import torch
from torch.utils.data import Dataset, DataLoader
import torch.utils.data.distributed
from torchvision import transforms
import numpy as np
from PIL import Image
import os
import random
import copy
import cv2
from utils import DistributedSamplerNoEvenlyDivisible
def _is_pil_image(img):
return isinstance(img, Image.Image)
def _is_numpy_image(img):
return isinstance(img, np.ndarray) and (img.ndim in {2, 3})
def preprocessing_transforms(mode):
return transforms.Compose([
ToTensor(mode=mode)
])
class NewDataLoader(object):
def __init__(self, args, mode):
if mode == 'train':
self.training_samples = DataLoadPreprocess(args, mode, transform=preprocessing_transforms(mode))
if args.distributed:
self.train_sampler = torch.utils.data.distributed.DistributedSampler(self.training_samples)
else:
self.train_sampler = None
self.data = DataLoader(self.training_samples, args.batch_size,
shuffle=(self.train_sampler is None),
num_workers=args.num_threads,
pin_memory=True,
sampler=self.train_sampler)
elif mode == 'online_eval':
self.testing_samples = DataLoadPreprocess(args, mode, transform=preprocessing_transforms(mode))
if args.distributed:
# self.eval_sampler = torch.utils.data.distributed.DistributedSampler(self.testing_samples, shuffle=False)
self.eval_sampler = DistributedSamplerNoEvenlyDivisible(self.testing_samples, shuffle=False)
else:
self.eval_sampler = None
self.data = DataLoader(self.testing_samples, 1,
shuffle=False,
num_workers=1,
pin_memory=True,
sampler=self.eval_sampler)
elif mode == 'test':
self.testing_samples = DataLoadPreprocess(args, mode, transform=preprocessing_transforms(mode))
self.data = DataLoader(self.testing_samples, 1, shuffle=False, num_workers=1)
else:
print('mode should be one of \'train, test, online_eval\'. Got {}'.format(mode))
class DataLoadPreprocess(Dataset):
def __init__(self, args, mode, transform=None, is_for_online_eval=False):
self.args = args
if mode == 'online_eval':
with open(args.filenames_file_eval, 'r') as f:
self.filenames = f.readlines()
else:
with open(args.filenames_file, 'r') as f:
self.filenames = f.readlines()
self.mode = mode
self.transform = transform
self.to_tensor = ToTensor
self.is_for_online_eval = is_for_online_eval
def __getitem__(self, idx):
sample_path = self.filenames[idx]
# focal = float(sample_path.split()[2])
focal = 518.8579
if self.mode == 'train':
if self.args.dataset == 'kitti':
rgb_file = sample_path.split()[0]
depth_file = os.path.join(sample_path.split()[0].split('/')[0], sample_path.split()[1])
if self.args.use_right is True and random.random() > 0.5:
rgb_file = rgb_file.replace('image_02', 'image_03')
depth_file = depth_file.replace('image_02', 'image_03')
else:
rgb_file = sample_path.split()[0]
depth_file = sample_path.split()[1]
image_path = os.path.join(self.args.data_path, rgb_file)
depth_path = os.path.join(self.args.gt_path, depth_file)
image = Image.open(image_path)
depth_gt = Image.open(depth_path)
if self.args.do_kb_crop is True:
height = image.height
width = image.width
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
depth_gt = depth_gt.crop((left_margin, top_margin, left_margin + 1216, top_margin + 352))
image = image.crop((left_margin, top_margin, left_margin + 1216, top_margin + 352))
# To avoid blank boundaries due to pixel registration
if self.args.dataset == 'nyu':
if self.args.input_height == 480:
depth_gt = np.array(depth_gt)
valid_mask = np.zeros_like(depth_gt)
valid_mask[45:472, 43:608] = 1
depth_gt[valid_mask==0] = 0
depth_gt = Image.fromarray(depth_gt)
else:
depth_gt = depth_gt.crop((43, 45, 608, 472))
image = image.crop((43, 45, 608, 472))
if self.args.do_random_rotate is True:
random_angle = (random.random() - 0.5) * 2 * self.args.degree
image = self.rotate_image(image, random_angle)
depth_gt = self.rotate_image(depth_gt, random_angle, flag=Image.NEAREST)
image = np.asarray(image, dtype=np.float32) / 255.0
depth_gt = np.asarray(depth_gt, dtype=np.float32)
depth_gt = np.expand_dims(depth_gt, axis=2)
if self.args.dataset == 'nyu':
depth_gt = depth_gt / 1000.0
else:
depth_gt = depth_gt / 256.0
if image.shape[0] != self.args.input_height or image.shape[1] != self.args.input_width:
image, depth_gt = self.random_crop(image, depth_gt, self.args.input_height, self.args.input_width)
image, depth_gt = self.train_preprocess(image, depth_gt)
image, depth_gt = self.Cut_Flip(image, depth_gt)
sample = {'image': image, 'depth': depth_gt, 'focal': focal}
else:
if self.mode == 'online_eval':
data_path = self.args.data_path_eval
else:
data_path = self.args.data_path
image_path = os.path.join(data_path, "./" + sample_path.split()[0])
image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0
image = cv2.resize(image, (640, 480))
if self.mode == 'online_eval':
gt_path = self.args.gt_path_eval
depth_path = os.path.join(gt_path, "./" + sample_path.split()[1])
if self.args.dataset == 'kitti':
depth_path = os.path.join(gt_path, sample_path.split()[0].split('/')[0], sample_path.split()[1])
has_valid_depth = False
try:
depth_gt = Image.open(depth_path)
has_valid_depth = True
except IOError:
depth_gt = False
# print('Missing gt for {}'.format(image_path))
if has_valid_depth:
depth_gt = np.asarray(depth_gt, dtype=np.uint16) # 2
depth_gt = np.bitwise_or(np.right_shift(depth_gt, 3), np.left_shift(depth_gt, 16 - 3)) # 3
depth_gt = np.expand_dims(depth_gt, axis=2)
if self.args.dataset == 'nyu':
depth_gt = depth_gt.astype(np.single) / 1000 # 4
depth_gt = depth_gt.astype(np.float32) # 5
else:
depth_gt = depth_gt / 256.0
if self.args.do_kb_crop is True:
height = image.shape[0]
width = image.shape[1]
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
image = image[top_margin:top_margin + 352, left_margin:left_margin + 1216, :]
if self.mode == 'online_eval' and has_valid_depth:
depth_gt = depth_gt[top_margin:top_margin + 352, left_margin:left_margin + 1216, :]
if self.mode == 'online_eval':
sample = {'image': image, 'depth': depth_gt, 'focal': focal, 'has_valid_depth': has_valid_depth}
else:
sample = {'image': image, 'focal': focal}
if self.transform:
sample = self.transform(sample)
return sample
def rotate_image(self, image, angle, flag=Image.BILINEAR):
result = image.rotate(angle, resample=flag)
return result
def random_crop(self, img, depth, height, width):
assert img.shape[0] >= height
assert img.shape[1] >= width
assert img.shape[0] == depth.shape[0]
assert img.shape[1] == depth.shape[1]
x = random.randint(0, img.shape[1] - width)
y = random.randint(0, img.shape[0] - height)
img = img[y:y + height, x:x + width, :]
depth = depth[y:y + height, x:x + width, :]
return img, depth
def train_preprocess(self, image, depth_gt):
# Random flipping
do_flip = random.random()
if do_flip > 0.5:
image = (image[:, ::-1, :]).copy()
depth_gt = (depth_gt[:, ::-1, :]).copy()
# Random gamma, brightness, color augmentation
do_augment = random.random()
if do_augment > 0.5:
image = self.augment_image(image)
return image, depth_gt
def augment_image(self, image):
# gamma augmentation
gamma = random.uniform(0.9, 1.1)
image_aug = image ** gamma
# brightness augmentation
if self.args.dataset == 'nyu':
brightness = random.uniform(0.75, 1.25)
else:
brightness = random.uniform(0.9, 1.1)
image_aug = image_aug * brightness
# color augmentation
colors = np.random.uniform(0.9, 1.1, size=3)
white = np.ones((image.shape[0], image.shape[1]))
color_image = np.stack([white * colors[i] for i in range(3)], axis=2)
image_aug *= color_image
image_aug = np.clip(image_aug, 0, 1)
return image_aug
def Cut_Flip(self, image, depth):
p = random.random()
if p < 0.5:
return image, depth
image_copy = copy.deepcopy(image)
depth_copy = copy.deepcopy(depth)
h, w, c = image.shape
N = 2
h_list = []
h_interval_list = [] # hight interval
for i in range(N-1):
h_list.append(random.randint(int(0.2*h), int(0.8*h)))
h_list.append(h)
h_list.append(0)
h_list.sort()
h_list_inv = np.array([h]*(N+1))-np.array(h_list)
for i in range(len(h_list)-1):
h_interval_list.append(h_list[i+1]-h_list[i])
for i in range(N):
image[h_list[i]:h_list[i+1], :, :] = image_copy[h_list_inv[i]-h_interval_list[i]:h_list_inv[i], :, :]
depth[h_list[i]:h_list[i+1], :, :] = depth_copy[h_list_inv[i]-h_interval_list[i]:h_list_inv[i], :, :]
return image, depth
def __len__(self):
return len(self.filenames)
class ToTensor(object):
def __init__(self, mode):
self.mode = mode
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
def __call__(self, sample):
image, focal = sample['image'], sample['focal']
image = self.to_tensor(image)
image = self.normalize(image)
if self.mode == 'test':
return {'image': image, 'focal': focal}
depth = sample['depth']
if self.mode == 'train':
depth = self.to_tensor(depth)
return {'image': image, 'depth': depth, 'focal': focal}
else:
has_valid_depth = sample['has_valid_depth']
return {'image': image, 'depth': depth, 'focal': focal, 'has_valid_depth': has_valid_depth}
def to_tensor(self, pic):
if not (_is_pil_image(pic) or _is_numpy_image(pic)):
raise TypeError(
'pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
if isinstance(pic, np.ndarray):
img = torch.from_numpy(pic.transpose((2, 0, 1)))
return img
# handle PIL Image
if pic.mode == 'I':
img = torch.from_numpy(np.array(pic, np.int32, copy=False))
elif pic.mode == 'I;16':
img = torch.from_numpy(np.array(pic, np.int16, copy=False))
else:
img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
# PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
if pic.mode == 'YCbCr':
nchannel = 3
elif pic.mode == 'I;16':
nchannel = 1
else:
nchannel = len(pic.mode)
img = img.view(pic.size[1], pic.size[0], nchannel)
img = img.transpose(0, 1).transpose(0, 2).contiguous()
if isinstance(img, torch.ByteTensor):
return img.float()
else:
return img