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# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# Dataloader for Spring
# --------------------------------------------------------
import os.path as osp
from glob import glob
import itertools
import numpy as np
import re
import cv2
import os
import sys
sys.path.append('/home/lipeng/ljh_code/Video_Depth_CVPR2025-main/dust3r_train')
from dust3r.datasets.base.base_stereo_view_dataset import BaseStereoViewDataset
from dust3r.utils.image import imread_cv2
TAG_FLOAT = 202021.25
def depth_read(filename):
"""Read depth data from file, return as numpy array."""
f = open(filename, "rb")
check = np.fromfile(f, dtype=np.float32, count=1)[0]
assert (
check == TAG_FLOAT
), " depth_read:: Wrong tag in flow file (should be: {0}, is: {1}). Big-endian machine? ".format(
TAG_FLOAT, check
)
width = np.fromfile(f, dtype=np.int32, count=1)[0]
height = np.fromfile(f, dtype=np.int32, count=1)[0]
size = width * height
assert (
width > 0 and height > 0 and size > 1 and size < 100000000
), " depth_read:: Wrong input size (width = {0}, height = {1}).".format(
width, height
)
depth = np.fromfile(f, dtype=np.float32, count=-1).reshape((height, width))
return depth
def cam_read(filename):
""" Read camera data, return (M,N) tuple.
M is the intrinsic matrix, N is the extrinsic matrix, so that
x = M*N*X,
with x being a point in homogeneous image pixel coordinates, X being a
point in homogeneous world coordinates.
"""
f = open(filename,'rb')
check = np.fromfile(f,dtype=np.float32,count=1)[0]
assert check == TAG_FLOAT, ' cam_read:: Wrong tag in flow file (should be: {0}, is: {1}). Big-endian machine? '.format(TAG_FLOAT,check)
M = np.fromfile(f,dtype='float64',count=9).reshape((3,3))
N = np.fromfile(f,dtype='float64',count=12).reshape((3,4))
return M,N
class SintelDatasets(BaseStereoViewDataset):
def __init__(self, *args, split, ROOT, **kwargs):
self.ROOT = ROOT # ROOT = "/media/8TB/tyhuang/video_depth/vkitti_2.0.3_proc"
super().__init__(*args, **kwargs)
self.dataset_label = 'Sintel'
test_scenes = []
scene_list = []
for scene in os.listdir(ROOT):
scene_list.append(osp.join(ROOT, scene))
self.pair_dict = {}
pair_num = 0
for scene in scene_list:
imgs = sorted(glob(osp.join(scene, '*.png')))
len_imgs = len(imgs)
# combinations = [(i, j) for i, j in itertools.combinations(range(len_imgs), 2)
# if abs(i - j) <= 15 or (abs(i - j) <= 30 and abs(i - j) % 5 == 0)]
combinations = [(i, j) for i, j in itertools.combinations(range(len_imgs), 2) if abs(i - j) <= 3]
for (i, j) in combinations:
self.pair_dict[pair_num] = [imgs[i], imgs[j]]
pair_num += 1
def __len__(self):
return len(self.pair_dict)
def _get_views(self, idx, resolution, rng):
views = []
for img_path in self.pair_dict[idx]:
rgb_image = imread_cv2(img_path)
depthmap_path = img_path.replace('MPI-Sintel-training_images', 'MPI-Sintel-depth-training').replace('final/','depth/').replace('.png','.dpt')
mask_path = img_path.replace('MPI-Sintel-training_images', 'MPI-Sintel-depth-training').replace('final/','dynamic_label_perfect/')
metadata_path = img_path.replace('MPI-Sintel-training_images', 'MPI-Sintel-depth-training').replace('final/','camdata_left/').replace('.png','.cam')
pred_depth = np.load(img_path.replace('final','depth_prediction_' + self.depth_prior_name).replace('.png', '.npz'))#['depth']
focal_length_px = pred_depth['focallength_px']
pred_depth = pred_depth['depth']
pred_depth = self.pixel_to_pointcloud(pred_depth, focal_length_px)
depthmap = depth_read(depthmap_path)
if os.path.exists(mask_path):
maskmap = imread_cv2(mask_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
maskmap = (maskmap / 255.0) > 0.1
#print(maskmap.max())
#maskmap = maskmap * (depthmap<100)
depthmap *= maskmap
intrinsics, extrinsics = cam_read(metadata_path)
intrinsics, extrinsics = np.array(intrinsics, dtype=np.float32), np.array(extrinsics, dtype=np.float32)
R = extrinsics[:3,:3]
t = extrinsics[:3,3]
camera_pose = np.eye(4, dtype=np.float32)
camera_pose[:3,:3] = R.T
camera_pose[:3,3] = -R.T @ t
#camera_pose = np.linalg.inv(camera_pose)
# max_depth = np.float32(metadata['maximum_depth'])
# depthmap = (depthmap.astype(np.float32) / 20.0)
# camera_pose[:3, 3] /= 20.0
# pred_depth = pred_depth/20.0
rgb_image, depthmap, pred_depth, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, pred_depth, intrinsics, resolution, rng=rng, info=img_path)
num_valid = (depthmap > 0.0).sum()
# assert num_valid > 0
# if num_valid==0:
# depthmap +=0.001
views.append(dict(
img=rgb_image,
depthmap=depthmap,
camera_pose=camera_pose,
camera_intrinsics=intrinsics,
dataset=self.dataset_label,
label=img_path,
instance=img_path,
pred_depth=pred_depth
))
return views
if __name__ == "__main__":
from dust3r.datasets.base.base_stereo_view_dataset import view_name
from dust3r.viz import SceneViz, auto_cam_size
from dust3r.utils.image import rgb
dataset = SintelDatasets(split='train', ROOT="/data/lipeng/ljh_data/MPI-Sintel/MPI-Sintel/MPI-Sintel-training_images/training/final", resolution=512, aug_crop=16)
a = len(dataset)
for idx in np.random.permutation(len(dataset)):
views = dataset[idx]
assert len(views) == 2
print(view_name(views[0]), view_name(views[1]))
viz = SceneViz()
poses = [views[view_idx]['camera_pose'] for view_idx in [0, 1]]
cam_size = max(auto_cam_size(poses), 0.001)
for view_idx in [0, 1]:
pts3d = views[view_idx]['pts3d']
valid_mask = views[view_idx]['valid_mask']
colors = rgb(views[view_idx]['img'])
# viz.add_pointcloud(pts3d, colors, valid_mask)
# viz.add_camera(pose_c2w=views[view_idx]['camera_pose'],
# focal=views[view_idx]['camera_intrinsics'][0, 0],
# color=(idx * 255, (1 - idx) * 255, 0),
# image=colors,
# cam_size=cam_size)
# viz.show() |