如何從 TXT 檔案載入影象和標籤

Tensorflow 文件中沒有解釋如何直接從 TXT 檔案載入影象和標籤。下面的程式碼說明了我是如何實現它的。但是，這並不意味著這是最好的方法，這種方式將有助於進一步的步驟。

例如，我在一個整數值{0,1}中載入標籤，而文件使用單熱向量[0,1]。

# Learning how to import images and labels from a TXT file
#
# TXT file format
#
# path/to/imagefile_1 label_1
# path/to/imagefile_2 label_2
# ...                 ...
#
# where label_X is either {0,1}

#Importing Libraries
import os
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes

#File containing the path to images and the labels [path/to/images label]
filename = '/path/to/List.txt'

#Lists where to store the paths and labels
filenames = []
labels = []

#Reading file and extracting paths and labels
with open(filename, 'r') as File:
    infoFile = File.readlines() #Reading all the lines from File
    for line in infoFile: #Reading line-by-line
        words = line.split() #Splitting lines in words using space character as separator
        filenames.append(words[0])
        labels.append(int(words[1]))

NumFiles = len(filenames)

#Converting filenames and labels into tensors
tfilenames = ops.convert_to_tensor(filenames, dtype=dtypes.string)
tlabels = ops.convert_to_tensor(labels, dtype=dtypes.int32)

#Creating a queue which contains the list of files to read and the value of the labels
filename_queue = tf.train.slice_input_producer([tfilenames, tlabels], num_epochs=10, shuffle=True, capacity=NumFiles)

#Reading the image files and decoding them
rawIm= tf.read_file(filename_queue[0])
decodedIm = tf.image.decode_png(rawIm) # png or jpg decoder

#Extracting the labels queue
label_queue = filename_queue[1]

#Initializing Global and Local Variables so we avoid warnings and errors
init_op = tf.group(tf.local_variables_initializer() ,tf.global_variables_initializer())

#Creating an InteractiveSession so we can run in iPython
sess = tf.InteractiveSession()

with sess.as_default():
    sess.run(init_op)
    
    # Start populating the filename queue.
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for i in range(NumFiles): #length of your filenames list
        nm, image, lb = sess.run([filename_queue[0], decodedIm, label_queue])
        
        print image.shape
        print nm
        print lb
        
        #Showing the current image
        plt.imshow(image)
        plt.show()

    coord.request_stop()
    coord.join(threads)