348 lines
10 KiB
Python
348 lines
10 KiB
Python
import sys
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import os
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import shutil
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import math
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') # add project root directory
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from dataset.shapenet import ShapeNetPartSegDataset
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from model.pointnet2_part_seg import PointNet2PartSegmentNet
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import torch_geometric.transforms as GT
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import torch
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import argparse
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from distutils.util import strtobool
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import numpy as np
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from sklearn.cluster import DBSCAN
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from sklearn.preprocessing import StandardScaler
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import open3d as o3d
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import pointcloud as pc
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def eval_sample(net, sample):
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'''
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sample: { 'points': tensor(n, 3), 'labels': tensor(n,) }
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return: (pred_label, gt_label) with labels shape (n,)
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'''
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net.eval()
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with torch.no_grad():
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# points: (n, 3)
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points, gt_label = sample['points'], sample['labels']
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n = points.shape[0]
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points = points.view(1, n, 3) # make a batch
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points = points.transpose(1, 2).contiguous()
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points = points.to(device, dtype)
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pred = net(points) # (batch_size, n, num_classes)
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pred_label = pred.max(2)[1]
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pred_label = pred_label.view(-1).cpu() # (n,)
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assert pred_label.shape == gt_label.shape
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return (pred_label, gt_label)
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def mini_color_table(index, norm=True):
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colors = [
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[0.5000, 0.5400, 0.5300], [0.8900, 0.1500, 0.2100], [0.6400, 0.5800, 0.5000],
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[1.0000, 0.3800, 0.0100], [1.0000, 0.6600, 0.1400], [0.4980, 1.0000, 0.0000],
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[0.4980, 1.0000, 0.8314], [0.9412, 0.9725, 1.0000], [0.5412, 0.1686, 0.8863],
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[0.5765, 0.4392, 0.8588], [0.3600, 0.1400, 0.4300], [0.5600, 0.3700, 0.6000],
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]
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color = colors[index % len(colors)]
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if not norm:
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color[0] *= 255
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color[1] *= 255
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color[2] *= 255
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return color
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def label2color(labels):
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'''
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labels: np.ndarray with shape (n, )
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colors(return): np.ndarray with shape (n, 3)
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'''
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num = labels.shape[0]
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colors = np.zeros((num, 3))
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minl, maxl = np.min(labels), np.max(labels)
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for l in range(minl, maxl + 1):
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colors[labels == l, :] = mini_color_table(l)
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return colors
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def clusterToColor(cluster, cluster_idx):
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colors = np.zeros(shape=(len(cluster), 3))
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point_idx = 0
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for point in cluster:
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colors[point_idx, :] = mini_color_table(cluster_idx)
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point_idx += 1
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return colors
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def normalize_pointcloud(pc):
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max = pc.max(axis=0)
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min = pc.min(axis=0)
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f = np.max([abs(max[0] - min[0]), abs(max[1] - min[1]), abs(max[2] - min[2])])
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pc[:, 0:3] /= f
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pc[:, 3:6] /= (np.linalg.norm(pc[:, 3:6], ord=2, axis=1, keepdims=True))
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return pc
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def farthest_point_sampling(pts, K):
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if pts.shape[0] < K:
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return pts
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def calc_distances(p0, points):
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return ((p0[:3] - points[:, :3]) ** 2).sum(axis=1)
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farthest_pts = np.zeros((K, pts.shape[1]))
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farthest_pts[0] = pts[np.random.randint(len(pts))]
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distances = calc_distances(farthest_pts[0], pts)
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for i in range(1, K):
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farthest_pts[i] = pts[np.argmax(distances)]
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distances = np.minimum(distances, calc_distances(farthest_pts[i], pts))
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return farthest_pts
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def append_onehotencoded_type(data, factor = 1.0):
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types = data[:, 6].astype(int)
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res = np.zeros((len(types), 4))
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res[np.arange(len(types)), types] = factor
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return np.column_stack((data, res))
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def append_normal_angles(data):
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def func(x):
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theta = math.acos(x[2]) / math.pi
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phi = (math.atan2(x[1], x[0]) + math.pi) / (2.0 * math.pi)
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return (theta, phi)
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res = np.array([func(xi) for xi in data[:, 3:6]])
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print(res)
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return np.column_stack((data, res))
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def extract_cube_clusters(data, cluster_dims, max_points_per_cluster):
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max = data[:,:3].max(axis=0)
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max += max * 0.01
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min = data[:,:3].min(axis=0)
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min -= min * 0.01
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size = (max - min)
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clusters = {}
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cluster_size = size / np.array(cluster_dims, dtype=np.float32)
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print('Min: ' + str(min) + ' Max: ' + str(max))
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print('Cluster Size: ' + str(cluster_size))
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for row in data:
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# print('Row: ' + str(row))
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cluster_pos = ((row[:3] - min) / cluster_size).astype(int)
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cluster_idx = cluster_dims[0] * cluster_dims[2] * cluster_pos[1] + cluster_dims[0] * cluster_pos[2] + cluster_pos[0]
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clusters.setdefault(cluster_idx, []).append(row)
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# Apply farthest point sampling to each cluster
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for key, cluster in clusters.items():
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c = np.vstack(cluster)
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clusters[key] = farthest_point_sampling(c, max_points_per_cluster)
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return clusters.values()
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def extract_clusters(data, selected_indices, eps, min_samples, metric='euclidean', algo='auto'):
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min_samples = min_samples * len(data)
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print('Clustering. Min Samples: ' + str(min_samples) + ' EPS: ' + str(eps))
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# 0,1,2 : pos
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# 3,4,5 : normal
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# 6: type index
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# 7,8,9,10: type index one hot encoded
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# 11,12: normal as angles
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db_res = DBSCAN(eps=eps, metric=metric, n_jobs=-1, algorithm=algo, min_samples=min_samples).fit(data[:, selected_indices])
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labels = db_res.labels_
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n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
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n_noise = list(labels).count(-1)
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print("Noise: " + str(n_noise) + " Clusters: " + str(n_clusters))
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clusters = {}
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for idx, l in enumerate(labels):
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if l is -1:
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continue
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clusters.setdefault(str(l), []).append(data[idx, :])
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npClusters = []
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for cluster in clusters.values():
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npClusters.append(np.array(cluster))
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return npClusters
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def draw_clusters(clusters):
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clouds = []
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cluster_idx = 0
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for cluster in clusters:
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cloud = o3d.PointCloud()
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cloud.points = o3d.Vector3dVector(cluster[:,:3])
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cloud.colors = o3d.Vector3dVector(clusterToColor(cluster, cluster_idx))
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clouds.append(cloud)
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cluster_idx += 1
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o3d.draw_geometries(clouds)
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def draw_sample_data(sample_data, colored_normals = False):
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cloud = o3d.PointCloud()
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cloud.points = o3d.Vector3dVector(sample_data[:,:3])
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cloud.colors = \
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o3d.Vector3dVector(label2color(sample_data[:, 6].astype(int)) if not colored_normals else sample_data[:, 3:6])
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o3d.draw_geometries([cloud])
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def recreate_folder(folder):
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if os.path.exists(folder) and os.path.isdir(folder):
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shutil.rmtree(folder)
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os.mkdir(folder)
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') # add project root directory
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parser = argparse.ArgumentParser()
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parser.add_argument('--npoints', type=int, default=2048, help='resample points number')
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parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_3.pth', help='model path')
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parser.add_argument('--sample_idx', type=int, default=0, help='select a sample to segment and view result')
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parser.add_argument('--headers', type=strtobool, default=True, help='if raw files come with headers')
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parser.add_argument('--collate_per_segment', type=strtobool, default=True, help='whether to look at pointclouds or sub')
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parser.add_argument('--has_variations', type=strtobool, default=False,
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help='whether a single pointcloud has variations '
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'named int(id)_pc.(xyz|dat) look at pointclouds or sub')
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opt = parser.parse_args()
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print(opt)
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if __name__ == '__main__':
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# Create dataset
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print('Create data set ..')
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dataset_folder = './data/raw/predict/'
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pointcloud_file = './pointclouds/0_pc.xyz'
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pcloud = pc.read_pointcloud(pointcloud_file)
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pcloud = normalize_pointcloud(pcloud)
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pcloud = append_normal_angles(pcloud)
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# pc = StandardScaler().fit_transform(pc)
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recreate_folder(dataset_folder)
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# Add full point cloud to prediction folder.
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recreate_folder(dataset_folder + '0_0' + '/')
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pc_fps = farthest_point_sampling(pcloud, opt.npoints)
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pc.write_pointcloud(dataset_folder + '0_0' + '/pc.xyz', pc_fps)
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pc_clusters = extract_cube_clusters(pcloud, [4,4,4], 1024)
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#pc_clusters = extract_clusters(pc, [0, 1, 2, 3, 4, 5], eps=0.1, min_samples=0.0001, metric='euclidean', algo='auto')
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# Add cluster point clouds to prediction folder.
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for idx, pcc in enumerate(pc_clusters):
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pcc = farthest_point_sampling(pcc, opt.npoints)
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recreate_folder(dataset_folder + str(idx) + '/')
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pc.write_pointcloud(dataset_folder + str(idx) + '/pc.xyz', pcc)
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#draw_sample_data(pcc, False)
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draw_clusters(pc_clusters)
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# Load dataset
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print('load dataset ..')
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test_transform = GT.Compose([GT.NormalizeScale(), ])
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test_dataset = ShapeNetPartSegDataset(
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mode='predict',
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root_dir='data',
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npoints=opt.npoints,
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refresh=True,
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collate_per_segment=opt.collate_per_segment,
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has_variations=opt.has_variations,
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headers=opt.headers
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)
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num_classes = test_dataset.num_classes()
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print('test dataset size: ', len(test_dataset))
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# Load model
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print('Construct model ..')
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device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
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dtype = torch.float
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# net = PointNetPartSegmentNet(num_classes)
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net = PointNet2PartSegmentNet(num_classes)
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net.load_state_dict(torch.load(opt.model, map_location=device.type))
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net = net.to(device, dtype)
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net.eval()
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result_clusters = []
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# Iterate over all the samples
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for sample in test_dataset:
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print('Eval test sample ..')
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pred_label, gt_label = eval_sample(net, sample)
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sample_data = np.column_stack((sample["points"].numpy(), sample["normals"].numpy(), pred_label.numpy()))
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print('Eval done.')
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sample_data = normalize_pointcloud(sample_data)
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sample_data = append_onehotencoded_type(sample_data, 1.0)
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sample_data = append_normal_angles(sample_data)
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print('Clustering ..')
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print('Shape: ' + str(sample_data.shape))
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clusters = extract_clusters(sample_data, [0, 1, 2, 3, 4, 5, 7, 8, 9, 10], eps=0.1, min_samples=0.0001, metric='euclidean', algo='auto')
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print('Clustering done. ' + str(len(clusters)) + " Clusters.")
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print(sample_data[:, 6])
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draw_sample_data(sample_data, False)
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result_clusters.extend(clusters)
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# result_clusters.append(sample_data)
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#draw_clusters(result_clusters)
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