269 lines
11 KiB
Python
269 lines
11 KiB
Python
import os
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import torch
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import numpy as np
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from torch.utils.data import Dataset
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from torch.utils import data
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import random
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import open3d as o3d
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import numpy as np
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import torch.nn.functional as F
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# taken from https://github.com/optas/latent_3d_points/blob/8e8f29f8124ed5fc59439e8551ba7ef7567c9a37/src/in_out.py
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synsetid_to_cate = {
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'02691156': 'airplane', '02773838': 'bag', '02801938': 'basket',
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'02808440': 'bathtub', '02818832': 'bed', '02828884': 'bench',
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'02876657': 'bottle', '02880940': 'bowl', '02924116': 'bus',
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'02933112': 'cabinet', '02747177': 'can', '02942699': 'camera',
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'02954340': 'cap', '02958343': 'car', '03001627': 'chair',
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'03046257': 'clock', '03207941': 'dishwasher', '03211117': 'monitor',
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'04379243': 'table', '04401088': 'telephone', '02946921': 'tin_can',
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'04460130': 'tower', '04468005': 'train', '03085013': 'keyboard',
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'03261776': 'earphone', '03325088': 'faucet', '03337140': 'file',
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'03467517': 'guitar', '03513137': 'helmet', '03593526': 'jar',
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'03624134': 'knife', '03636649': 'lamp', '03642806': 'laptop',
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'03691459': 'speaker', '03710193': 'mailbox', '03759954': 'microphone',
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'03761084': 'microwave', '03790512': 'motorcycle', '03797390': 'mug',
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'03928116': 'piano', '03938244': 'pillow', '03948459': 'pistol',
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'03991062': 'pot', '04004475': 'printer', '04074963': 'remote_control',
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'04090263': 'rifle', '04099429': 'rocket', '04225987': 'skateboard',
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'04256520': 'sofa', '04330267': 'stove', '04530566': 'vessel',
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'04554684': 'washer', '02992529': 'cellphone',
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'02843684': 'birdhouse', '02871439': 'bookshelf',
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# '02858304': 'boat', no boat in our dataset, merged into vessels
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# '02834778': 'bicycle', not in our taxonomy
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}
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cate_to_synsetid = {v: k for k, v in synsetid_to_cate.items()}
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class Uniform15KPC(Dataset):
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def __init__(self, root_dir, subdirs, tr_sample_size=10000,
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te_sample_size=10000, split='train', scale=1.,
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normalize_per_shape=False, box_per_shape=False,
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random_subsample=False,
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normalize_std_per_axis=False,
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all_points_mean=None, all_points_std=None,
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input_dim=3, use_mask=False):
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self.root_dir = root_dir
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self.split = split
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self.in_tr_sample_size = tr_sample_size
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self.in_te_sample_size = te_sample_size
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self.subdirs = subdirs
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self.scale = scale
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self.random_subsample = random_subsample
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self.input_dim = input_dim
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self.use_mask = use_mask
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self.box_per_shape = box_per_shape
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if use_mask:
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self.mask_transform = PointCloudMasks(radius=5, elev=5, azim=90)
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self.all_cate_mids = []
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self.cate_idx_lst = []
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self.all_points = []
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for cate_idx, subd in enumerate(self.subdirs):
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# NOTE: [subd] here is synset id
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sub_path = os.path.join(root_dir, subd, self.split)
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if not os.path.isdir(sub_path):
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print("Directory missing : %s" % sub_path)
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continue
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all_mids = []
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for x in os.listdir(sub_path):
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if not x.endswith('.npy'):
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continue
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all_mids.append(os.path.join(self.split, x[:-len('.npy')]))
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# NOTE: [mid] contains the split: i.e. "train/<mid>" or "val/<mid>" or "test/<mid>"
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for mid in all_mids:
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# obj_fname = os.path.join(sub_path, x)
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obj_fname = os.path.join(root_dir, subd, mid + ".npy")
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try:
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point_cloud = np.load(obj_fname) # (15k, 3)
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except:
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continue
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assert point_cloud.shape[0] == 15000
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self.all_points.append(point_cloud[np.newaxis, ...])
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self.cate_idx_lst.append(cate_idx)
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self.all_cate_mids.append((subd, mid))
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# Shuffle the index deterministically (based on the number of examples)
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self.shuffle_idx = list(range(len(self.all_points)))
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random.Random(38383).shuffle(self.shuffle_idx)
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self.cate_idx_lst = [self.cate_idx_lst[i] for i in self.shuffle_idx]
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self.all_points = [self.all_points[i] for i in self.shuffle_idx]
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self.all_cate_mids = [self.all_cate_mids[i] for i in self.shuffle_idx]
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# Normalization
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self.all_points = np.concatenate(self.all_points) # (N, 15000, 3)
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self.normalize_per_shape = normalize_per_shape
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self.normalize_std_per_axis = normalize_std_per_axis
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if all_points_mean is not None and all_points_std is not None: # using loaded dataset stats
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self.all_points_mean = all_points_mean
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self.all_points_std = all_points_std
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elif self.normalize_per_shape: # per shape normalization
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B, N = self.all_points.shape[:2]
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self.all_points_mean = self.all_points.mean(axis=1).reshape(B, 1, input_dim)
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if normalize_std_per_axis:
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self.all_points_std = self.all_points.reshape(B, N, -1).std(axis=1).reshape(B, 1, input_dim)
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else:
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self.all_points_std = self.all_points.reshape(B, -1).std(axis=1).reshape(B, 1, 1)
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elif self.box_per_shape:
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B, N = self.all_points.shape[:2]
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self.all_points_mean = self.all_points.min(axis=1).reshape(B, 1, input_dim)
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self.all_points_std = self.all_points.max(axis=1).reshape(B, 1, input_dim) - self.all_points.min(axis=1).reshape(B, 1, input_dim)
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else: # normalize across the dataset
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self.all_points_mean = self.all_points.reshape(-1, input_dim).mean(axis=0).reshape(1, 1, input_dim)
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if normalize_std_per_axis:
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self.all_points_std = self.all_points.reshape(-1, input_dim).std(axis=0).reshape(1, 1, input_dim)
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else:
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self.all_points_std = self.all_points.reshape(-1).std(axis=0).reshape(1, 1, 1)
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self.all_points = (self.all_points - self.all_points_mean) / self.all_points_std
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if self.box_per_shape:
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self.all_points = self.all_points - 0.5
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self.train_points = self.all_points[:, :10000]
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self.test_points = self.all_points[:, 10000:]
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self.tr_sample_size = min(10000, tr_sample_size)
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self.te_sample_size = min(5000, te_sample_size)
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print("Total number of data:%d" % len(self.train_points))
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print("Min number of points: (train)%d (test)%d"
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% (self.tr_sample_size, self.te_sample_size))
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assert self.scale == 1, "Scale (!= 1) is deprecated"
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def get_pc_stats(self, idx):
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if self.normalize_per_shape or self.box_per_shape:
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m = self.all_points_mean[idx].reshape(1, self.input_dim)
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s = self.all_points_std[idx].reshape(1, -1)
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return m, s
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return self.all_points_mean.reshape(1, -1), self.all_points_std.reshape(1, -1)
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def renormalize(self, mean, std):
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self.all_points = self.all_points * self.all_points_std + self.all_points_mean
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self.all_points_mean = mean
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self.all_points_std = std
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self.all_points = (self.all_points - self.all_points_mean) / self.all_points_std
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self.train_points = self.all_points[:, :10000]
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self.test_points = self.all_points[:, 10000:]
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def __len__(self):
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return len(self.train_points)
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def __getitem__(self, idx):
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tr_out = self.train_points[idx]
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if self.random_subsample:
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tr_idxs = np.random.choice(tr_out.shape[0], self.tr_sample_size)
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else:
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tr_idxs = np.arange(self.tr_sample_size)
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tr_out = torch.from_numpy(tr_out[tr_idxs, :]).float()
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te_out = self.test_points[idx]
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if self.random_subsample:
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te_idxs = np.random.choice(te_out.shape[0], self.te_sample_size)
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else:
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te_idxs = np.arange(self.te_sample_size)
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te_out = torch.from_numpy(te_out[te_idxs, :]).float()
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m, s = self.get_pc_stats(idx)
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cate_idx = self.cate_idx_lst[idx]
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sid, mid = self.all_cate_mids[idx]
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out = {
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'idx': idx,
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'train_points': tr_out,
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'test_points': te_out,
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'mean': m, 'std': s, 'cate_idx': cate_idx,
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'sid': sid, 'mid': mid
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}
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if self.use_mask:
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# masked = torch.from_numpy(self.mask_transform(self.all_points[idx]))
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# ss = min(masked.shape[0], self.in_tr_sample_size//2)
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# masked = masked[:ss]
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#
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# tr_mask = torch.ones_like(masked)
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# masked = torch.cat([masked, torch.zeros(self.in_tr_sample_size - ss, 3)],dim=0)#F.pad(masked, (self.in_tr_sample_size-masked.shape[0], 0), "constant", 0)
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#
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# tr_mask = torch.cat([tr_mask, torch.zeros(self.in_tr_sample_size- ss, 3)],dim=0)#F.pad(tr_mask, (self.in_tr_sample_size-tr_mask.shape[0], 0), "constant", 0)
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# out['train_points_masked'] = masked
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# out['train_masks'] = tr_mask
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tr_mask = self.mask_transform(tr_out)
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out['train_masks'] = tr_mask
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return out
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class ShapeNet15kPointClouds(Uniform15KPC):
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def __init__(self, root_dir="data/ShapeNetCore.v2.PC15k",
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categories=['airplane'], tr_sample_size=10000, te_sample_size=2048,
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split='train', scale=1., normalize_per_shape=False,
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normalize_std_per_axis=False, box_per_shape=False,
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random_subsample=False,
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all_points_mean=None, all_points_std=None,
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use_mask=False):
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self.root_dir = root_dir
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self.split = split
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assert self.split in ['train', 'test', 'val']
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self.tr_sample_size = tr_sample_size
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self.te_sample_size = te_sample_size
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self.cates = categories
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if 'all' in categories:
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self.synset_ids = list(cate_to_synsetid.values())
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else:
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self.synset_ids = [cate_to_synsetid[c] for c in self.cates]
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# assert 'v2' in root_dir, "Only supporting v2 right now."
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self.gravity_axis = 1
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self.display_axis_order = [0, 2, 1]
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super(ShapeNet15kPointClouds, self).__init__(
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root_dir, self.synset_ids,
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tr_sample_size=tr_sample_size,
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te_sample_size=te_sample_size,
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split=split, scale=scale,
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normalize_per_shape=normalize_per_shape, box_per_shape=box_per_shape,
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normalize_std_per_axis=normalize_std_per_axis,
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random_subsample=random_subsample,
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all_points_mean=all_points_mean, all_points_std=all_points_std,
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input_dim=3, use_mask=use_mask)
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class PointCloudMasks(object):
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'''
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render a view then save mask
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'''
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def __init__(self, radius : float=10, elev: float =45, azim:float=315, ):
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self.radius = radius
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self.elev = elev
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self.azim = azim
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def __call__(self, points):
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pcd = o3d.geometry.PointCloud()
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pcd.points = o3d.utility.Vector3dVector(points)
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camera = [self.radius * np.sin(90-self.elev) * np.cos(self.azim),
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self.radius * np.cos(90 - self.elev),
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self.radius * np.sin(90 - self.elev) * np.sin(self.azim),
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]
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# camera = [0,self.radius,0]
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_, pt_map = pcd.hidden_point_removal(camera, self.radius)
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mask = torch.zeros_like(points)
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mask[pt_map] = 1
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return mask #points[pt_map]
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####################################################################################
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