用 Keras 创建自己的图像标题生成器
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2021-02-22 15:24
了解图像字幕生成器如何使用编码器-解码器工作 知道如何使用Keras创建自己的图像标题生成器
import numpy as np
from numpy import array
import matplotlib.pyplot as plt
%matplotlib inline
import string
import os
import glob
from PIL import Image
from time import time
from keras import Input, layers
from keras import optimizers
from keras.optimizers import Adam
from keras.preprocessing import sequence
from keras.preprocessing import image
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import LSTM, Embedding, Dense, Activation, Flatten, Reshape, Dropout
from keras.layers.wrappers import Bidirectional
from keras.layers.merge import add
from keras.applications.inception_v3 import InceptionV3
from keras.applications.inception_v3 import preprocess_input
from keras.models import Model
from keras.utils import to_categorical
token_path = "../input/flickr8k/Data/Flickr8k_text/Flickr8k.token.txt"
train_images_path = '../input/flickr8k/Data/Flickr8k_text/Flickr_8k.trainImages.txt'
test_images_path = '../input/flickr8k/Data/Flickr8k_text/Flickr_8k.testImages.txt'
images_path = '../input/flickr8k/Data/Flicker8k_Dataset/'
glove_path = '../input/glove6b'
doc = open(token_path,'r').read()
print(doc[:410])
descriptions = dict()
for line in doc.split('\n'):
tokens = line.split()
if len(line) > 2:
image_id = tokens[0].split('.')[0]
image_desc = ' '.join(tokens[1:])
if image_id not in descriptions:
descriptions[image_id] = list()
descriptions[image_id].append(image_desc)
table = str.maketrans('', '', string.punctuation)
for key, desc_list in descriptions.items():
for i in range(len(desc_list)):
desc = desc_list[i]
desc = desc.split()
desc = [word.lower() for word in desc]
desc = [w.translate(table) for w in desc]
desc_list[i] = ' '.join(desc)
pic = '1000268201_693b08cb0e.jpg'
x=plt.imread(images_path+pic)
plt.imshow(x)
plt.show()
descriptions['1000268201_693b08cb0e']
vocabulary = set()
for key in descriptions.keys():
[vocabulary.update(d.split()) for d in descriptions[key]]
print('Original Vocabulary Size: %d' % len(vocabulary))
lines = list()
for key, desc_list in descriptions.items():
for desc in desc_list:
lines.append(key + ' ' + desc)
new_descriptions = '\n'.join(lines)
doc = open(train_images_path,'r').read()
dataset = list()
for line in doc.split('\n'):
if len(line) > 1:
identifier = line.split('.')[0]
dataset.append(identifier)
train = set(dataset)
train_img和test_img列表中：img = glob.glob(images_path + '*.jpg')
train_images = set(open(train_images_path, 'r').read().strip().split('\n'))
train_img = []
for i in img:
if i[len(images_path):] in train_images:
train_img.append(i)
test_images = set(open(test_images_path, 'r').read().strip().split('\n'))
test_img = []
for i in img:
if i[len(images_path):] in test_images:
test_img.append(i)
train_descriptions = dict()
for line in new_descriptions.split('\n'):
tokens = line.split()
image_id, image_desc = tokens[0], tokens[1:]
if image_id in train:
if image_id not in train_descriptions:
train_descriptions[image_id] = list()
desc = 'startseq ' + ' '.join(image_desc) + ' endseq'
train_descriptions[image_id].append(desc)
all_train_captions = []
for key, val in train_descriptions.items():
for cap in val:
all_train_captions.append(cap)
word_count_threshold = 10
word_counts = {}
nsents = 0
for sent in all_train_captions:
nsents += 1
for w in sent.split(' '):
word_counts[w] = word_counts.get(w, 0) + 1
vocab = [w for w in word_counts if word_counts[w] >= word_count_threshold]
print('Vocabulary = %d' % (len(vocab)))
ixtoword = {}
wordtoix = {}
ix = 1
for w in vocab:
wordtoix[w] = ix
ixtoword[ix] = w
ix += 1
vocab_size = len(ixtoword) + 1
all_desc = list()
for key in train_descriptions.keys():
[all_desc.append(d) for d in train_descriptions[key]]
lines = all_desc
max_length = max(len(d.split()) for d in lines)
print('Description Length: %d' % max_length)
embeddings_index = {}
f = open(os.path.join(glove_path, 'glove.6B.200d.txt'), encoding="utf-8")
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
embedding_dim = 200
embedding_matrix = np.zeros((vocab_size, embedding_dim))
for word, i in wordtoix.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
model = InceptionV3(weights='imagenet')
model_new = Model(model.input, model.layers[-2].output)
由于我们使用的是InceptionV3，因此我们需要对输入进行预处理，然后再将其输入模型。因此，我们定义了一个预处理函数以将图像重塑为（299 x 299）并馈入Keras的preprocess_input（）函数。
def preprocess(image_path):
img = image.load_img(image_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
return x
def encode(image):
image = preprocess(image)
fea_vec = model_new.predict(image)
fea_vec = np.reshape(fea_vec, fea_vec.shape[1])
return fea_vec
encoding_train = {}
for img in train_img:
encoding_train[img[len(images_path):]] = encode(img)
train_features = encoding_train
encoding_test = {}
for img in test_img:
encoding_test[img[len(images_path):]] = encode(img)
inputs1 = Input(shape=(2048,))
fe1 = Dropout(0.5)(inputs1)
fe2 = Dense(256, activation='relu')(fe1)
inputs2 = Input(shape=(max_length,))
se1 = Embedding(vocab_size, embedding_dim, mask_zero=True)(inputs2)
se2 = Dropout(0.5)(se1)
se3 = LSTM(256)(se2)
decoder1 = add([fe2, se3])
decoder2 = Dense(256, activation='relu')(decoder1)
outputs = Dense(vocab_size, activation='softmax')(decoder2)
model = Model(inputs=[inputs1, inputs2], outputs=outputs)
model.summary()
Input_3是输入到嵌入层的最大长度为34的部分标题。这是单词映射到200-d手套嵌入的地方。紧随其后的是0.5的落差，以避免过度拟合。然后将其输入LSTM中以处理序列。Input_2是我们的InceptionV3网络提取的图像矢量。紧随其后的是0.5的落差，以避免过度拟合，然后将其馈入“全连接”层。model.layers[2].set_weights([embedding_matrix])
model.layers[2].trainable = False
Categorical_Crossentropy作为Loss函数并使用Adam作为优化器来编译模型。model.compile(loss='categorical_crossentropy', optimizer='adam')
def data_generator(descriptions, photos, wordtoix, max_length, num_photos_per_batch):
X1, X2, y = list(), list(), list()
n=0
# loop for ever over images
while 1:
for key, desc_list in descriptions.items():
n+=1
# retrieve the photo feature
photo = photos[key+'.jpg']
for desc in desc_list:
# encode the sequence
seq = [wordtoix[word] for word in desc.split(' ') if word in wordtoix]
# split one sequence into multiple X, y pairs
for i in range(1, len(seq)):
# split into input and output pair
in_seq, out_seq = seq[:i], seq[i]
# pad input sequence
in_seq = pad_sequences([in_seq], maxlen=max_length)[0]
# encode output sequence
out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]
# store
X1.append(photo)
X2.append(in_seq)
y.append(out_seq)
if n==num_photos_per_batch:
yield ([array(X1), array(X2)], array(y))
X1, X2, y = list(), list(), list()
n=0
epochs = 30
batch_size = 3
steps = len(train_descriptions)//batch_size
generator = data_generator(train_descriptions, train_features, wordtoix, max_length, batch_size)
model.fit(generator, epochs=epochs, steps_per_epoch=steps, verbose=1)
def greedySearch(photo):
in_text = 'startseq'
for i in range(max_length):
sequence = [wordtoix[w] for w in in_text.split() if w in wordtoix]
sequence = pad_sequences([sequence], maxlen=max_length)
yhat = model.predict([photo,sequence], verbose=0)
yhat = np.argmax(yhat)
word = ixtoword[yhat]
in_text += ' ' + word
if word == 'endseq':
break
final = in_text.split()
final = final[1:-1]
final = ' '.join(final)
return final
def beam_search_predictions(image, beam_index = 3):
start = [wordtoix["startseq"]]
start_word = [[start, 0.0]]
while len(start_word[0][0]) < max_length:
temp = []
for s in start_word:
par_caps = sequence.pad_sequences([s[0]], maxlen=max_length, padding='post')
preds = model.predict([image,par_caps], verbose=0)
word_preds = np.argsort(preds[0])[-beam_index:]
# Getting the top (n) predictions and creating a
# new list so as to put them via the model again
for w in word_preds:
next_cap, prob = s[0][:], s[1]
next_cap.append(w)
prob += preds[0][w]
temp.append([next_cap, prob])
start_word = temp
# Sorting according to the probabilities
start_word = sorted(start_word, reverse=False, key=lambda l: l[1])
# Getting the top words
start_word = start_word[-beam_index:]
start_word = start_word[-1][0]
intermediate_caption = [ixtoword[i] for i in start_word]
final_caption = []
for i in intermediate_caption:
if i != 'endseq':
final_caption.append(i)
else:
break
final_caption = ' '.join(final_caption[1:])
return final_caption
pic = '2398605966_1d0c9e6a20.jpg'
image = encoding_test[pic].reshape((1,2048))
x=plt.imread(images_path+pic)
plt.imshow(x)
plt.show()
print("Greedy Search:",greedySearch(image))
print("Beam Search, K = 3:",beam_search_predictions(image, beam_index = 3))
print("Beam Search, K = 5:",beam_search_predictions(image, beam_index = 5))
print("Beam Search, K = 7:",beam_search_predictions(image, beam_index = 7))
print("Beam Search, K = 10:",beam_search_predictions(image, beam_index = 10))
pic = list(encoding_test.keys())[1]
image = encoding_test[pic].reshape((1,2048))
x=plt.imread(images_path+pic)
plt.imshow(x)
plt.show()
print("Greedy:",greedySearch(image))
print("Beam Search, K = 3:",beam_search_predictions(image, beam_index = 3))
print("Beam Search, K = 5:",beam_search_predictions(image, beam_index = 5))
print("Beam Search, K = 7:",beam_search_predictions(image, beam_index = 7))
作者：沂水寒城，CSDN博客专家，个人研究方向：机器学习、深度学习、NLP、CV
Blog: http://yishuihancheng.blog.csdn.net
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