python实现hough变换
时间: 2023-10-10 22:10:51 浏览: 62
Hough变换是一种图像处理算法,用于在图像中检测直线、圆等几何形状。下面是Python实现Hough变换的代码:
首先,我们需要导入必要的库:
```python
import numpy as np
import cv2
import math
import matplotlib.pyplot as plt
```
接下来,我们读取一张图片,并将其转换为灰度图:
```python
img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
```
然后,我们对灰度图进行边缘检测,这里使用Canny边缘检测算法:
```python
edges = cv2.Canny(gray,50,150,apertureSize = 3)
```
接下来,我们定义Hough变换的函数:
```python
def hough_transform(img, angle_step=1, lines_are_white=True, value_threshold=5):
"""
Hough transform for lines
Input:
img - 2D binary image with nonzeros representing edges
angle_step - Spacing between angles to use every n-th angle
between -90 and 90 degrees. Default step is 1.
lines_are_white - boolean indicating whether lines to be detected are white
value_threshold - Pixel values above or below the value_threshold are edges
Returns:
accumulator - 2D array of the hough transform accumulator
theta - array of angles used in computation, in radians.
rhos - array of rho values. Max size is 2 times the diagonal
distance of the input image.
"""
# Rho and Theta ranges
thetas = np.deg2rad(np.arange(-90.0, 90.0, angle_step))
width, height = img.shape
diag_len = int(round(math.sqrt(width * width + height * height)))
rhos = np.linspace(-diag_len, diag_len, diag_len * 2.0)
# Cache some resuable values
cos_t = np.cos(thetas)
sin_t = np.sin(thetas)
num_thetas = len(thetas)
# Hough accumulator array of theta vs rho
accumulator = np.zeros((2 * diag_len, num_thetas), dtype=np.uint8)
yc, xc = np.nonzero(img)
# Vote in the hough accumulator
for i in range(len(xc)):
x = xc[i]
y = yc[i]
for t_idx in range(num_thetas):
# Calculate rho. diag_len is added for a positive index
rho = int(round(x * cos_t[t_idx] + y * sin_t[t_idx]) + diag_len)
accumulator[rho, t_idx] += 1
return accumulator, thetas, rhos
```
最后,我们调用Hough变换函数,并将结果可视化:
```python
accumulator, thetas, rhos = hough_transform(edges)
# Show the accumulator array
plt.imshow(accumulator, cmap='jet', extent=[np.rad2deg(thetas[-1]), np.rad2deg(thetas[0]), rhos[-1], rhos[0]])
plt.title('Hough transform accumulator')
plt.xlabel('Angles (degrees)')
plt.ylabel('Distance (pixels)')
plt.show()
```
这样,我们就完成了Hough变换的实现。
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)