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import os
import cv2
import json
import itertools
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 Co3d(BaseManyViewDataset):
def __init__(self, mask_bg=True, use_comb=True,
scene_class=None, scene_id=None,
num_seq=100, num_frames=5,
min_thresh=10, max_thresh=100,
full_video=False, lb=0, ub=30,
kf_every=1, *args, ROOT, **kwargs):
self.ROOT = ROOT
super().__init__(*args, **kwargs)
assert mask_bg in (True, False, 'rand')
self.mask_bg = mask_bg
self.num_seq = num_seq
self.num_frames = num_frames
self.max_thresh = max_thresh
self.min_thresh = min_thresh
self.full_video = full_video
self.kf_every = kf_every
self.use_comb = use_comb
self.scenes, self.scene_list = self.load_scene(scene_class, scene_id)
self.combinations, self.num_seq = self.get_combinations(use_comb, lb, ub)
self.invalidate = {scene: {} for scene in self.scene_list}
def get_combinations(self, use_comb, lb, ub):
if use_comb and not self.full_video:
print('Using combinations')
combinations = list(itertools.combinations(range(100), self.num_frames))
combinations = [combo for combo in combinations if all(lb < abs(x-y) <= ub and abs(x-y) % 5 == 0 for x, y in zip(combo, combo[1:]))]
num_seq = len(combinations)
print('Number of sequences:', num_seq)
else:
combinations = None
num_seq = self.num_seq
return combinations, num_seq
def load_scene(self, scene_class=None, scene_id=None):
print('Loading scenes')
with open(osp.join(self.ROOT, f'selected_seqs_{self.split}.json'), 'r') as f:
scenes = json.load(f)
if scene_class is not None:
scenes = {k: v for k, v in scenes.items() if k == scene_class}
else:
scenes = {k: v for k, v in scenes.items() if len(v) > 0} # k is class (apple), v is corresponding list
if scene_id is not None:
scenes = {(k, k2): v2 for k, v in scenes.items() for k2, v2 in v.items() if k2 == scene_id}
else:
scenes = {(k, k2): v2 for k, v in scenes.items()
for k2, v2 in v.items()} # k is class (apple), k2 is instance (110_13051_23361), v2 is list of image idx
scene_list = list(scenes.keys())
return scenes, scene_list
def __len__(self):
return len(self.scene_list) * self.num_seq
def _get_views(self, idx, resolution, rng, attempts=0):
obj, instance = self.scene_list[idx // self.num_seq]
image_pool = self.scenes[obj, instance]
if self.use_comb and not self.full_video:
frame_idx = self.combinations[idx % len(self.combinations)]
last = len(image_pool)-1
imgs_idxs = [max(0, min(im_idx + rng.integers(-4, 5), last)) for im_idx in frame_idx]
else:
img_idx = range(0, len(image_pool))
imgs_idxs = self.sample_frames(img_idx, rng)
if resolution not in self.invalidate[obj, instance]: # flag invalid images
self.invalidate[obj, instance][resolution] = [False for _ in range(len(image_pool))]
mask_bg = (self.mask_bg == True) or (self.mask_bg == 'rand' and rng.choice(2))
imgs_idxs = deque(imgs_idxs)
max_depth_min = 1e8
max_depth_max = 0.
max_depth_first = None
views = []
while len(imgs_idxs) > 0:
im_idx = imgs_idxs.popleft()
if self.invalidate[obj, instance][resolution][im_idx]:
# search for a valid image
random_direction = 2 * rng.choice(2) - 1
for offset in range(1, len(image_pool)):
tentative_im_idx = (im_idx + (random_direction * offset)) % len(image_pool)
if not self.invalidate[obj, instance][resolution][tentative_im_idx]:
im_idx = tentative_im_idx
break
view_idx = image_pool[im_idx]
impath = osp.join(self.ROOT, obj, instance, 'images', f'frame{view_idx:06d}.jpg')
# load camera params
input_metadata = np.load(impath.replace('jpg', 'npz'))
camera_pose = input_metadata['camera_pose'].astype(np.float32)
intrinsics = input_metadata['camera_intrinsics'].astype(np.float32)
rgb_image = imread_cv2(impath)
depthmap = imread_cv2(impath.replace('images', 'depths') + '.geometric.png', cv2.IMREAD_UNCHANGED)
depthmap = (depthmap.astype(np.float32) / 65535) * np.nan_to_num(input_metadata['maximum_depth'])
if mask_bg:
# load object mask
maskpath = osp.join(self.ROOT, obj, instance, 'masks', f'frame{view_idx:06d}.png')
maskmap = imread_cv2(maskpath, cv2.IMREAD_UNCHANGED).astype(np.float32)
maskmap = (maskmap / 255.0) > 0.1
# update the depthmap with mask
depthmap *= maskmap
rgb_image, depthmap, intrinsics = self._crop_resize_if_necessary(
rgb_image, depthmap, intrinsics, resolution, rng=rng, info=impath)
num_valid = (depthmap > 0.0).sum()
if num_valid == 0:
# problem, invalidate image and retry
self.invalidate[obj, instance][resolution][im_idx] = True
imgs_idxs.appendleft(im_idx)
continue
if input_metadata['maximum_depth'] > max_depth_max:
max_depth_max = input_metadata['maximum_depth']
if input_metadata['maximum_depth'] < max_depth_min:
max_depth_min = input_metadata['maximum_depth']
if max_depth_first is None:
max_depth_first = input_metadata['maximum_depth']
views.append(dict(
img=rgb_image,
depthmap=depthmap,
camera_pose=camera_pose,
camera_intrinsics=intrinsics,
dataset='Co3d_v2',
label=osp.join(obj, instance),
instance=osp.split(impath)[1]
))
if max_depth_max / max_depth_min > 100. or max_depth_max / max_depth_first > 10.:
new_idx = rng.integers(0, self.__len__()-1)
return self._get_views(new_idx, resolution, rng)
return views
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