stuff
This commit is contained in:
Markus Friedrich
parent
ce7ff0ae7c
commit
4e1fcdfd43
@ -152,4 +152,18 @@ def draw_clusters(clusters):
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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)
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o3d.draw_geometries(clouds)
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def write_clusters(path, clusters, type_column=6):
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file = open(path, "w")
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file.write(str(len(clusters)) + "\n")
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for cluster in clusters:
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print("Types: ", cluster[:, type_column])
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types = np.unique(cluster[:, type_column], axis=0)
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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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@ -139,7 +139,7 @@ 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):
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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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@ -165,11 +165,15 @@ def extract_cube_clusters(data, cluster_dims, max_points_per_cluster):
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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] = farthest_point_sampling(c, max_points_per_cluster)
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if c.shape[0] < min_points_per_cluster:
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continue
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return clusters.values()
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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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@ -186,7 +190,6 @@ def extract_clusters(data, selected_indices, eps, min_samples, metric='euclidean
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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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@ -198,7 +201,6 @@ def extract_clusters(data, selected_indices, eps, min_samples, metric='euclidean
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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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@ -241,7 +243,7 @@ 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_3.pth', help='model path')
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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('--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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@ -260,31 +262,40 @@ if __name__ == '__main__':
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dataset_folder = './data/raw/predict/'
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pointcloud_file = './pointclouds/0_pc.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 = append_normal_angles(pcloud)
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# pcloud = farthest_point_sampling(pcloud, opt.npoints)
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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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# 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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# 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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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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#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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@ -293,7 +304,7 @@ if __name__ == '__main__':
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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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refresh=False,
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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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@ -301,8 +312,6 @@ if __name__ == '__main__':
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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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@ -315,33 +324,68 @@ if __name__ == '__main__':
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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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labeled_dataset = None
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# Iterate over all the samples
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# Iterate over all the samples and predict
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for sample in test_dataset:
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print('Eval test sample ..')
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# Predict
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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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#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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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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