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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# # Data loading based on https://github.com/NVIDIA/flownet2-pytorch
import os
import numpy as np
import os.path as osp
from PIL import Image
from tqdm import tqdm
import csv
import imageio
# Check for endianness, based on Daniel Scharstein's optical flow code.
# Using little-endian architecture, these two should be equal.
TAG_FLOAT = 202021.25
TAG_CHAR = "PIEH"
def depth_read(filename):
"""Read depth data from file, return as numpy array."""
f = open(filename, "rb")
check = np.fromfile(f, dtype=np.float32, count=1)[0]
assert (
check == TAG_FLOAT
), " depth_read:: Wrong tag in flow file (should be: {0}, is: {1}). Big-endian machine? ".format(
TAG_FLOAT, check
)
width = np.fromfile(f, dtype=np.int32, count=1)[0]
height = np.fromfile(f, dtype=np.int32, count=1)[0]
size = width * height
assert (
width > 0 and height > 0 and size > 1 and size < 100000000
), " depth_read:: Wrong input size (width = {0}, height = {1}).".format(
width, height
)
depth = np.fromfile(f, dtype=np.float32, count=-1).reshape((height, width))
return depth
def extract_sintel(
root,
depth_root,
sample_len=-1,
csv_save_path="",
datatset_name="",
saved_rgb_dir="",
saved_disp_dir="",
):
scenes_names = os.listdir(root)
all_samples = []
for i, seq_name in enumerate(tqdm(scenes_names)):
all_img_names = os.listdir(os.path.join(root, seq_name))
all_img_names = [x for x in all_img_names if x.endswith(".png")]
all_img_names.sort()
all_img_names = sorted(all_img_names, key=lambda x: int(x.split(".")[0][-4:]))
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} / {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, all_img_names[idx])
depth_path = osp.join(
depth_root, seq_name, all_img_names[idx][:-3] + "dpt"
)
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]
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"{datatset_name}/{seq_name}_rgb_left.mp4"
)
sample["filepath_disparity"] = os.path.join(
f"{datatset_name}/{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_sintel(
root="path/to/Sintel-Depth/training_image/clean",
depth_root="path/to/Sintel-Depth/MPI-Sintel-depth-training-20150305/training/depth",
saved_rgb_dir="./benchmark/datasets/",
saved_disp_dir="./benchmark/datasets/",
csv_save_path=f"./benchmark/datasets/sintel.csv",
sample_len=-1,
datatset_name="sintel",
)
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