mirror of
https://github.com/Tencent/DepthCrafter.git
synced 2024-09-25 23:28:07 +03:00
125 lines
3.9 KiB
Python
125 lines
3.9 KiB
Python
import os
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import numpy as np
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import os.path as osp
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from PIL import Image
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from tqdm import tqdm
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import csv
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import imageio
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def _read_image(img_rel_path) -> np.ndarray:
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image_to_read = img_rel_path
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image = Image.open(image_to_read) # [H, W, rgb]
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image = np.asarray(image)
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return image
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def depth_read(filename):
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depth_in = _read_image(filename)
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depth_decoded = depth_in / 1000.0
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return depth_decoded
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def extract_scannet(
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root,
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sample_len=-1,
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csv_save_path="",
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datatset_name="",
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scene_number=16,
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scene_frames_len=120,
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stride=1,
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saved_rgb_dir="",
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saved_disp_dir="",
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):
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scenes_names = os.listdir(root)
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scenes_names = sorted(scenes_names)[:scene_number]
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all_samples = []
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for i, seq_name in enumerate(tqdm(scenes_names)):
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all_img_names = os.listdir(osp.join(root, seq_name, "color"))
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all_img_names = [x for x in all_img_names if x.endswith(".jpg")]
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all_img_names = sorted(all_img_names, key=lambda x: int(x.split(".")[0]))
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all_img_names = all_img_names[:scene_frames_len:stride]
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print(f"sequence frame number: {len(all_img_names)}")
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seq_len = len(all_img_names)
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step = sample_len if sample_len > 0 else seq_len
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for ref_idx in range(0, seq_len, step):
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print(f"Progress: {seq_name}, {ref_idx // step + 1} / {seq_len//step}")
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video_imgs = []
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video_depths = []
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if (ref_idx + step) <= seq_len:
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ref_e = ref_idx + step
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else:
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continue
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for idx in range(ref_idx, ref_e):
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im_path = osp.join(root, seq_name, "color", all_img_names[idx])
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depth_path = osp.join(
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root, seq_name, "depth", all_img_names[idx][:-3] + "png"
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)
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depth = depth_read(depth_path)
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disp = depth
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video_depths.append(disp)
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video_imgs.append(np.array(Image.open(im_path)))
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disp_video = np.array(video_depths)[:, None]
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img_video = np.array(video_imgs)[..., 0:3]
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disp_video = disp_video[:, :, 8:-8, 11:-11]
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img_video = img_video[:, 8:-8, 11:-11, :]
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data_root = saved_rgb_dir + datatset_name
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disp_root = saved_disp_dir + datatset_name
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os.makedirs(data_root, exist_ok=True)
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os.makedirs(disp_root, exist_ok=True)
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img_video_dir = data_root
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disp_video_dir = disp_root
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img_video_path = os.path.join(img_video_dir, f"{seq_name}_rgb_left.mp4")
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disp_video_path = os.path.join(disp_video_dir, f"{seq_name}_disparity.npz")
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imageio.mimsave(
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img_video_path, img_video, fps=15, quality=9, macro_block_size=1
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)
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np.savez(disp_video_path, disparity=disp_video)
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sample = {}
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sample["filepath_left"] = os.path.join(
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f"{datatset_name}/{seq_name}_rgb_left.mp4"
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)
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sample["filepath_disparity"] = os.path.join(
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f"{datatset_name}/{seq_name}_disparity.npz"
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)
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all_samples.append(sample)
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filename_ = csv_save_path
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os.makedirs(os.path.dirname(filename_), exist_ok=True)
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fields = ["filepath_left", "filepath_disparity"]
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with open(filename_, "w") as csvfile:
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writer = csv.DictWriter(csvfile, fieldnames=fields)
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writer.writeheader()
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writer.writerows(all_samples)
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print(f"{filename_} has been saved.")
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if __name__ == "__main__":
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extract_scannet(
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root="path/to/ScanNet_v2/raw/scans_test",
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saved_rgb_dir="./benchmark/datasets/",
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saved_disp_dir="./benchmark/datasets/",
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csv_save_path=f"./benchmark/datasets/scannet.csv",
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sample_len=-1,
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datatset_name="scannet",
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scene_number=100,
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scene_frames_len=90 * 3,
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stride=3,
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)
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