<filename>dataset/preparation/preparedata_train.py
import os
import sys
import subprocess

from joblib import Parallel, delayed
import numpy as np
import imageio
imageio.plugins.freeimage.download()
from imageio.plugins import freeimage
import h5py
from lz4.block import decompress
import scipy.misc
import cv2

from path import Path

path = os.path.join(os.path.dirname(os.path.abspath(__file__)))

def dump_example(dataset_name):
    print("Converting {:}.h5 ...".format(dataset_name))
    file = h5py.File(os.path.join(path, "traindata", "{:}.h5".format(dataset_name)), "r")
    
    for (seq_idx, seq_name) in enumerate(file):
        if dataset_name == 'scenes11_train':
            scale = 0.4
        else:
            scale = 1

        if ((dataset_name == 'sun3d_train_1.6m_to_infm' and seq_idx == 7) or \
            (dataset_name == 'sun3d_train_0.4m_to_0.8m' and seq_idx == 15) or \
            (dataset_name == 'scenes11_train' and (seq_idx == 2758 or seq_idx == 4691 or seq_idx == 7023 or seq_idx == 11157 or seq_idx == 17168 or seq_idx == 19595))):
            continue        # Skip error files

        print("Processing sequence {:d}/{:d}".format(seq_idx, len(file)))
        dump_dir = os.path.join(path, '../train', dataset_name + "_" + "{:05d}".format(seq_idx))
        if not os.path.isdir(dump_dir):
            os.mkdir(dump_dir)
        dump_dir = Path(dump_dir)
        sequence = file[seq_name]["frames"]["t0"]
        poses = []
        for (f_idx, f_name) in enumerate(sequence):
            frame = sequence[f_name]
            for dt_type in frame:
                dataset = frame[dt_type]
                img = dataset[...]
                if dt_type == "camera":
                    if f_idx == 0:
                        intrinsics = np.array([[img[0], 0, img[3]], [0, img[1], img[4]], [0, 0, 1]])
                    pose = np.array([[img[5],img[8],img[11],img[14]*scale], [img[6],img[9],img[12],img[15]*scale], [img[7],img[10],img[13],img[16]*scale]])
                    poses.append(pose.tolist())
                elif dt_type == "depth":
                    dimension = dataset.attrs["extents"]
                    depth = np.array(np.frombuffer(decompress(img.tobytes(), dimension[0] * dimension[1] * 2), dtype = np.float16)).astype(np.float32)
                    depth = depth.reshape(dimension[0], dimension[1])*scale

                    dump_depth_file = dump_dir/'{:04d}.npy'.format(f_idx)
                    np.save(dump_depth_file, depth)
                elif dt_type == "image":
                    img = imageio.imread(img.tobytes())
                    dump_img_file = dump_dir/'{:04d}.jpg'.format(f_idx)
                    scipy.misc.imsave(dump_img_file, img)

        dump_cam_file = dump_dir/'cam.txt'
        np.savetxt(dump_cam_file, intrinsics)
        poses_file = dump_dir/'poses.txt'
        np.savetxt(poses_file, np.array(poses).reshape(-1, 12), fmt='%.6e')

        if len(dump_dir.files('*.jpg')) < 2:
            dump_dir.rmtree()

def preparedata():
    num_threads = 1
    SUB_DATASET_NAMES = ([
        "rgbd_10_to_20_3d_train", "rgbd_10_to_20_handheld_train", "rgbd_10_to_20_simple_train", "rgbd_20_to_inf_3d_train", "rgbd_20_to_inf_handheld_train", "rgbd_20_to_inf_simple_train",
        "sun3d_train_0.01m_to_0.1m", "sun3d_train_0.1m_to_0.2m", "sun3d_train_0.2m_to_0.4m", "sun3d_train_0.4m_to_0.8m", "sun3d_train_0.8m_to_1.6m", "sun3d_train_1.6m_to_infm",
        "scenes11_train", 
    ])

    dump_root = os.path.join(path, '../train')
    if not os.path.isdir(dump_root):
        os.mkdir(dump_root)

    if num_threads == 1:
        for scene in SUB_DATASET_NAMES:
            dump_example(scene)
    else:
        Parallel(n_jobs=num_threads)(delayed(dump_example)(scene) for scene in SUB_DATASET_NAMES)

    np.random.seed(8964)
    dump_root = Path(dump_root)
    subdirs = dump_root.dirs()
    canonic_prefixes = set([subdir.basename()[:-2] for subdir in subdirs])
    with open(dump_root / 'train.txt', 'w') as tf:
        with open(dump_root / 'val.txt', 'w') as vf:
            for pr in canonic_prefixes:
                corresponding_dirs = dump_root.dirs('{}*'.format(pr))
                if np.random.random() < 0.1:
                    for s in corresponding_dirs:
                        vf.write('{}\n'.format(s.name))
                else:
                    for s in corresponding_dirs:
                        tf.write('{}\n'.format(s.name))

    print("Finished Converting Data.")

if __name__ == "__main__":
    preparedata()

<filename>dataset/preparation/preparedata_train.py
import os
import sys
import subprocess

from joblib import Parallel, delayed
import numpy as np
import imageio
imageio.plugins.freeimage.download()
from imageio.plugins import freeimage
import h5py
from lz4.block import decompress
import scipy.misc
import cv2

from path import Path

path = os.path.join(os.path.dirname(os.path.abspath(__file__)))

def dump_example(dataset_name):
    print("Converting {:}.h5 ...".format(dataset_name))
    file = h5py.File(os.path.join(path, "traindata", "{:}.h5".format(dataset_name)), "r")
    
    for (seq_idx, seq_name) in enumerate(file):
        if dataset_name == 'scenes11_train':
            scale = 0.4
        else:
            scale = 1

        if ((dataset_name == 'sun3d_train_1.6m_to_infm' and seq_idx == 7) or \
            (dataset_name == 'sun3d_train_0.4m_to_0.8m' and seq_idx == 15) or \
            (dataset_name == 'scenes11_train' and (seq_idx == 2758 or seq_idx == 4691 or seq_idx == 7023 or seq_idx == 11157 or seq_idx == 17168 or seq_idx == 19595))):
            continue        # Skip error files

        print("Processing sequence {:d}/{:d}".format(seq_idx, len(file)))
        dump_dir = os.path.join(path, '../train', dataset_name + "_" + "{:05d}".format(seq_idx))
        if not os.path.isdir(dump_dir):
            os.mkdir(dump_dir)
        dump_dir = Path(dump_dir)
        sequence = file[seq_name]["frames"]["t0"]
        poses = []
        for (f_idx, f_name) in enumerate(sequence):
            frame = sequence[f_name]
            for dt_type in frame:
                dataset = frame[dt_type]
                img = dataset[...]
                if dt_type == "camera":
                    if f_idx == 0:
                        intrinsics = np.array([[img[0], 0, img[3]], [0, img[1], img[4]], [0, 0, 1]])
                    pose = np.array([[img[5],img[8],img[11],img[14]*scale], [img[6],img[9],img[12],img[15]*scale], [img[7],img[10],img[13],img[16]*scale]])
                    poses.append(pose.tolist())
                elif dt_type == "depth":
                    dimension = dataset.attrs["extents"]
                    depth = np.array(np.frombuffer(decompress(img.tobytes(), dimension[0] * dimension[1] * 2), dtype = np.float16)).astype(np.float32)
                    depth = depth.reshape(dimension[0], dimension[1])*scale

                    dump_depth_file = dump_dir/'{:04d}.npy'.format(f_idx)
                    np.save(dump_depth_file, depth)
                elif dt_type == "image":
                    img = imageio.imread(img.tobytes())
                    dump_img_file = dump_dir/'{:04d}.jpg'.format(f_idx)
                    scipy.misc.imsave(dump_img_file, img)

        dump_cam_file = dump_dir/'cam.txt'
        np.savetxt(dump_cam_file, intrinsics)
        poses_file = dump_dir/'poses.txt'
        np.savetxt(poses_file, np.array(poses).reshape(-1, 12), fmt='%.6e')

        if len(dump_dir.files('*.jpg')) < 2:
            dump_dir.rmtree()

def preparedata():
    num_threads = 1
    SUB_DATASET_NAMES = ([
        "rgbd_10_to_20_3d_train", "rgbd_10_to_20_handheld_train", "rgbd_10_to_20_simple_train", "rgbd_20_to_inf_3d_train", "rgbd_20_to_inf_handheld_train", "rgbd_20_to_inf_simple_train",
        "sun3d_train_0.01m_to_0.1m", "sun3d_train_0.1m_to_0.2m", "sun3d_train_0.2m_to_0.4m", "sun3d_train_0.4m_to_0.8m", "sun3d_train_0.8m_to_1.6m", "sun3d_train_1.6m_to_infm",
        "scenes11_train", 
    ])

    dump_root = os.path.join(path, '../train')
    if not os.path.isdir(dump_root):
        os.mkdir(dump_root)

    if num_threads == 1:
        for scene in SUB_DATASET_NAMES:
            dump_example(scene)
    else:
        Parallel(n_jobs=num_threads)(delayed(dump_example)(scene) for scene in SUB_DATASET_NAMES)

    np.random.seed(8964)
    dump_root = Path(dump_root)
    subdirs = dump_root.dirs()
    canonic_prefixes = set([subdir.basename()[:-2] for subdir in subdirs])
    with open(dump_root / 'train.txt', 'w') as tf:
        with open(dump_root / 'val.txt', 'w') as vf:
            for pr in canonic_prefixes:
                corresponding_dirs = dump_root.dirs('{}*'.format(pr))
                if np.random.random() < 0.1:
                    for s in corresponding_dirs:
                        vf.write('{}\n'.format(s.name))
                else:
                    for s in corresponding_dirs:
                        tf.write('{}\n'.format(s.name))

    print("Finished Converting Data.")

if __name__ == "__main__":
    preparedata()

# Skip error files