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Running
on
Zero
File size: 3,951 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 _read_image(img_rel_path) -> np.ndarray:
image_to_read = img_rel_path
image = Image.open(image_to_read) # [H, W, rgb]
image = np.asarray(image)
return image
def depth_read(filename):
depth_in = _read_image(filename)
depth_decoded = depth_in / 1000.0
return depth_decoded
def extract_scannet(
root,
sample_len=-1,
csv_save_path="",
datatset_name="",
scene_number=16,
scene_frames_len=120,
stride=1,
saved_rgb_dir="",
saved_disp_dir="",
):
scenes_names = os.listdir(root)
scenes_names = sorted(scenes_names)[:scene_number]
all_samples = []
for i, seq_name in enumerate(tqdm(scenes_names)):
all_img_names = os.listdir(osp.join(root, seq_name, "color"))
all_img_names = [x for x in all_img_names if x.endswith(".jpg")]
all_img_names = sorted(all_img_names, key=lambda x: int(x.split(".")[0]))
all_img_names = all_img_names[:scene_frames_len:stride]
print(f"sequence frame number: {len(all_img_names)}")
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, "color", all_img_names[idx])
depth_path = osp.join(
root, seq_name, "depth", all_img_names[idx][:-3] + "png"
)
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]
disp_video = disp_video[:, :, 8:-8, 11:-11]
img_video = img_video[:, 8:-8, 11:-11, :]
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=9, 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_scannet(
root="path/to/ScanNet_v2/raw/scans_test",
saved_rgb_dir="./benchmark/datasets/",
saved_disp_dir="./benchmark/datasets/",
csv_save_path=f"./benchmark/datasets/scannet.csv",
sample_len=-1,
datatset_name="scannet",
scene_number=100,
scene_frames_len=90 * 3,
stride=3,
)
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