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eval_nyu_dataset_loader.py
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eval_nyu_dataset_loader.py
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"""
Created on Thu Feb 1 18:07:52 2018
@author: norbot
"""
from __future__ import print_function, division
import os
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import data_transform
from PIL import Image, ImageOps
import h5py
imagenet_mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
imagenet_std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
imagenet_eigval = np.array([0.2175, 0.0188, 0.0045], dtype=np.float32)
imagenet_eigvec = np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]], dtype=np.float32)
class NyuDepthDataset(Dataset):
# nyu depth dataset
def __init__(self, csv_file, root_dir, split, n_sample=200, input_format = 'img'):
"""
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.rgbd_frame = pd.read_csv(csv_file)
self.root_dir = root_dir
self.split = split
self.input_format = input_format
self.n_sample = n_sample
def __len__(self):
return len(self.rgbd_frame)
def __getitem__(self, idx):
# read input image
if self.input_format == 'img':
rgb_name = os.path.join(self.root_dir,
self.rgbd_frame.iloc[idx, 0])
with open(rgb_name, 'rb') as fRgb:
rgb_image = Image.open(rgb_name).convert('RGB')
depth_name = os.path.join(self.root_dir,
self.rgbd_frame.iloc[idx, 1])
with open(depth_name, 'rb') as fDepth:
depth_image = Image.open(depth_name)
# read input hdf5
elif self.input_format == 'hdf5':
file_name = os.path.join(self.root_dir,
self.rgbd_frame.iloc[idx, 0])
rgb_h5, depth_h5 = self.load_h5(file_name)
rgb_image = Image.fromarray(rgb_h5, mode='RGB')
depth_image = Image.fromarray(depth_h5.astype('float32'), mode='F')
else:
print('error: the input format is not supported now!')
return None
_s = np.random.uniform(1.0, 1.5)
s = np.int(240*_s)
degree = np.random.uniform(-5.0, 5.0)
if self.split == 'train':
tRgb = data_transform.Compose([transforms.Resize(s),
data_transform.Rotation(degree),
transforms.ColorJitter(brightness = 0.4, contrast = 0.4, saturation = 0.4),
# data_transform.Lighting(0.1, imagenet_eigval, imagenet_eigvec)])
transforms.CenterCrop((228, 304)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
transforms.ToPILImage()])
tDepth = data_transform.Compose([transforms.Resize(s),
data_transform.Rotation(degree),
transforms.CenterCrop((228, 304))])
rgb_image = tRgb(rgb_image)
depth_image = tDepth(depth_image)
if np.random.uniform()<0.5:
rgb_image = rgb_image.transpose(Image.FLIP_LEFT_RIGHT)
depth_image = depth_image.transpose(Image.FLIP_LEFT_RIGHT)
rgb_image = transforms.ToTensor()(rgb_image)
if self.input_format == 'img':
depth_image = transforms.ToTensor()(depth_image)
else:
depth_image = data_transform.ToTensor()(depth_image)
depth_image = depth_image.div(_s)
sparse_image = self.createSparseDepthImage(depth_image, self.n_sample)
rgbd_image = torch.cat((rgb_image, sparse_image), 0)
sample = {'rgbd': rgbd_image, 'depth': depth_image}
elif self.split == 'val':
tRgb = data_transform.Compose([transforms.Resize(240),
transforms.CenterCrop((228, 304)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
transforms.ToPILImage()])
tDepth = data_transform.Compose([transforms.Resize(240),
transforms.CenterCrop((228, 304))])
rgb_raw = tDepth(rgb_image)
rgb_image = tRgb(rgb_image)
depth_image = tDepth(depth_image)
rgb_image = transforms.ToTensor()(rgb_image)
rgb_raw = transforms.ToTensor()(rgb_raw)
if self.input_format == 'img':
depth_image = transforms.ToTensor()(depth_image)
else:
depth_image = data_transform.ToTensor()(depth_image)
sparse_image = self.createSparseDepthImage(depth_image, self.n_sample)
rgbd_image = torch.cat((rgb_image, sparse_image), 0)
sample = {'rgbd': rgbd_image, 'depth': depth_image, 'raw_rgb': rgb_raw }
return sample
def createSparseDepthImage(self, depth_image, n_sample):
random_mask = torch.zeros(1, depth_image.size(1), depth_image.size(2))
n_pixels = depth_image.size(1) * depth_image.size(2)
n_valid_pixels = torch.sum(depth_image>0.0001)
perc_sample = n_sample/n_pixels
random_mask = torch.bernoulli(torch.ones_like(random_mask)*perc_sample)
sparse_depth = torch.mul(depth_image, random_mask)
return sparse_depth
def load_h5(self, h5_filename):
f = h5py.File(h5_filename, 'r')
rgb = f['rgb'][:].transpose(1,2,0)
depth = f['depth'][:]
return (rgb, depth)