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import os
import cv2
import json
import numpy as np
import os.path as osp
from collections import deque
from dust3r.utils.image import imread_cv2
from .base_many_view_dataset import BaseManyViewDataset
class DTU(BaseManyViewDataset):
def __init__(self, num_seq=49, num_frames=5,
min_thresh=10, max_thresh=30,
test_id=None, full_video=False,
sample_pairs=False, kf_every=1,
*args, ROOT, **kwargs):
self.ROOT = ROOT
super().__init__(*args, **kwargs)
self.num_seq = num_seq
self.num_frames = num_frames
self.max_thresh = max_thresh
self.min_thresh = min_thresh
self.test_id = test_id
self.full_video = full_video
self.kf_every = kf_every
self.sample_pairs = sample_pairs
# load all scenes
self.load_all_scenes(ROOT)
def __len__(self):
return len(self.scene_list) * self.num_seq
def load_all_scenes(self, base_dir):
if self.test_id is None:
self.scene_list = os.listdir(osp.join(base_dir))
print(f"Found {len(self.scene_list)} scenes in split {self.split}")
else:
if isinstance(self.test_id, list):
self.scene_list = self.test_id
else:
self.scene_list = [self.test_id]
print(f"Test_id: {self.test_id}")
def load_cam_mvsnet(self, file, interval_scale=1):
""" read camera txt file """
cam = np.zeros((2, 4, 4))
words = file.read().split()
# read extrinsic
for i in range(0, 4):
for j in range(0, 4):
extrinsic_index = 4 * i + j + 1
cam[0][i][j] = words[extrinsic_index]
# read intrinsic
for i in range(0, 3):
for j in range(0, 3):
intrinsic_index = 3 * i + j + 18
cam[1][i][j] = words[intrinsic_index]
if len(words) == 29:
cam[1][3][0] = words[27]
cam[1][3][1] = float(words[28]) * interval_scale
cam[1][3][2] = 192
cam[1][3][3] = cam[1][3][0] + cam[1][3][1] * cam[1][3][2]
elif len(words) == 30:
cam[1][3][0] = words[27]
cam[1][3][1] = float(words[28]) * interval_scale
cam[1][3][2] = words[29]
cam[1][3][3] = cam[1][3][0] + cam[1][3][1] * cam[1][3][2]
elif len(words) == 31:
cam[1][3][0] = words[27]
cam[1][3][1] = float(words[28]) * interval_scale
cam[1][3][2] = words[29]
cam[1][3][3] = words[30]
else:
cam[1][3][0] = 0
cam[1][3][1] = 0
cam[1][3][2] = 0
cam[1][3][3] = 0
extrinsic = cam[0].astype(np.float32)
intrinsic = cam[1].astype(np.float32)
return intrinsic, extrinsic
def _get_views(self, idx, resolution, rng):
scene_id = self.scene_list[idx // self.num_seq]
seq_id = idx % self.num_seq
print('Scene ID:', scene_id)
image_path = osp.join(self.ROOT, scene_id, 'images')
depth_path = osp.join(self.ROOT, scene_id, 'depths')
mask_path = osp.join(self.ROOT, scene_id, 'binary_masks')
cam_path = osp.join(self.ROOT, scene_id, 'cams')
pairs_path = osp.join(self.ROOT, scene_id, 'pair.txt')
if not self.full_video:
img_idxs = self.sample_pairs(pairs_path, seq_id)
else:
img_idxs = sorted(os.listdir(image_path))
img_idxs = self.sample_frame_idx(img_idxs, rng, full_video=self.full_video)
views = []
imgs_idxs = deque(img_idxs)
while len(imgs_idxs) > 0:
im_idx = imgs_idxs.pop()
impath = osp.join(image_path, im_idx)
depthpath = osp.join(depth_path, im_idx.replace('.jpg', '.npy'))
campath = osp.join(cam_path, im_idx.replace('.jpg', '_cam.txt'))
maskpath = osp.join(mask_path, im_idx.replace('.jpg', '.png'))
rgb_image = imread_cv2(impath)
depthmap = np.load(depthpath)
depthmap = np.nan_to_num(depthmap.astype(np.float32), 0.0)
mask = imread_cv2(maskpath, cv2.IMREAD_UNCHANGED)/255.0
mask = mask.astype(np.float32)
mask[mask>0.5] = 1.0
mask[mask<0.5] = 0.0
mask = cv2.resize(mask, (depthmap.shape[1], depthmap.shape[0]), interpolation=cv2.INTER_NEAREST)
kernel = np.ones((10, 10), np.uint8) # Define the erosion kernel
mask = cv2.erode(mask, kernel, iterations=1)
depthmap = depthmap * mask
cur_intrinsics, camera_pose = self.load_cam_mvsnet(open(campath, 'r'))
intrinsics = cur_intrinsics[:3, :3]
camera_pose = np.linalg.inv(camera_pose)
rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, intrinsics, resolution, rng=rng, info=impath)
views.append(dict(
img=rgb_image,
depthmap=depthmap,
camera_pose=camera_pose,
camera_intrinsics=intrinsics,
dataset='dtu',
label=osp.join(scene_id, im_idx),
instance=osp.split(impath)[1],
))
return views
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