以下是我的多图像分类代码。我收到了一个错误;我认为这是因为加载和其他方面的某些方面的不匹配。
错误消息从代码结束的地方开始。任何人都可以看到问题吗？
#importing necessary packages
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from PIL import Image
import tflearn
import tensorflow as tf
import tflearn
#for writing text files
import glob
import os
import random
#reading images from a text file
from tflearn.data_utils import image_preloader
import math
IMAGE_FOLDER = 'C:/Users/kdeepshi/Desktop/PyforE/Face-Detection/Train'
TRAIN_DATA = 'C:/Users/kdeepshi/Desktop/PyforE/Face-Detection/training_data.txt'
TEST_DATA = 'C:/Users/kdeepshi/Desktop/PyforE/Face-Detection/test_data.txt'
VALIDATION_DATA = 'C:/Users/kdeepshi/Desktop/PyforE/Face-Detection/validation_data.txt'
train_proportion=0.7
test_proportion=0.2
validation_proportion=0.1
#read the image directories
filenames_image = os.listdir(IMAGE_FOLDER)
#shuffling the data is important otherwise the model will be fed with a single class data for a long time and
#network will not learn properly
random.shuffle(filenames_image)
#total number of images
total=len(filenames_image)
## *****training data********
fr = open(TRAIN_DATA, 'w')
train_files=filenames_image[0: int(train_proportion*total)]
for filename in train_files:
if filename[0:4] == 'Mark':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 0\n')
elif filename[0:5] == 'lucas':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 1\n')
elif filename[0:3] == 'Ann':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 2\n')
elif filename[0:5] == 'Henry':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 3\n')
elif filename[0:5] == 'Hanna':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 4\n')
elif filename[0:4] == 'Jack':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 5\n')
elif filename[0:5] == 'Harry':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 6\n')
elif filename[0:3] == 'Lui':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 7\n')
elif filename[0:6] == 'Karlos':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 8\n')
elif filename[0:4] == 'Guan':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 9\n')
fr.close()
## *****testing data********
fr = open(TEST_DATA, 'w')
test_files=filenames_image[int(math.ceil(train_proportion*total)):int(math.ceil((train_proportion+test_proportion)*total))]
for filename in test_files:
if filename[0:4] == 'Mark':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 0\n')
elif filename[0:5] == 'lucas':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 1\n')
elif filename[0:3] == 'Ann':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 2\n')
elif filename[0:5] == 'Henry':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 3\n')
elif filename[0:5] == 'Hanna':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 4\n')
elif filename[0:4] == 'Jack':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 5\n')
elif filename[0:5] == 'Harry':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 6\n')
elif filename[0:3] == 'Lui':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 7\n')
elif filename[0:6] == 'Karlos':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 8\n')
elif filename[0:4] == 'Guan':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 9\n')
fr.close()
## *****validation data********
fr = open(VALIDATION_DATA, 'w')
valid_files=filenames_image[int(math.ceil((train_proportion+test_proportion)*total)):total]
for filename in valid_files:
if filename[0:4] == 'Mark':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 0\n')
elif filename[0:5] == 'lucas':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 1\n')
elif filename[0:3] == 'Ann':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 2\n')
elif filename[0:5] == 'Henry':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 3\n')
elif filename[0:5] == 'Hanna':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 4\n')
elif filename[0:4] == 'Jack':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 5\n')
elif filename[0:5] == 'Harry':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 6\n')
elif filename[0:3] == 'Lui':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 7\n')
elif filename[0:6] == 'Karlos':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 8\n')
elif filename[0:4] == 'Guan':
fr.write(IMAGE_FOLDER + '/'+ filename + ' 9\n')
fr.close()
#Importing data
X_train, Y_train = image_preloader(TRAIN_DATA, image_shape=(56,56),mode='file', categorical_labels=True,normalize=True)
X_test, Y_test = image_preloader(TEST_DATA, image_shape=(56,56),mode='file', categorical_labels=True,normalize=True)
X_val, Y_val = image_preloader(VALIDATION_DATA, image_shape=(56,56),mode='file', categorical_labels=True,normalize=True)
print ("Dataset")
print ("Number of training images {}".format(len(X_train)))
print ("Number of testing images {}".format(len(X_test)))
print ("Number of validation images {}".format(len(X_val)))
print ("Shape of an image {}" .format(X_train[1].shape))
print ("Shape of label:{} ,number of classes: {}".format(Y_train[1].shape,len(Y_train[1])))
#Sample Image
plt.imshow(X_train[1])
plt.axis('off')
plt.title('Sample image with label {}'.format(Y_train[1]))
plt.show()
print(type(X_test))
#input image
x=tf.placeholder(tf.float32,shape=[None,56,56,3] , name='input_image')
#input class
y_=tf.placeholder(tf.float32,shape=[None, 10] , name='input_class')
input_layer=x
print("Hiiiiiiii No error till this point")
#convolutional layer 1 --convolution+RELU activation
conv_layer1=tflearn.layers.conv.conv_2d(input_layer, nb_filter=64, filter_size=5, strides=[1,1,1,1],
padding='same', activation='relu', regularizer="L2", name='conv_layer_1')
#2x2 max pooling layer
out_layer1=tflearn.layers.conv.max_pool_2d(conv_layer1, 10)
#second convolutional layer
conv_layer2=tflearn.layers.conv.conv_2d(out_layer1, nb_filter=128, filter_size=5, strides=[1,1,1,1],
padding='same', activation='relu', regularizer="L2", name='conv_layer_2')
out_layer2=tflearn.layers.conv.max_pool_2d(conv_layer2, 10)
# third convolutional layer
conv_layer3=tflearn.layers.conv.conv_2d(out_layer2, nb_filter=128, filter_size=5, strides=[1,1,1,1],
padding='same', activation='relu', regularizer="L2", name='conv_layer_2')
out_layer3=tflearn.layers.conv.max_pool_2d(conv_layer3, 10)
#fully connected layer1
fcl= tflearn.layers.core.fully_connected(out_layer3, 4096, activation='relu' , name='FCL-1')
fcl_dropout_1 = tflearn.layers.core.dropout(fcl, 0.8)
#fully connected layer2
fc2= tflearn.layers.core.fully_connected(fcl_dropout_1, 4096, activation='relu' , name='FCL-2')
fcl_dropout_2 = tflearn.layers.core.dropout(fc2, 0.8)
#softmax layer output
y_predicted = tflearn.layers.core.fully_connected(fcl_dropout_2, 10, activation='softmax', name='output')
#loss function
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_predicted+np.exp(-10)), reduction_indices=[1]))
#optimiser -
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#calculating accuracy of our model
correct_prediction = tf.equal(tf.argmax(y_predicted,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# session parameters
sess = tf.InteractiveSession()
#initialising variables
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
save_path="C:/Users/kdeepshi/Desktop/PyforE/Face-Detection/mark2.ckpt"
g = tf.get_default_graph()
# every operations in our graph
[op.name for op in g.get_operations()]
epoch=5000
batch_size=20
previous_batch=0
for i in range(epoch):
#batch wise training
if previous_batch >= len(X_train) :
previous_batch=0
current_batch=previous_batch+batch_size
x_input=X_train[previous_batch:current_batch]
x_images=np.array(x_input)
x_images=np.reshape(x_images,[batch_size,56,56,3])
y_input=Y_train[previous_batch:current_batch]
y_label=np.reshape(y_input,[batch_size,10])
previous_batch=previous_batch+batch_size
_,loss=sess.run([train_step, cross_entropy], feed_dict={x: x_images,y_: y_label})
if i%500==0:
n=50 #number of test samples
X_test=np.array(X_test)
x_test_images=np.reshape(X_test[0:n],[n,56,56,3])
y_test_labels=np.reshape(Y_test[0:n],[n,10])
Accuracy=sess.run(accuracy,feed_dict={x: x_test_images ,y_: y_test_labels})
print("Iteration no :{} , Accuracy:{} , Loss : {}" .format(i,Accuracy,loss))
saver.save(sess, save_path, global_step = i)
elif i % 100 ==0:
print("Iteration no :{} Loss : {}" .format(i,loss))
x_input=X_val
x_images=np.reshape(x_input,[len(X_val),56,56,3])
y_input=Y_val
y_label=np.reshape(y_input,[len(Y_val),10])
Accuracy_validation=sess.run(accuracy,feed_dict={x: x_images ,y_: y_label})
Accuracy_validation=round(Accuracy_validation*100,2)
print("Accuracy in the validation dataset: {} %".format(Accuracy_validation))
Test_FOLDER = 'C:/Users/kdeepshi/Desktop/PyforE/Face-Detection/Test'
filenames_image = os.listdir(Test_FOLDER)
total=len(filenames_image)
print(total)
test_files=filenames_image[0: int(total)]
for filename in test_files:
marty=Image.open(Test_FOLDER+'/'+filename)
marty_resize=marty.resize((56,56),Image.ANTIALIAS)
marty_resize=np.array(marty_resize)
marty_test=marty_resize/np.max(marty_resize).astype(float)
marty_test=np.reshape(marty_test,[1,56,56,3])
c=sess.run(y_predicted, feed_dict={x: marty_test})
d= np.argmax(c)
#test your own images
#test_image=Image.open('/path to file')
#test_image= process_img(test_image)
#predicted_array= sess.run(y_predicted, feed_dict={x: test_image})
#predicted_class= np.argmax(predicted_array)
if d==0:
print("This is Mark\n")
elif d==1:
print("This is lucas\n")
elif d==2:
print("This is Ann")
elif d==3:
print("This is Henrry\n")
elif d==4:
print("This is Hanna\n")
elif d==5:
print("This is Jack")
elif d==6:
print("This is Harry\n")
elif d==7:
print("This is Lui")
elif d==8:
print("This is Karlos\n")
elif d==9:
print("This is guan\n")
这是错误：
C：\用户\ kdeepshi \桌面\ PyforE \面部识别＆GT; multi.py
此计算机不支持curses（请安装/重新安装 诅咒以获得最佳体验）
数据集
训练图像数量22
测试图像数量6
验证图像数量3
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期待读取12个字节 但只有0.跳过标签270
“跳过代码％s”％（尺寸，len（数据），代码））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期望读取6个字节 但只得到0.跳过标签271
“跳过代码％s”％（尺寸，len（数据），代码））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期待读取8个字节 但只有0.跳过标签272“跳过标签％s”％（尺寸， len（数据），标签））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期望读取64个字节 但只得到0.跳过标签282“跳过标签％s”％（尺寸， len（数据），标签））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期望读取64个字节 但只得到0.跳过标签283“跳过标签％s”％（尺寸， len（数据），标签））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期待读取20个字节 但只有0.跳过标签306“跳过标签％s”％（尺寸， len（数据），标签））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期望读取24个字节 但只有0.跳过标签529“跳过标签％s”％（尺寸， len（数据），标签））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期望读取24个字节 但只有0.跳过标签532“跳过标签％s”％（尺寸， len（数据），标签））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：692： UserWarning：可能损坏EXIF数据。期待读取40个字节 但只有0.跳过标签33432“跳过标签％s”％（尺寸， len（数据），标签））
C：\ ProgramData \ Anaconda3 \ lib中\站点包\ PIL \ TiffImagePlugin.py：709： UserWarning：损坏的EXIF数据。期望只读取2个字节 得到0. warnings.warn（str（msg））
图像的形状（56,56,3）
标签形状：（10，），班级数：10
答案 0 :(得分：1)
从PIL模块中的图像读取EXIF数据时出现问题。我倾向于认为这是PIL中的错误，而不是图像损坏。由于深度学习不需要此数据，因此您只需清理文件即可。为此，请下载ExifTool并运行以下命令：
exiftool -r -all= -ext JPEG D:\datasets\ImageNet\train
-r：递归
-all：将所有EXIF数据设置为null
-ext：文件扩展名
这在Windows和Linux上均可使用。