①编程显示H、S、I三分量图像,观察其直方图,思考并尝试使用阈值分割法寻找物体表面矩形(如图中红框所示)的中心。
时间: 2024-05-09 16:15:00 浏览: 13
首先,需要加载H、S、I三个通道的图像,并显示它们:
```python
import cv2
import numpy as np
# 加载图像
img = cv2.imread('image.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, i = cv2.split(hsv)
# 显示图像
cv2.imshow('H', h)
cv2.imshow('S', s)
cv2.imshow('I', i)
cv2.waitKey(0)
```
然后,可以分别查看三个通道图像的直方图:
```python
import matplotlib.pyplot as plt
# 绘制直方图
fig, axs = plt.subplots(3, 1, figsize=(10, 10))
axs[0].hist(h.ravel(), 256, [0, 256])
axs[0].set_title('Histogram of H')
axs[1].hist(s.ravel(), 256, [0, 256])
axs[1].set_title('Histogram of S')
axs[2].hist(i.ravel(), 256, [0, 256])
axs[2].set_title('Histogram of I')
plt.show()
```
接下来,可以使用阈值分割法寻找物体表面矩形的中心。首先,我们需要选择一个适当的阈值。可以使用Otsu方法自动计算阈值:
```python
# 自动计算阈值
ret, thresh = cv2.threshold(i, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow('Thresh', thresh)
cv2.waitKey(0)
```
然后,可以使用形态学操作(如开运算和闭运算)来平滑二值图像,并找到物体表面的轮廓:
```python
# 形态学操作
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
# 查找轮廓
contours, hierarchy = cv2.findContours(closing, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0, 0, 255), 2)
cv2.imshow('Contours', img)
cv2.waitKey(0)
```
最后,可以计算物体表面矩形的中心:
```python
# 计算矩形中心
M = cv2.moments(contours[0])
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
cv2.circle(img, (cx, cy), 5, (0, 255, 0), -1)
cv2.imshow('Result', img)
cv2.waitKey(0)
```
完整代码如下:
```python
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 加载图像
img = cv2.imread('image.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, i = cv2.split(hsv)
# 显示图像
cv2.imshow('H', h)
cv2.imshow('S', s)
cv2.imshow('I', i)
cv2.waitKey(0)
# 绘制直方图
fig, axs = plt.subplots(3, 1, figsize=(10, 10))
axs[0].hist(h.ravel(), 256, [0, 256])
axs[0].set_title('Histogram of H')
axs[1].hist(s.ravel(), 256, [0, 256])
axs[1].set_title('Histogram of S')
axs[2].hist(i.ravel(), 256, [0, 256])
axs[2].set_title('Histogram of I')
plt.show()
# 自动计算阈值
ret, thresh = cv2.threshold(i, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow('Thresh', thresh)
cv2.waitKey(0)
# 形态学操作
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
# 查找轮廓
contours, hierarchy = cv2.findContours(closing, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0, 0, 255), 2)
cv2.imshow('Contours', img)
cv2.waitKey(0)
# 计算矩形中心
M = cv2.moments(contours[0])
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
cv2.circle(img, (cx, cy), 5, (0, 255, 0), -1)
cv2.imshow('Result', img)
cv2.waitKey(0)
```
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