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generate_disp.py
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generate_disp.py
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import argparse
import os
import numpy as np
import scipy.misc as ssc
import kitti_util
def generate_dispariy_from_velo(pc_velo, height, width, calib):
pts_2d = calib.project_velo_to_image(pc_velo)
fov_inds = (pts_2d[:, 0] < width - 1) & (pts_2d[:, 0] >= 0) & \
(pts_2d[:, 1] < height - 1) & (pts_2d[:, 1] >= 0)
fov_inds = fov_inds & (pc_velo[:, 0] > 2)
imgfov_pc_velo = pc_velo[fov_inds, :]
imgfov_pts_2d = pts_2d[fov_inds, :]
imgfov_pc_rect = calib.project_velo_to_rect(imgfov_pc_velo)
depth_map = np.zeros((height, width)) - 1
imgfov_pts_2d = np.round(imgfov_pts_2d).astype(int)
for i in range(imgfov_pts_2d.shape[0]):
depth = imgfov_pc_rect[i, 2]
depth_map[int(imgfov_pts_2d[i, 1]), int(imgfov_pts_2d[i, 0])] = depth
baseline = 0.54
disp_map = (calib.f_u * baseline) / depth_map
return disp_map
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Generate Disparity')
parser.add_argument('--data_path', type=str, default='~/Kitti/object/training/')
parser.add_argument('--split_file', type=str, default='~/Kitti/object/train.txt')
args = parser.parse_args()
assert os.path.isdir(args.data_path)
lidar_dir = args.data_path + '/velodyne/'
calib_dir = args.data_path + '/calib/'
image_dir = args.data_path + '/image_2/'
disparity_dir = args.data_path + '/disparity/'
assert os.path.isdir(lidar_dir)
assert os.path.isdir(calib_dir)
assert os.path.isdir(image_dir)
if not os.path.isdir(disparity_dir):
os.makedirs(disparity_dir)
lidar_files = [x for x in os.listdir(lidar_dir) if x[-3:] == 'bin']
lidar_files = sorted(lidar_files)
assert os.path.isfile(args.split_file)
with open(args.split_file, 'r') as f:
file_names = [x.strip() for x in f.readlines()]
for fn in lidar_files:
predix = fn[:-4]
if predix not in file_names:
continue
calib_file = '{}/{}.txt'.format(calib_dir, predix)
calib = kitti_util.Calibration(calib_file)
# load point cloud
lidar = np.fromfile(lidar_dir + '/' + fn, dtype=np.float32).reshape((-1, 4))[:, :3]
image_file = '{}/{}.png'.format(image_dir, predix)
image = ssc.imread(image_file)
height, width = image.shape[:2]
disp = generate_dispariy_from_velo(lidar, height, width, calib)
np.save(disparity_dir + '/' + predix, disp)
print('Finish Disparity {}'.format(predix))