File size: 1,871 Bytes
6123f34 |
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 |
import tensorflow as tf
import numpy as np
from tensorflow.keras.applications.inception_v3 import preprocess_input
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Paths
token_path = 'saved_models/Flickr8k.token.txt'
train_images_path = 'saved_models/Flickr_8k.trainImages.txt'
test_images_path = 'saved_models/Flickr_8k.testImages.txt'
model_path = 'saved_models/Final_Image_Captioning.h5'
def preprocess_image(image_path):
img = image.load_img(image_path, target_size=(299, 299))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = preprocess_input(img)
return img
def generate_caption(image_file):
# Load the pre-trained model
model = load_model(model_path)
# Preprocess the image
img = preprocess_image(image_file)
# Generate the caption
caption = generate_caption_from_image(model, img)
return caption
def generate_caption_from_image(model, img):
max_length = 34
start_token = "<start>"
end_token = "<end>"
wordtoix = np.load("wordtoix.npy", allow_pickle=True).item()
ixtoword = np.load("ixtoword.npy", allow_pickle=True).item()
initial_state = [np.zeros((1, 256)), np.zeros((1, 256))]
# Generate caption using greedy search
caption = start_token
for _ in range(max_length):
sequence = [wordtoix[word] for word in caption.split() if word in wordtoix]
sequence = pad_sequences([sequence], maxlen=max_length)
y_pred = model.predict([img, sequence] + initial_state)
y_pred = np.argmax(y_pred)
word = ixtoword[y_pred]
caption += " " + word
if word == end_token:
break
# Remove start and end tokens
caption = " ".join(caption.split()[1:-1])
return caption
|