stuff
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Markus Friedrich
parent
f2cc070d04
commit
e4cc447f68
@ -207,14 +207,15 @@ def normalize_pointcloud(pc, factor=1.0):
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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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def hierarchical_clustering(data, selected_indices_0, selected_indices_1, 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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clusters = cluster_dbscan(data, selected_indices_0, 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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# cluster = normalize_pointcloud(cluster)
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sub_clusters = cluster_dbscan(cluster, selected_indices_1, 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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@ -86,18 +86,6 @@ 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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@ -140,43 +128,6 @@ 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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@ -262,37 +213,44 @@ 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/1_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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# for 0_0.xyz: pc.hierarchical_clustering(pcloud, [0, 1, 2, 3, 4, 5], eps=0.1, min_samples=5)
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#pc_clusters = pc.cluster_dbscan(pcloud, [0, 1, 2, 3,4,5], eps=0.5, 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.hierarchical_clustering(pcloud, selected_indices_0=[0, 1, 2, 3, 4, 5],
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selected_indices_1=[0, 1, 2, 3, 4, 5], eps=0.1, min_samples=5)
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# pc.cluster_cubes(pcloud, [4, 4, 4])
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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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# 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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draw_clusters(pc_clusters)
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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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@ -328,7 +286,7 @@ if __name__ == '__main__':
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net.eval()
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labeled_dataset = None
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result_clusters = []
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# Iterate over all the samples and predict
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for sample in test_dataset:
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@ -340,7 +298,7 @@ if __name__ == '__main__':
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else:
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sample_data = np.column_stack((sample["points"].numpy(), sample["normals"], pred_label.numpy()))
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draw_sample_data(sample_data, False)
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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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@ -360,14 +318,14 @@ if __name__ == '__main__':
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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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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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@ -376,22 +334,8 @@ if __name__ == '__main__':
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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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# TODO: Take result clusters and cluster them by primitive type.
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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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pc.write_clusters("clusters.txt", result_clusters)
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