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Running
on
Zero
File size: 4,468 Bytes
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import os
import numpy as np
import os.path as osp
from PIL import Image
from tqdm import tqdm
import csv
import imageio
def depth_read(filename):
# loads depth map D from png file
# and returns it as a numpy array,
depth_png = np.array(Image.open(filename), dtype=int)
# make sure we have a proper 16bit depth map here.. not 8bit!
assert np.max(depth_png) > 255
depth = depth_png.astype(np.float64) / 256.0
depth[depth_png == 0] = -1.0
return depth
def extract_kitti(
root,
depth_root,
sample_len=-1,
csv_save_path="",
datatset_name="",
saved_rgb_dir="",
saved_disp_dir="",
start_frame=0,
end_frame=110,
):
scenes_names = os.listdir(depth_root)
all_samples = []
for i, seq_name in enumerate(tqdm(scenes_names)):
all_img_names = os.listdir(
osp.join(depth_root, seq_name, "proj_depth/groundtruth/image_02")
)
all_img_names = [x for x in all_img_names if x.endswith(".png")]
print(f"sequence frame number: {len(all_img_names)}")
all_img_names.sort()
all_img_names = sorted(all_img_names, key=lambda x: int(x.split(".")[0][-4:]))
all_img_names = all_img_names[start_frame:end_frame]
seq_len = len(all_img_names)
step = sample_len if sample_len > 0 else seq_len
for ref_idx in range(0, seq_len, step):
print(f"Progress: {seq_name}, {ref_idx // step + 1} / {seq_len//step}")
video_imgs = []
video_depths = []
if (ref_idx + step) <= seq_len:
ref_e = ref_idx + step
else:
continue
for idx in range(ref_idx, ref_e):
im_path = osp.join(
root, seq_name[0:10], seq_name, "image_02/data", all_img_names[idx]
)
depth_path = osp.join(
depth_root,
seq_name,
"proj_depth/groundtruth/image_02",
all_img_names[idx],
)
depth = depth_read(depth_path)
disp = depth
video_depths.append(disp)
video_imgs.append(np.array(Image.open(im_path)))
disp_video = np.array(video_depths)[:, None]
img_video = np.array(video_imgs)[..., 0:3]
def even_or_odd(num):
if num % 2 == 0:
return num
else:
return num - 1
height = disp_video.shape[-2]
width = disp_video.shape[-1]
height = even_or_odd(height)
width = even_or_odd(width)
disp_video = disp_video[:, :, 0:height, 0:width]
img_video = img_video[:, 0:height, 0:width]
data_root = saved_rgb_dir + datatset_name
disp_root = saved_disp_dir + datatset_name
os.makedirs(data_root, exist_ok=True)
os.makedirs(disp_root, exist_ok=True)
img_video_dir = data_root
disp_video_dir = disp_root
img_video_path = os.path.join(img_video_dir, f"{seq_name}_rgb_left.mp4")
disp_video_path = os.path.join(disp_video_dir, f"{seq_name}_disparity.npz")
imageio.mimsave(
img_video_path, img_video, fps=15, quality=10, macro_block_size=1
)
np.savez(disp_video_path, disparity=disp_video)
sample = {}
sample["filepath_left"] = os.path.join(f"KITTI/{seq_name}_rgb_left.mp4")
sample["filepath_disparity"] = os.path.join(
f"KITTI/{seq_name}_disparity.npz"
)
all_samples.append(sample)
filename_ = csv_save_path
os.makedirs(os.path.dirname(filename_), exist_ok=True)
fields = ["filepath_left", "filepath_disparity"]
with open(filename_, "w") as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fields)
writer.writeheader()
writer.writerows(all_samples)
print(f"{filename_} has been saved.")
if __name__ == "__main__":
extract_kitti(
root="path/to/KITTI/raw_data",
depth_root="path/to/KITTI/data_depth_annotated/val",
saved_rgb_dir="./benchmark/datasets/",
saved_disp_dir="./benchmark/datasets/",
csv_save_path=f"./benchmark/datasets/KITTI.csv",
sample_len=-1,
datatset_name="KITTI",
start_frame=0,
end_frame=110,
)
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