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dataset.py
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dataset.py
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import os
import cv2
import numpy as np
from torch.utils.data import DataLoader
import torch
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
import pandas as pd
from utils import random_click
from glob import glob
from torch.utils.data.distributed import DistributedSampler
from conf.global_settings import modality_datapath_map
def get_dataloader(args):
g =torch.Generator()
data_path = modality_datapath_map[args.modality]
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((args.image_size, args.image_size)),
])
transform_train_seg = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((args.out_size, args.out_size),
interpolation=transforms.InterpolationMode.NEAREST),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((args.image_size, args.image_size)),
])
transform_test_seg = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((args.out_size, args.out_size),
interpolation=transforms.InterpolationMode.NEAREST),
])
if args.modality == "zju-rgbp":
'''zju_rgbp data'''
zju_rgbp_train_dataset = ZJU_RGBP(args, data_path, transform=transform_train,
transform_msk=transform_train_seg, mode='train')
zju_rgbp_test_dataset = ZJU_RGBP(args, data_path, transform=transform_test,
transform_msk=transform_test_seg, mode='val')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(zju_rgbp_train_dataset)
test_sampler = DistributedSampler(zju_rgbp_test_dataset)
nice_train_loader = DataLoader(zju_rgbp_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(zju_rgbp_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
'''end'''
elif args.modality == "nir":
'''nir data'''
nir_train_dataset = NIR(args, data_path, transform = transform_train,
transform_msk= transform_train_seg, mode = 'train')
nir_test_dataset = NIR(args, data_path, transform = transform_test,
transform_msk= transform_test_seg, mode = 'test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(nir_train_dataset)
test_sampler = DistributedSampler(nir_test_dataset)
nice_train_loader = DataLoader(nir_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(nir_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == "rgbnir":
'''nir data'''
nir_train_dataset = RGBNIR(args, data_path, transform = transform_train,
transform_msk= transform_train_seg, mode = 'train')
nir_test_dataset = RGBNIR(args, data_path, transform = transform_test,
transform_msk= transform_test_seg, mode = 'test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(nir_train_dataset)
test_sampler = DistributedSampler(nir_test_dataset)
nice_train_loader = DataLoader(nir_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(nir_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == "rgbt":
'''rgb and thermal glass data'''
nir_train_dataset = RGBThermal(args, data_path, transform=transform_train,
transform_msk=transform_train_seg, mode='train')
nir_test_dataset = RGBThermal(args, data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(nir_train_dataset)
test_sampler = DistributedSampler(nir_test_dataset)
nice_train_loader = DataLoader(nir_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(nir_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == "t":
'''rgb and thermal glass data'''
nir_train_dataset = Thermal(args, data_path, transform=transform_train,
transform_msk=transform_train_seg, mode='train')
nir_test_dataset = Thermal(args, data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(nir_train_dataset)
test_sampler = DistributedSampler(nir_test_dataset)
nice_train_loader = DataLoader(nir_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(nir_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == "rgbt_500":
'''rgb and thermal glass data'''
rgbt_train_dataset = RGBThermal_500(args, data_path, transform=transform_train,
transform_msk=transform_train_seg, mode='train')
rgbt_test_dataset = RGBThermal_500(args, data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(rgbt_train_dataset)
test_sampler = DistributedSampler(rgbt_test_dataset)
nice_train_loader = DataLoader(rgbt_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(rgbt_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == "t_500":
'''rgb and thermal glass data'''
t_train_dataset = Thermal_500(args, data_path, transform=transform_train,
transform_msk=transform_train_seg, mode='train')
t_test_dataset = Thermal_500(args, data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(t_train_dataset)
test_sampler = DistributedSampler(t_test_dataset)
nice_train_loader = DataLoader(t_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(t_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == "pgsnet_rgbp":
pgsnet_train_dataset = PGSNet_RGBP(args, data_path, transform=transform_train,
transform_msk=transform_train_seg, mode='train')
pgsnet_test_dataset = PGSNet_RGBP(args, data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(pgsnet_train_dataset)
test_sampler = DistributedSampler(pgsnet_test_dataset)
nice_train_loader = DataLoader(pgsnet_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(pgsnet_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == "pgsnet_p":
pgsnet_train_dataset = PGSNet_P(args, data_path, transform=transform_train,
transform_msk=transform_train_seg, mode='train')
pgsnet_test_dataset = PGSNet_P(args, data_path, transform=transform_test,
transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(pgsnet_train_dataset)
test_sampler = DistributedSampler(pgsnet_test_dataset)
nice_train_loader = DataLoader(pgsnet_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(pgsnet_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == 'rgbd':
nyudv2_train_dataset = NYUDv2_RGBD(data_path, transform=transform_train, transform_msk=transform_train_seg,
mode='train')
nyudv2_test_dataset = NYUDv2_RGBD(data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(nyudv2_train_dataset)
test_sampler = DistributedSampler(nyudv2_test_dataset)
nice_train_loader = DataLoader(nyudv2_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(nyudv2_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == 'd':
nyudv2_train_dataset = NYUDv2_D(data_path, transform=transform_train, transform_msk=transform_train_seg,
mode='train')
nyudv2_test_dataset = NYUDv2_D(data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(nyudv2_train_dataset)
test_sampler = DistributedSampler(nyudv2_test_dataset)
nice_train_loader = DataLoader(nyudv2_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(nyudv2_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == 'hha':
nyudv2_train_dataset = NYUDv2_HHA(data_path, transform=transform_train, transform_msk=transform_train_seg,
mode='train')
nyudv2_test_dataset = NYUDv2_HHA(data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(nyudv2_train_dataset)
test_sampler = DistributedSampler(nyudv2_test_dataset)
nice_train_loader = DataLoader(nyudv2_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(nyudv2_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == 'rgbhha':
nyudv2_train_dataset = NYUDv2_RGBHHA(data_path, transform=transform_train, transform_msk=transform_train_seg,
mode='train' )
nyudv2_test_dataset = NYUDv2_RGBHHA(data_path, transform=transform_test, transform_msk=transform_test_seg,
mode='test')
train_sampler, test_sampler = None, None
if args.ddp:
train_sampler = DistributedSampler(nyudv2_train_dataset)
test_sampler = DistributedSampler(nyudv2_test_dataset)
nice_train_loader = DataLoader(nyudv2_train_dataset, batch_size=args.b, sampler=train_sampler, num_workers=0,
pin_memory=True, generator=g)
nice_test_loader = DataLoader(nyudv2_test_dataset, batch_size=1, sampler=test_sampler, num_workers=0,
pin_memory=True, generator=g)
elif args.modality == 'taskonnomy_10':
raise NotImplementedError
else:
raise NotImplementedError
return nice_train_loader, nice_test_loader, train_sampler, test_sampler
class ZJU_RGBP(Dataset):
def __init__(self, args, data_path, transform=None, transform_msk=None, mode='train', prompt='click', plane=False):
# mean/std statistics of each modality channel
self.mean = torch.Tensor([141.1251, 141.37535, 138.99559, -0.01452719, -0.014152956, -0.016532706, 0.15173349, 0.1489902, 0.17018148])[:,None,None]
self.std = torch.Tensor([139.9553, 140.5757, 141.13524, 0.38503787, 0.38248295, 0.3834839, 0.13344945, 0.13081141, 0.14260896])[:,None,None]
self.gt_list_path = os.path.join(data_path, mode, 'label_sam')
self.name_list = []
self.gt_list = []
for file_name in os.listdir(self.gt_list_path):
img_path = [os.path.join(data_path, mode, angle, file_name.replace('.png', '_' + angle + '°.png')) for angle
in ['0', '45', '90', '135']]
self.name_list.append(img_path)
self.gt_list.append(os.path.join(self.gt_list_path, file_name))
self.mode = mode
self.prompt = prompt
self.img_size = args.image_size
self.transform = transform
self.transform_msk = transform_msk
def __len__(self):
return len(self.name_list)
def raw2rgbp(self, images):
I1, I2, I3, I4 = images
I = 0.5 * (I1 + I2 + I3 + I4) + 1e-4
Q = I1 - I3
U = I2 - I4
Q[Q == 0] = 0.0001
I[I == 0] = 0.0001
DoLP = np.sqrt(np.square(Q) + np.square(U)) / I
DoLP[DoLP > 1] = 1
AoLP = 0.5 * np.arctan(U / Q)
# normalized_I = I / 255/ 2
# normalized_AoLP = (AoLP+np.pi/4)/(np.pi/2)
# normalized_DoLP = DoLP
# rgbp = np.concatenate([normalized_I, normalized_AoLP, normalized_DoLP], axis=-1)
rgbp = np.concatenate([I, AoLP, DoLP], axis=-1)
return rgbp
def __getitem__(self, index):
"""Get the images"""
img_path = self.name_list[index]
gt_path = self.gt_list[index]
raw = [np.array(Image.open(angle_path), dtype=np.float32) for angle_path in img_path]
img = self.raw2rgbp(raw)
gt = Image.open(gt_path).convert('I')
newsize = (self.img_size, self.img_size)
mask = gt.resize(newsize, Image.NEAREST)
CNT_THRESHOLD = 10000
if self.mode == 'train':
mask = np.array(mask)
mask_values, counts = np.unique(mask, return_counts=True)
region_ids = np.delete(mask_values, np.where(counts < CNT_THRESHOLD))[1:]
region_id = np.random.choice(region_ids)
pt = random_click(np.array(mask), 1, region_id)[None]
pt = np.ascontiguousarray(pt)
point_label = np.array([1])
valid_region_ids = np.array([region_id])
else:
pt = []
point_label = []
valid_region_ids = []
if self.prompt == 'click':
for region_id, region_cnt in zip(*np.unique(mask, return_counts=True)):
if region_id > 0 and region_cnt > CNT_THRESHOLD:
pt.append(random_click(np.array(mask), 1, region_id, middle=True)[None])
point_label.append(1)
valid_region_ids.append(region_id)
pt = np.concatenate(pt, axis=0) # [num_click, 2]
point_label = np.array(point_label)
valid_region_ids = np.array(valid_region_ids)
if self.transform:
state = torch.get_rng_state()
img = self.transform(img)
torch.set_rng_state(state)
if self.transform_msk:
mask = self.transform_msk(mask)
name = gt_path.split('/')[-1].split(".jpg")[0]
image_meta_dict = {'filename_or_obj': name}
if self.mode == 'train':
mask[mask != region_id] = 0
mask[mask == region_id] = 1
# normalization
norm_img = (img - self.mean) / self.std
# new_modality = torch.from_numpy(np.array(raw)).permute(0,3,1,2)
# new_modality = torch.nn.functional.interpolate(new_modality, (256,256))
return {
'orig_img': img[:3].flip(0),
'new_modality': (norm_img[3:6]/4+0.5),
'image': norm_img,
'label': mask,
'p_label': point_label,
'pt': pt,
'valid_region_ids': valid_region_ids,
'image_meta_dict': image_meta_dict,
}
class PGSNet_RGBP(Dataset):
def __init__(self, args, data_path, transform=None, transform_msk=None, mode='train', prompt='click', plane=False):
# mean/std statistics of each modality channel
self.mean = torch.Tensor([0.20545432, 0.21862635, 0.15604725, 0.47199312, 0.4637443, 0.48603472, 0.102636375, 0.10431647, 0.11925355])[:,None,None]
self.std = torch.Tensor([0.19210099, 0.20711783, 0.18405455, 0.28202084, 0.28334478, 0.25937688, 0.11682395, 0.118849464, 0.12313089])[:,None,None]
self.all_name_list = sorted(glob(data_path + "/{}/image/*tiff".format(mode)))
# ignore broken
broken_file = ['20210808013065', '20210811002205']
self.name_list = [i for i in self.all_name_list if not any([i.__contains__(broken) for broken in broken_file])]
# semantic
# self.gt_list = [x.replace("image", "mask").replace("rgb.tiff", "mask.png") for x in self.name_list]
# instance
self.gt_list = [x.replace("image", "instance_mask").replace("rgb.tiff", "instance_mask.png") for x in self.name_list]
self.mode = mode
self.prompt = prompt
self.img_size = args.image_size
self.transform = transform
self.transform_msk = transform_msk
def __len__(self):
return len(self.name_list)
def raw2rgbp(self, img_path):
rgb_path = img_path
tiff_image = Image.open(rgb_path)
rgb_image_array = np.array(tiff_image)
aolp_path = img_path.replace("image","aolp").replace("rgb","aolp")
tiff_image = Image.open(aolp_path)
aolp_image_array = np.array(tiff_image)
dolp_path = img_path.replace("image","dolp").replace("rgb","dolp")
tiff_image = Image.open(dolp_path)
dolp_image_array = np.array(tiff_image)
rgbp = np.concatenate([rgb_image_array, aolp_image_array, dolp_image_array], axis=-1)
return rgbp
def __getitem__(self, index):
"""Get the images"""
img_path = self.name_list[index]
gt_path = self.gt_list[index]
img = self.raw2rgbp(img_path)
gt = Image.open(gt_path).convert('I')
newsize = (self.img_size, self.img_size)
mask = gt.resize(newsize, Image.NEAREST)
CNT_THRESHOLD = 100
if self.mode == 'train':
mask = np.array(mask)
mask_values, counts = np.unique(mask, return_counts=True)
region_ids = np.delete(mask_values, np.where(counts < CNT_THRESHOLD))[1:]
region_id = np.random.choice(region_ids)
pt = random_click(np.array(mask), 1, region_id)[None]
pt = np.ascontiguousarray(pt)
point_label = np.array([1])
valid_region_ids = np.array([region_id])
else:
pt = []
point_label = []
valid_region_ids = []
if self.prompt == 'click':
for region_id, region_cnt in zip(*np.unique(mask, return_counts=True)):
if region_id > 0 and region_cnt > CNT_THRESHOLD:
pt.append(random_click(np.array(mask), 1, region_id, middle=True)[None])
point_label.append(1)
valid_region_ids.append(region_id)
pt = np.concatenate(pt, axis=0) # [num_click, 2]
point_label = np.array(point_label)
valid_region_ids = np.array(valid_region_ids)
if self.transform:
state = torch.get_rng_state()
img = self.transform(img)
torch.set_rng_state(state)
if self.transform_msk:
mask = self.transform_msk(mask)
name = gt_path.split('/')[-1].split(".jpg")[0]
image_meta_dict = {'filename_or_obj': name}
if self.mode == 'train':
mask[mask != region_id] = 0
mask[mask == region_id] = 1
# normalization
norm_img = (img - self.mean) / self.std
return {
'orig_img': img[:3]*255,
'new_modality': img[3:4]*255,
'image': norm_img,
'label': mask,
'p_label': point_label,
'pt': pt,
'valid_region_ids': valid_region_ids,
'image_meta_dict': image_meta_dict,
}
class PGSNet_P(Dataset):
def __init__(self, args, data_path, transform=None, transform_msk=None, mode='train', prompt='click', plane=False):
# mean/std statistics of each modality channel
self.mean = torch.Tensor([0.47199312, 0.4637443, 0.48603472, 0.102636375, 0.10431647, 0.11925355])[:,None,None]
self.std = torch.Tensor([0.28202084, 0.28334478, 0.25937688, 0.11682395, 0.118849464, 0.12313089])[:,None,None]
self.all_name_list = sorted(glob(data_path + "/{}/image/*tiff".format(mode)))
# ignore broken
broken_file = ['20210808013065', '20210811002205']
self.name_list = [i for i in self.all_name_list if not any([i.__contains__(broken) for broken in broken_file])]
# semantic
# self.gt_list = [x.replace("image", "mask").replace("rgb.tiff", "mask.png") for x in self.name_list]
# instance
self.gt_list = [x.replace("image", "instance_mask").replace("rgb.tiff", "instance_mask.png") for x in self.name_list]
self.mode = mode
self.prompt = prompt
self.img_size = args.image_size
self.transform = transform
self.transform_msk = transform_msk
self.img_id = 0
def __len__(self):
return len(self.name_list)
def raw2p(self, img_path):
rgb_path = img_path
tiff_image = Image.open(rgb_path)
rgb_image_array = np.array(tiff_image)
aolp_path = img_path.replace("image","aolp").replace("rgb","aolp")
tiff_image = Image.open(aolp_path)
aolp_image_array = np.array(tiff_image)
dolp_path = img_path.replace("image","dolp").replace("rgb","dolp")
tiff_image = Image.open(dolp_path)
dolp_image_array = np.array(tiff_image)
rgbp = np.concatenate([aolp_image_array, dolp_image_array], axis=-1)
return rgbp
def raw2rgb(self, img_path):
rgb_path = img_path
tiff_image = Image.open(rgb_path)
rgb_image_array = np.array(tiff_image)
return rgb_image_array
def __getitem__(self, index):
"""Get the images"""
img_path = self.name_list[index]
gt_path = self.gt_list[index]
img = self.raw2p(img_path)
gt = Image.open(gt_path).convert('I')
newsize = (self.img_size, self.img_size)
mask = gt.resize(newsize, Image.NEAREST)
CNT_THRESHOLD = 100
if self.mode == 'train':
mask = np.array(mask)
mask_values, counts = np.unique(mask, return_counts=True)
region_ids = np.delete(mask_values, np.where(counts < CNT_THRESHOLD))[1:]
region_id = np.random.choice(region_ids)
pt = random_click(np.array(mask), 1, region_id)[None]
pt = np.ascontiguousarray(pt)
point_label = np.array([1])
valid_region_ids = np.array([region_id])
else:
pt = []
point_label = []
valid_region_ids = []
if self.prompt == 'click':
for region_id, region_cnt in zip(*np.unique(mask, return_counts=True)):
if region_id > 0 and region_cnt > CNT_THRESHOLD:
pt.append(random_click(np.array(mask), 1, region_id, middle=True)[None])
point_label.append(1)
valid_region_ids.append(region_id)
pt = np.concatenate(pt, axis=0) # [num_click, 2]
point_label = np.array(point_label)
valid_region_ids = np.array(valid_region_ids)
if self.transform:
state = torch.get_rng_state()
img = self.transform(img)
torch.set_rng_state(state)
if self.transform_msk:
mask = self.transform_msk(mask)
name = gt_path.split('/')[-1].split(".jpg")[0]
image_meta_dict = {'filename_or_obj': name}
if self.mode == 'train':
mask[mask != region_id] = 0
mask[mask == region_id] = 1
# normalization
img = (img - self.mean) / self.std
# =========================================
# rgb_img = (self.transform(self.raw2rgb(img_path)) * 255).permute(1, 2, 0).type(torch.uint8).cpu().numpy()
# rgb_img = Image.fromarray(rgb_img)
# rgb_img.save('polarization_projected/%03d_rgb.png' % (self.img_id))
# self.img_id += 1
# ===========================================
return {
'orig_img': img[:3]*255,
'new_modality': img[3:4]*255,
'image': img,
'label': mask,
'p_label': point_label,
'pt': pt,
'valid_region_ids': valid_region_ids,
'image_meta_dict': image_meta_dict,
}
class ZJU_RGBP_RGB(Dataset):
def __init__(self, args, data_path, transform=None, transform_msk=None, mode='train', prompt='click', plane=False):
# mean/std statistics of each modality channel
self.mean = torch.Tensor([141.1251, 141.37535, 138.99559])[:,None,None]
self.std = torch.Tensor([139.9553, 140.5757, 141.13524])[:,None,None]
self.gt_list_path = os.path.join(data_path, mode, 'label_sam')
self.name_list = []
self.gt_list = []
for file_name in os.listdir(self.gt_list_path):
img_path = [os.path.join(data_path, mode, angle, file_name.replace('.png', '_' + angle + '°.png')) for angle
in ['0', '45', '90', '135']]
self.name_list.append(img_path)
self.gt_list.append(os.path.join(self.gt_list_path, file_name))
self.mode = mode
self.prompt = prompt
self.img_size = args.image_size
self.transform = transform
self.transform_msk = transform_msk
def __len__(self):
return len(self.name_list)
def raw2rgb(self, images):
I1, I2, I3, I4 = images
I = 0.5 * (I1 + I2 + I3 + I4) + 1e-4
Q = I1 - I3
U = I2 - I4
Q[Q == 0] = 0.0001
I[I == 0] = 0.0001
return I
def __getitem__(self, index):
"""Get the images"""
img_path = self.name_list[index]
gt_path = self.gt_list[index]
img = [np.array(Image.open(angle_path), dtype=np.float32) for angle_path in img_path]
img = self.raw2rgb(img)
gt = Image.open(gt_path).convert('I')
newsize = (self.img_size, self.img_size)
mask = gt.resize(newsize, Image.NEAREST)
CNT_THRESHOLD = 10000
if self.mode == 'train':
mask = np.array(mask)
mask_values, counts = np.unique(mask, return_counts=True)
region_ids = np.delete(mask_values, np.where(counts < CNT_THRESHOLD))[1:]
region_id = np.random.choice(region_ids)
pt = random_click(np.array(mask), 1, region_id)[None]
pt = np.ascontiguousarray(pt)
point_label = np.array([1])
valid_region_ids = np.array([region_id])
else:
pt = []
point_label = []
valid_region_ids = []
if self.prompt == 'click':
for region_id, region_cnt in zip(*np.unique(mask, return_counts=True)):
if region_id > 0 and region_cnt > CNT_THRESHOLD:
pt.append(random_click(np.array(mask), 1, region_id, middle=True)[None])
point_label.append(1)
valid_region_ids.append(region_id)
pt = np.concatenate(pt, axis=0) # [num_click, 2]
point_label = np.array(point_label)
valid_region_ids = np.array(valid_region_ids)
if self.transform:
state = torch.get_rng_state()
img = self.transform(img)
torch.set_rng_state(state)
if self.transform_msk:
mask = self.transform_msk(mask)
name = gt_path.split('/')[-1].split(".jpg")[0]
image_meta_dict = {'filename_or_obj': name}
if self.mode == 'train':
mask[mask != region_id] = 0
mask[mask == region_id] = 1
# normalization
img = (img - self.mean)/self.std
return {
'image': img,
'label': mask,
'p_label': point_label,
'pt': pt,
'valid_region_ids': valid_region_ids,
'image_meta_dict': image_meta_dict,
}
class NYUDv2_RGBD(Dataset):
def __init__(self, data_path, transform=None, transform_msk=None, mode='train', prompt='click', plane=False,
samples_per=1):
self.rgbd_mean = torch.Tensor([123.675/255, 116.28/255, 103.53/255, (123.675+116.28+103.53)/3/255])[:,None,None]
self.rgbd_std = torch.Tensor([58.395/255, 57.12/255, 57.375/255, (58.395+57.12+57.375)/3/255])[:,None,None]
self._split_name = mode
self.prompt = prompt
self._data_path = data_path
self._img_path = os.path.join(self._data_path, 'RGB')
self._gt_path = os.path.join(self._data_path, 'Mask_gt')
self._hha_path = os.path.join(self._data_path, 'HHA')
self._depth_path = os.path.join(self._data_path, 'Depth')
self.transform = transform
self.transform_msk = transform_msk
self._file_names = os.path.join(self._data_path, self._split_name + '.txt')
self._name_list = []
f = open(self._file_names, "r")
lines = f.readlines()
for line in lines:
self._name_list.append(line.split('\t')[0])
len_dataset = int(len(self._name_list) * samples_per)
self._name_list = self._name_list[:len_dataset]
self.img_size = 1024
def __getitem__(self, index):
rgb_name = self._name_list[index]
if rgb_name in ['RGB/1133.jpg', 'RGB/398.jpg']: # broken files
rgb_name = 'RGB/1134.jpg'
label_name = rgb_name.replace('RGB', 'Mask_gt').replace('jpg', 'png')
color_label_name = rgb_name.replace('RGB', 'ColoredLabel').replace('jpg', 'png')
hha_name = rgb_name.replace('RGB', 'HHA')
depth_name = rgb_name.replace('RGB', 'Depth').replace('jpg', 'npy')
img_path = os.path.join(self._data_path, rgb_name)
color_label_path = os.path.join(self._data_path, color_label_name)
gt_path = os.path.join(self._data_path, label_name)
hha_path = os.path.join(self._data_path, hha_name)
depth_path = os.path.join(self._data_path, depth_name)
img_rgb = Image.open(img_path).convert('RGB')
img_d = np.load(depth_path, allow_pickle=True)
img_d = (img_d - np.min(img_d)) / (np.max(img_d) - np.min(img_d))
img_d = img_d * 255
img_d = img_d.astype(np.uint8)
img = np.concatenate((np.array(img_rgb), img_d[:,:,None]), axis=-1)
gt = Image.open(gt_path).convert('I')
newsize = (self.img_size, self.img_size)
mask = gt.resize(newsize, Image.NEAREST)
if self._split_name == 'train':
region_ids = np.unique(mask)[1:]
region_id = np.random.choice(region_ids)
pt = random_click(np.array(mask), 1, region_id)[np.newaxis, :]
pt = np.ascontiguousarray(pt)
point_label = np.array([1])
valid_region_ids = np.array([region_id])
else:
pt = []
point_label = []
valid_region_ids = []
if self.prompt == 'click':
for region_id in np.unique(mask):
if region_id > 0:
pt.append(random_click(np.array(mask), 1, region_id, middle=True)[None, :])
point_label.append(1)
valid_region_ids.append(region_id)
pt = np.concatenate(pt, axis=0)
point_label = np.array(point_label)
valid_region_ids = np.array(valid_region_ids)
if self.transform:
state = torch.get_rng_state()
img = self.transform(img)
torch.set_rng_state(state)
if self.transform_msk:
mask = self.transform_msk(mask)
name = rgb_name.split('/')[-1].split(".jpg")[0]
image_meta_dict = {'filename_or_obj': name}
norm_img = (img - self.rgbd_mean)/self.rgbd_std
if self._split_name == 'train':
mask[mask != region_id] = 0
mask[mask == region_id] = 1
return {
'orig_img': img[:3],
'new_modality': img[3:],
'image': norm_img,
'label': mask,
'p_label': point_label,
'pt': pt,
'valid_region_ids': valid_region_ids,
'image_meta_dict': image_meta_dict,
}
def _fetch_data(self, img_path, gt_path, hha_path, dtype=None):
img = self._open_image(img_path)
gt = self._open_image(gt_path, cv2.IMREAD_GRAYSCALE, dtype=dtype)
hha = self._open_image(hha_path)
return img, gt, hha
def __len__(self):
return len(self._name_list)
@classmethod
def get_class_colors(*args):
def uint82bin(n, count=8):
"""returns the binary of integer n, count refers to amount of bits"""
return ''.join([str((n >> y) & 1) for y in range(count - 1, -1, -1)])
N = 41
cmap = np.zeros((N, 3), dtype=np.uint8)
for i in range(N):
r, g, b = 0, 0, 0
id = i
for j in range(7):
str_id = uint82bin(id)
r = r ^ (np.uint8(str_id[-1]) << (7 - j))
g = g ^ (np.uint8(str_id[-2]) << (7 - j))
b = b ^ (np.uint8(str_id[-3]) << (7 - j))
id = id >> 3
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
class_colors = cmap.tolist()
return class_colors
@staticmethod
def _open_image(filepath, mode=cv2.IMREAD_COLOR, dtype=None):
img = np.array(cv2.imread(filepath, mode), dtype=dtype)
return img
@classmethod
def get_class_names(*args):
return ['wall','floor','cabinet','bed','chair','sofa','table','door','window','bookshelf','picture','counter','blinds',
'desk','shelves','curtain','dresser','pillow','mirror','floor mat','clothes','ceiling','books','refridgerator',
'television','paper','towel','shower curtain','box','whiteboard','person','night stand','toilet',
'sink','lamp','bathtub','bag','otherstructure','otherfurniture','otherprop']
@classmethod
def transform_label(cls, pred, name):
label = np.zeros(pred.shape)
ids = np.unique(pred)
for id in ids:
label[np.where(pred == id)] = cls.trans_labels[id]
new_name = (name.split('.')[0]).split('_')[:-1]
new_name = '_'.join(new_name) + '.png'
return label, new_name
class NYUDv2_D(Dataset):
def __init__(self, data_path, transform=None, transform_msk=None, mode='train', prompt='click', plane=False,
samples_per=1,if_hha=False):
self.d_mean = torch.Tensor([(123.675+116.28+103.53)/3/255])[:,None,None]
self.d_std = torch.Tensor([(58.395+57.12+57.375)/3/255])[:,None,None]
self._split_name = mode
self.prompt = prompt
self._data_path = data_path
self._img_path = os.path.join(self._data_path, 'RGB')
self._gt_path = os.path.join(self._data_path, 'Mask_gt')
self._hha_path = os.path.join(self._data_path, 'HHA')
self._depth_path = os.path.join(self._data_path, 'Depth')
self.transform = transform
self.transform_msk = transform_msk
self._file_names = os.path.join(self._data_path, self._split_name + '.txt')
self._name_list = []
f = open(self._file_names, "r")
lines = f.readlines()
for line in lines:
self._name_list.append(line.split('\t')[0])
len_dataset = int(len(self._name_list) * samples_per)
self._name_list = self._name_list[:len_dataset]
self.img_size = 1024
self.if_hha=if_hha
def __getitem__(self, index):
rgb_name = self._name_list[index]
if rgb_name in ['RGB/1133.jpg', 'RGB/398.jpg']: # broken files
rgb_name = 'RGB/1134.jpg'
label_name = rgb_name.replace('RGB', 'Mask_gt').replace('jpg', 'png')
color_label_name = rgb_name.replace('RGB', 'ColoredLabel').replace('jpg', 'png')
hha_name = rgb_name.replace('RGB', 'HHA')
depth_name = rgb_name.replace('RGB', 'Depth').replace('jpg', 'npy')
img_path = os.path.join(self._data_path, rgb_name)
color_label_path = os.path.join(self._data_path, color_label_name)
gt_path = os.path.join(self._data_path, label_name)
hha_path = os.path.join(self._data_path, hha_name)
depth_path = os.path.join(self._data_path, depth_name)
img_d = np.load(depth_path, allow_pickle=True)
img_d = (img_d - np.min(img_d)) / (np.max(img_d) - np.min(img_d))
img_d = img_d * 255
img_d = img_d.astype(np.uint8)
img = img_d[:,:,None]
gt = Image.open(gt_path).convert('I')
newsize = (self.img_size, self.img_size)
mask = gt.resize(newsize, Image.NEAREST)
if self._split_name == 'train':
region_ids = np.unique(mask)[1:]
region_id = np.random.choice(region_ids)
pt = random_click(np.array(mask), 1, region_id)[np.newaxis, :]
pt = np.ascontiguousarray(pt)
point_label = np.array([1])
valid_region_ids = np.array([region_id])
else:
pt = []
point_label = []
valid_region_ids = []
if self.prompt == 'click':
for region_id in np.unique(mask):
if region_id > 0:
pt.append(random_click(np.array(mask), 1, region_id, middle=True)[None, :])
point_label.append(1)
valid_region_ids.append(region_id)
pt = np.concatenate(pt, axis=0)
point_label = np.array(point_label)
valid_region_ids = np.array(valid_region_ids)
if self.transform:
state = torch.get_rng_state()
img = self.transform(img)
torch.set_rng_state(state)
if self.transform_msk:
mask = self.transform_msk(mask)
name = rgb_name.split('/')[-1].split(".jpg")[0]
image_meta_dict = {'filename_or_obj': name}
norm_img = (img - self.d_mean)/self.d_std
if self._split_name == 'train':
mask[mask != region_id] = 0
mask[mask == region_id] = 1
return {
'orig_img': img.repeat(3,1,1),
'new_modality': img,
'image': norm_img,
'label': mask,
'p_label': point_label,
'pt': pt,
'valid_region_ids': valid_region_ids,
'image_meta_dict': image_meta_dict,
}
def _fetch_data(self, img_path, gt_path, hha_path, dtype=None):
img = self._open_image(img_path)
gt = self._open_image(gt_path, cv2.IMREAD_GRAYSCALE, dtype=dtype)
hha = self._open_image(hha_path)
return img, gt, hha
def __len__(self):
return len(self._name_list)
@classmethod
def get_class_colors(*args):
def uint82bin(n, count=8):
"""returns the binary of integer n, count refers to amount of bits"""
return ''.join([str((n >> y) & 1) for y in range(count - 1, -1, -1)])
N = 41
cmap = np.zeros((N, 3), dtype=np.uint8)
for i in range(N):
r, g, b = 0, 0, 0
id = i
for j in range(7):
str_id = uint82bin(id)
r = r ^ (np.uint8(str_id[-1]) << (7 - j))
g = g ^ (np.uint8(str_id[-2]) << (7 - j))
b = b ^ (np.uint8(str_id[-3]) << (7 - j))
id = id >> 3
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
class_colors = cmap.tolist()
return class_colors
@staticmethod
def _open_image(filepath, mode=cv2.IMREAD_COLOR, dtype=None):
img = np.array(cv2.imread(filepath, mode), dtype=dtype)
return img
@classmethod
def get_class_names(*args):
return ['wall','floor','cabinet','bed','chair','sofa','table','door','window','bookshelf','picture','counter','blinds',
'desk','shelves','curtain','dresser','pillow','mirror','floor mat','clothes','ceiling','books','refridgerator',
'television','paper','towel','shower curtain','box','whiteboard','person','night stand','toilet',
'sink','lamp','bathtub','bag','otherstructure','otherfurniture','otherprop']
@classmethod
def transform_label(cls, pred, name):
label = np.zeros(pred.shape)
ids = np.unique(pred)
for id in ids:
label[np.where(pred == id)] = cls.trans_labels[id]
new_name = (name.split('.')[0]).split('_')[:-1]
new_name = '_'.join(new_name) + '.png'
return label, new_name
class NYUDv2_RGBHHA(Dataset):
def __init__(self, data_path, transform=None, transform_msk=None, mode='train', prompt='click', plane=False,
samples_per=1):
self.rgbhha_mean = torch.Tensor([123.675/255, 116.28/255, 103.53/255, 123.675/255, 116.28/255, 103.53/255])[:,None,None]
self.rgbhha_std = torch.Tensor([58.395/255, 57.12/255, 57.375/255, 58.395/255, 57.12/255, 57.375/255])[:,None,None]
self._split_name = mode
self.prompt = prompt
self._data_path = data_path