# Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
import os
import random
import numpy as np
import copy
from PIL import Image # using pillow-simd for increased speed
import torch
import torch.utils.data as data
from torchvision import transforms
def pil_loader(path):
# open path as file to avoid ResourceWarning
# (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
class MonoDataset(data.Dataset):
"""Superclass for monocular dataloaders
Args:
data_path
filenames
height
width
frame_idxs
num_scales
is_train
img_ext
"""
def __init__(self,
data_path,
filenames,
height,
width,
frame_idxs,
num_scales,
is_train=False,
img_ext='.jpg'):
super(MonoDataset, self).__init__()
self.data_path = data_path
self.filenames = filenames
self.height = height
self.width = width
self.num_scales = num_scales
self.interp = Image.ANTIALIAS
self.frame_idxs = frame_idxs
self.is_train = is_train
self.img_ext = img_ext
self.loader = pil_loader
self.to_tensor = transforms.ToTensor()
# We need to specify augmentations differently in newer versions of torchvision.
# We first try the newer tuple version; if this fails we fall back to scalars
try:
self.brightness = (0.8, 1.2)
self.contrast = (0.8, 1.2)
self.saturation = (0.8, 1.2)
self.hue = (-0.1, 0.1)
transforms.ColorJitter.get_params(
self.brightness, self.contrast, self.saturation, self.hue)
except TypeError:
self.brightness = 0.2
self.contrast = 0.2
self.saturation = 0.2
self.hue = 0.1
self.resize = {}
for i in range(self.num_scales):
s = 2 ** i
self.resize[i] = transforms.Resize((self.height // s, self.width // s),
interpolation=self.interp)
self.load_depth = self.check_depth()
def preprocess(self, inputs, color_aug):
"""Resize colour images to the required scales and augment if required
We create the color_aug object in advance and apply the same augmentation to all
images in this item. This ensures that all images input to the pose network receive the
same augmentation.
"""
for k in list(inputs):
frame = inputs[k]
if "color" in k:
n, im, i = k
for i in range(self.num_scales):
inputs[(n, im, i)] = self.resize[i](inputs[(n, im, i - 1)])
for k in list(inputs):
f = inputs[k]
if "color" in k:
n, im, i = k
inputs[(n, im, i)] = self.to_tensor(f)
inputs[(n + "_aug", im, i)] = self.to_tensor(color_aug(f))
def __len__(self):
return len(self.filenames)
def __getitem__(self, index):
"""Returns a single training item from the dataset as a dictionary.
Values correspond to torch tensors.
Keys in the dictionary are either strings or tuples:
("color", <frame_id>, <scale>) for raw colour images,
("color_aug", <frame_id>, <scale>) for augmented colour images,
("K", scale) or ("inv_K", scale) for camera intrinsics,
"stereo_T" for camera extrinsics, and
"depth_gt" for ground truth depth maps.
<frame_id> is either:
an integer (e.g. 0, -1, or 1) representing the temporal step relative to 'index',
or
"s" for the opposite image in the stereo pair.
<scale> is an integer representing the scale of the image relative to the fullsize image:
-1 images at native resolution as loaded from disk
0 images resized to (self.width, self.height )
1 images resized to (self.width // 2, self.height // 2)
2 images resized to (self.width // 4, self.height // 4)
3 images resized to (self.width // 8, self.height // 8)
"""
inputs = {}
do_color_aug = self.is_train and random.random() > 0.5
do_flip = self.is_train and random.random() > 0.5
line = self.filenames[index].split()
folder = line[0]
if len(line) == 3:
frame_index = int(line[1])
else:
frame_index = 0
if len(line) == 3:
side = line[2]
else:
side = None
for i in self.frame_idxs:
if i == "s":
other_side = {"r": "l", "l": "r"}[side]
inputs[("color", i, -1)] = self.get_color(folder, frame_index, other_side, do_flip)
else:
inputs[("color", i, -1)] = self.get_color(folder, frame_index + i, side, do_flip)
# adjusting intrinsics to match each scale in the pyramid
for scale in range(self.num_scales):
K = self.K.copy()
K[0, :] *= self.width // (2 ** scale)
K[1, :] *= self.height // (2 ** scale)
inv_K = np.linalg.pinv(K)
inputs[("K", scale)] = torch.from_numpy(K)
inputs[("inv_K", scale)] = torch.from_numpy(inv_K)
if do_color_aug:
color_aug = transforms.ColorJitter.get_params(
self.brightness, self.contrast, self.saturation, self.hue)
else:
color_aug = (lambda x: x)
self.preprocess(inputs, color_aug)
for i in self.frame_idxs:
del inputs[("color", i, -1)]
del inputs[("color_aug", i, -1)]
if self.load_depth:
depth_gt = self.get_depth(folder, frame_index, side, do_flip)
inputs["depth_gt"] = np.expand_dims(depth_gt, 0)
inputs["depth_gt"] = torch.from_numpy(inputs["depth_gt"].astype(np.float32))
if "s" in self.frame_idxs:
stereo_T = np.eye(4, dtype=np.float32)
baseline_sign = -1 if do_flip else 1
side_sign = -1 if side == "l" else 1
stereo_T[0, 3] = side_sign * baseline_sign * 0.1
inputs["stereo_T"] = torch.from_numpy(stereo_T)
return inputs
def get_color(self, folder, frame_index, side, do_flip):
raise NotImplementedError
def check_depth(self):
raise NotImplementedError
def get_depth(self, folder, frame_index, side, do_flip):
raise NotImplementedError
