Spaces:
Sleeping
Sleeping
File size: 2,099 Bytes
d34f465 325e144 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
import pickle
CapGenerator = tf.keras.models.load_model('CapGen.h5')
VGGMod = tf.keras.models.load_model('VGGModel.h5')
max_length = 35
with open('models/tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
vocab_size = len(tokenizer.word_index) + 1
def idx_to_word(integer, tokenizer):
for word, index in tokenizer.word_index.items():
if index == integer:
return word
return None
def predict_caption(model, image, tokenizer, max_length=max_length):
# add start tag for generation process
in_text = 'startseq'
# iterate over the max length of sequence
for i in range(max_length):
# encode input sequence
sequence = tokenizer.texts_to_sequences([in_text])[0]
# pad the sequence
sequence = pad_sequences([sequence], max_length)
# predict next word
yhat = model.predict([image, sequence], verbose=0)
# get index with high probability
yhat = np.argmax(yhat)
# convert index to word
word = idx_to_word(yhat, tokenizer)
# stop if word not found
if word is None:
break
# append word as input for generating next word
in_text += " " + word
# stop if we reach end tag
if word == 'endseq':
break
return in_text
def feature_extractor(image):
# Img to np array
image = img_to_array(image)
# Reshaping
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
# Preprocessing for passing through VGG16
image = preprocess_input(image)
feature = VGGMod.predict(image, verbose=0)
return feature
def generate_caption(image_name):
y_pred = predict_caption(CapGenerator, feature_extractor(image_name), tokenizer, max_length)
y_pred = y_pred[8:-7].upper()
return y_pred
|