NYU-V2数据集预处理

我下载的是带标签的NYUV2数据集，下载写来的格式是.mat
原图，深度图，labels的提取方式参考此博客

提取各类名称保存为txt

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import h5py
import os
from PIL import Image

f=h5py.File(\"./nyu_depth_v2_labeled.mat\")

ft = open(\'names.txt\', \'w+\')
#print(f[\"names\"].shape) #打印查看类别个数，共894类
for j in range (893):
    name = f[\"names\"][0][j]
    obj = f[name]
    strr = \"\".join(chr(i) for i in obj[:])
    ft.write(strr + \'\\n\')

ft.close()

提取原图images（RGB图）

import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import h5py
import os
f=h5py.File(\"./nyu_depth_v2_labeled.mat\")
images=f[\"images\"]
images=np.array(images)
 
path_converted=\'./nyu_images\'
if not os.path.isdir(path_converted):
    os.makedirs(path_converted)
 
from PIL import Image
images_number=[]
for i in range(len(images)):
    print(i)
    images_number.append(images[i])
    a=np.array(images_number[i])
#    print len(img)
    #img=img.reshape(3,480,640)
 #   print img.shape
    r = Image.fromarray(a[0]).convert(\'L\')
    g = Image.fromarray(a[1]).convert(\'L\')
    b = Image.fromarray(a[2]).convert(\'L\')
    img = Image.merge(\"RGB\", (r, g, b))
    img = img.transpose(Image.ROTATE_270)
   # plt.imshow(img)
   # plt.axis(\'off\')
   # plt.show()
    iconpath=\'./nyu_images/\'+str(i)+\'.jpg\'
    img.save(iconpath,optimize=True)

提取深度图（depth map）

import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import h5py
import os
from PIL import Image
 
f=h5py.File(\"./nyu_depth_v2_labeled.mat\")
depths=f[\"depths\"]
depths=np.array(depths)
path_converted=\'./nyu_depths/\'
if not os.path.isdir(path_converted):
    os.makedirs(path_converted)
max=depths.max()
print (depths.shape)
print (depths.max())
print (depths.min())
 
depths=depths/max*255
depths=depths.transpose((0,2,1))
print (depths.max())
print (depths.min())
 
for i in range(len(depths)):
    print (str(i)+\'.png\')
    depths_img=Image.fromarray(np.uint8(depths[i]))
    depths_img=depths_img.transpose(Image.FLIP_LEFT_RIGHT)
 
    iconpath = path_converted + str(i) + \'.png\'
    depths_img.save(iconpath, \'PNG\', optimize=True)

提取labels

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import h5py
import os
from PIL import Image
f=h5py.File(\"./nyu_depth_v2_labeled.mat\")
labels=f[\"labels\"]
labels=np.array(labels)
 
path_converted=\'./nyu_labels\'
if not os.path.isdir(path_converted):
    os.makedirs(path_converted)
 
labels_number=[]
for i in range(len(labels)):
    labels_number.append(labels[i])
    labels_0=np.array(labels_number[i])
    #print labels_0.shape
    print (type(labels_0))
    label_img=Image.fromarray(np.uint8(labels_number[i]))
    #label_img = label_img.rotate(270)
    label_img = label_img.transpose(Image.ROTATE_270)
 
    iconpath=\'./nyu_labels/\'+str(i)+\'.png\'
    label_img.save(iconpath, \'PNG\', optimize=True)