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dataset_ptam.py
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import numpy as np
import cv2
import os
import time
from collections import defaultdict, namedtuple
from threading import Thread, Lock
from multiprocessing import Process, Queue
import pdb
class ImageReader(object):
def __init__(self, ids, timestamps=None, cam=None):
self.ids = ids
self.timestamps = timestamps
self.cam = cam
self.cache = dict()
self.idx = 0
self.ahead = 10 # 10 images ahead of current index
self.waiting = 1.5 # waiting time
self.preload_thread = Thread(target=self.preload)
self.thread_started = False
def read(self, path):
img = cv2.imread(path, -1)
if self.cam is None:
return img
else:
return self.cam.rectify(img)
def preload(self):
idx = self.idx
t = float('inf')
while True:
if time.time() - t > self.waiting:
return
if self.idx == idx:
time.sleep(1e-2)
continue
for i in range(self.idx, self.idx + self.ahead):
if i not in self.cache and i < len(self.ids):
self.cache[i] = self.read(self.ids[i])
if self.idx + self.ahead > len(self.ids):
return
idx = self.idx
t = time.time()
def __len__(self):
return len(self.ids)
def __getitem__(self, idx):
self.idx = idx
# if not self.thread_started:
# self.thread_started = True
# self.preload_thread.start()
if idx in self.cache:
img = self.cache[idx]
del self.cache[idx]
else:
img = self.read(self.ids[idx])
return img
def __iter__(self):
for i, timestamp in enumerate(self.timestamps):
yield timestamp, self[i]
@property
def dtype(self):
return self[0].dtype
@property
def shape(self):
return self[0].shape
class ICLNUIMDataset(object):
'''
path example: 'path/to/your/ICL-NUIM R-GBD Dataset/living_room_traj0_frei_png'
'''
cam = namedtuple('camera', 'fx fy cx cy scale')(
481.20, 480.0, 319.5, 239.5, 5000)
def __init__(self, path):
path = os.path.expanduser(path)
self.rgb = ImageReader(self.listdir(os.path.join(path, 'rgb')))
self.depth = ImageReader(self.listdir(os.path.join(path, 'depth')))
self.timestamps = None
def sort(self, xs):
return sorted(xs, key=lambda x:int(x[:-4]))
def listdir(self, dir):
files = [_ for _ in os.listdir(dir) if _.endswith('.png')]
return [os.path.join(dir, _) for _ in self.sort(files)]
def __len__(self):
return len(self.rgb)
def make_pair(matrix, threshold=1):
assert (matrix >= 0).all()
pairs = []
base = defaultdict(int)
while True:
i = matrix[:, 0].argmin()
min0 = matrix[i, 0]
j = matrix[0, :].argmin()
min1 = matrix[0, j]
if min0 < min1:
i, j = i, 0
else:
i, j = 0, j
if min(min1, min0) < threshold:
pairs.append((i + base['i'], j + base['j']))
matrix = matrix[i + 1:, j + 1:]
base['i'] += (i + 1)
base['j'] += (j + 1)
if min(matrix.shape) == 0:
break
return pairs
class Sfm_houseRGBDDataset(object):
'''
path example: 'path/to/your/TUM R-GBD Dataset/rgbd_dataset_freiburg1_xyz'
'''
cam = namedtuple('camera', 'fx fy cx cy scale')(
726.21081542969,726.21081542969,359.2048034668,202.47247314453, 5000.)
def __init__(self, path, register=True):
path = os.path.expanduser(path)
if not register:
rgb_ids, rgb_timestamps = self.listdir(path, 'rgb', ext='.png')
depth_ids, depth_timestamps = self.listdir(path, 'depth')
else:
rgb_imgs, rgb_timestamps = self.listdir(path, 'rgb', ext='.png')
depth_imgs, depth_timestamps = self.listdir(path, 'depth')
interval = (rgb_timestamps[1:] - rgb_timestamps[:-1]).mean() * 2/3
matrix = np.abs(rgb_timestamps[:, np.newaxis] - depth_timestamps)
pairs = make_pair(matrix, interval)
rgb_ids = []
depth_ids = []
for i, j in pairs:
rgb_ids.append(rgb_imgs[i])
depth_ids.append(depth_imgs[j])
self.rgb = ImageReader(rgb_ids, rgb_timestamps)
self.depth = ImageReader(depth_ids, depth_timestamps)
self.timestamps = rgb_timestamps
def sort(self, xs, st = 3):
return sorted(xs, key=lambda x:float(x[st:-4]))
def listdir(self, path, split='rgb', ext='.png'):
imgs, timestamps = [], []
files = [x for x in os.listdir(os.path.join(path, split)) if x.endswith(ext)]
st = 0
for name in self.sort(files,st):
imgs.append(os.path.join(path, split, name))
timestamp = float(name[st:-len(ext)].rstrip('.'))
timestamps.append(timestamp)
return imgs, np.array(timestamps)
def __getitem__(self, idx):
return self.rgb[idx], self.depth[idx]
def __len__(self):
return len(self.rgb)
class ReplicaRGBDDataset(object):
'''
path example: 'path/to/your/TUM R-GBD Dataset/rgbd_dataset_freiburg1_xyz'
'''
cam = namedtuple('camera', 'fx fy cx cy scale')(
600., 600., 599.5, 339.5, 6553.5)
def __init__(self, path, register=True):
path = os.path.expanduser(path)
if not register:
rgb_ids, rgb_timestamps = self.listdir(path, 'rgb', ext='.jpg')
depth_ids, depth_timestamps = self.listdir(path, 'depth')
else:
rgb_imgs, rgb_timestamps = self.listdir(path, 'rgb', ext='.jpg')
depth_imgs, depth_timestamps = self.listdir(path, 'depth')
interval = (rgb_timestamps[1:] - rgb_timestamps[:-1]).mean() * 2/3
matrix = np.abs(rgb_timestamps[:, np.newaxis] - depth_timestamps)
pairs = make_pair(matrix, interval)
rgb_ids = []
depth_ids = []
for i, j in pairs:
rgb_ids.append(rgb_imgs[i])
depth_ids.append(depth_imgs[j])
self.rgb = ImageReader(rgb_ids, rgb_timestamps)
self.depth = ImageReader(depth_ids, depth_timestamps)
self.timestamps = rgb_timestamps
def sort(self, xs, st = 3):
return sorted(xs, key=lambda x:float(x[st:-4]))
def listdir(self, path, split='rgb', ext='.png'):
imgs, timestamps = [], []
files = [x for x in os.listdir(os.path.join(path, split)) if x.endswith(ext)]
st = 5
for name in self.sort(files,st):
imgs.append(os.path.join(path, split, name))
timestamp = float(name[st:-len(ext)].rstrip('.'))
timestamps.append(timestamp)
return imgs, np.array(timestamps)
def __getitem__(self, idx):
return self.rgb[idx], self.depth[idx]
def __len__(self):
return len(self.rgb)
class NeuralSurfaceRGBDDataset(object):
'''
path example: 'path/to/your/TUM R-GBD Dataset/rgbd_dataset_freiburg1_xyz'
'''
cam = namedtuple('camera', 'fx fy cx cy scale')(
554.2562584220408, 554.2562584220408, 320., 240., 1000)
def __init__(self, path, register=True):
path = os.path.expanduser(path)
if not register:
rgb_ids, rgb_timestamps = self.listdir(path, 'images')
depth_ids, depth_timestamps = self.listdir(path, 'depth_filtered')
else:
rgb_imgs, rgb_timestamps = self.listdir(path, 'images')
depth_imgs, depth_timestamps = self.listdir(path, 'depth_filtered')
interval = (rgb_timestamps[1:] - rgb_timestamps[:-1]).mean() * 2/3
matrix = np.abs(rgb_timestamps[:, np.newaxis] - depth_timestamps)
pairs = make_pair(matrix, interval)
rgb_ids = []
depth_ids = []
for i, j in pairs:
rgb_ids.append(rgb_imgs[i])
depth_ids.append(depth_imgs[j])
self.rgb = ImageReader(rgb_ids, rgb_timestamps)
self.depth = ImageReader(depth_ids, depth_timestamps)
self.timestamps = rgb_timestamps
def sort(self, xs, st = 3):
return sorted(xs, key=lambda x:float(x[st:-4]))
def listdir(self, path, split='rgb', ext='.png'):
imgs, timestamps = [], []
files = [x for x in os.listdir(os.path.join(path, split)) if x.endswith(ext)]
st = 3 if split == "images" else 5
for name in self.sort(files,st):
imgs.append(os.path.join(path, split, name))
timestamp = float(name[st:-len(ext)].rstrip('.'))
timestamps.append(timestamp)
return imgs, np.array(timestamps)
def __getitem__(self, idx):
return self.rgb[idx], self.depth[idx]
def __len__(self):
return len(self.rgb)
class TUMRGBDDataset(object):
'''
path example: 'path/to/your/TUM R-GBD Dataset/rgbd_dataset_freiburg1_xyz'
'''
cam = namedtuple('camera', 'fx fy cx cy scale')(
481.20, 480.0, 319.5, 239.5, 5000)
'''
cam = namedtuple('camera', 'fx fy cx cy scale')(
525.0, 525.0, 319.5, 239.5, 5000)
'''
def __init__(self, path, register=True):
path = os.path.expanduser(path)
if not register:
rgb_ids, rgb_timestamps = self.listdir(path, 'rgb')
depth_ids, depth_timestamps = self.listdir(path, 'depth')
else:
rgb_imgs, rgb_timestamps = self.listdir(path, 'rgb')
depth_imgs, depth_timestamps = self.listdir(path, 'depth')
interval = (rgb_timestamps[1:] - rgb_timestamps[:-1]).mean() * 2/3
matrix = np.abs(rgb_timestamps[:, np.newaxis] - depth_timestamps)
pairs = make_pair(matrix, interval)
rgb_ids = []
depth_ids = []
for i, j in pairs:
rgb_ids.append(rgb_imgs[i])
depth_ids.append(depth_imgs[j])
self.rgb = ImageReader(rgb_ids, rgb_timestamps)
self.depth = ImageReader(depth_ids, depth_timestamps)
self.timestamps = rgb_timestamps
def sort(self, xs):
return sorted(xs, key=lambda x:float(x[:-4]))
def listdir(self, path, split='rgb', ext='.png'):
imgs, timestamps = [], []
files = [x for x in os.listdir(os.path.join(path, split)) if x.endswith(ext)]
for name in self.sort(files):
imgs.append(os.path.join(path, split, name))
timestamp = float(name[:-len(ext)].rstrip('.'))
timestamps.append(timestamp)
return imgs, np.array(timestamps)
def __getitem__(self, idx):
return self.rgb[idx], self.depth[idx]
def __len__(self):
return len(self.rgb)