用opencv结合分水岭算法和细胞计数并在图上标记细胞数字,写出具体代码
时间: 2024-03-23 11:39:25 浏览: 61
好的,根据您的要求,我会给出具体的代码实现。
首先,您需要安装OpenCV库,然后导入以下所需的库:
```python
import cv2
import numpy as np
from matplotlib import pyplot as plt
```
接下来,我们需要读入一张细胞图像,这里我们以细胞图像`cell.jpg`为例:
```python
img = cv2.imread('cell.jpg')
```
然后,我们需要进行预处理,包括灰度化、二值化等操作:
```python
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
```
然后,我们需要对图像进行形态学操作,以便于进行分割:
```python
# 进行腐蚀操作
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel,iterations = 2)
# 进行膨胀操作
sure_bg = cv2.dilate(opening,kernel,iterations=3)
```
接下来,我们可以通过距离变换来获取前景区域:
```python
# 距离变换
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# 找到未知区域
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
```
然后,我们可以通过分水岭算法来进行分割:
```python
# 分水岭算法
ret, markers = cv2.connectedComponents(sure_fg)
# 添加标记
markers = markers+1
# 将未知区域标记为0
markers[unknown==255] = 0
# 应用分水岭算法
markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]
```
最后,我们可以通过以下代码来标记细胞数字并显示图像:
```python
# 标记细胞数字
font = cv2.FONT_HERSHEY_SIMPLEX
count = 0
for i in range(1,ret):
x,y,w,h = cv2.boundingRect(np.uint8(markers==i))
if w*h>100:
count += 1
cv2.putText(img,str(count),(x,y-5), font, 0.5,(255,255,255),2,cv2.LINE_AA)
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
# 显示图像
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.show()
```
这样,我们就完成了细胞计数并在图上标记细胞数字的任务。完整代码如下:
```python
import cv2
import numpy as np
from matplotlib import pyplot as plt
# 读入图像
img = cv2.imread('cell.jpg')
# 灰度化和二值化处理
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# 形态学处理
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel,iterations = 2)
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# 距离变换
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
# 分水岭算法
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers+1
markers[unknown==255] = 0
markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]
# 标记细胞数字
font = cv2.FONT_HERSHEY_SIMPLEX
count = 0
for i in range(1,ret):
x,y,w,h = cv2.boundingRect(np.uint8(markers==i))
if w*h>100:
count += 1
cv2.putText(img,str(count),(x,y-5), font, 0.5,(255,255,255),2,cv2.LINE_AA)
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
# 显示图像
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.show()
```
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