"""ScanObjectNN download: http://103.24.77.34/scanobjectnn/h5_files.zip."""

import os

import h5py
import numpy as np
from torch.utils.data import Dataset

os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"


def download():
    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
    DATA_DIR = os.path.join(BASE_DIR, "data")
    if not os.path.exists(DATA_DIR):
        os.mkdir(DATA_DIR)
    if not os.path.exists(os.path.join(DATA_DIR, "h5_files")):
        # note that this link only contains the hardest perturbed variant (PB_T50_RS).
        # for full versions, consider the following link.
        www = "https://web.northeastern.edu/smilelab/xuma/datasets/h5_files.zip"
        # www = 'http://103.24.77.34/scanobjectnn/h5_files.zip'
        zipfile = os.path.basename(www)
        os.system(f"wget {www}  --no-check-certificate; unzip {zipfile}")
        os.system(f"mv {zipfile[:-4]} {DATA_DIR}")
        os.system("rm %s" % (zipfile))


def load_scanobjectnn_data(partition):
    download()
    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
    all_data = []
    all_label = []

    h5_name = BASE_DIR + "/data/h5_files/main_split/" + partition + "_objectdataset_augmentedrot_scale75.h5"
    f = h5py.File(h5_name, mode="r")
    data = f["data"][:].astype("float32")
    label = f["label"][:].astype("int64")
    f.close()
    all_data.append(data)
    all_label.append(label)
    all_data = np.concatenate(all_data, axis=0)
    all_label = np.concatenate(all_label, axis=0)
    return all_data, all_label


def translate_pointcloud(pointcloud):
    xyz1 = np.random.uniform(low=2.0 / 3.0, high=3.0 / 2.0, size=[3])
    xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])

    return np.add(np.multiply(pointcloud, xyz1), xyz2).astype("float32")


class ScanObjectNN(Dataset):
    def __init__(self, num_points, partition="training"):
        self.data, self.label = load_scanobjectnn_data(partition)
        self.num_points = num_points
        self.partition = partition

    def __getitem__(self, item):
        pointcloud = self.data[item][: self.num_points]
        label = self.label[item]
        if self.partition == "training":
            pointcloud = translate_pointcloud(pointcloud)
            np.random.shuffle(pointcloud)
        return pointcloud, label

    def __len__(self):
        return self.data.shape[0]


if __name__ == "__main__":
    train = ScanObjectNN(1024)
    test = ScanObjectNN(1024, "test")
    for data, label in train:
        print(data.shape)
        print(label)