Merge remote-tracking branch 'origin/master'
# Conflicts: # predict/predict.py
This commit is contained in:
Markus Friedrich
commit
f2cc070d04
@ -148,6 +148,8 @@ class CustomShapeNet(InMemoryDataset):
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####################################
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# This is where you define the keys
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attr_dict = dict(y=y, pos=points[:, :3 if not self.with_normals else 6])
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if not self.with_normals:
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attr_dict.update(normals=points[:, 3:6])
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####################################
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if self.collate_per_element:
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data = Data(**attr_dict)
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@ -28,8 +28,9 @@ def eval_sample(net, sample):
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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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f = points.shape[1]
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points = points.view(1, n, 3) # make a batch
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points = points.view(1, n, f) # 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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@ -85,6 +86,18 @@ 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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@ -127,6 +140,43 @@ 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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@ -178,7 +228,7 @@ def draw_clusters(clusters):
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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.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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@ -194,9 +244,10 @@ 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_0.pth', help='model path')
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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('--with_normals', type=strtobool, default=True, help='if training will include normals')
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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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@ -211,46 +262,41 @@ 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_0.xyz'
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pointcloud_file = './pointclouds/1_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 = pc.normalize_pointcloud(pcloud, 1)
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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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#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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# 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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# 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.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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# pc = StandardScaler().fit_transform(pc)
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recreate_folder(dataset_folder)
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for idx, pcc in enumerate(pc_clusters):
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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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# 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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@ -259,8 +305,9 @@ if __name__ == '__main__':
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test_dataset = ShapeNetPartSegDataset(
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mode='predict',
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root_dir='data',
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with_normals=opt.with_normals,
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npoints=opt.npoints,
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refresh=False,
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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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@ -273,7 +320,8 @@ 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 = PointNet2PartSegmentNet(num_classes)
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# net = PointNetPartSegmentNet(num_classes)
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net = PointNet2PartSegmentNet(num_classes, with_normals=opt.with_normals)
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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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@ -287,20 +335,63 @@ if __name__ == '__main__':
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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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if opt.with_normals:
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sample_data = np.column_stack((sample["points"].numpy(), pred_label.numpy()))
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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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#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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print("prediction done")
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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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