155 lines
4.3 KiB
Python
155 lines
4.3 KiB
Python
import numpy as np
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import open3d as o3d
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from sklearn.cluster import DBSCAN
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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 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 read_pointcloud(path, delimiter=' ', hasHeader=True):
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with open(path, 'r') as f:
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if hasHeader:
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# Get rid of the Header
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_ = f.readline()
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# This itrates over all lines, splits them anc converts values to floats. Will fail on wrong values.
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pc = [[float(x) for x in line.rstrip().split(delimiter)] for line in f if line != '']
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return np.asarray(pc)
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def write_pointcloud(file, pc, numCols=6):
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np.savetxt(file, pc[:,:numCols], header=str(len(pc)) + ' ' + str(numCols), comments='')
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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 cluster_per_column(pc, column):
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clusters = []
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for i in range(0, int(np.max(pc[:, column]))):
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cluster_pc = pc[pc[:, column] == i, :]
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clusters.append(cluster_pc)
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return clusters
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def cluster_cubes(data, cluster_dims):
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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] = c # farthest_point_sampling(c, max_points_per_cluster)
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return clusters.values()
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def cluster_dbscan(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) + "Selected Indices: " + str(selected_indices))
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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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for cluster_idx, cluster in enumerate(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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o3d.draw_geometries(clouds) |