自适应阈值Canny边缘检测
时间: 2023-10-21 21:06:37 浏览: 43
自适应阈值Canny边缘检测是一种改进的Canny边缘检测算法,它可以根据图像的局部特征自动调整阈值,从而提高边缘检测的鲁棒性和精度。它的基本思路是先计算图像的局部方差,然后根据局部方差自适应地计算阈值,最后使用Canny算法进行边缘检测。这种方法可以有效地处理图像中的噪声和光照变化等问题,从而得到更加准确的边缘检测结果。
相关问题
自适应阈值canny边缘检测步骤matlab
以下是自适应阈值Canny边缘检测的MATLAB代码步骤:
1.读取图像并转换为灰度图像
```matlab
img = imread('image.jpg');
gray = rgb2gray(img);
```
2.使用高斯滤波器平滑图像
```matlab
sigma = 1.5;
gaussian = fspecial('gaussian', [5 5], sigma);
smoothed = imfilter(gray, gaussian);
```
3.计算图像的梯度幅值和方向
```matlab
[Gx, Gy] = imgradientxy(smoothed);
[grad_mag, grad_dir] = imgradient(Gx, Gy);
```
4.进行非极大值抑制
```matlab
nms = imhmax(grad_mag, 0);
```
5.自适应确定Canny算法的分割门限
```matlab
med = median(nms(:));
low = 0.66 * med;
high = 1.33 * med;
```
6.使用双阈值法提取边缘
```matlab
edges = edge(gray, 'canny', [low high]);
```
7.显示结果
```matlab
imshow(edges);
```
自适应阈值canny边缘检测步骤
自适应阈值Canny边缘检测算法的步骤如下:
1. 对输入图像进行高斯滤波,以平滑图像并去除噪声。
```python
import cv2
img = cv2.imread('image.jpg', 0)
blur = cv2.GaussianBlur(img, (3, 3), 0)
```
2. 计算图像中每个像素的梯度幅值和方向。
```python
sobelx = cv2.Sobel(blur, cv2.CV_64F, 1, 0, ksize=3)
sobely = cv2.Sobel(blur, cv2.CV_64F, 0, 1, ksize=3)
mag = cv2.magnitude(sobelx, sobely)
theta = cv2.phase(sobelx, sobely, angleInDegrees=True)
```
3. 对梯度方向进行四舍五入,将其转换为只有0、45、90、135度四个方向。
```python
theta = np.round(theta / 45) * 45 % 180
```
4. 对梯度幅值进行非最大抑制,以保留边缘的细节信息。
```python
rows, cols = mag.shape
for i in range(1, rows - 1):
for j in range(1, cols - 1):
if (0 <= theta[i, j] < 22.5) or (157.5 <= theta[i, j] <= 180):
if (mag[i, j] > mag[i, j - 1]) and (mag[i, j] > mag[i, j + 1]):
mag[i, j] = mag[i, j]
else:
mag[i, j] = 0
elif (22.5 <= theta[i, j] < 67.5):
if (mag[i, j] > mag[i - 1, j - 1]) and (mag[i, j] > mag[i + 1, j + 1]):
mag[i, j] = mag[i, j]
else:
mag[i, j] = 0
elif (67.5 <= theta[i, j] < 112.5):
if (mag[i, j] > mag[i - 1, j]) and (mag[i, j] > mag[i + 1, j]):
mag[i, j] = mag[i, j]
else:
mag[i, j] = 0
elif (112.5 <= theta[i, j] < 157.5):
if (mag[i, j] > mag[i - 1, j + 1]) and (mag[i, j] > mag[i + 1, j - 1]):
mag[i, j] = mag[i, j]
else:
mag[i, j] = 0
```
5. 使用自适应阈值进行双阈值处理,以确定边缘像素。
```python
mag_max = np.max(mag)
mag_min = np.min(mag)
threshold1 = mag_min + 0.2 * (mag_max - mag_min)
threshold2 = mag_min + 0.8 * (mag_max - mag_min)
rows, cols = mag.shape
for i in range(rows):
for j in range(cols):
if mag[i, j] < threshold1:
mag[i, j] = 0
elif mag[i, j] > threshold2:
mag[i, j] = 255
else:
mag[i, j] = 50
```
6. 对双阈值处理后的图像进行边缘连接,以得到完整的边缘。
```python
edges = cv2.Canny(mag, threshold1, threshold2)
```