def read_data(self, list_of_videos):
T_o = self.T_o
T_a = self.T_a
max_sq_len = 0
totLabels= []
for vid in list_of_videos:
start_To=0
file_ptr = open(vid, 'r')
content = file_ptr.read().split()[:-1]
#a scalar value used in normalizing the input length,
#the paper specify that should be eaulq to the mean number of action per video
T = (1.0/self.alpha)*len(content)
#label_seq, length_seq = get_label_length_seq(content)
while start_To+T_o+T_a < len(content):
obs = content[start_To:start_To+T_o]
target = content[start_To+T_o+T_a]
label_seq, length_seq = get_label_length_seq(obs)
#What i'm actually obeserving. p_seq is a matrix of 48 columns like classes + 1 where it stores the lenght of the action
#Morover it has 25 rows beacuse the max number in a video is 25 for breakfast
p_seq = []
seq_len = 0
for i in label_seq:
p_seq.append(np.zeros((self.nClasses+1)))
#Lenght of the action obeserved
p_seq[-1][-1] = length_seq[label_seq.index(i)]/T
#1-hot encoding
p_seq[-1][self.actions_dict[i]] = 1
seq_len += 1
if i not in totLabels:
totLabels.append(i)
#padding to zero every other free slot
if(seq_len > max_sq_len):
max_sq_len = seq_len
for j in range(self.max_seq_sz - seq_len):
p_seq.append(np.zeros((self.nClasses+1)))
#The target is gonna be the next action in 1-hot fashion
#label_target,_= get_label_length_seq(target)
p_tar = np.zeros((self.nClasses))
p_tar[ self.actions_dict[target]] = 1
example = [p_seq, p_tar]
self.list_of_examples.append(example)
start_To = start_To+T_a
random.shuffle(self.list_of_examples)
return
def predict(self, sess, model_save_path, input_x, sigma, actions_dict):
self.saver.restore(sess, model_save_path)
result = sess.run([self.prediction], feed_dict={self.input_vid: input_x})[0]
result = np.reshape(result,[self.nRows, 48])
result = self.__post_process(result, sigma)
output = []
for i in range(len(result)):
output.append(actions_dict.keys()[actions_dict.values().index(np.argmax(result[i]))])
label_seq, length_seq = get_label_length_seq(output)
return label_seq, length_seq
recog_sq[str(init)] = ["start"]
for init in range(8):
target_sq[str(init)] = ["start"]
T = (1.0 / args.alpha) * vid_len
while start_To + S_enc_frame + S_ant_frame + beta_frame < len(
content):
k = 0
for stride in range(
start_To + S_enc_frame + beta_frame,
start_To + S_enc_frame + S_ant_frame + beta_frame,
beta_frame):
obs = content[start_To:start_To + stride]
target = content[start_To + S_enc_frame + S_ant_frame +
beta_frame]
label_seq, length_seq = get_label_length_seq(obs)
#What i'm actually obeserving. p_seq is a matrix of 48 columns like classes + 1 where it stores the lenght of the action
#Morover it has 25 rows beacuse the max number in a video is 25 for breakfast
p_seq = []
seq_len = 0
for i in label_seq:
p_seq.append(np.zeros((nClasses + 1)))
#Lenght of the action obeserved
p_seq[-1][-1] = length_seq[label_seq.index(i)] / T
#1-hot encoding
p_seq[-1][actions_dict[i]] = 1
seq_len += 1
#padding to zero every other free slot
if (seq_len > max_sq_len):
max_sq_len = seq_len
for j in range(args.max_seq_sz - seq_len):
for obs_p in obs_percentages:
if args.input_type == "decoded":
file_ptr = open(args.decoded_path+"/obs"+str(obs_p)+"/"+f_name+'.txt', 'r')
observed_content = file_ptr.read().split('\n')[:-1]
vid_len = int(len(observed_content)/obs_p)
elif args.input_type == "gt":
observed_content = content[:int(obs_p*vid_len)]
T = (1.0/args.alpha)*vid_len
for pred_p in pred_percentages:
pred_len = int(pred_p*vid_len)
output_len = pred_len + len(observed_content)
label_seq, length_seq = get_label_length_seq(observed_content)
with tf.Session() as sess:
label_seq, length_seq = model.predict(sess, model_restore_path, pred_len, label_seq, length_seq, actions_dict, T)
recognition = []
for i in range(len(label_seq)):
recognition = np.concatenate((recognition, [label_seq[i]]*int(length_seq[i])))
recognition = recognition[:output_len]
#write results to file
f_name = vid.split('/')[-1].split('.')[0]
path=args.results_save_path+"/obs"+str(obs_p)+"-pred"+str(pred_p)
write_predictions(path, f_name, recognition)
elif args.model == "cnn":
model = ModelCNN(args.nRows, nClasses)
n_T = np.zeros(len(classes))
n_F = np.zeros(len(classes))
totalGT = []
totalRecog = []
IoU = 0
index_act = int(args.nactions)
div = 0
for filename in filelist:
gt, recog = read_sequences(
filename, args.ground_truth_path,
obs_percentage) # gt e recog hano la stessa lunghezza
if (recog != 0):
label_seq_gt, length_seq_gt = get_label_length_seq(gt)
label_seq_recog, length_seq_recog = get_label_length_seq(recog)
gt_dict = get_label_dict(gt)
recog_dict = get_label_dict(recog)
if (len(label_seq_gt) >= index_act):
act = label_seq_gt[index_act - 1]
start_gt = gt_dict[index_act - 1][1]
stop_gt = gt_dict[index_act - 1][2]
# if(start_gt!=0):
# print("ciao")
# gt_dict = get_label_dict(recog_dict)
act_recog = recog[start_gt:stop_gt]
observed_content = content[:int(
obs_p * vid_len)] # observed_actions_per_frame
unobserved_content = content[int(obs_p * vid_len):]
T = (1.0 / args.alpha) * vid_len
pred_percentages = [.1, .2, .3, .5, .7, .8]
pred_percentages = [
pred_p for pred_p in pred_percentages
if obs_p + pred_p <= 1.0
]
for pred_p in pred_percentages:
pred_len = int(pred_p * vid_len) # num_frames_to_predict
output_len = pred_len + len(
observed_content) # write out observed + predictions
label_seq, length_seq = get_label_length_seq(
observed_content)
obs_label_seq, obs_length_seq = list(label_seq), list(
length_seq)
num_obs_actions = len(obs_label_seq)
with tf.Session() as sess:
label_seq, length_seq = model.predict(
sess, model_restore_path, pred_len, label_seq,
length_seq, actions_dict, T)
recognition = []
for i in range(len(label_seq)):
recognition = np.concatenate(
(recognition, [label_seq[i]] * int(length_seq[i])))
recognition = recognition[:output_len]
# write results to file
f_name = vid.split('/')[-1].split('.')[0]
