Gaussian blur based on jupyter

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1. Experiment introduction

1. Experimental content

In this experiment, we will learn about Gaussian Blur.

2. Experimental points

Gaussian blur image
Testing performance with a high-pass filter

3. Experimental environment

Python 3.6.6
numpy
matplotlib
cv2

2. Experimental steps

1 Import resources and display images

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

%matplotlib inline

# 读入图像
image = cv2.imread('images/brain_MR.jpg')

# 制作图像副本
image_copy = np.copy(image)

# 将颜色更改为RGB（从BGR）
image_copy = cv2.cvtColor(image_copy, cv2.COLOR_BGR2RGB)

plt.imshow(image_copy)

<matplotlib.image.AxesImage at 0x7f9b454dae48>

insert image description here

2 Gaussian blur image

# 转换为灰度用于过滤
gray = cv2.cvtColor(image_copy, cv2.COLOR_RGB2GRAY)

# 创建高斯模糊图像
gray_blur = cv2.GaussianBlur(gray, (9, 9), 0)

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))

ax1.set_title('original gray')
ax1.imshow(gray, cmap='gray')

ax2.set_title('blurred image')
ax2.imshow(gray_blur, cmap='gray')

<matplotlib.image.AxesImage at 0x7f9b453f16d8>

insert image description here

3 Testing performance with a high-pass filter

# 高通滤波器

# 3x3 Sobel滤波器用于边缘检测
sobel_x = np.array([[ -1, 0, 1], 
                   [ -2, 0, 2], 
                   [ -1, 0, 1]])


sobel_y = np.array([[ -1, -2, -1], 
                   [ 0, 0, 0], 
                   [ 1, 2, 1]])


# 使用filter2D过滤原始和模糊的灰度图像
filtered = cv2.filter2D(gray, -1, sobel_x)

filtered_blurred = cv2.filter2D(gray_blur, -1, sobel_y)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))

ax1.set_title('original gray')
ax1.imshow(filtered, cmap='gray')

ax2.set_title('blurred image')
ax2.imshow(filtered_blurred, cmap='gray')

<matplotlib.image.AxesImage at 0x7f9b453713c8>

insert image description here

# 创建一个阈值，将所有过滤的像素设置为白色
# 在一定的阈值之上

retval, binary_image = cv2.threshold(filtered_blurred, 50, 255, cv2.THRESH_BINARY)

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

<matplotlib.image.AxesImage at 0x7ff2b018be10>

insert image description here

Summarize

Gaussian blur is a commonly used image processing technique, which achieves a blur effect by weighting and averaging all pixels in the image and surrounding pixels. Usually, the weighting coefficient is calculated according to the Gaussian function, which can make the image smoother and remove noise and detail information. Gaussian blur is widely used in computer vision, image processing, image recognition and other fields, such as face recognition, license plate recognition, edge detection, etc. At the same time, in order to ensure the quality and speed of the blur effect, it is necessary to pay attention to the selection of the Gaussian kernel size, the normalization of the gray value, and the design of the concurrent algorithm.