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testing_caption_generator.py
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testing_caption_generator.py
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from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.applications.xception import Xception
from keras.models import load_model
from pickle import load
import numpy as np
import cv2
import matplotlib.pyplot as plt
import argparse
def extract_features(filename, model):
print("File Path: \"" + filename + "\"")
try:
image = cv2.imread(filename, -1)
except:
print("ERROR: Couldn't open image! Make sure the image path and extension is correct.")
image = cv2.resize(image, (299, 299))
image = np.array(image)
# For images that has 4 channels, we convert them into 3 channels
if image.shape[2] == 4:
image = image[..., :3]
image = np.expand_dims(image, axis=0)
image = image / 127.5
image = image - 1.0
feature = model.predict(image)
return feature
def word_for_id(integer, tokenizer):
for word, index in tokenizer.word_index.items():
if index == integer:
return word
return None
def generate_desc(model, tokenizer, photo, max_length):
in_text = 'start'
for i in range(max_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = pad_sequences([sequence], maxlen=max_length)
pred = model.predict([photo,sequence], verbose=0)
pred = np.argmax(pred)
word = word_for_id(pred, tokenizer)
if word is None:
break
in_text += ' ' + word
if word == 'end':
break
return in_text
def main():
# Example image: 'flickr8k-dataset/111537222_07e56d5a30.jpg'
# Command: python testing_caption_generator.py -i ./flickr8k-dataset/111537222_07e56d5a30.jpg
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--image', required=True, help="Image Path")
args = vars(parser.parse_args())
img_path = args['image']
max_length = 32
tokenizer = load(open("tokenizer.p","rb"))
model = load_model('models/model_9.h5')
xception_model = Xception(include_top=False, pooling="avg")
photo = extract_features(img_path, xception_model)
img = cv2.imread(img_path, 0)
description = generate_desc(model, tokenizer, photo, max_length)
if description != 'start':
description = description[6:]
if description[-3:] == 'end':
description = description[:-3]
print("\n")
print("Caption: " + description)
plt.imshow(img)
if __name__ == '__main__':
main()