故障样本诊断——python_故障诊断中,如何用python画图

今天写的，主要有：

（1）正常的图片，故障的图片，先求LBP特征。因为LBP有光照不变性和旋转不变性
（2）把图片分成上下两部分，这样可以使故障的部分显得更显著
（3）融合PSRN和SSIM两种方法，这样会有更好的鲁邦性

"""

"""
import os
import cv2
import time
import numpy as np
from skimage.transform import rotate
from skimage.feature import local_binary_pattern
from skimage import data, io
from skimage.color import label2rgb
import skimage
import math
####
def compute_psnr(img1, img2):
    if isinstance(img1,str):
        img1=io.imread(img1)
    if isinstance(img2,str):
        img2=io.imread(img2)
    mse = np.mean( (img1/255. - img2/255.) ** 2 )
    if mse < 1.0e-10:
       return 1000000000000
    PIXEL_MAX = 1
    psnr = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
    return mse, psnr

def ssim(img1, img2):
    C1 = (0.01 * 255) ** 2
    C2 = (0.03 * 255) ** 2
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    # kernel = cv2.getGaussianKernel(11, 1.5)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())
    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1 ** 2
    mu2_sq = mu2 ** 2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 =
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