如何使用caffe爲FCN創建自己的數據集？

Question

如何將圖像轉換爲lmdb與咖啡fcn？你知道，用caffe很容易創建自己的圖像分類數據集，但是如何爲fcn創建自己的數據集語義片段？

Answer 1

使用此代碼。進行必要的路徑更改。使用前請仔細閱讀代碼。

import caffe 
import lmdb 
from PIL import Image 
import numpy as np 
import glob 
from random import shuffle 

# Initialize the Image set: 

NumberTrain = 1111 # Number of Training Images 

NumberTest = 1112 # Number of Testing Images 

Rheight = 380 # Required Height 

Rwidth = 500 # Required Width 

LabelHeight = 380 # Downscaled height of the label 

LabelWidth = 500 # Downscaled width of the label 


# Read the files in the Data Folder 

inputs_data_train = sorted(glob.glob("/home/<user>/caffe-with_crop/examples/fcn-32s/TrainData/*.jpg")) 
inputs_data_valid = sorted(glob.glob("/home/<user>/caffe-with_crop/examples/fcn-32s/ValData/*.jpg")) 
inputs_label = sorted(glob.glob("/home/<user>/caffe-with_crop/examples/fcn-32s/VOC2011/SegmentationClass/*.png")) 

shuffle(inputs_data_train) # Shuffle the DataSet 
shuffle(inputs_data_valid) # Shuffle the DataSet 

inputs_Train = inputs_data_train[:NumberTrain] # Extract the training data from the complete set 

inputs_Test = inputs_data_valid[:NumberTest] # Extract the testing data from the complete set 

# Creating LMDB for Training Data 

print("Creating Training Data LMDB File ..... ") 

in_db = lmdb.open('/home/<user>/caffe-with_crop/examples/fcn-32s/TrainVOC_Data_lmdb',map_size=int(1e14)) 

with in_db.begin(write=True) as in_txn: 

    for in_idx, in_ in enumerate(inputs_Train): 
     print in_idx 
     im = np.array(Image.open(in_)) # or load whatever ndarray you need 
     Dtype = im.dtype 
     im = im[:,:,::-1] 
     im = Image.fromarray(im) 
     im = im.resize([Rheight, Rwidth], Image.ANTIALIAS) 
     im = np.array(im,Dtype)  

     im = im.transpose((2,0,1)) 
     im_dat = caffe.io.array_to_datum(im) 
     in_txn.put('{:0>10d}'.format(in_idx),im_dat.SerializeToString()) 

in_db.close() 

# Creating LMDB for Training Labels 

print("Creating Training Label LMDB File ..... ") 

in_db = lmdb.open('/home/<user>/caffe-with_crop/examples/fcn-32s/TrainVOC_Label_lmdb',map_size=int(1e14)) 

with in_db.begin(write=True) as in_txn: 

    for in_idx, in_ in enumerate(inputs_Train): 
     print in_idx 
     in_label = in_[:-25]+'VOC2011/SegmentationClass/'+in_[-15:-3]+'png' # Change the numbers as per requirement 
     L = np.array(Image.open(in_)) # or load whatever ndarray you need 
     Dtype = L.dtype 
     L = L[:,:,::-1] 
     Limg = Image.fromarray(L) 
     Limg = Limg.resize([LabelHeight, LabelWidth],Image.NEAREST) # To resize the Label file to the required size 

     L = np.array(Limg,Dtype) 

     L = L.reshape(L.shape[0],L.shape[1],1) 

     L = L.transpose((2,0,1)) 

     L_dat = caffe.io.array_to_datum(L) 
     in_txn.put('{:0>10d}'.format(in_idx),L_dat.SerializeToString()) 

in_db.close() 

# Creating LMDB for Testing Data 

print("Creating Testing Data LMDB File ..... ") 

in_db = lmdb.open('/home/<user>/caffe-with_crop/examples/fcn-32s/TestVOC_Data_lmdb',map_size=int(1e14)) 

with in_db.begin(write=True) as in_txn: 

    for in_idx, in_ in enumerate(inputs_Test): 
     print in_idx  
     im = np.array(Image.open(in_)) # or load whatever ndarray you need 
     Dtype = im.dtype 
     im = im[:,:,::-1] 
     im = Image.fromarray(im) 
     im = im.resize([Rheight, Rwidth], Image.ANTIALIAS) 
     im = np.array(im,Dtype)  

     im = im.transpose((2,0,1)) 
     im_dat = caffe.io.array_to_datum(im) 
     in_txn.put('{:0>10d}'.format(in_idx),im_dat.SerializeToString()) 

in_db.close() 

# Creating LMDB for Testing Labels 

print("Creating Testing Label LMDB File ..... ") 

in_db = lmdb.open('/home/<user>/caffe-with_crop/examples/fcn-32s/TestVOC_Label_lmdb',map_size=int(1e14)) 

with in_db.begin(write=True) as in_txn: 

    for in_idx, in_ in enumerate(inputs_Test): 
     print in_idx  
     in_label = in_[:-25]+'VOC2011/SegmentationClass/'+in_[-15:-3]+'png' # Change the numbers as per requirement 
     L = np.array(Image.open(in_)) # or load whatever ndarray you need 
     Dtype = L.dtype 
     L = L[:,:,::-1] 
     Limg = Image.fromarray(L) 
     Limg = Limg.resize([LabelHeight, LabelWidth],Image.NEAREST) # To resize the Label file to the required size 

     L = np.array(Limg,Dtype) 

     L = L.reshape(L.shape[0],L.shape[1],1) 

     L = L.transpose((2,0,1)) 

     L_dat = caffe.io.array_to_datum(L) 
     in_txn.put('{:0>10d}'.format(in_idx),L_dat.SerializeToString()) 

in_db.close()