PointCNN/mnist/data_utils.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import h5py
import plyfile
import numpy as np
from matplotlib import cm
import scipy.spatial.distance as distance


def save_ply(points, filename, colors=None, normals=None):
    vertex = np.array([tuple(p) for p in points], dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4')])
    n = len(vertex)
    desc = vertex.dtype.descr

    if normals is not None:
        vertex_normal = np.array([tuple(n) for n in normals], dtype=[('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4')])
        assert len(vertex_normal) == n
        desc = desc + vertex_normal.dtype.descr

    if colors is not None:
        vertex_color = np.array([tuple(c * 255) for c in colors],
                                dtype=[('red', 'u1'), ('green', 'u1'), ('blue', 'u1')])
        assert len(vertex_color) == n
        desc = desc + vertex_color.dtype.descr

    vertex_all = np.empty(n, dtype=desc)

    for prop in vertex.dtype.names:
        vertex_all[prop] = vertex[prop]

    if normals is not None:
        for prop in vertex_normal.dtype.names:
            vertex_all[prop] = vertex_normal[prop]

    if colors is not None:
        for prop in vertex_color.dtype.names:
            vertex_all[prop] = vertex_color[prop]

    ply = plyfile.PlyData([plyfile.PlyElement.describe(vertex_all, 'vertex')], text=False)
    if not os.path.exists(os.path.dirname(filename)):
        os.makedirs(os.path.dirname(filename))
    ply.write(filename)


def save_ply_property(points, property, property_max, filename, cmap_name='Set1'):
    point_num = points.shape[0]
    colors = np.full(points.shape, 0.5)
    cmap = cm.get_cmap(cmap_name)
    for point_idx in range(point_num):
        colors[point_idx] = cmap(property[point_idx] / property_max)[:3]
    save_ply(points, filename, colors)


def save_ply_batch(points_batch, file_path, points_num=None):
    batch_size = points_batch.shape[0]
    if type(file_path) != list:
        basename = os.path.splitext(file_path)[0]
        ext = '.ply'
    for batch_idx in range(batch_size):
        point_num = points_batch.shape[1] if points_num is None else points_num[batch_idx]
        if type(file_path) == list:
            save_ply(points_batch[batch_idx][:point_num], file_path[batch_idx])
        else:
            save_ply(points_batch[batch_idx][:point_num], '%s_%04d%s' % (basename, batch_idx, ext))


def save_ply_property_batch(points_batch, property_batch, file_path, points_num=None, property_max=None,
                            cmap_name='Set1'):
    batch_size = points_batch.shape[0]
    if type(file_path) != list:
        basename = os.path.splitext(file_path)[0]
        ext = '.ply'
    property_max = np.max(property_batch) if property_max is None else property_max
    for batch_idx in range(batch_size):
        point_num = points_batch.shape[1] if points_num is None else points_num[batch_idx]
        if type(file_path) == list:
            save_ply_property(points_batch[batch_idx][:point_num], property_batch[batch_idx][:point_num],
                              property_max, file_path[batch_idx], cmap_name)
        else:
            save_ply_property(points_batch[batch_idx][:point_num], property_batch[batch_idx][:point_num],
                              property_max, '%s_%04d%s' % (basename, batch_idx, ext), cmap_name)


def save_ply_point_with_normal(data_sample, folder):
    for idx, sample in enumerate(data_sample):
        filename_pts = os.path.join(folder, '{:08d}.ply'.format(idx))
        save_ply(sample[..., :3], filename_pts, normals=sample[..., 3:])


def grouped_shuffle(inputs):
    for idx in range(len(inputs) - 1):
        assert (len(inputs[idx]) == len(inputs[idx + 1]))

    shuffle_indices = np.arange(inputs[0].shape[0])
    np.random.shuffle(shuffle_indices)
    outputs = []
    for idx in range(len(inputs)):
        outputs.append(inputs[idx][shuffle_indices, ...])
    return outputs


def load_cls(filelist):
    points = []
    labels = []

    folder = os.path.dirname(filelist)
    for line in open(filelist):
        filename = os.path.basename(line.rstrip())
        data = h5py.File(os.path.join(folder, filename))
        if 'normal' in data:
            points.append(np.concatenate([data['data'][...], data['data'][...]], axis=-1).astype(np.float32))
        else:
            points.append(data['data'][...].astype(np.float32))
        labels.append(np.squeeze(data['label'][:]).astype(np.int32))
    return (np.concatenate(points, axis=0),
            np.concatenate(labels, axis=0))


def load_cls_train_val(filelist, filelist_val):
    data_train, label_train = grouped_shuffle(load_cls(filelist))
    data_val, label_val = load_cls(filelist_val)
    return data_train, label_train, data_val, label_val


def load_seg(filelist):
    points = []
    labels = []
    point_nums = []
    labels_seg = []

    folder = os.path.dirname(filelist)
    for line in open(filelist):
        filename = os.path.basename(line.rstrip())
        data = h5py.File(os.path.join(folder, filename))
        points.append(data['data'][...].astype(np.float32))
        labels.append(data['label'][...].astype(np.int32))
        point_nums.append(data['data_num'][...].astype(np.int32))
        labels_seg.append(data['label_seg'][...].astype(np.int32))
    return (np.concatenate(points, axis=0),
            np.concatenate(labels, axis=0),
            np.concatenate(point_nums, axis=0),
            np.concatenate(labels_seg, axis=0))