Canny的python代码
时间: 2023-04-02 19:04:16 浏览: 63
以下是Canny算法的Python代码:
```python
import cv2
import numpy as np
def canny(image):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), )
canny = cv2.Canny(blur, 50, 150)
return canny
image = cv2.imread('test.jpg')
canny = canny(image)
cv2.imshow('result', canny)
cv2.waitKey()
```
这段代码实现了Canny算法的边缘检测功能,将图片读入后,先将其转换为灰度图像,然后进行高斯模糊,最后使用Canny函数进行边缘检测。
相关问题
canny算法python代码
以下是基于Python OpenCV库实现Canny算法的代码示例:
```python
import cv2
# 读取图像
img = cv2.imread('image.jpg', 0)
# Canny边缘检测
edges = cv2.Canny(img, 100, 200)
# 显示结果
cv2.imshow('Original image', img)
cv2.imshow('Canny edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
在这个例子中,我们首先读取了一张名为'image.jpg'的图像,并将其转换为灰度图像。然后,我们使用cv2.Canny()函数对图像进行边缘检测。该函数接受三个参数:输入图像、低阈值和高阈值。低阈值和高阈值用于控制边缘的灵敏度和准确度。最后,我们使用cv2.imshow()函数显示原始图像和检测到的边缘。
canny算子python代码
Canny算子是一种常用的边缘检测算法,其Python实现如下:
```python
import cv2
import numpy as np
def canny(image, low_threshold, high_threshold):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
canny = cv2.Canny(blur, low_threshold, high_threshold)
return canny
```
其中,`image`为输入的图像,`low_threshold`和`high_threshold`为双阈值滤波的低阈值和高阈值。函数首先将图像转换为灰度图像,然后进行高斯模糊,最后使用Canny算子进行边缘检测。
另外,如果需要使用PyTorch实现Canny算子,可以使用以下代码:
```python
import torch
import torch.nn.functional as F
def canny_pytorch(image, low_threshold, high_threshold):
gray = torch.mean(image, dim=0, keepdim=True)
blur = F.conv2d(gray, torch.ones(1, 1, 5, 5) / 25, padding=2)
dx = F.conv2d(blur, torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32).unsqueeze(0).unsqueeze(0), padding=1)
dy = F.conv2d(blur, torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32).unsqueeze(0).unsqueeze(0), padding=1)
gradient = torch.sqrt(dx ** 2 + dy ** 2)
theta = torch.atan2(dy, dx)
theta[theta < 0] += np.pi
theta[theta >= np.pi * 7 / 8] = 0
theta[(theta >= np.pi * 1 / 8) & (theta < np.pi * 3 / 8)] = np.pi / 4
theta[(theta >= np.pi * 3 / 8) & (theta < np.pi * 5 / 8)] = np.pi / 2
theta[(theta >= np.pi * 5 / 8) & (theta < np.pi * 7 / 8)] = np.pi * 3 / 4
suppressed = gradient.clone()
for i in range(1, suppressed.shape[2] - 1):
for j in range(1, suppressed.shape[3] - 1):
if theta[0, 0, i, j] == 0:
if (gradient[0, 0, i, j] <= gradient[0, 0, i, j - 1]) or (gradient[0, 0, i, j] <= gradient[0, 0, i, j + 1]):
suppressed[0, 0, i, j] = 0
elif theta[0, 0, i, j] == np.pi / 4:
if (gradient[0, 0, i, j] <= gradient[0, 0, i - 1, j + 1]) or (gradient[0, 0, i, j] <= gradient[0, 0, i + 1, j - 1]):
suppressed[0, 0, i, j] = 0
elif theta[0, 0, i, j] == np.pi / 2:
if (gradient[0, 0, i, j] <= gradient[0, 0, i - 1, j]) or (gradient[0, 0, i, j] <= gradient[0, 0, i + 1, j]):
suppressed[0, 0, i, j] = 0
elif theta[0, 0, i, j] == np.pi * 3 / 4:
if (gradient[0, 0, i, j] <= gradient[0, 0, i - 1, j - 1]) or (gradient[0, 0, i, j] <= gradient[0, 0, i + 1, j + 1]):
suppressed[0, 0, i, j] = 0
suppressed[gradient < low_threshold] = 0
suppressed[gradient > high_threshold] = 255
for i in range(1, suppressed.shape[2] - 1):
for j in range(1, suppressed.shape[3] - 1):
if suppressed[0, 0, i, j] == 255:
if (suppressed[0, 0, i - 1, j] == 0) and (suppressed[0, 0, i + 1, j] == 0) and (suppressed[0, 0, i, j - 1] == 0) and (suppressed[0, 0, i, j + 1] == 0):
suppressed[0, 0, i, j] = 0
return suppressed
```
其中,`image`为输入的图像,`low_threshold`和`high_threshold`为双阈值滤波的低阈值和高阈值。函数首先将图像转换为灰度图像,然后进行高斯模糊,接着计算图像的梯度和方向,进行非极大值抑制和双阈值滤波,最后进行边缘连接。