SHOT特征描述子二值化代码
时间: 2023-11-12 15:13:27 浏览: 37
SHOT特征描述子二值化代码可以参考以下示例代码:
```python
import numpy as np
def binary_encode(descriptor, threshold=0):
"""
将SHOT特征描述子二值化
:param descriptor: SHOT特征描述子
:param threshold: 二值化阈值,默认为0
:return: 二值化后的SHOT特征描述子
"""
binary_descriptor = np.zeros_like(descriptor)
binary_descriptor[descriptor > threshold] = 1
return binary_descriptor
```
在上述代码中,我们通过将大于阈值的元素设置为1,其余元素设置为0的方式实现了SHOT特征描述子的二值化。调用该函数时,可以指定二值化阈值,如果不指定则默认为0。
相关问题
SHOT描述子二值化代码
以下是使用PyTorch实现SHOT描述子二值化的示例代码:
```python
import torch
import numpy as np
def binary_encode(x, threshold=0):
"""
将输入的向量进行二值化编码
:param x: 输入的向量,可以是numpy数组或PyTorch张量
:param threshold: 阈值,用于将向量中小于等于该值的元素设置为0,大于该值的元素设置为1,默认为0
:return: 二值化后的向量
"""
if isinstance(x, np.ndarray):
x = torch.from_numpy(x)
x_binary = torch.zeros_like(x)
x_binary[x > threshold] = 1
x_binary[x <= threshold] = 0
return x_binary
def shot_descriptor(points, normals, pcd, search_radius, num_angles=9, num_bins=10):
"""
计算SHOT描述子
:param points: 关键点的坐标,大小为[N,3]
:param normals: 关键点的法向量,大小为[N,3]
:param pcd: 点云数据,大小为[M,3]
:param search_radius: 搜索半径
:param num_angles: 角度分割数,默认为9
:param num_bins: 距离分割数,默认为10
:return: SHOT描述子,大小为[N,num_angles*num_bins*2]
"""
N = points.shape[0]
M = pcd.shape[0]
device = points.device
# 计算每个关键点的KNN
knn_indices = torch.empty((N, 100), dtype=torch.long, device=device)
knn_dists = torch.empty((N, 100), dtype=torch.float32, device=device)
pcd_tensor = torch.from_numpy(pcd).to(device)
for i in range(N):
query_point = points[i].unsqueeze(0)
dists, indices = torch.topk(torch.norm(pcd_tensor - query_point, dim=1), k=100, largest=False)
knn_indices[i] = indices
knn_dists[i] = dists
# 计算每个关键点的SHOT描述子
descriptors = torch.zeros((N, num_angles * num_bins * 2), dtype=torch.float32, device=device)
for i in range(N):
query_point = points[i]
query_normal = normals[i]
knn = pcd_tensor[knn_indices[i]]
knn_normals = normals[knn_indices[i]]
# 对每个KNN点计算相对坐标和距离
knn_coords = knn - query_point.unsqueeze(0)
knn_dists_norm = knn_dists[i] / search_radius
# 将相对坐标和距离组合成新的特征向量
knn_features = torch.cat((knn_coords, knn_dists_norm.unsqueeze(1)), dim=1)
# 计算每个特征向量的角度和距离
angles = torch.acos(torch.sum(knn_features * query_normal.unsqueeze(0), dim=1) / torch.norm(knn_features, dim=1))
distances = torch.norm(knn_features[:, :3], dim=1)
# 将角度和距离进行分组
angle_bins = torch.floor(angles / (np.pi / num_angles)).type(torch.long)
dist_bins = torch.floor(distances / (search_radius / num_bins)).type(torch.long)
# 对每个分组进行编码
for j in range(num_angles):
for k in range(num_bins):
indices = (angle_bins == j) & (dist_bins == k)
if indices.any():
# 对每个分组内的特征向量进行平均
features = knn_features[indices]
mean_features = torch.mean(features, dim=0)
# 将每个分组的平均向量作为描述子的一部分
start_idx = j * num_bins * 2 + k * 2
descriptors[i, start_idx:start_idx+2] = mean_features[:2].cpu()
# 对每个描述子进行二值化编码
binary_descriptors = binary_encode(descriptors)
return binary_descriptors
```
这段代码实现了计算SHOT描述子,并对每个描述子进行二值化编码。其中,`points`和`normals`分别是关键点的坐标和法向量,`pcd`是点云数据,`search_radius`是搜索半径,`num_angles`和`num_bins`分别是角度和距离的分割数。函数返回二值化后的SHOT描述子。
PCL中SHOT描述子二值化代码
在PCL中,可以使用 `pcl::SHOTColorEstimation` 类来计算SHOT描述子。对于二值化,可以使用以下代码:
```cpp
pcl::SHOTColorEstimation<pcl::PointXYZRGB, pcl::Normal, pcl::SHOT1344> shot;
shot.setInputCloud(cloud);
shot.setInputNormals(normals);
shot.setSearchMethod(tree);
pcl::PointCloud<pcl::SHOT1344>::Ptr shot_desc(new pcl::PointCloud<pcl::SHOT1344>);
shot.compute(*shot_desc);
// 将SHOT描述子二值化
for (size_t i = 0; i < shot_desc->size(); ++i) {
for (size_t j = 0; j < 1344; j++) {
if (shot_desc->points[i].descriptor[j] > 0) {
shot_desc->points[i].descriptor[j] = 1;
}
}
}
```
这里假设输入点云和法向量分别为 `cloud` 和 `normals`,搜索方法为 `tree`,计算得到的SHOT描述子存储在 `shot_desc` 中。在二值化过程中,遍历每个描述子的维度,如果维度的值大于0,则将其赋值为1。