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ACNet_data_nyuv2_eval.py
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ACNet_data_nyuv2_eval.py
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import numpy as np
import scipy.io
import imageio
import h5py
import os
from torch.utils.data import Dataset
import matplotlib
import matplotlib.colors
import skimage.transform
import random
import torchvision
import torch
image_h = 480
image_w = 640
train_file = 'G:/data/nyuv2/train.txt'
test_file = 'G:/data/nyuv2/test.txt'
#test_file = train_file
def make_dataset_fromlst(listfilename):
"""
NYUlist format:
imagepath seglabelpath depthpath HHApath
"""
images = []
segs = []
depths = []
with open(listfilename) as f:
content = f.readlines()
for x in content:
imgname, depthname, segname = x.strip().split(' ')
imgname = imgname.replace('/home/hxx', 'G:')
depthname = depthname.replace('/home/hxx', 'G:')
segname = segname.replace('/home/hxx', 'G:')
images += [imgname]
segs += [segname]
depths += [depthname]
return {'images':images, 'labels':segs, 'depths':depths}
class SUNRGBD(Dataset):
def __init__(self, transform=None, phase_train=True, data_dir=None):
self.phase_train = phase_train
self.transform = transform
result = make_dataset_fromlst(train_file)
self.img_dir_train = result['images']
self.depth_dir_train = result['depths']
self.label_dir_train = result['labels']
result = make_dataset_fromlst(test_file)
self.img_dir_test = result['images']
self.depth_dir_test = result['depths']
self.label_dir_test = result['labels']
def __len__(self):
if self.phase_train:
return len(self.img_dir_train)
else:
return len(self.img_dir_test)
def __getitem__(self, idx):
if self.phase_train:
img_dir = self.img_dir_train
depth_dir = self.depth_dir_train
label_dir = self.label_dir_train
else:
img_dir = self.img_dir_test
depth_dir = self.depth_dir_test
label_dir = self.label_dir_test
label = np.load(label_dir[idx])
depth = np.load(depth_dir[idx])
image = np.load(img_dir[idx])
sample = {'image': image, 'depth': depth, 'label': label}
if self.transform:
sample = self.transform(sample)
return sample
class RandomHSV(object):
"""
Args:
h_range (float tuple): random ratio of the hue channel,
new_h range from h_range[0]*old_h to h_range[1]*old_h.
s_range (float tuple): random ratio of the saturation channel,
new_s range from s_range[0]*old_s to s_range[1]*old_s.
v_range (int tuple): random bias of the value channel,
new_v range from old_v-v_range to old_v+v_range.
Notice:
h range: 0-1
s range: 0-1
v range: 0-255
"""
def __init__(self, h_range, s_range, v_range):
assert isinstance(h_range, (list, tuple)) and \
isinstance(s_range, (list, tuple)) and \
isinstance(v_range, (list, tuple))
self.h_range = h_range
self.s_range = s_range
self.v_range = v_range
def __call__(self, sample):
img = sample['image']
img_hsv = matplotlib.colors.rgb_to_hsv(img)
img_h, img_s, img_v = img_hsv[:, :, 0], img_hsv[:, :, 1], img_hsv[:, :, 2]
h_random = np.random.uniform(min(self.h_range), max(self.h_range))
s_random = np.random.uniform(min(self.s_range), max(self.s_range))
v_random = np.random.uniform(-min(self.v_range), max(self.v_range))
img_h = np.clip(img_h * h_random, 0, 1)
img_s = np.clip(img_s * s_random, 0, 1)
img_v = np.clip(img_v + v_random, 0, 255)
img_hsv = np.stack([img_h, img_s, img_v], axis=2)
img_new = matplotlib.colors.hsv_to_rgb(img_hsv)
return {'image': img_new, 'depth': sample['depth'], 'label': sample['label']}
class scaleNorm(object):
def __call__(self, sample):
image, depth, label = sample['image'], sample['depth'], sample['label']
# Bi-linear
image = skimage.transform.resize(image, (image_h, image_w), order=1,
mode='reflect', preserve_range=True)
# Nearest-neighbor
depth = skimage.transform.resize(depth, (image_h, image_w), order=0,
mode='reflect', preserve_range=True)
label = skimage.transform.resize(label, (image_h, image_w), order=0,
mode='reflect', preserve_range=True)
return {'image': image, 'depth': depth, 'label': label}
class RandomScale(object):
def __init__(self, scale):
self.scale_low = min(scale)
self.scale_high = max(scale)
def __call__(self, sample):
image, depth, label = sample['image'], sample['depth'], sample['label']
target_scale = random.uniform(self.scale_low, self.scale_high)
# (H, W, C)
target_height = int(round(target_scale * image.shape[0]))
target_width = int(round(target_scale * image.shape[1]))
# Bi-linear
image = skimage.transform.resize(image, (target_height, target_width),
order=1, mode='reflect', preserve_range=True)
# Nearest-neighbor
depth = skimage.transform.resize(depth, (target_height, target_width),
order=0, mode='reflect', preserve_range=True)
label = skimage.transform.resize(label, (target_height, target_width),
order=0, mode='reflect', preserve_range=True)
return {'image': image, 'depth': depth, 'label': label}
class RandomCrop(object):
def __init__(self, th, tw):
self.th = th
self.tw = tw
def __call__(self, sample):
image, depth, label = sample['image'], sample['depth'], sample['label']
h = image.shape[0]
w = image.shape[1]
i = random.randint(0, h - self.th)
j = random.randint(0, w - self.tw)
return {'image': image[i:i + image_h, j:j + image_w, :],
'depth': depth[i:i + image_h, j:j + image_w],
'label': label[i:i + image_h, j:j + image_w]}
class RandomFlip(object):
def __call__(self, sample):
image, depth, label = sample['image'], sample['depth'], sample['label']
if random.random() > 0.5:
image = np.fliplr(image).copy()
depth = np.fliplr(depth).copy()
label = np.fliplr(label).copy()
return {'image': image, 'depth': depth, 'label': label}
# Transforms on torch.*Tensor
class Normalize(object):
def __call__(self, sample):
image, depth = sample['image'], sample['depth']
image = image / 255
# image = torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])(image)
image = torchvision.transforms.Normalize(mean=[0.4850042694973687, 0.41627756261047333, 0.3981809741523051],
std=[0.26415541082494515, 0.2728415392982039, 0.2831175140191598])(image)
depth = torchvision.transforms.Normalize(mean=[2.8424503515351494],
std=[0.9932836506164299])(depth)
sample['image'] = image
sample['depth'] = depth
return sample
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
image, depth, label = sample['image'], sample['depth'], sample['label']
# Generate different label scales
label2 = skimage.transform.resize(label, (label.shape[0] // 2, label.shape[1] // 2),
order=0, mode='reflect', preserve_range=True)
label3 = skimage.transform.resize(label, (label.shape[0] // 4, label.shape[1] // 4),
order=0, mode='reflect', preserve_range=True)
label4 = skimage.transform.resize(label, (label.shape[0] // 8, label.shape[1] // 8),
order=0, mode='reflect', preserve_range=True)
label5 = skimage.transform.resize(label, (label.shape[0] // 16, label.shape[1] // 16),
order=0, mode='reflect', preserve_range=True)
# swap color axis because
# numpy image: H x W x C
# torch image: C X H X W
image = image.transpose((2, 0, 1))
depth = np.expand_dims(depth, 0).astype(np.float)
return {'image': torch.from_numpy(image).float(),
'depth': torch.from_numpy(depth).float(),
'label': torch.from_numpy(label).float(),
'label2': torch.from_numpy(label2).float(),
'label3': torch.from_numpy(label3).float(),
'label4': torch.from_numpy(label4).float(),
'label5': torch.from_numpy(label5).float()}