sobel_train.py~

import cv2
import numpy as np
import matplotlib.image as matimg
import matplotlib.pyplot as pyplot

def abs_sobel_thresh(img, kernel_size = 3,orient='x', thresh_min=0, thresh_max=255):
    
    # Apply the following steps to img
    # 1) Convert to grayscale
    # 2) Take the derivative in x or y given orient = 'x' or 'y'
    # 3) Take the absolute value of the derivative or gradient
    # 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
    # 5) Create a mask of 1's where the scaled gradient magnitude 
            # is > thresh_min and < thresh_max
    # 6) Return this mask as your binary_output image
    
    
    copied  = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)

    if orient == 'x':
        sobel_x = cv2.Sobel(copied,cv2.CV_64F,1,0,ksize=kernel_size)
    elif orient == 'y':
        sobel_x = cv2.Sobel(copied,cv2.CV_64F,0,1,ksize=kernel_size)

    #先去绝对值，然后伸缩为0->255
    abs_sobel_x = np.absolute(sobel_x)
    abs_sobel_x = np.uint8(255*abs_sobel_x/np.max(abs_sobel_x))
    binary_output = np.zeros_like(abs_sobel_x)
    binary_output[(abs_sobel_x> thresh_min) & (abs_sobel_x < thresh_max)] = 1
    return binary_output

def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
    
    # Apply the following steps to img
    # 1) Convert to grayscale
    # 2) Take the gradient in x and y separately
    # 3) Calculate the magnitude 
    # 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
    # 5) Create a binary mask where mag thresholds are met
    # 6) Return this mask as your binary_output image
    gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
    sobel_x = np.abs(cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=sobel_kernel))
    sobel_y = np.abs(cv2.Sobel(gray,cv2.CV_64F,0,1,ksize=sobel_kernel))
    magnitude = np.sqrt(sobel_x**2 + sobel_y**2)
    
    magnitude = np.uint8(255*magnitude/np.max(magnitude))
    binary_output = np.zeros_like(magnitude)
    binary_output[(magnitude > mag_thresh[0]) & (magnitude < mag_thresh[1])] = 1
    return binary_output

def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
    
    # Apply the following steps to img
    # 1) Convert to grayscale
    # 2) Take the gradient in x and y separately
    # 3) Take the absolute value of the x and y gradients
    # 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient 
    # 5) Create a binary mask where direction thresholds are met
    # 6) Return this mask as your binary_output image
    gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
    #不指定ksize，内核大小不生效
    sobel_x = cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=sobel_kernel)
    sobel_y = cv2.Sobel(gray,cv2.CV_64F,0,1,ksize=sobel_kernel)
   
    abs_orientataion = np.arctan2(np.absolute(sobel_y),np.absolute(sobel_x))
    # abs_orientataion = np.abs(orientation)
    binary_output = np.zeros_like(abs_orientataion)
    binary_output[(abs_orientataion>=thresh[0]) & (abs_orientataion<=thresh[1])]=1
    return binary_output


def show(img):
    pyplot.imshow(img,cmap='gray')
    pyplot.show()

img = matimg.imread('sobel_train.png')

# filter horizontal line
x_sobel = abs_sobel_thresh(img=img, kernel_size=5,orient='x', thresh_min=30,thresh_max=100)

y_sobel = abs_sobel_thresh(img=img, kernel_size=5,orient='y', thresh_min=30,thresh_max=100)

mag_sobel = mag_thresh(img,sobel_kernel=15,mag_thresh=(30,100))
direct_sobel = dir_threshold(img,sobel_kernel=15,thresh=(0.7,1.4))


combined = np.zeros_like(img)
combined[(mag_sobel==1) & (direct_sobel==1) | ((x_sobel == 1) &(y_sobel == 1))] = 1
# pyplot.subplot(2,2,1)
pyplot.imshow(combined)
pyplot.show()