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PreprocessText.py
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PreprocessText.py
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from os import listdir
from numpy import argmax
from numpy import array
from pickle import dump
from pickle import load
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model
from keras.models import load_model
from keras.utils import to_categorical
from keras.utils import plot_model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Embedding
from keras.layers import Dropout
from keras.layers.merge import add
from keras.layers import RepeatVector
from keras.layers import TimeDistributed
from keras.layers.merge import concatenate
from keras.layers.pooling import GlobalMaxPooling2D
from keras.callbacks import ModelCheckpoint
from nltk.translate.bleu_score import corpus_bleu
import string
from AuxilaryFunctions import *
# extract descriptions for images
def load_descriptions(doc):
mapping = dict()
# process lines
for line in doc.split('\n'):
# split line by white space
tokens = line.split()
if len(line) < 2:
continue
# take the first token as the image id, the rest as the description
image_id, image_desc = tokens[0], tokens[1:]
# remove filename from image id
image_id = image_id.split('.')[0]
# convert description tokens back to string
image_desc = ' '.join(image_desc)
# create the list if needed
if image_id not in mapping:
mapping[image_id] = image_desc
return mapping
# covert a dictionary of clean descriptions to a list of descriptions
def to_lines(descriptions):
all_desc = list()
for key in descriptions.keys():
[all_desc.append(d) for d in descriptions[key]]
return all_desc
# fit a tokenizer given caption descriptions
def create_tokenizer(descriptions):
lines = to_lines(descriptions)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(lines)
return tokenizer
# calculate the length of the description with the most words
def max_length(descriptions):
lines = to_lines(descriptions)
return max(len(d.split()) for d in lines)