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图像的边缘检测 -- 利用 OpenCV 的 Sobel 函数

OpenCV gradients

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('dave.jpg',0)

laplacian = cv2.Laplacian(img,cv2.CV_64F)
sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize = 5)
sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize = 5)

plt.subplot(2, 2, 1)
plt.imshow(img, cmap = 'gray')
plt.title('Original')
plt.xticks([])
plt.yticks([])
plt.subplot(2, 2, 2)
plt.imshow(laplacian, cmap = 'gray')
plt.title('Laplacian')
plt.xticks([])
plt.yticks([])
plt.subplot(2, 2, 3)
plt.imshow(sobelx, cmap = 'gray')
plt.title('Sobel X')
plt.xticks([])
plt.yticks([])
plt.subplot(2, 2, 4)
plt.imshow(sobely, cmap = 'gray')
plt.title('Sobel Y')
plt.xticks([])
plt.yticks([])

plt.show()

sobel 就是用一个 n * n 的 kernel 去对图像做卷积，算出各种梯度之类等等。

sobel 是带有方向的，我们可以用一个 kernel 计算在 x 轴方向上的梯度，也可以用另外一个 kernel 计算在 y 轴 方向上的梯度。 当然也可以算出梯度的绝对值，同时也计算出两者的向量和的绝对值。还可以算出梯度的方向。

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def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
    binary_output =  np.zeros_like(absgraddir)
    binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
    return binary_output

下面有个简单的练习，我们如何在以下图片中把车道线找出来的




最终我们得到的图片应该是这个样子的：

def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    if orient == 'x':
        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
    if orient == 'y':
        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
    scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
    binary_output = np.zeros_like(scaled_sobel)
    binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
    return binary_output

def mag_thresh(image, sobel_kernel=3, mag_thresh=(0, 255)):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize = sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize = sobel_kernel)
    grad_mag = np.sqrt(soblex ** 2 + sobely ** 2)
    # rescale to 8 bit
    scale_factor = np.max(gradmag) / 255
    grad_mag = (grad_mag / scale_factor).astype(np.uint8)
    mag_binary = np.zeros_like(sobel)
    mag_binary[(grad_mag > mag_thresh[0] ) & (grad_mag < mag_thresh[1]) ]
    return mag_binary

def dir_threshold(image, sobel_kernel=3, thresh=(0, np.pi/2)):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize = sobel_kernel)
    sobelx = np.absolute(sobelx)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0 ,1, ksize = sobel_kernel)
    sobely = np.absolute(sobely)
    direction = np.arctan2(sobely, sobelx)
    binary_output = np.zeros_like(direction)
    binary_output[(direction >= thresh[0]) & (direction <= thresh[1])] = 1
    return binary_output

    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize = sobel_kernel)
    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize = sobel_kernel)
    return dir_binary

# Choose a Sobel kernel size
ksize = 3 # Choose a larger odd number to smooth gradient measurements

# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh=(0, 255))
grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh=(0, 255))
mag_binary = mag_thresh(image, sobel_kernel=ksize, mag_thresh=(0, 255))
dir_binary = dir_threshold(image, sobel_kernel=ksize, thresh=(0, np.pi/2))