Creating a Filter, Edge Detection

小飞侠xp

发布于 2018-08-28 17:52:22

4370

发布于 2018-08-28 17:52:22

Import resources and display image

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

import cv2
import numpy as np

%matplotlib inline

# Read in the image
image = mpimg.imread('images/curved_lane.jpg')

plt.imshow(image)

Convert the image to grayscale

# Convert to grayscale for filtering
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

plt.imshow(gray, cmap='gray')

TODO: Create a custom kernel

Below, you've been given one common type of edge detection filter: a Sobel operator.

The Sobel filter is very commonly used in edge detection and in finding patterns in intensity in an image. Applying a Sobel filter to an image is a way of taking (an approximation) of the derivative of the image in the x or y direction, separately. The operators look as follows.

# Create a custom kernel

# 3x3 array for edge detection
sobel_y = np.array([[ -1, -2, -1], 
                   [ 0, 0, 0], 
                   [ 1, 2, 1]])

## TODO: Create and apply a Sobel x operator


# Filter the image using filter2D, which has inputs: (grayscale image, bit-depth, kernel)  
filtered_image = cv2.filter2D(gray, -1, sobel_y)
#-1 指的是输出与输入类型一致
plt.imshow(filtered_image, cmap='gray')

最后一步也是计算机视觉相当实用的一步,就是将图像转换为二值图像,也就是说将其转换成纯粹的黑白图像强度最高的边缘就会十分显眼了 Create binary image

reteval ,binary_image = cv2.threshold(filtered_image,100,255,cv2.THRESH_BINARY)
plt.imshow(binary_image,cmap ='gray')

常见的噪声有噪点和模糊的细节,检测这类边缘时,高通过滤器会强化图像里的这些噪声,通常我们需要增加一步操作才能确保噪声得不到强化。在像这样使用高通过滤器之前，我们要用低通过滤器来对图像进行模糊处理以便降噪

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原始发表：2018.08.16 ，如有侵权请联系 cloudcommunity@tencent.com 删除

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