def write_dict(self, dict, base_path):
cleared_direcories = []
for key, value in dict.items():
path = os.path.join(self.f_prefix, base_path, key[0])
ext_removed_file_name = remove_file_extention(key[1])
file_name = ext_removed_file_name + "_" + str(key[2])
file_name = add_file_extention(file_name, 'txt')
self.dataloader.write_dataset(value, file_name, path)
def main():
parser = argparse.ArgumentParser()
# frame rate of video
parser.add_argument('--frame', type=int, default=1,
help='Frame of video created from plots')
# gru model
parser.add_argument('--gru', action="store_true", default=False,
help='Visualization of GRU model')
# number of validation dataset
parser.add_argument('--num_of_data', type=int, default=3,
help='Number of validation data will be visualized (If 0 is given, will work on test data mode)')
# drive support
parser.add_argument('--drive', action="store_true", default=False,
help='Use Google drive or not')
# minimum lenght of trajectory
parser.add_argument('--min_traj', type=int, default=3,
help='Min. treshold of number of frame to be removed from a sequence')
# percentage of peds will be taken for each frame
parser.add_argument('--max_ped_ratio', type=float, default=0.8,
help='Percentage of pedestrian will be illustrated in a plot for a sequence')
# maximum ped numbers
parser.add_argument('--max_target_ped', type=int, default=20,
help='Maximum number of peds in final plot')
# method to be visualized
parser.add_argument('--method', type=int, default=1,
help='Method of lstm will be used (1 = social lstm, 2 = obstacle lstm, 3 = vanilla lstm)')
# Parse the parameters
args = parser.parse_args()
prefix = ''
f_prefix = '.'
if args.drive is True:
prefix='drive/semester_project/social_lstm_final/'
f_prefix = 'drive/semester_project/social_lstm_final'
model_name = "LSTM"
method_name = get_method_name(args.method)
if args.gru:
model_name = "GRU"
plot_file_directory = 'validation'
# Directories
if args.num_of_data is 0:
plot_file_directory = 'test'
# creation of paths
save_plot_directory = os.path.join(f_prefix, 'plot',method_name, model_name,'plots/')
plot_directory = os.path.join(f_prefix, 'plot', method_name, model_name, plot_file_directory)
video_directory = os.path.join(f_prefix, 'plot',method_name, model_name,'videos/')
plot_file_name = get_all_file_names(plot_directory)
num_of_data = np.clip(args.num_of_data, 0, len(plot_file_name))
plot_file_name = random.sample(plot_file_name, num_of_data)
for file_index in range(len(plot_file_name)):
file_name = plot_file_name[file_index]
folder_name = remove_file_extention(file_name)
print("Now processing: ", file_name)
file_path = os.path.join(plot_directory, file_name)
video_save_directory = os.path.join(video_directory, folder_name)
figure_save_directory = os.path.join(save_plot_directory, folder_name)
# remove existed plots
clear_folder(video_save_directory)
clear_folder(figure_save_directory)
if not os.path.exists(video_save_directory):
os.makedirs(video_save_directory)
if not os.path.exists(figure_save_directory):
os.makedirs(figure_save_directory)
try:
f = open(file_path, 'rb')
except FileNotFoundError:
print("File not found: %s"%file_path)
continue
results = pickle.load(f)
result_arr = np.array(results)
true_trajectories = np.array(result_arr[:,0])
pred_trajectories = np.array(result_arr[:,1])
frames = np.array(result_arr[:, 4])
target_id_trajs = []
args.max_target_ped = np.clip(args.max_target_ped, 0, len(results)-1)
min_r = -10
max_r = 10
plot_offset = 1
for i in range(len(results)):
print("##########################################################################################")
name = 'sequence' + str(i).zfill(5)
print("Now processing seq: ",name)
if args.num_of_data is 0: #test data visualization
target_traj = plot_trajectories(results[i][0], results[i][1], results[i][2], results[i][3], results[i][4], name, figure_save_directory, args.min_traj ,args.max_ped_ratio, results[i][5], [min_r, max_r, plot_offset], results[i][6])
else:
target_traj = plot_trajectories(results[i][0], results[i][1], results[i][2], results[i][3],results[i][4], name, figure_save_directory, args.min_traj ,args.max_ped_ratio, results[i][5], [min_r, max_r, plot_offset], 20)
target_traj.append(results[i][2])#pedlist
target_traj.append(results[i][3])#lookup
target_id_trajs.append(target_traj)
save_video(figure_save_directory, video_save_directory, plot_file_name[file_index], args.frame)
plot_target_trajs(target_id_trajs, figure_save_directory, args.max_target_ped, plot_offset)