python3 Circular-LBP(圆形局部二值化算法)的实现及批量处理
- Circular-LBP算法的实现
由于Circula-LBP算法较传统的LBP特征提取效果更好，近期实验需要，所以用python3实现了Circular-LBP，代码如下：
import numpy as np
import cv2
from matplotlib import pyplot as plt
import math#双极性插值
def bilinear_interpolation(x, y, img):x1, y1 = int(r), int(c)x2, y2 = math.ceil(r), math.ceil(c)r1 = (x2 - x) / (x2 - x1) * get_pixel_else_0(img, x1, y1) + (x - x1) / (x2 - x1) * get_pixel_else_0(img, x2, y1)r2 = (x2 - x) / (x2 - x1) * get_pixel_else_0(img, x1, y2) + (x - x1) / (x2 - x1) * get_pixel_else_0(img, x2, y2)return (y2 - y) / (y2 - y1) * r1 + (y - y1) / (y2 - y1) * r2 #阈值设置
def thresholded(center, pixels):out = []for a in pixels:if a >= center:out.append(1)else:out.append(0)return out#像素返回
def get_pixel_else_0(l, idx, idy):if idx < int(len(l)) - 1 and idy < len(l[0]):return l[idx,idy]else:return 0#读取图像
img = cv2.imread('C:/Users/qgl/Desktop/articles/test1.jpg', 0)
transformed_img = cv2.imread('C:/Users/qgl/Desktop/articles/test1.jpg', 0)#相邻像素P和半径R
P = 8 # number of pixels
R = 1 # radius for x in range(0, len(img)):for y in range(0, len(img[0])):center = img[x,y]pixels = []for point in range(0, P):r = x + R * math.cos(2 * math.pi * point / P)c = y - R * math.sin(2 * math.pi * point / P)if r < 0 or c < 0:pixels.append(0)continue if int(r) == r:if int(c) != c:c1 = int(c)c2 = math.ceil(c)w1 = (c2 - c) / (c2 - c1)w2 = (c - c1) / (c2 - c1)pixels.append(int((w1 * get_pixel_else_0(img, int(r), int(c)) + \w2 * get_pixel_else_0(img, int(r), math.ceil(c))) / (w1 + w2)))else:pixels.append(get_pixel_else_0(img, int(r), int(c)))elif int(c) == c:r1 = int(r)r2 = math.ceil(r)w1 = (r2 - r) / (r2 - r1)w2 = (r - r1) / (r2 - r1) pixels.append((w1 * get_pixel_else_0(img, int(r), int(c)) + \w2 * get_pixel_else_0(img, math.ceil(r), int(c))) / (w1 + w2))else:pixels.append(bilinear_interpolation(r, c, img))values = thresholded(center, pixels)res = 0for a in range(0, len(values)):res += values[a] * (2 ** a)transformed_img.itemset((x,y), res)print (x)cv2.imshow('image', img)
cv2.imshow('thresholded image', transformed_img)
cv2.imwrite('C:...'+'/'+'1.jpg',transformed_img)
- Circular-LBP的批量处理
关于批量处理，需要修改之前算法中LBP函数的输出部分，转为return：
return transformed_img
再重新def定义一个循环函数，对目标路径下的每一张图像进行处理：
def tran(src,drc,P,R):list = os.listdir(src)#遍历数据集的所有图片sum = 0for i in list:try:img = cv2.imread(src+'/'+i,0)#按list的顺序读取每一张图片transformed_img = cv2.imread(src+'/'+i,0)#按list的顺序读取每一张图片cv2.imshow('img',img)#显示图片transformed_img = LBP(img,P,R,transformed_img)#调用下方定义的LBP（）函数，得到LBP计算后的图片cv2.imwrite(drc+'/'+i,transformed_img)#将得到的每张图片写入新的目标文件夹里sum = int(sum)+1print(i+'is finished, number is'+str(sum))except:print('error in'+i)
对数据集进行处理，就可以进行后续的分类工作了。