# OpenCV半小时掌握基本操作之分水岭算法

【OpenCV】⚠️高手勿入! 半小时学会基本操作 ⚠️ 分水岭算法

## 概述

OpenCV 是一个跨平台的计算机视觉库, 支持多语言, 功能强大. 今天小白就带大家一起携手走进 OpenCV 的世界.

1. 读取图片
2. 转换成灰度图
3. 二值化
4. 距离变换
5. 寻找种子
6. 生成 Marker
7. 分水岭变换

## 连通域

```cv2.connectedComponents(image, labels=None, connectivity=None, ltype=None)
```

• image: 输入图像, 必须是 uint8 二值图像
• labels 图像上每一像素的标记, 用数字 1, 2, 3 表示

## 分水岭

```cv2.watershed(image, markers)
```

• image: 输入图像
• markers: 种子, 包含不同区域的轮廓

## 代码实战

```import numpy as np
import cv2
from matplotlib import pyplot as plt

def watershed(image):
"""分水岭算法"""

# 卷积核
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

# 均值迁移滤波
blur = cv2.pyrMeanShiftFiltering(image, 10, 100)

# 转换成灰度图
image_gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)

# 二值化
ret1, thresh1 = cv2.threshold(image_gray, 0, 255, cv2.THRESH_OTSU)

# 开运算
open = cv2.morphologyEx(thresh1, cv2.MORPH_OPEN, kernel, iterations=2)

# 膨胀
dilate = cv2.dilate(open, kernel, iterations=3)

# 距离变换
dist = cv2.distanceTransform(dilate, cv2.DIST_L2, 3)
dist = cv2.normalize(dist, 0, 1.0, cv2.NORM_MINMAX)
print(dist.max())

# 二值化
ret2, thresh2 = cv2.threshold(dist, dist.max() * 0.6, 255, cv2.THRESH_BINARY)
thresh2 = np.uint8(thresh2)

# 分水岭计算
unknown = cv2.subtract(dilate, thresh2)
ret3, component = cv2.connectedComponents(thresh2)
print(ret3)

# 分水岭计算
markers = component + 1
markers[unknown == 255] = 0
result = cv2.watershed(image, markers=markers)
image[result == -1] = [0, 0, 255]

# 图片展示
image_show((image, blur, image_gray, thresh1, open, dilate), (dist, thresh2, unknown, component, markers, image))

return image

def image_show(graph1, graph2):
"""绘制图片"""

# 图像1
original, blur, gray, binary1, open, dilate = graph1

# 图像2
dist, binary2, unknown, component, markers, result = graph2

f, ax = plt.subplots(3, 2, figsize=(12, 16))

# 绘制子图
ax[0, 0].imshow(cv2.cvtColor(original, cv2.COLOR_BGR2RGB))
ax[0, 1].imshow(cv2.cvtColor(blur, cv2.COLOR_BGR2RGB))
ax[1, 0].imshow(gray, "gray")
ax[1, 1].imshow(binary1, "gray")
ax[2, 0].imshow(open, "gray")
ax[2, 1].imshow(dilate, "gray")

# 标题
ax[0, 0].set_title("original")
ax[0, 1].set_title("image blur")
ax[1, 0].set_title("image gray")
ax[1, 1].set_title("image binary1")
ax[2, 0].set_title("image open")
ax[2, 1].set_title("image dilate")

plt.show()

f, ax = plt.subplots(3, 2, figsize=(12, 16))

# 绘制子图
ax[0, 0].imshow(dist, "gray")
ax[0, 1].imshow(binary2, "gray")
ax[1, 0].imshow(unknown, "gray")
ax[1, 1].imshow(component, "gray")
ax[2, 0].imshow(markers, "gray")
ax[2, 1].imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))

# 标题
ax[0, 0].set_title("image distance")
ax[0, 1].set_title("image binary2")
ax[1, 0].set_title("image unknown")
ax[1, 1].set_title("image component")
ax[2, 0].set_title("image markers")
ax[2, 1].set_title("result")

plt.show()

if __name__ == "__main__":
# 读取图片