python实现canny算法

很高兴为您解答，Python可以使用OpenCV库来实现Canny边缘检测算法。具体可以通过以下代码来实现：

import cv2

img = cv2.imread('image.jpg', 0) # 读取灰度图像
edges = cv2.Canny(img, 100, 200) # Canny边缘检测
cv2.imshow('Canny', edges) # 显示结果
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，第一行代码读取一张灰度图像，第二行代码使用Canny边缘检测算法，第三行代码显示边缘检测结果。

希望这可以帮助您！

相关问题

使用python实现canny边缘算法

好的，以下是使用Python实现Canny边缘算法的代码：

import cv2

# 读取图片
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# 高斯滤波
img_blur = cv2.GaussianBlur(img, (3, 3), 0)

# 计算梯度
sobelx = cv2.Sobel(img_blur, cv2.CV_64F, 1, 0, ksize=3)
sobely = cv2.Sobel(img_blur, cv2.CV_64F, 0, 1, ksize=3)
grad = cv2.sqrt(cv2.addWeighted(cv2.pow(sobelx, 2.0), 1.0, cv2.pow(sobely, 2.0), 1.0, 0.0))

# 非极大值抑制
gradient_direction = cv2.phase(sobelx, sobely, angleInDegrees=True)
gradient_direction = gradient_direction % 180
gradient_direction[gradient_direction < 22.5] = 0
gradient_direction[(gradient_direction >= 22.5) & (gradient_direction < 67.5)] = 45
gradient_direction[(gradient_direction >= 67.5) & (gradient_direction < 112.5)] = 90
gradient_direction[(gradient_direction >= 112.5) & (gradient_direction < 157.5)] = 135
gradient_direction[gradient_direction >= 157.5] = 0
nms = cv2.copyMakeBorder(grad, 1, 1, 1, 1, cv2.BORDER_CONSTANT, value=0)
for i in range(1, nms.shape[0] - 1):
    for j in range(1, nms.shape[1] - 1):
        if gradient_direction[i - 1, j - 1] == 0:
            if nms[i, j] < nms[i, j - 1] or nms[i, j] < nms[i, j + 1]:
                nms[i, j] = 0
        elif gradient_direction[i - 1, j - 1] == 45:
            if nms[i, j] < nms[i - 1, j + 1] or nms[i, j] < nms[i + 1, j - 1]:
                nms[i, j] = 0
        elif gradient_direction[i - 1, j - 1] == 90:
            if nms[i, j] < nms[i - 1, j] or nms[i, j] < nms[i + 1, j]:
                nms[i, j] = 0
        else:
            if nms[i, j] < nms[i - 1, j - 1] or nms[i, j] < nms[i + 1, j + 1]:
                nms[i, j] = 0
nms = nms[1:-1, 1:-1]

# 双阈值检测
threshold_low = 0.05 * grad.max()
threshold_high = 0.15 * grad.max()
edges = cv2.Canny(img, threshold_low, threshold_high)

# 显示结果
cv2.imshow('Original Image', img)
cv2.imshow('Canny Edge Detection', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码首先使用`cv2.imread`函数读取图片，然后使用`cv2.GaussianBlur`函数进行高斯滤波，降低噪声的影响，接着使用`cv2.Sobel`函数计算梯度，然后进行非极大值抑制，最后使用双阈值检测得到边缘图像。最后使用`cv2.imshow`函数显示原图和边缘图像。

python实现canny边缘检测算法

Canny边缘检测是一种经典的图像边缘检测算法，下面是Python实现代码:

import cv2
import numpy as np

# 读取图片
img = cv2.imread('image.jpg')

# 灰度化处理
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 高斯平滑滤波
gaussian = cv2.GaussianBlur(gray, (5, 5), 0)

# 计算梯度
sobelx = cv2.Sobel(gaussian, cv2.CV_64F, 1, 0, ksize=3)
sobely = cv2.Sobel(gaussian, cv2.CV_64F, 0, 1, ksize=3)
mag = np.sqrt(sobelx**2 + sobely**2)
mag = np.uint8(mag)

# 非极大值抑制
height, width = mag.shape
non_max = np.zeros((height, width), dtype=np.uint8)
for i in range(1, height - 1):
    for j in range(1, width - 1):
        if sobelx[i, j] == 0:
            tangent = 1e5
        else:
            tangent = sobely[i, j] / sobelx[i, j]
        if mag[i, j] >= mag[i-1, j-1] * tangent and mag[i, j] >= mag[i+1, j+1] * tangent:
            non_max[i, j] = 255
        elif mag[i, j] >= mag[i-1, j] and mag[i, j] >= mag[i+1, j]:
            non_max[i, j] = 255
        elif mag[i, j] >= mag[i, j-1] * tangent and mag[i, j] >= mag[i, j+1] * tangent:
            non_max[i, j] = 255
        elif mag[i, j] >= mag[i, j-1] and mag[i, j] >= mag[i, j+1]:
            non_max[i, j] = 255

# 双阈值和连接边缘
weak = 75
strong = 255
result = np.zeros((height, width), dtype=np.uint8)
result[mag > weak] = 1
result[mag > strong] = 255
for i in range(1, height - 1):
    for j in range(1, width - 1):
        if result[i, j] == 1:
            if result[i-1, j-1] == 255 or result[i-1, j] == 255 or result[i-1, j+1] == 255 or result[i, j-1] == 255 or result[i, j+1] == 255 or result[i+1, j-1] == 255 or result[i+1, j] == 255 or result[i+1, j+1] == 255:
                result[i, j] = 255
            else:
                result[i, j] = 0

# 显示图片
cv2.imshow('Canny Edge Detection', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

以上代码中使用了OpenCV库实现了Canny边缘检测算法，其中包括灰度化处理、高斯平滑滤波、计算梯度、非极大值抑制、双阈值和连接边缘等步骤，最终结果显示在窗口中。