def demoImage(i):
img=mpimg.imread('images/anh'+str(i)+'.jpg')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
captions=generate_new_caption(vgg_feat[i-1])
for caption in captions:
speak.Speak(utils.listToSentence(caption['sen'][1:]))
def captionImage(image_path):
img=mpimg.imread(image_path)
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
img_feat=extract_feautures.get_vgg_feat(image_path)
captions=generate_new_caption(img_feat)
for caption in captions:
speak.Speak(utils.listToSentence(caption['sen'][1:]))
def generate_new_caption( img_feat):
tf.reset_default_graph()
config=utils.Config()
model_gen=model.Model(config,1)
save_file =path+ '/weight_model/'+model_gen.config.model_name+'/train_model.ckpt'
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, save_file)
captions=model_gen.generate_caption(sess,img_feat)[0:4]
for caption in captions:
print(utils.listToSentence(caption['sen']), 'score: ', caption['score'])
return captions
def test_modal(start,end):
tf.reset_default_graph()
config=utils.Config()
model_test=model.Model(config,1)
save_file =path+ '/weight_model/'+model_test.config.model_name+'/train_model.ckpt'
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, save_file)
for i in range(200000)[start:end]:
img=mpimg.imread(path+'/data/'+materialwv.dataset+'_Dataset/'+materialwv.getImageLink(i))
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
captions=model_test.generate_caption(sess,materialwv.getImageFeat(i))[0:4]
for caption in captions:
print(str(i),utils.listToSentence(caption['sen']), 'score: ', caption['score'])
def gen_test_caption():
tf.reset_default_graph()
config=utils.Config()
model_test=model.Model(config,1)
save_file =path+ '/weight_model/'+model_test.config.model_name+'/train_model.ckpt'
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, save_file)
file=open(materialwv.result_path+"result_"+"beam"+str(utils.beam_size)+".txt","w")
start = time.time()
for i in range(200000)[utils.total_train_img+utils.total_validate_img:utils.total_train_img+utils.total_validate_img+utils.total_test_img]:
captions=model_test.generate_caption(sess,materialwv.getImageFeat(i))[0]['sen']
caption=utils.listToSentence(captions[1:len(captions)-1])
file.write(caption+"\n")
if i%100==0:
print(i, "time",str(time.time()-start), caption)
start=time.time()
file.close()