def run_deep_dream_simple(self,
img,
steps = 100,
learning_rate = 1.0,
update_frequency = 5):
updates = []
progress_bar = ProgressBar(steps)
progress_bar.start()
steps_remaining = steps
steps_done = 0
while steps_remaining:
if steps_remaining>update_frequency:
run_steps = tf.constant(update_frequency)
else:
run_steps = tf.constant(steps_remaining)
steps_remaining -= run_steps
steps_done += run_steps
img = self(img, run_steps, tf.constant(learning_rate))
updates.append(img.numpy())
progress_bar.update(steps_done)
return img, updates
vel = './velodyne/'
img = './image_2/'
cal = './calib/'
lab = './label_2/'
listOfGTFiles = [("%06d" % int(f.split("_")[0]))
for f in os.listdir("../gt_database/")
if os.stat("../gt_database/" + f).st_size == 0]
#listOfAllGTFiles = [("%06d" % int(f.split("_")[0])) for f in os.listdir("../gt_database/") ]
#listOfFiles = [ f[:-4] for f in os.listdir("./calib/") if f[:-4] not in listOfAllGTFiles ]
print("Removing bad data. This may take several minutes.")
# removing bad data
prog_bar1 = ProgressBar()
prog_bar1.start(len(listOfGTFiles)) # +len(listOfFiles))
for f in listOfGTFiles:
os.system("rm -f " + vel + f + ".bin ")
os.system("rm -f " + img + f + ".jpg ")
os.system("rm -f " + cal + f + ".txt ")
os.system("rm -f " + lab + f + ".txt ")
prog_bar1.print_bar()
#for f in listOfFiles:
# os.system("rm -f "+vel+f+".bin ")
# os.system("rm -f "+img+f+".jpg ")
# os.system("rm -f "+cal+f+".txt ")
# os.system("rm -f "+lab+f+".txt ")
# prog_bar1.print_bar()
# indexing
