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export_gt_depth.py
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export_gt_depth.py
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from __future__ import absolute_import, division, print_function
import os
import argparse
import numpy as np
import PIL.Image as pil
import cv2
from pathlib import Path
from utils import readlines
from kitti_utils import generate_depth_map
def export_gt_depths_kitti(opt):
"""
Generate ground-truth data and store as .npz file
"""
split_folder = os.path.join(os.path.dirname(__file__), "splits", opt.split)
lines = readlines(os.path.join(split_folder, "test_files.txt"))
print("Exporting ground truth depths for {}".format(opt.split))
gt_depths = []
for line in lines:
folder, frame_id, _ = line.split()
frame_id = int(frame_id)
if opt.split == "eigen":
calib_dir = os.path.join(opt.data_path, folder.split("/")[0])
velo_filename = os.path.join(opt.data_path, folder,
"velodyne_points/data", "{:010d}.bin".format(frame_id))
gt_depth = generate_depth_map(calib_dir, velo_filename, 2, True)
elif opt.split == "eigen_benchmark":
gt_depth_path = os.path.join(opt.data_path, folder, "proj_depth",
"groundtruth", "image_02", "{:010d}.png".format(frame_id))
gt_depth = np.array(pil.open(gt_depth_path)).astype(np.float32) / 256
gt_depths.append(gt_depth.astype(np.float32))
output_path = os.path.join(split_folder, "gt_depths.npz")
print("Saving to {}".format(opt.split))
np.savez_compressed(output_path, data=np.array(gt_depths))
def export_gt_depths_cityscapes(opt):
"""
Load ground-truth in the dataset an store as .npz file
"""
split_folder = os.path.join(os.path.dirname(__file__), "splits")
gt_depths = []
print("Exporting ground truth depths for {}".format(opt.dataset))
folder_path = opt.data_path
all_imgs = sorted(list(Path(folder_path).glob('**/*.png')))
for line in all_imgs:
# gt_depth_path = os.path.join(opt.data_path, line)
gt_depth = cv2.imread(str(line), cv2.IMREAD_UNCHANGED)
gt_depth = (cv2.resize(gt_depth, (1242, 375), cv2.INTER_AREA))
gt_depths.append(gt_depth.astype(np.float32))
output_path = os.path.join(split_folder, "gt_depths_cityscapes.npz")
print("Saving to {}".format(output_path))
np.savez_compressed(output_path, data=np.array(gt_depths))
def main():
parser = argparse.ArgumentParser(description='export_gt_depth')
parser.add_argument('--data_path',
type=str,
help='path to the root of the KITTI data',
required=True)
parser.add_argument('--dataset',
type=str,
help='which split to export gt from',
required=True,
choices=["kitti", "cityscapes"])
parser.add_argument('--split',
type=str,
help='which split to export gt from',
choices=["eigen", "eigen_benchmark"])
opt = parser.parse_args()
if opt.dataset == 'kitti':
export_gt_depths_kitti(opt)
elif opt.dataset == 'cityscapes':
export_gt_depths_cityscapes(opt)
if __name__ == "__main__":
main()