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data_utils.py
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data_utils.py
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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
def save_ply(points, filename, colors=None, normals=None):
vertex = np.core.records.fromarrays(points.transpose(), names='x, y, z', formats='f4, f4, f4')
n = len(vertex)
desc = vertex.dtype.descr
if normals is not None:
vertex_normal = np.core.records.fromarrays(normals.transpose(), names='nx, ny, nz', formats='f4, f4, f4')
assert len(vertex_normal) == n
desc = desc + vertex_normal.dtype.descr
if colors is not None:
vertex_color = np.core.records.fromarrays(colors.transpose() * 255, names='red, green, blue',
formats='u1, u1, 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='tab20'):
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):
if property[point_idx] == 0:
colors[point_idx] = np.array([0, 0, 0])
else:
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_color_batch(points_batch, colors_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], colors_batch[batch_idx][:point_num])
else:
save_ply(points_batch[batch_idx][:point_num], '%s_%04d%s' % (basename, batch_idx, ext),
colors_batch[batch_idx][:point_num])
def save_ply_property_batch(points_batch, property_batch, file_path, points_num=None, property_max=None,
cmap_name='tab20'):
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['normal'][...]], axis=-1).astype(np.float32))
else:
points.append(data['data'][...].astype(np.float32))
labels.append(np.squeeze(data['label'][:]).astype(np.int64))
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 is_h5_list(filelist):
return all([line.strip()[-3:] == '.h5' for line in open(filelist)])
def load_seg_list(filelist):
folder = os.path.dirname(filelist)
return [os.path.join(folder, line.strip()) for line in open(filelist)]
def load_seg(filelist):
points = []
labels = []
point_nums = []
labels_seg = []
indices_split_to_full = []
folder = os.path.dirname(filelist)
for line in open(filelist):
data = h5py.File(os.path.join(folder, line.strip()))
points.append(data['data'][...].astype(np.float32))
labels.append(data['label'][...].astype(np.int64))
point_nums.append(data['data_num'][...].astype(np.int32))
labels_seg.append(data['label_seg'][...].astype(np.int64))
if 'indices_split_to_full' in data:
indices_split_to_full.append(data['indices_split_to_full'][...].astype(np.int64))
return (np.concatenate(points, axis=0),
np.concatenate(labels, axis=0),
np.concatenate(point_nums, axis=0),
np.concatenate(labels_seg, axis=0),
np.concatenate(indices_split_to_full, axis=0) if indices_split_to_full else None)
def balance_classes(labels):
_, inverse, counts = np.unique(labels, return_inverse=True, return_counts=True)
counts_max = np.amax(counts)
repeat_num_avg_unique = counts_max / counts
repeat_num_avg = repeat_num_avg_unique[inverse]
repeat_num_floor = np.floor(repeat_num_avg)
repeat_num_probs = repeat_num_avg - repeat_num_floor
repeat_num = repeat_num_floor + (np.random.rand(repeat_num_probs.shape[0]) < repeat_num_probs)
return repeat_num.astype(np.int64)