_covisible_points[i, j],
}
for (i, j) in valid_pairs
]
# Print some info
print("[Info] Successfully initialized dataset")
print("\t Name: ETH3D")
print("----------------------------------------")
def download_eth3d(self):
data_dir = DATA_PATH / self.conf.data_dir
tmp_dir = data_dir.parent / "ETH3D_tmp"
if tmp_dir.exists():
shutil.rmtree(tmp_dir)
tmp_dir.mkdir(exist_ok=True, parents=True)
url_base = "https://cvg-data.inf.ethz.ch/SOLD2/SOLD2_ETH3D_undistorted/"
zip_name = "ETH3D_undistorted.zip"
zip_path = tmp_dir / zip_name
torch.hub.download_url_to_file(url_base + zip_name, zip_path)
with zipfile.ZipFile(zip_path, "r") as zip_ref:
zip_ref.extractall(tmp_dir)
shutil.move(tmp_dir / zip_name.split(".")[0], data_dir)
def get_dataset(self, split):
return ETH3DDataset(self.conf)
def _read_image(self, img_path):
img = load_image(img_path, grayscale=self.grayscale)
shape = img.shape[-2:]
# instead of INTER_AREA this does bilinear interpolation with antialiasing
img_data = ImagePreprocessor({"resize": max(shape) // self.downsize_factor})(
img
)
return img_data
def read_depth(self, depth_path):
if self.downsize_factor != 8:
raise ValueError(
"Undistorted depth only available for low res"
+ " images(downsize_factor = 8)."
)
depth_img = cv2.imread(depth_path, cv2.IMREAD_ANYDEPTH)
depth_img = depth_img.astype(np.float32) / 256
return depth_img
def __getitem__(self, idx):
"""Returns the data associated to a pair of images (reference, target)
that are co-visible."""
data = self.data[idx]
# Load the images
view0 = data.pop("view0")
view1 = data.pop("view1")
view0 = {**view0, **self._read_image(view0["img_path"])}
view1 = {**view1, **self._read_image(view1["img_path"])}
view0["scales"] = np.array([1.0, 1]).astype(np.float32)
view1["scales"] = np.array([1.0, 1]).astype(np.float32)
# Load the depths
view0["depth"] = self.read_depth(view0["depth_path"])
view1["depth"] = self.read_depth(view1["depth_path"])
outputs = {
**data,
"view0": view0,
"view1": view1,
"name": f"{view0['name']}_{view1['name']}",
}
return outputs
def __len__(self):
return len(self.data)
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