# Euclidean-PCL-Python (0%)

## 1. Eucliean Cluster

> 중요 : colorlist is a static variable you should define it outside the function getcolorlist, I defined it after if \_name == ‘\_\_main‘: get\_color\_list.color\_list = \[]

```python
def do_euclidean_clustering(white_cloud):
    '''
    :param cloud_objects:
    :return: cluster cloud and cluster indices
    '''
    tree = white_cloud.make_kdtree()

    # Create Cluster-Mask Point Cloud to visualize each cluster separately
    ec = white_cloud.make_EuclideanClusterExtraction()
    ec.set_ClusterTolerance(0.015)
    ec.set_MinClusterSize(50)
    ec.set_MaxClusterSize(20000)
    ec.set_SearchMethod(tree)
    cluster_indices = ec.Extract()
    cluster_color = get_color_list(len(cluster_indices))

    color_cluster_point_list = []

    for j, indices in enumerate(cluster_indices):
        for i, indice in enumerate(indices):
            color_cluster_point_list.append([white_cloud[indice][0],
                                             white_cloud[indice][1],
                                             white_cloud[indice][2],
                                             rgb_to_float(cluster_color[j])])

    cluster_cloud = pcl.PointCloud_PointXYZRGB()
    cluster_cloud.from_list(color_cluster_point_list)
    return cluster_cloud,cluster_indices

# Euclidean Clustering


white_cloud= XYZRGB_to_XYZ(cloud_objects)
cluster_cloud,cluster_indices = do_euclidean_clustering(white_cloud)
```

Yuchao's blogspot

```python
# Euclidean Clustering
white_cloud = XYZRGB_to_XYZ(white_cloud) # <type 'pcl._pcl.PointCloud'>
tree = white_cloud.make_kdtree() # <type 'pcl._pcl.KdTree'>
ec = white_cloud.make_EuclideanClusterExtraction()
ec.set_ClusterTolerance(0.02) # for hammer
ec.set_MinClusterSize(10)
ec.set_MaxClusterSize(250)
ec.set_SearchMethod(tree)
cluster_indices = ec.Extract() # indices for each cluster (a list of lists)
# Assign a color to each cluster
cluster_color = get_color_list(len(cluster_indices))
color_cluster_point_list = []
for j, indices in enumerate(cluster_indices):
for i, indice in enumerate(indices):
color_cluster_point_list.append([white_cloud[indice][0], white_cloud[indice][1], white_cloud[indice][2], rgb_to_float(cluster_color[j])])
# Create new cloud containing all clusters, each with unique color
cluster_cloud = pcl.PointCloud_PointXYZRGB()
cluster_cloud.from_list(color_cluster_point_list)
# publish to cloud
ros_cluster_cloud = pcl_to_ros(cluster_cloud)
# save to local
pcl.save(cluster_cloud, 'cluster.pcd')
```

분리수행

```python
# Euclidean Clustering
def euclid_cluster(cloud):
    white_cloud = XYZRGB_to_XYZ(cloud) # Apply function to convert XYZRGB to XYZ
    tree = white_cloud.make_kdtree()
    ec = white_cloud.make_EuclideanClusterExtraction()
    ec.set_ClusterTolerance(0.015)
    ec.set_MinClusterSize(20)
    ec.set_MaxClusterSize(3000)
    ec.set_SearchMethod(tree)
    cluster_indices = ec.Extract()

    return cluster_indices, white_cloud



def cluster_mask(cluster_indices, white_cloud):
    # Create Cluster-Mask Point Cloud to visualize each cluster separately
    #Assign a color corresponding to each segmented object in scene
    cluster_color = get_color_list(len(cluster_indices))

    color_cluster_point_list = []

    for j, indices in enumerate(cluster_indices):
        for i, indice in enumerate(indices):
            color_cluster_point_list.append([
                                            white_cloud[indice][0],
                                            white_cloud[indice][1],
                                            white_cloud[indice][2],
                                            rgb_to_float( cluster_color[j] )
                                           ])

    #Create new cloud containing all clusters, each with unique color
    cluster_cloud = pcl.PointCloud_PointXYZRGB()
    cluster_cloud.from_list(color_cluster_point_list)

    return cluster_cloud


cluster_indices, white_cloud = euclid_cluster(white_cloud)
get_color_list.color_list = []
```

적절한 eps 정하는 방법 : 1. StandardScaler 2. MinMaxScaler

## Region growing segmentation


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://pcl.gitbook.io/tutorial/part-2/part02-chapter01/part02-chapter01-euclidean-pcl-python.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.