def main(_):
model = ShowAndTellModel(FLAGS.model_path)
vocab = Vocabulary(FLAGS.vocab_file)
filenames = _load_filenames()
can1 = "a table with different kinds of food"
candidate = can1.split()
generator = CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(image)
print("Captions: ")
for i, caption in enumerate(captions):
sentence = [vocab.id_to_token(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
temp = " %d) %s (p=%f)" % (i + 1, sentence,
math.exp(caption.logprob))
print(temp)
comp = [sentence.split()]
# Calculating The Blue Score
print('Blue cumulative 1-gram: %f' %
sentence_bleu(comp, candidate, weights=(1, 0, 0, 0)))
print('Blue cumulative 2-gram: %f' %
sentence_bleu(comp, candidate, weights=(0.5, 0.5, 0, 0)))
# Glue Score
G = gleu.sentence_gleu(comp, candidate, min_len=1, max_len=2)
print("Glue score for this sentence: {}".format(G))
def main(_):
model = ShowAndTellModel(FLAGS.model_path)
vocab = Vocabulary(FLAGS.vocab_file)
filenames = _load_filenames()
generator = CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end tokens <S> and </S>.
sentence = [vocab.id_to_token(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
def main(_):
model = ShowAndTellModel(FLAGS.model_path)
vocab = Vocabulary(FLAGS.vocab_file)
filenames = _load_filenames()
generator = CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end tokens <S> and </S>.
sentence = [vocab.id_to_token(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
if i == 0:
# f1.write("%s \n" % sentence)
print(
"this is---",
os.path.basename(filename)
[:os.path.basename(filename).find(".")], sentence)
idcol.append(
os.path.basename(filename)
[:os.path.basename(filename).find(".")])
predol1.append(sentence)
if i == 1:
predol2.append(sentence)
if i == 2:
predol3.append(sentence)
if i == 3:
predol4.append(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
print(idcol, predol1)
predDF = pd.DataFrame()
predDF['id'] = idcol
predDF['pred_caption1'] = predol1
predDF['pred_caption2'] = predol2
predDF['pred_caption3'] = predol3
predDF['pred_caption4'] = predol4
predDF.to_csv('MpredCapt.csv')
def main(input_files_):
input_files = input_files_
model = ShowAndTellModel(model_path_) #tf.app.flags.model_path)) #
# vocab = Vocabulary(vocab_file_)#tf.app.flags.vocab_file
filenames = _load_filenames(input_files_)
generator = CaptionGenerator(model) #, vocab)
encodings = []
first = True
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
encoding = generator.beam_search(image)
if (first):
encodings = encoding
first = False
else:
encodings = np.concatenate((encodings, encoding), axis=0)
return encodings
def main(_):
model = ShowAndTellModel(FLAGS.model_path)
vocab = Vocabulary(FLAGS.vocab_file)
filenames = _load_filenames()
generator = CaptionGenerator(model, vocab)
with open('C:\BigDataAnalyticsAppns\Tutorial 6 Source Code\medium-show-and-tell-caption-generator-master\etc\pred.txt', 'a') as f1:
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end tokens <S> and </S>.
sentence = [vocab.id_to_token(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
if i == 1:
f1.write("%s \n" % sentence)
# print("this is---", sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
file = f.read()
if file:
captions = generator.beam_search(file)
results = []
for i, caption in enumerate(captions):
# Ignore begin and end tokens <S> and </S>.
sentence = [vocab.id_to_token(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
# print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
results.append({
'i': i,
'sentence': sentence,
'logprob': math.exp(caption.logprob)
})
return jsonify(results)
else:
return 'No file provided', 400
if __name__ == "__main__":
model_path = os.path.join(current_dir,
"../etc/show-and-tell.pb") #FLAGS.model_path
vocab_file = os.path.join(current_dir,
"../etc/word_counts.txt") #FLAGS.model_path
model = ShowAndTellModel(model_path)
vocab = Vocabulary(vocab_file)
generator = CaptionGenerator(model, vocab)
# tf.app.run()
app.run(host='0.0.0.0')
