如何对于利用ct成像,已经提取出来的植物根系的二值化图片,图片为450张512x512的,图片为png格式,图片分辨率为0.3mm,将它堆栈成三维图像,三维骨架化以后,再去计算三维根系的总长度,利用3.8版本python,举个详细的例子
时间: 2024-02-27 19:57:03 浏览: 28
很抱歉,刚才的回答中出现了重复,这里给您提供一个详细的Python代码示例,用于将450张512x512的二值化PNG图像堆叠成三维图像,并进行三维骨架化和长度计算。
首先,您需要安装以下Python库:
- NumPy:用于处理数组数据
- OpenCV:用于图像处理
- scikit-image:用于图像处理和三维重构
- mahotas:用于三维骨架化
您可以使用以下命令安装这些库:
```
pip install numpy opencv-python scikit-image mahotas
```
然后,您可以按照以下步骤进行操作:
1. 导入必要的库
```python
import cv2
import numpy as np
import os
from skimage import measure
import mahotas
```
2. 指定文件路径和阈值
```python
data_path = '/path/to/data/folder/'
threshold = 150
```
3. 定义函数来处理每个图像并将结果添加到一个列表中
```python
def process_image(path):
# 读取图像并进行阈值处理
img = cv2.imread(path, 0)
binary = np.zeros_like(img)
binary[img > threshold] = 1
# 进行骨架化并计算长度
skeleton = mahotas.skeletonize(binary)
distances = mahotas.distance(skeleton)
total_length = np.sum(distances)
return skeleton, total_length
# 处理每个图像并将结果添加到列表中
skeletons = []
lengths = []
for i in range(1, 451):
filename = f"root_{i}.png"
path = os.path.join(data_path, filename)
skeleton, length = process_image(path)
skeletons.append(skeleton)
lengths.append(length)
```
4. 将所有骨架化图像堆叠成三维数组
```python
stacked = np.stack(skeletons, axis=0)
```
5. 使用scikit-image库的marching_cubes_lewiner函数将二值化图像转换为三维网格
```python
verts, faces, _, _ = measure.marching_cubes_lewiner(stacked)
```
6. 计算三维根系的总长度
```python
# 计算每个骨架化像素之间的欧几里得距离
distances = np.sqrt(np.sum(np.square(np.diff(np.argwhere(stacked), axis=0)), axis=1))
# 将所有距离相加以获得总长度
total_length = np.sum(distances)
```
完整的代码示例:
```python
import cv2
import numpy as np
import os
from skimage import measure
import mahotas
# 指定文件路径和阈值
data_path = '/path/to/data/folder/'
threshold = 150
# 定义函数来处理每个图像并将结果添加到一个列表中
def process_image(path):
# 读取图像并进行阈值处理
img = cv2.imread(path, 0)
binary = np.zeros_like(img)
binary[img > threshold] = 1
# 进行骨架化并计算长度
skeleton = mahotas.skeletonize(binary)
distances = mahotas.distance(skeleton)
total_length = np.sum(distances)
return skeleton, total_length
# 处理每个图像并将结果添加到列表中
skeletons = []
lengths = []
for i in range(1, 451):
filename = f"root_{i}.png"
path = os.path.join(data_path, filename)
skeleton, length = process_image(path)
skeletons.append(skeleton)
lengths.append(length)
# 将所有骨架化图像堆叠成三维数组
stacked = np.stack(skeletons, axis=0)
# 将二值化图像转换为三维网格
verts, faces, _, _ = measure.marching_cubes_lewiner(stacked)
# 计算每个骨架化像素之间的欧几里得距离
distances = np.sqrt(np.sum(np.square(np.diff(np.argwhere(stacked), axis=0)), axis=1))
# 将所有距离相加以获得总长度
total_length = np.sum(distances)
print(f"根系的总长度为:{total_length}")
```