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