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predict_realtime.py
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predict_realtime.py
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import functools
import operator
import os
import cv2
import time
import numpy as np
import extract_features
import config
import model
class VideoDescriptionRealTime(object):
"""
Initialize the parameters for the model
"""
def __init__(self, config):
self.latent_dim = config.latent_dim
self.num_encoder_tokens = config.num_encoder_tokens
self.num_decoder_tokens = config.num_decoder_tokens
self.time_steps_encoder = config.time_steps_encoder
self.max_probability = config.max_probability
# models
self.tokenizer, self.inf_encoder_model, self.inf_decoder_model = model.inference_model()
self.inf_decoder_model = None
self.save_model_path = config.save_model_path
self.test_path = config.test_path
self.search_type = config.search_type
self.num = 0
def greedy_search(self, loaded_array):
"""
:param f: the loaded numpy array after creating videos to frames and extracting features
:return: the final sentence which has been predicted greedily
"""
inv_map = self.index_to_word()
states_value = self.inf_encoder_model.predict(loaded_array.reshape(-1, 80, 4096))
target_seq = np.zeros((1, 1, 1500))
final_sentence = ''
target_seq[0, 0, self.tokenizer.word_index['bos']] = 1
for i in range(15):
output_tokens, h, c = self.inf_decoder_model.predict([target_seq] + states_value)
states_value = [h, c]
output_tokens = output_tokens.reshape(self.num_decoder_tokens)
y_hat = np.argmax(output_tokens)
if y_hat == 0:
continue
if inv_map[y_hat] is None:
break
if inv_map[y_hat] == 'eos':
break
else:
final_sentence = final_sentence + inv_map[y_hat] + ' '
target_seq = np.zeros((1, 1, 1500))
target_seq[0, 0, y_hat] = 1
return final_sentence
def decode_sequence2bs(self, input_seq):
states_value = self.inf_encoder_model.predict(input_seq)
target_seq = np.zeros((1, 1, self.num_decoder_tokens))
target_seq[0, 0, self.tokenizer.word_index['bos']] = 1
self.beam_search(target_seq, states_value, [], [], 0)
return decode_seq
def beam_search(self, target_seq, states_value, prob, path, lens):
"""
:param target_seq: the array that is fed into the model to predict the next word
:param states_value: previous state that is fed into the lstm cell
:param prob: probability of predicting a word
:param path: list of words from each sentence
:param lens: number of words
:return: final sentence
"""
global decode_seq
node = 2
output_tokens, h, c = self.inf_decoder_model.predict(
[target_seq] + states_value)
output_tokens = output_tokens.reshape(self.num_decoder_tokens)
sampled_token_index = output_tokens.argsort()[-node:][::-1]
states_value = [h, c]
for i in range(node):
if sampled_token_index[i] == 0:
sampled_char = ''
else:
sampled_char = list(self.tokenizer.word_index.keys())[
list(self.tokenizer.word_index.values()).index(sampled_token_index[i])]
MAX_LEN = 12
if sampled_char != 'eos' and lens <= MAX_LEN:
p = output_tokens[sampled_token_index[i]]
if sampled_char == '':
p = 1
prob_new = list(prob)
prob_new.append(p)
path_new = list(path)
path_new.append(sampled_char)
target_seq = np.zeros((1, 1, self.num_decoder_tokens))
target_seq[0, 0, sampled_token_index[i]] = 1.
self.beam_search(target_seq, states_value, prob_new, path_new, lens + 1)
else:
p = output_tokens[sampled_token_index[i]]
prob_new = list(prob)
prob_new.append(p)
p = functools.reduce(operator.mul, prob_new, 1)
if p > self.max_probability:
decode_seq = path
self.max_probability = p
def decoded_sentence_tuning(self, decoded_sentence):
# tuning sentence
decode_str = []
filter_string = ['bos', 'eos']
uni_gram = {}
last_string = ""
for idx2, c in enumerate(decoded_sentence):
if c in uni_gram:
uni_gram[c] += 1
else:
uni_gram[c] = 1
if last_string == c and idx2 > 0:
continue
if c in filter_string:
continue
if len(c) > 0:
decode_str.append(c)
if idx2 > 0:
last_string = c
return decode_str
def index_to_word(self):
# inverts word tokenizer
index_to_word = {value: key for key, value in self.tokenizer.word_index.items()}
return index_to_word
def get_test_data(self):
# loads the features array
file_list = os.listdir(os.path.join(self.test_path, 'video'))
# with open(os.path.join(self.test_path, 'testing.txt')) as testing_file:
# lines = testing_file.readlines()
# file_name = lines[self.num].strip()
file_name = file_list[self.num]
path = os.path.join(self.test_path, 'feat', file_name + '.npy')
if os.path.exists(path):
f = np.load(path)
else:
model = extract_features.model_cnn_load()
f = extract_features.extract_features(file_name, model)
if self.num < len(file_list):
self.num += 1
else:
self.num = 0
return f, file_name
def test(self):
X_test, filename = self.get_test_data()
# generate inference test outputs
if self.search_type == 'greedy':
sentence_predicted = self.greedy_search(X_test.reshape((-1, 80, 4096)))
else:
sentence_predicted = ''
decoded_sentence = self.decode_sequence2bs(X_test.reshape((-1, 80, 4096)))
decode_str = self.decoded_sentence_tuning(decoded_sentence)
for d in decode_str:
sentence_predicted = sentence_predicted + d + ' '
# re-init max prob
self.max_probability = -1
return sentence_predicted, filename
def main(self, filename, caption):
"""
:param filename: the video to load
:param caption: final caption
:return:
"""
# 1. Initialize reading video object
cap1 = cv2.VideoCapture(os.path.join(self.test_path, 'video', filename))
cap2 = cv2.VideoCapture(os.path.join(self.test_path, 'video', filename))
caption = '[' + ' '.join(caption.split()[1:]) + ']'
# 2. Cycle through pictures
while cap1.isOpened():
ret, frame = cap2.read()
ret2, frame2 = cap1.read()
if ret:
imS = cv2.resize(frame, (480, 300))
cv2.putText(imS, caption, (100, 270), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0),
2, cv2.LINE_4)
cv2.imshow("VIDEO CAPTIONING", imS)
if ret2:
imS = cv2.resize(frame, (480, 300))
cv2.imshow("ORIGINAL", imS)
else:
break
# Quit playing
key = cv2.waitKey(25)
if key == 27: # Button esc
break
# 3. Free resources
cap1.release()
cap2.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
video_to_text = VideoDescriptionRealTime(config)
while True:
print('.........................\nGenerating Caption:\n')
start = time.time()
video_caption, file = video_to_text.test()
end = time.time()
sentence = ''
print(sentence)
for text in video_caption.split():
sentence = sentence + ' ' + text
print('\n.........................\n')
print(sentence)
print('\n.........................\n')
print('It took {:.2f} seconds to generate caption'.format(end-start))
video_to_text.main(file, sentence)
play_video = input('Should I play the video? ')
if play_video.lower() == 'y':
continue
elif play_video.lower() == 'n':
break
else:
print('Could not understand type (y) for yes and (n) for no')
continue