python numpy 图象加噪及高斯滤波

问题：

1、imageio.imwrite 能保存np.array为图片 但是保存不了Image.fromarray的图像 找原因

2、有时候需要连起来才能保存图像，找原因

im_salt = Image.fromarray(img1)
im_salt = img1.convert('RGB')  

3、要系统学习一下numpy的用法

4、code

引用

from PIL import Image
import numpy as np
from numpy import*
from pylab import *
import random
import imageio

添加高斯噪声

def noise_Gaussian(im,mean,variance):
    img = np.asarray(im)
    img1 = img.copy()
    img1 = np.array(img1/255, dtype=float)
    noise = np.random.normal(0, 0.05, img1.shape)
    out = img1 + noise
    out = np.uint8(out*255)
    # im_Gaussian = Image.fromarray(out)
    return out

添加椒盐噪声

def noise_salt(im,persentage):
    img = np.asarray(im)
    img1 = img.copy()  
    noiseNum=int(persentage*img1.shape[0]*img1.shape[1])

    for i in range(noiseNum):
        w = random.randint(0,img1.shape[0]-1)
        h = random.randint(0,img1.shape[1]-1)
        if random.randint(0,1) ==0:
            img1[h,w] =0
        else:
            img1[h,w] = 255
    im_salt = Image.fromarray(img1)
    # im_salt = img1.convert('RGB')    
    return im_salt

添加脉冲噪声

def noise_pulse(im,persentage):
    img = np.asarray(im)
    img2 = img.copy()  
    noiseNum=int(persentage*img2.shape[0]*img2.shape[1])
    for i in range(noiseNum):
        w = random.randint(0,img2.shape[0]-1)
        h = random.randint(0,img2.shape[1]-1)
        img2[h,w] = 255
    im_pulse = Image.fromarray(img2)
    im_pulse = im_pulse.convert('RGB')    
    return im_pulse

高斯滤波器

def gaussian_filter(im, K_size=3, sigma=1.3):
    img = np.asarray(np.uint8(im))
    if len(img.shape) == 3:
        H, W, C = img.shape
    else:
        img = np.expand_dims(img, axis=-1)
        H, W, C = img.shape
 
    ## Zero padding
    pad = K_size // 2
    out = np.zeros((H + pad * 2, W + pad * 2, C), dtype=np.float)
    out[pad: pad + H, pad: pad + W] = img.copy().astype(np.float)
 
    ## prepare Kernel
    K = np.zeros((K_size, K_size), dtype=np.float)
    for x in range(-pad, -pad + K_size):
        for y in range(-pad, -pad + K_size):
            K[y + pad, x + pad] = np.exp( -(x ** 2 + y ** 2) / (2 * (sigma ** 2)))
    K /= (2 * np.pi * sigma * sigma) 
    K /= K.sum()
    tmp = out.copy()
 
    # filtering
    for y in range(H):
       for x in range(W):
            for c in range(C): 
                out[pad + y, pad + x, c] = np.sum(K * tmp[y: y + K_size, x: x + K_size, c])
    out = np.clip(out, 0, 255)
    out = out[pad: pad + H, pad: pad + W].astype(np.uint8)
    return out

main() 添加高斯噪声，并高斯滤波去噪

# 读入图片
img = Image.open("./lena.jpg")
im_Gaussian = noise_Gaussian(im,0,0.05)
imageio.imwrite("im_Gaussian.jpg",im_Gaussian)
im_Gaussian_gaussian = gaussian_filter(im_Gaussian,sigma=1.3)
imageio.imwrite("im_Gaussian_gaussian_sigma1.3.jpg",im_Gaussian_gaussian)

添加椒盐噪声，并高斯去噪

im_pulse = noise_pulse(im,0.005)
im_pulse = np.asarray(im_pulse)
imageio.imwrite("im_pulse.jpg",im_pulse)
im_pulse_gaussian = gaussian_filter(im_pulse,sigma=10.3)
imageio.imwrite("im_pulse_gaussian_sigma10.3.jpg",im_pulse_gaussian)

添加脉冲噪声，并高斯去噪

im_pulse = noise_pulse(im,0.005)
im_pulse = np.asarray(im_pulse)
imageio.imwrite("im_pulse.jpg",im_pulse)
im_pulse_gaussian = gaussian_filter(im_pulse,sigma=10.3)
imageio.imwrite("im_pulse_gaussian_sigma10.3.jpg",im_pulse_gaussian)

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