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import os.path as osp | |
import json | |
import itertools | |
from collections import deque | |
import sys | |
sys.path.append(osp.join(osp.dirname(__file__), "..", "..")) | |
import cv2 | |
import numpy as np | |
import time | |
from dust3r.datasets.base.base_multiview_dataset import BaseMultiViewDataset | |
from dust3r.utils.image import imread_cv2 | |
class Co3d_Multi(BaseMultiViewDataset): | |
def __init__(self, mask_bg="rand", *args, ROOT, **kwargs): | |
self.ROOT = ROOT | |
super().__init__(*args, **kwargs) | |
assert mask_bg in (True, False, "rand") | |
self.mask_bg = mask_bg | |
self.is_metric = False | |
self.dataset_label = "Co3d_v2" | |
# load all scenes | |
with open(osp.join(self.ROOT, f"selected_seqs_{self.split}.json"), "r") as f: | |
self.scenes = json.load(f) | |
self.scenes = {k: v for k, v in self.scenes.items() if len(v) > 0} | |
self.scenes = { | |
(k, k2): v2 for k, v in self.scenes.items() for k2, v2 in v.items() | |
} | |
self.scene_list = list(self.scenes.keys()) | |
cut_off = ( | |
self.num_views if not self.allow_repeat else max(self.num_views // 3, 3) | |
) | |
self.cut_off = cut_off | |
self.all_ref_imgs = [ | |
(key, value) | |
for key, values in self.scenes.items() | |
for value in values[: len(values) - cut_off + 1] | |
] | |
self.invalidate = {scene: {} for scene in self.scene_list} | |
self.invalid_scenes = {scene: False for scene in self.scene_list} | |
def __len__(self): | |
return len(self.all_ref_imgs) | |
def _get_metadatapath(self, obj, instance, view_idx): | |
return osp.join(self.ROOT, obj, instance, "images", f"frame{view_idx:06n}.npz") | |
def _get_impath(self, obj, instance, view_idx): | |
return osp.join(self.ROOT, obj, instance, "images", f"frame{view_idx:06n}.jpg") | |
def _get_depthpath(self, obj, instance, view_idx): | |
return osp.join( | |
self.ROOT, obj, instance, "depths", f"frame{view_idx:06n}.jpg.geometric.png" | |
) | |
def _get_maskpath(self, obj, instance, view_idx): | |
return osp.join(self.ROOT, obj, instance, "masks", f"frame{view_idx:06n}.png") | |
def _read_depthmap(self, depthpath, input_metadata): | |
depthmap = imread_cv2(depthpath, cv2.IMREAD_UNCHANGED) | |
depthmap = (depthmap.astype(np.float32) / 65535) * np.nan_to_num( | |
input_metadata["maximum_depth"] | |
) | |
return depthmap | |
def _get_views(self, idx, resolution, rng, num_views): | |
invalid_seq = True | |
scene_info, ref_img_idx = self.all_ref_imgs[idx] | |
while invalid_seq: | |
while self.invalid_scenes[scene_info]: | |
idx = rng.integers(low=0, high=len(self.all_ref_imgs)) | |
scene_info, ref_img_idx = self.all_ref_imgs[idx] | |
obj, instance = scene_info | |
image_pool = self.scenes[obj, instance] | |
if len(image_pool) < self.cut_off: | |
print("Invalid scene!") | |
self.invalid_scenes[scene_info] = True | |
continue | |
imgs_idxs, ordered_video = self.get_seq_from_start_id( | |
num_views, ref_img_idx, image_pool, rng | |
) | |
if resolution not in self.invalidate[obj, instance]: # flag invalid images | |
self.invalidate[obj, instance][resolution] = [ | |
False for _ in range(len(image_pool)) | |
] | |
# decide now if we mask the bg | |
mask_bg = (self.mask_bg == True) or ( | |
self.mask_bg == "rand" and rng.choice(2, p=[0.9, 0.1]) | |
) | |
views = [] | |
imgs_idxs = deque(imgs_idxs) | |
while len(imgs_idxs) > 0: # some images (few) have zero depth | |
if ( | |
len(image_pool) - sum(self.invalidate[obj, instance][resolution]) | |
< self.cut_off | |
): | |
print("Invalid scene!") | |
invalid_seq = True | |
self.invalid_scenes[scene_info] = True | |
break | |
im_idx = imgs_idxs.pop() | |
if self.invalidate[obj, instance][resolution][im_idx]: | |
# search for a valid image | |
ordered_video = False | |
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 = self._get_impath(obj, instance, view_idx) | |
depthpath = self._get_depthpath(obj, instance, view_idx) | |
# load camera params | |
metadata_path = self._get_metadatapath(obj, instance, view_idx) | |
input_metadata = np.load(metadata_path) | |
camera_pose = input_metadata["camera_pose"].astype(np.float32) | |
intrinsics = input_metadata["camera_intrinsics"].astype(np.float32) | |
# load image and depth | |
rgb_image = imread_cv2(impath) | |
depthmap = self._read_depthmap(depthpath, input_metadata) | |
if mask_bg: | |
# load object mask | |
maskpath = self._get_maskpath(obj, instance, view_idx) | |
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.append(im_idx) | |
continue | |
# generate img mask and raymap mask | |
img_mask, ray_mask = self.get_img_and_ray_masks( | |
self.is_metric, len(views), rng | |
) | |
views.append( | |
dict( | |
img=rgb_image, | |
depthmap=depthmap, | |
camera_pose=camera_pose, | |
camera_intrinsics=intrinsics, | |
dataset=self.dataset_label, | |
label=osp.join(obj, instance), | |
instance=osp.split(impath)[1], | |
is_metric=self.is_metric, | |
is_video=ordered_video, | |
quantile=np.array(0.9, dtype=np.float32), | |
img_mask=img_mask, | |
ray_mask=ray_mask, | |
camera_only=False, | |
depth_only=False, | |
single_view=False, | |
reset=False, | |
) | |
) | |
if len(views) == num_views and not all( | |
[view["instance"] == views[0]["instance"] for view in views] | |
): | |
invalid_seq = False | |
return views | |