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import os
import pickle
import requests
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import numpy as np
from keras.src.utils import pad_sequences
from matplotlib import pyplot as plt
from keras.models import load_model
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.preprocessing.text import Tokenizer
from PIL import Image
def load_model_from_path(model_path):
model_link=os.path.abspath(model_path)
if os.path.exists(model_link):
try:
model = load_model(model_link)
print(f"Model from {model_link} loaded successfully!")
return model
except Exception as e:
print(f"Error loading model from {model_link}: {e}")
else:
print(f"File not found: {model_link}")
return None
def tokenizer_load(path):
with open(path, 'rb') as file:
tokenizer = pickle.load(file)
return tokenizer
def download_image(url, save_path):
try:
response = requests.get(url, stream=True, timeout=10)
response.raise_for_status() # Raise an error for bad responses (4xx and 5xx)
with open(save_path, 'wb') as file:
for chunk in response.iter_content(1024):
file.write(chunk)
return save_path
except Exception as e:
print(f"Error downloading image {url}: {e}")
return None
def extract_image_features_one(model, img_path):
try:
if img_path.startswith("http"):
temp_path = "temp_image.jpg"
img_path = download_image(img_path, temp_path)
if img_path is None:
return None
if not os.path.exists(img_path):
print(f"Error: Image path does not exist - {img_path}")
return None
image = load_img(img_path, target_size=(224, 224))
img_array = img_to_array(image)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
feature = model.predict(img_array, verbose=0)
if feature is None:
print(f"Error: Model returned None for image - {img_path}")
return feature
except Exception as e:
print(f"Exception in feature extraction: {e}")
return None
finally:
if temp_path and os.path.exists(temp_path):
os.remove(temp_path)
def idx_to_word(integer,tokenizer):
for word ,index in tokenizer.word_index.items():
if index == integer:
return word
return None
def extract_captions(mapping):
captions_list = []
for key in mapping:
captions_list.extend(mapping[key])
return captions_list
def prepare_tokenizer(captions_list):
tokenizer = Tokenizer()
tokenizer.fit_on_texts(captions_list)
vocab_size = len(tokenizer.word_index) + 1
return tokenizer, vocab_size
def calculate_max_length(captions_list):
return max(len(caption.split()) for caption in captions_list)
def predict_caption(model, image, tokenizer, max_length):
in_text = 'startseq'
for i in range(max_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = pad_sequences([sequence], maxlen=max_length, padding='post')
yhat = model.predict([image, sequence], verbose=0)
yhat = np.argmax(yhat)
word = idx_to_word(yhat, tokenizer)
if word is None:
break
in_text += " " + word
if word == 'endseq':
break
return in_text
def generate_caption(image_path,vgg16_model,model,tokenizer):
features_image = extract_image_features_one(vgg16_model, image_path)
if features_image is None:
print("Error: No features extracted from the image.")
y_pred = predict_caption(model, features_image, tokenizer, 18)
return y_pred
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