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import open3d as o3d
import numpy as np
import os
import cv2
import collections
from evaluation.constants import SCANNETPP_LABELS, SCANNETPP_IDS
import torch
BaseImage = collections.namedtuple(
"Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
BaseCamera = collections.namedtuple(
"Camera", ["id", "model", "width", "height", "params"])
def qvec2rotmat(qvec):
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 Image(BaseImage):
def qvec2rotmat(self):
return qvec2rotmat(self.qvec)
@property
def world_to_camera(self) -> np.ndarray:
R = qvec2rotmat(self.qvec)
t = self.tvec
world2cam = np.eye(4)
world2cam[:3, :3] = R
world2cam[:3, 3] = t
return world2cam
class Camera(BaseCamera):
@property
def K(self):
K = np.eye(3)
if self.model == "SIMPLE_PINHOLE" or self.model == "SIMPLE_RADIAL" or self.model == "RADIAL" or self.model == "SIMPLE_RADIAL_FISHEYE" or self.model == "RADIAL_FISHEYE":
K[0, 0] = self.params[0]
K[1, 1] = self.params[0]
K[0, 2] = self.params[1]
K[1, 2] = self.params[2]
elif self.model == "PINHOLE" or self.model == "OPENCV" or self.model == "OPENCV_FISHEYE" or self.model == "FULL_OPENCV" or self.model == "FOV" or self.model == "THIN_PRISM_FISHEYE":
K[0, 0] = self.params[0]
K[1, 1] = self.params[1]
K[0, 2] = self.params[2]
K[1, 2] = self.params[3]
else:
raise NotImplementedError
return K
def read_images_text(path):
images = {}
with open(path, "r") as fid:
while True:
line = fid.readline()
if not line:
break
line = line.strip()
if len(line) > 0 and line[0] != "#":
elems = line.split()
image_id = int(elems[0])
qvec = np.array(tuple(map(float, elems[1:5])))
tvec = np.array(tuple(map(float, elems[5:8])))
camera_id = int(elems[8])
image_name = elems[9]
elems = fid.readline().split()
xys = np.column_stack([tuple(map(float, elems[0::3])),
tuple(map(float, elems[1::3]))])
point3D_ids = np.array(tuple(map(int, elems[2::3])))
images[image_id] = Image(
id=image_id, qvec=qvec, tvec=tvec,
camera_id=camera_id, name=image_name,
xys=xys, point3D_ids=point3D_ids)
return images
def read_cameras_text(path):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasText(const std::string& path)
void Reconstruction::ReadCamerasText(const std::string& path)
"""
cameras = {}
with open(path, "r") as fid:
while True:
line = fid.readline()
if not line:
break
line = line.strip()
if len(line) > 0 and line[0] != "#":
elems = line.split()
camera_id = int(elems[0])
model = elems[1]
width = int(elems[2])
height = int(elems[3])
params = np.array(tuple(map(float, elems[4:])))
cameras[camera_id] = Camera(id=camera_id, model=model,
width=width, height=height,
params=params)
return cameras
class ScanNetPPDataset:
def __init__(self, seq_name) -> None:
self.seq_name = seq_name
self.root = f'./data/scannetpp/data/{seq_name}'
self.rgb_dir = f'{self.root}/iphone/rgb'
self.depth_dir = f'{self.root}/iphone/render_depth'
self.segmentation_dir = f'{self.root}/output/mask'
self.object_dict_dir = f'{self.root}/output/object'
self.point_cloud_path = f'./data/scannetpp/pcld_0.25/{seq_name}.pth'
self.load_meta_data()
self.depth_scale = 1000.0
self.image_size = (1920, 1440)
def load_meta_data(self):
self.frame_id_list = []
cameras = read_cameras_text(os.path.join(self.root, 'iphone/colmap', "cameras.txt"))
images = read_images_text(os.path.join(self.root, 'iphone/colmap', "images.txt"))
camera = next(iter(cameras.values()))
fx, fy, cx, cy = camera.params[:4]
intrinsics = {}
extrinsics = {}
for _, image in (images.items()):
image_id = int(image.name.split('.')[0].split('_')[1])
self.frame_id_list.append(image_id)
world_to_camera = image.world_to_camera
extrinsics[image_id] = np.linalg.inv(world_to_camera)
intrinsics[image_id] = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
self.extrinsics = extrinsics
self.intrinsics = intrinsics
def get_frame_list(self, stride):
return self.frame_id_list[::stride]
def get_intrinsics(self, frame_id):
intrinsic_matrix = self.intrinsics[frame_id]
intrinisc_cam_parameters = o3d.camera.PinholeCameraIntrinsic()
intrinisc_cam_parameters.set_intrinsics(self.image_size[0], self.image_size[1], intrinsic_matrix[0, 0], intrinsic_matrix[1, 1], intrinsic_matrix[0, 2], intrinsic_matrix[1, 2])
return intrinisc_cam_parameters
def get_extrinsic(self, frame_id):
return self.extrinsics[frame_id]
def get_depth(self, frame_id):
depth_path = os.path.join(self.depth_dir, 'frame_%06d.png' % frame_id)
depth = cv2.imread(depth_path, -1)
depth = depth / self.depth_scale
depth = depth.astype(np.float32)
return depth
def get_rgb(self, frame_id, change_color=True):
rgb_path = os.path.join(self.rgb_dir, 'frame_%06d.jpg' % frame_id)
rgb = cv2.imread(rgb_path)
if change_color:
rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)
return rgb
def get_segmentation(self, frame_id, align_with_depth=False):
segmentation_path = os.path.join(self.segmentation_dir, 'frame_%06d.png' % frame_id)
if not os.path.exists(segmentation_path):
assert False, f"Segmentation not found: {segmentation_path}"
segmentation = cv2.imread(segmentation_path, cv2.IMREAD_UNCHANGED)
return segmentation
def get_frame_path(self, frame_id):
rgb_path = os.path.join(self.rgb_dir, 'frame_%06d.jpg' % frame_id)
segmentation_path = os.path.join(self.segmentation_dir, 'frame_%06d.png' % frame_id)
return rgb_path, segmentation_path
def get_label_features(self):
label_features_dict = np.load(f'data/text_features/scannetpp.npy', allow_pickle=True).item()
return label_features_dict
def get_scene_points(self):
data = torch.load(self.point_cloud_path)
points = np.asarray(data['sampled_coords'])
return points
def get_label_id(self):
self.class_id = SCANNETPP_IDS
self.class_label = SCANNETPP_LABELS
self.label2id = {}
self.id2label = {}
for label, id in zip(self.class_label, self.class_id):
self.label2id[label] = id
self.id2label[id] = label
return self.label2id, self.id2label |