/
dataset.py
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/
dataset.py
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import imgviz
from torch.utils.data import Dataset, DataLoader
import torch
import numpy as np
import cv2
import os
from utils import enlarge_bbox, get_bbox2d, get_bbox2d_batch, box_filter
import glob
from torchvision import transforms
import image_transforms
import open3d
import time
def next_live_data(track_to_map_IDT, inited):
while True:
if track_to_map_IDT.empty():
if inited:
return None # no new frame, use kf buffer
else: # blocking until get the first frame
continue
else:
Buffer_data = track_to_map_IDT.get(block=False)
break
if Buffer_data is not None:
image, depth, T, obj, bbox_dict, kf_id = Buffer_data
del Buffer_data
T_obj = torch.eye(4)
sample = {"image": image, "depth": depth, "T": T, "T_obj": T_obj,
"obj": obj, "bbox_dict": bbox_dict, "frame_id": kf_id}
return sample
else:
print("getting nothing?")
exit(-1)
# return None
def init_loader(cfg, multi_worker=True):
if cfg.dataset_format == "Replica":
dataset = Replica(cfg)
elif cfg.dataset_format == "ScanNet":
dataset = ScanNet(cfg)
else:
print("Dataset format {} not found".format(cfg.dataset_format))
exit(-1)
# init dataloader
if multi_worker:
# multi worker loader
dataloader = DataLoader(dataset, batch_size=None, shuffle=False, sampler=None,
batch_sampler=None, num_workers=4, collate_fn=None,
pin_memory=True, drop_last=False, timeout=0,
worker_init_fn=None, generator=None, prefetch_factor=2,
persistent_workers=True)
else:
# single worker loader
dataloader = DataLoader(dataset, batch_size=None, shuffle=False, sampler=None,
batch_sampler=None, num_workers=0)
return dataloader
class Replica(Dataset):
def __init__(self, cfg):
self.imap_mode = cfg.imap_mode
self.root_dir = cfg.dataset_dir
traj_file = os.path.join(self.root_dir, "traj_w_c.txt")
self.Twc = np.loadtxt(traj_file, delimiter=" ").reshape([-1, 4, 4])
self.depth_transform = transforms.Compose(
[image_transforms.DepthScale(cfg.depth_scale),
image_transforms.DepthFilter(cfg.max_depth)])
# background semantic classes: undefined--1, undefined-0 beam-5 blinds-12 curtain-30 ceiling-31 floor-40 pillar-60 vent-92 wall-93 wall-plug-95 window-97 rug-98
self.background_cls_list = [5,12,30,31,40,60,92,93,95,97,98,79]
# Not sure: door-37 handrail-43 lamp-47 pipe-62 rack-66 shower-stall-73 stair-77 switch-79 wall-cabinet-94 picture-59
self.bbox_scale = 0.2 # 1 #1.5 0.9== s=1/9, s=0.2
def __len__(self):
return len(os.listdir(os.path.join(self.root_dir, "depth")))
def __getitem__(self, idx):
bbox_dict = {}
rgb_file = os.path.join(self.root_dir, "rgb", "rgb_" + str(idx) + ".png")
depth_file = os.path.join(self.root_dir, "depth", "depth_" + str(idx) + ".png")
inst_file = os.path.join(self.root_dir, "semantic_instance", "semantic_instance_" + str(idx) + ".png")
obj_file = os.path.join(self.root_dir, "semantic_class", "semantic_class_" + str(idx) + ".png")
depth = cv2.imread(depth_file, -1).astype(np.float32).transpose(1,0)
image = cv2.imread(rgb_file).astype(np.uint8)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).transpose(1,0,2)
obj = cv2.imread(obj_file, cv2.IMREAD_UNCHANGED).astype(np.int32).transpose(1,0) # uint16 -> int32
inst = cv2.imread(inst_file, cv2.IMREAD_UNCHANGED).astype(np.int32).transpose(1,0) # uint16 -> int32
bbox_scale = self.bbox_scale
if self.imap_mode:
obj = np.zeros_like(obj)
else:
obj_ = np.zeros_like(obj)
inst_list = []
batch_masks = []
for inst_id in np.unique(inst):
inst_mask = inst == inst_id
# if np.sum(inst_mask) <= 2000: # too small 20 400
# continue
sem_cls = np.unique(obj[inst_mask]) # sem label, only interested obj
assert sem_cls.shape[0] != 0
if sem_cls in self.background_cls_list:
continue
obj_mask = inst == inst_id
batch_masks.append(obj_mask)
inst_list.append(inst_id)
if len(batch_masks) > 0:
batch_masks = torch.from_numpy(np.stack(batch_masks))
cmins, cmaxs, rmins, rmaxs = get_bbox2d_batch(batch_masks)
for i in range(batch_masks.shape[0]):
w = rmaxs[i] - rmins[i]
h = cmaxs[i] - cmins[i]
if w <= 10 or h <= 10: # too small todo
continue
bbox_enlarged = enlarge_bbox([rmins[i], cmins[i], rmaxs[i], cmaxs[i]], scale=bbox_scale,
w=obj.shape[1], h=obj.shape[0])
# inst_list.append(inst_id)
inst_id = inst_list[i]
obj_[batch_masks[i]] = 1
# bbox_dict.update({int(inst_id): torch.from_numpy(np.array(bbox_enlarged).reshape(-1))}) # batch format
bbox_dict.update({inst_id: torch.from_numpy(np.array(
[bbox_enlarged[1], bbox_enlarged[3], bbox_enlarged[0], bbox_enlarged[2]]))}) # bbox order
inst[obj_ == 0] = 0 # for background
obj = inst
bbox_dict.update({0: torch.from_numpy(np.array([int(0), int(obj.shape[0]), 0, int(obj.shape[1])]))}) # bbox order
T = self.Twc[idx] # could change to ORB-SLAM pose or else
T_obj = np.eye(4) # obj pose, if dynamic
sample = {"image": image, "depth": depth, "T": T, "T_obj": T_obj,
"obj": obj, "bbox_dict": bbox_dict, "frame_id": idx}
if image is None or depth is None:
print(rgb_file)
print(depth_file)
raise ValueError
if self.depth_transform:
sample["depth"] = self.depth_transform(sample["depth"])
return sample
class ScanNet(Dataset):
def __init__(self, cfg):
self.imap_mode = cfg.imap_mode
self.root_dir = cfg.dataset_dir
self.color_paths = sorted(glob.glob(os.path.join(
self.root_dir, 'color', '*.jpg')), key=lambda x: int(os.path.basename(x)[:-4]))
self.depth_paths = sorted(glob.glob(os.path.join(
self.root_dir, 'depth', '*.png')), key=lambda x: int(os.path.basename(x)[:-4]))
self.inst_paths = sorted(glob.glob(os.path.join(
self.root_dir, 'instance-filt', '*.png')), key=lambda x: int(os.path.basename(x)[:-4])) # instance-filt
self.sem_paths = sorted(glob.glob(os.path.join(
self.root_dir, 'label-filt', '*.png')), key=lambda x: int(os.path.basename(x)[:-4])) # label-filt
self.load_poses(os.path.join(self.root_dir, 'pose'))
self.n_img = len(self.color_paths)
self.depth_transform = transforms.Compose(
[image_transforms.DepthScale(cfg.depth_scale),
image_transforms.DepthFilter(cfg.max_depth)])
# self.rgb_transform = rgb_transform
self.W = cfg.W
self.H = cfg.H
self.fx = cfg.fx
self.fy = cfg.fy
self.cx = cfg.cx
self.cy = cfg.cy
self.edge = cfg.mw
self.intrinsic_open3d = open3d.camera.PinholeCameraIntrinsic(
width=self.W,
height=self.H,
fx=self.fx,
fy=self.fy,
cx=self.cx,
cy=self.cy,
)
self.min_pixels = 1500
# from scannetv2-labels.combined.tsv
#1-wall, 3-floor, 16-window, 41-ceiling, 232-light switch 0-unknown? 21-pillar 161-doorframe, shower walls-128, curtain-21, windowsill-141
self.background_cls_list = [-1, 0, 1, 3, 16, 41, 232, 21, 161, 128, 21]
self.bbox_scale = 0.2
self.inst_dict = {}
def load_poses(self, path):
self.poses = []
pose_paths = sorted(glob.glob(os.path.join(path, '*.txt')),
key=lambda x: int(os.path.basename(x)[:-4]))
for pose_path in pose_paths:
with open(pose_path, "r") as f:
lines = f.readlines()
ls = []
for line in lines:
l = list(map(float, line.split(' ')))
ls.append(l)
c2w = np.array(ls).reshape(4, 4)
self.poses.append(c2w)
def __len__(self):
return self.n_img
def __getitem__(self, index):
bbox_scale = self.bbox_scale
color_path = self.color_paths[index]
depth_path = self.depth_paths[index]
inst_path = self.inst_paths[index]
sem_path = self.sem_paths[index]
color_data = cv2.imread(color_path).astype(np.uint8)
color_data = cv2.cvtColor(color_data, cv2.COLOR_BGR2RGB)
depth_data = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
depth_data = np.nan_to_num(depth_data, nan=0.)
T = None
if self.poses is not None:
T = self.poses[index]
if np.any(np.isinf(T)):
if index + 1 == self.__len__():
print("pose inf!")
return None
return self.__getitem__(index + 1)
H, W = depth_data.shape
color_data = cv2.resize(color_data, (W, H), interpolation=cv2.INTER_LINEAR)
if self.edge:
edge = self.edge # crop image edge, there are invalid value on the edge of the color image
color_data = color_data[edge:-edge, edge:-edge]
depth_data = depth_data[edge:-edge, edge:-edge]
if self.depth_transform:
depth_data = self.depth_transform(depth_data)
bbox_dict = {}
if self.imap_mode:
inst_data = np.zeros_like(depth_data).astype(np.int32)
else:
inst_data = cv2.imread(inst_path, cv2.IMREAD_UNCHANGED)
inst_data = cv2.resize(inst_data, (W, H), interpolation=cv2.INTER_NEAREST).astype(np.int32)
sem_data = cv2.imread(sem_path, cv2.IMREAD_UNCHANGED)#.astype(np.int32)
sem_data = cv2.resize(sem_data, (W, H), interpolation=cv2.INTER_NEAREST)
if self.edge:
edge = self.edge
inst_data = inst_data[edge:-edge, edge:-edge]
sem_data = sem_data[edge:-edge, edge:-edge]
inst_data += 1 # shift from 0->1 , 0 is for background
# box filter
track_start = time.time()
masks = []
classes = []
# convert to list of arrays
obj_ids = np.unique(inst_data)
for obj_id in obj_ids:
mask = inst_data == obj_id
sem_cls = np.unique(sem_data[mask])
if sem_cls in self.background_cls_list:
inst_data[mask] = 0 # set to background
continue
masks.append(mask)
classes.append(obj_id)
T_CW = np.linalg.inv(T)
inst_data = box_filter(masks, classes, depth_data, self.inst_dict, self.intrinsic_open3d, T_CW, min_pixels=self.min_pixels)
merged_obj_ids = np.unique(inst_data)
for obj_id in merged_obj_ids:
mask = inst_data == obj_id
bbox2d = get_bbox2d(mask, bbox_scale=bbox_scale)
if bbox2d is None:
inst_data[mask] = 0 # set to bg
else:
min_x, min_y, max_x, max_y = bbox2d
bbox_dict.update({int(obj_id): torch.from_numpy(np.array([min_x, max_x, min_y, max_y]).reshape(-1))}) # batch format
bbox_time = time.time()
# print("bbox time ", bbox_time - filter_time)
cv2.imshow("inst", imgviz.label2rgb(inst_data))
cv2.waitKey(1)
print("frame {} track time {}".format(index, bbox_time-track_start))
bbox_dict.update({0: torch.from_numpy(np.array([int(0), int(inst_data.shape[1]), 0, int(inst_data.shape[0])]))}) # bbox order
# wrap data to frame dict
T_obj = np.identity(4)
sample = {"image": color_data.transpose(1,0,2), "depth": depth_data.transpose(1,0), "T": T, "T_obj": T_obj}
if color_data is None or depth_data is None:
print(color_path)
print(depth_path)
raise ValueError
sample.update({"obj": inst_data.transpose(1,0)})
sample.update({"bbox_dict": bbox_dict})
return sample