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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
from dust3r.datasets.base.base_stereo_view_dataset import BaseStereoViewDataset
from dust3r.utils.image import imread_cv2
def readPFM(file):
file = open(file, 'rb')
color = None
width = None
height = None
scale = None
endian = None
header = file.readline().rstrip()
if header == b'PF':
color = True
elif header == b'Pf':
color = False
else:
raise Exception('Not a PFM file.')
dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline())
if dim_match:
width, height = map(int, dim_match.groups())
else:
raise Exception('Malformed PFM header.')
scale = float(file.readline().rstrip())
if scale < 0: # little-endian
endian = '<'
scale = -scale
else:
endian = '>' # big-endian
data = np.fromfile(file, endian + 'f')
shape = (height, width, 3) if color else (height, width)
data = np.reshape(data, shape)
data = np.flipud(data)
return data
class TartanairDatasets(BaseStereoViewDataset):
def __init__(self, *args, split, ROOT, **kwargs):
self.ROOT = ROOT # ROOT = "/media/tyhuang/T9/videodepth_data/spring_proc/train"
super().__init__(*args, **kwargs)
self.dataset_label = 'Tartanair'
test_scenes = []
scene_list = []
for scene in os.listdir(ROOT):
#scene_list.append(osp.join(ROOT, scene))
if scene not in test_scenes and split == 'train':
if 'Hard' not in scene:
scene_list.append(osp.join(ROOT, scene))
if scene in test_scenes and split == 'test':
if 'Hard' not in scene:
scene_list.append(osp.join(ROOT, scene))
self.pair_dict = {}
pair_num = 0
for scene in scene_list:
imgs = sorted(glob(osp.join(scene, '*_rgb.jpg')))
len_imgs = len(imgs)
# combinations = [(i, j) for i, j in itertools.combinations(range(len_imgs), 2)
# if abs(i - j) <= 10 or (abs(i - j) <= 20 and abs(i - j) % 3 == 0)]
combinations = [(i, j) for i, j in itertools.combinations(range(len_imgs), 2) if abs(i - j) <= 10 ]
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('_rgb.jpg', '_depth.pfm')
mask_path = img_path.replace('_rgb.jpg', '_mask.png')
metadata_path = img_path.replace('_rgb.jpg', '_metadata.npz')
pred_depth = np.load(img_path.replace('.jpg', '_pred_depth_' + self.depth_prior_name + '.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 = readPFM(depthmap_path)
maskmap = imread_cv2(mask_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
maskmap = (maskmap / 255.0) > 0.1
#maskmap = maskmap * (depthmap<100)
depthmap *= maskmap
metadata = np.load(metadata_path)
intrinsics = np.float32(metadata['camera_intrinsics'])
camera_pose = np.float32(metadata['camera_pose'])
# max_depth = np.float32(metadata['maximum_depth'])
#pred_depth = depthmap.copy()
# depthmap = (depthmap.astype(np.float32) / 10.0)
# pred_depth = pred_depth#/20.0
# camera_pose[:3, 3] /= 10.0
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()
# if num_valid==0:
# depthmap +=1
#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 = SpringDatasets(split='train', ROOT="/media/8TB/tyhuang/video_depth/spring_proc/train", 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() |