def read_data(self, list_of_videos):
for vid in list_of_videos:
file_ptr = open(vid, 'r')
content = file_ptr.read().split('\n')[:-1]
obs_perc = [.1, .2, .3, .5]
for i in range(len(obs_perc)):
observed_content = content[:int(obs_perc[i]*len(content))]
input_vid = encode_content(observed_content, self.nRows, self.nCols, self.actions_dict)
input_vid = np.reshape(input_vid, [self.nRows, self.nCols, 1])
target_content = content[int(obs_perc[i]*len(content)):int((0.5+obs_perc[i])*len(content))]
target = encode_content(target_content, self.nRows, self.nCols, self.actions_dict)
target = np.reshape(target, [self.nRows, self.nCols, 1])
example = [input_vid, target]
self.list_of_examples.append(example)
random.shuffle(self.list_of_examples)
return
observed_content=[]
vid_len = 0
if args.input_type == "gt":
file_ptr = open(vid, 'r')
content = file_ptr.read().split('\n')[:-1]
vid_len = len(content)
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)]
input_x = encode_content(observed_content, args.nRows, nClasses, actions_dict)
input_x = [np.reshape(input_x, [args.nRows, nClasses, 1])]
with tf.Session() as sess:
label_seq, length_seq = model.predict(sess, model_restore_path, input_x, args.sigma, actions_dict)
recognition = []
for i in range(len(label_seq)):
recognition = np.concatenate((recognition, [label_seq[i]]*int(0.5*vid_len*length_seq[i]/args.nRows)))
recognition = np.concatenate((observed_content,recognition))
diff = int((0.5+obs_p)*vid_len)-len(recognition)
for i in range(diff):
recognition = np.concatenate((recognition, [label_seq[-1]]))
#write results to file
for pred_p in pred_percentages:
path=args.results_save_path+"/obs"+str(obs_p)+"-pred"+str(pred_p)