stuff
This commit is contained in:
Markus Friedrich
parent
4e1fcdfd43
commit
851b0ce01e
@ -3,6 +3,22 @@ import open3d as o3d
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from sklearn.cluster import DBSCAN
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from pyod.models.knn import KNN
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from pyod.models.sod import SOD
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from pyod.models.abod import ABOD
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from pyod.models.sos import SOS
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from pyod.models.pca import PCA
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from pyod.models.ocsvm import OCSVM
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from pyod.models.mcd import MCD
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from pyod.models.lof import LOF
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from pyod.models.cof import COF
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from pyod.models.cblof import CBLOF
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from pyod.models.loci import LOCI
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from pyod.models.hbos import HBOS
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from pyod.models.lscp import LSCP
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from pyod.models.feature_bagging import FeatureBagging
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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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@ -75,7 +91,11 @@ def cluster_per_column(pc, column):
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return clusters
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def cluster_cubes(data, cluster_dims):
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def cluster_cubes(data, cluster_dims, max_points_per_cluster=-1, min_points_per_cluster=-1):
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if cluster_dims[0] is 1 and cluster_dims[1] is 1 and cluster_dims[2] is 1:
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print("no need to cluster.")
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return [data]
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max = data[:,:3].max(axis=0)
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max += max * 0.01
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@ -101,18 +121,23 @@ def cluster_cubes(data, cluster_dims):
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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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final_clusters = []
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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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if c.shape[0] < min_points_per_cluster and -1 is not min_points_per_cluster:
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continue
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return clusters.values()
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if max_points_per_cluster is not -1:
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final_clusters.append(farthest_point_sampling(c, max_points_per_cluster))
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else:
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final_clusters.append(c)
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return final_clusters
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def cluster_dbscan(data, selected_indices, eps, min_samples, metric='euclidean', algo='auto'):
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def cluster_dbscan(data, selected_indices, eps, min_samples=5, 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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# 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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@ -120,13 +145,13 @@ def cluster_dbscan(data, selected_indices, eps, min_samples, metric='euclidean',
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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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db_res = DBSCAN(eps=eps, metric=metric, n_jobs=-1, min_samples=min_samples, algorithm=algo).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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# 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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@ -167,3 +192,49 @@ def write_clusters(path, clusters, type_column=6):
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np.savetxt(file, types, header='', comments='')
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np.savetxt(file, cluster[:, :6], header=str(len(cluster)) + ' ' + str(6), comments='')
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def normalize_pointcloud(pc, factor=1.0):
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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 * factor)
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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 hierarchical_clustering(data, selected_indices, eps, min_samples=5, metric='euclidean', algo='auto'):
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total_clusters = []
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clusters = cluster_dbscan(data, selected_indices, eps, min_samples, metric=metric, algo=algo)
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for cluster in clusters:
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sub_clusters = cluster_dbscan(cluster, selected_indices, eps, min_samples, metric=metric, algo=algo)
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total_clusters.extend(sub_clusters)
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return total_clusters
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def filter_clusters(clusters, min_size):
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filtered_clusters = []
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for c in clusters:
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if len(c) >= min_size:
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filtered_clusters.append(c)
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return filtered_clusters
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def split_outliers(pc, columns):
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clf = KNN()#FeatureBagging() # detector_list=[LOF(), KNN()]
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clf.fit(pc[:, columns])
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# LOF, kNN
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return pc[clf.labels_ == 0], pc[clf.labels_ == 1]
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@ -85,18 +85,6 @@ def clusterToColor(cluster, cluster_idx):
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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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@ -139,43 +127,6 @@ def append_normal_angles(data):
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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, min_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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final_clusters = []
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for key, cluster in clusters.items():
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c = np.vstack(cluster)
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if c.shape[0] < min_points_per_cluster:
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continue
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final_clusters.append(farthest_point_sampling(c, max_points_per_cluster))
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return final_clusters
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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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@ -243,7 +194,7 @@ sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') # add proj
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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_30.pth', help='model path')
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parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_0.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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@ -260,41 +211,46 @@ if __name__ == '__main__':
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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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pointcloud_file = './pointclouds/0_0.xyz'
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# Load and pre-process point cloud
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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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# pcloud = farthest_point_sampling(pcloud, opt.npoints)
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pcloud = pc.normalize_pointcloud(pcloud, 1)
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# Test: Pre-predict clustering
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print("point cloud size: ", pcloud.shape)
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clusters = extract_clusters(pcloud, [0, 1, 2, 3, 4, 5], eps=0.10, min_samples=0.005,
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metric='euclidean', algo='auto')
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#draw_clusters(clusters)
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#a, b = pc.split_outliers(pcloud, [3, 4, 5])
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#draw_sample_data(a, True)
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#draw_sample_data(b, True)
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#pcloud = a
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# pc = StandardScaler().fit_transform(pc)
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recreate_folder(dataset_folder)
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# for 0_0.xyz: pc.hierarchical_clustering(pcloud, [0, 1, 2, 3, 4, 5], eps=0.1, min_samples=5)
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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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# Add cluster point clouds to prediction folder.
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pc_clusters = extract_cube_clusters(pcloud, [4, 4, 4], 2048, 100)
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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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# pc_clusters = pc.hierarchical_clustering(pcloud, [0, 1, 2, 3, 4, 5], eps=0.1, min_samples=5)
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# pc_clusters = pc.filter_clusters(pc_clusters, 100)
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pc_clusters = [pcloud]
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print("NUM CLUSTERS: ", len(pc_clusters))
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draw_clusters(pc_clusters)
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for c in pc_clusters:
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draw_sample_data(c, True)
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print("Cluster Size: ", len(c))
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# draw_sample_data(pcloud)
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pc_clusters = pc.cluster_cubes(pcloud, [1, 1, 1])
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recreate_folder(dataset_folder)
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for idx, pcc in enumerate(pc_clusters):
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print("Cluster shape: ", pcc.shape)
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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_sample_data(pcc, False)
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# Load dataset
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print('load dataset ..')
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@ -317,7 +273,6 @@ if __name__ == '__main__':
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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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@ -334,58 +289,18 @@ if __name__ == '__main__':
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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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draw_sample_data(sample_data, False)
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#print("Sample Datat: ", sample_data[:5, :])
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#print('Eval done.')
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print("PRED LABEL: ", pred_label)
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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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if labeled_dataset is None:
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labeled_dataset = sample_data
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else:
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labeled_dataset = np.vstack((labeled_dataset, sample_data))
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#draw_clusters(result_clusters)
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print("prediction done")
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draw_sample_data(labeled_dataset, False)
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print("point cloud size: ", labeled_dataset.shape)
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print("Min: ", np.min(labeled_dataset[:, :3]))
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print("Max: ", np.max(labeled_dataset[:, :3]))
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print("Min: ", np.min(pcloud[:, :3]))
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print("Max: ", np.max(pcloud[:, :3]))
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#print("Data Set: ", labeled_dataset[:5, :])
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labeled_dataset = normalize_pointcloud(labeled_dataset)
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labeled_dataset = append_normal_angles(labeled_dataset)
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#labeled_dataset = farthest_point_sampling(labeled_dataset, opt.npoints)
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labeled_dataset = append_onehotencoded_type(labeled_dataset, 1.0)
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clusters = extract_clusters(labeled_dataset, [0, 1, 2, 3, 4, 5], eps=0.10, min_samples=0.005,
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metric='euclidean', algo='auto')
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#total_clusters = []
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#for cluster in clusters:
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# sub_clusters = extract_clusters(cluster, [7,8,9], eps=0.10, min_samples=0.05,
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# metric='euclidean', algo='auto')
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# total_clusters.extend(sub_clusters)
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draw_clusters(clusters)
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pc.write_clusters("clusters.txt", clusters)
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