python opencv根据图片给出canny算法得到的边缘图
时间: 2023-12-24 16:03:52 浏览: 30
如何去除噪声?
可以使用高斯模糊(Gaussian Blur)来去除噪声,具体做法是先对原图进行高斯模糊处理,然后再使用 Canny 算法得到边缘图。高斯模糊可以通过cv2.GaussianBlur()函数来实现,该函数提供了一些参数来控制高斯核的大小和标准差。一般情况下,我们可以使用 3x3 的高斯核进行模糊处理。
相关问题
不用OPENCV库用python实现canny边缘检测算法
可以使用Python的NumPy和Matplotlib库来实现Canny边缘检测算法。以下是一个简单的实现示例:
```python
import numpy as np
import matplotlib.pyplot as plt
def canny_edge_detection(image, sigma=1, kernel_size=5, low_threshold=0.1, high_threshold=0.3):
# Convert image to grayscale
gray = np.mean(image, axis=2)
# Apply Gaussian blur
blurred = np.zeros_like(gray)
kernel = np.zeros((kernel_size, kernel_size))
for i in range(kernel_size):
for j in range(kernel_size):
kernel[i, j] = np.exp(-((i - kernel_size // 2) ** 2 + (j - kernel_size // 2) ** 2) / (2 * sigma ** 2))
kernel /= np.sum(kernel)
for i in range(kernel_size // 2, gray.shape[0] - kernel_size // 2):
for j in range(kernel_size // 2, gray.shape[1] - kernel_size // 2):
blurred[i, j] = np.sum(gray[i - kernel_size // 2:i + kernel_size // 2 + 1, j - kernel_size // 2:j + kernel_size // 2 + 1] * kernel)
# Compute gradient magnitude and direction
dx = np.zeros_like(blurred)
dy = np.zeros_like(blurred)
for i in range(1, blurred.shape[0] - 1):
for j in range(1, blurred.shape[1] - 1):
dx[i, j] = blurred[i, j + 1] - blurred[i, j - 1]
dy[i, j] = blurred[i + 1, j] - blurred[i - 1, j]
magnitude = np.sqrt(dx ** 2 + dy ** 2)
direction = np.arctan2(dy, dx)
# Non-maximum suppression
suppressed = np.zeros_like(magnitude)
for i in range(1, magnitude.shape[0] - 1):
for j in range(1, magnitude.shape[1] - 1):
angle = direction[i, j] * 180 / np.pi
if angle < 0:
angle += 180
if (angle >= 0 and angle < 22.5) or (angle >= 157.5 and angle < 180):
if magnitude[i, j] >= magnitude[i, j - 1] and magnitude[i, j] >= magnitude[i, j + 1]:
suppressed[i, j] = magnitude[i, j]
elif (angle >= 22.5 and angle < 67.5):
if magnitude[i, j] >= magnitude[i - 1, j - 1] and magnitude[i, j] >= magnitude[i + 1, j + 1]:
suppressed[i, j] = magnitude[i, j]
elif (angle >= 67.5 and angle < 112.5):
if magnitude[i, j] >= magnitude[i - 1, j] and magnitude[i, j] >= magnitude[i + 1, j]:
suppressed[i, j] = magnitude[i, j]
elif (angle >= 112.5 and angle < 157.5):
if magnitude[i, j] >= magnitude[i - 1, j + 1] and magnitude[i, j] >= magnitude[i + 1, j - 1]:
suppressed[i, j] = magnitude[i, j]
# Double thresholding and edge tracking
low_threshold *= np.max(suppressed)
high_threshold *= np.max(suppressed)
edges = np.zeros_like(suppressed)
strong_i, strong_j = np.where(suppressed >= high_threshold)
weak_i, weak_j = np.where((suppressed >= low_threshold) & (suppressed < high_threshold))
edges[strong_i, strong_j] = 1
while len(weak_i) > 0:
i, j = weak_i[0], weak_j[0]
weak_i, weak_j = np.delete(weak_i, 0), np.delete(weak_j, 0)
if edges[i + 1, j] == 1 or edges[i - 1, j] == 1 or edges[i, j + 1] == 1 or edges[i, j - 1] == 1 or edges[i + 1, j + 1] == 1 or edges[i - 1, j - 1] == 1 or edges[i + 1, j - 1] == 1 or edges[i - 1, j + 1] == 1:
edges[i, j] = 1
return edges
# Test the function on a sample image
image = plt.imread('lena.png')
edges = canny_edge_detection(image)
plt.imshow(edges, cmap='gray')
plt.show()
```
这个实现使用了高斯滤波器来平滑图像,计算梯度幅值和方向,进行非极大值抑制,双阈值处理和边缘跟踪。可以通过调整参数来控制算法的性能。
python图像边缘检测_python Canny边缘检测算法的实现
Canny边缘检测是一种常用的图像处理算法,它可以检测图像中物体的边缘,并将其转换为二值化图像。下面是Python实现Canny边缘检测算法的示例代码:
``` python
import cv2
import numpy as np
# 读取图像
img = cv2.imread("test.jpg")
# 转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用高斯滤波平滑图像
blur = cv2.GaussianBlur(gray, (3, 3), 0)
# 计算梯度
dx = cv2.Sobel(blur, cv2.CV_16S, 1, 0)
dy = cv2.Sobel(blur, cv2.CV_16S, 0, 1)
# 计算梯度的幅值和方向
edge = cv2.Canny(dx, dy, 50, 150)
# 显示结果
cv2.imshow("original image", img)
cv2.imshow("edge detection", edge)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
在上述代码中,我们首先读取了一张图像,并将其转换为灰度图像。接下来,我们使用高斯滤波平滑了图像,然后使用Sobel算子计算了图像的梯度。最后,我们使用Canny算法计算了梯度的幅值和方向,并将其转换为二值化图像进行显示。
在运行上述代码之前,需要先安装OpenCV库。
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