python实现gabor滤波器提取纹理特征 提取指静脉纹理特征 指静脉切割代码

#!/usr/bin/python
#coding:utf-8
import numpy as np
import os
import cv2

def pathFile(path):
    return os.getcwd() + '/' + path

def brightestColumn(img):
    w, h = img.shape
    r = range(h / 2, h - 1)
    c = range(0, w - 1)
    return img[c][:,r].sum(axis=0).argmax()

#构建GABOR滤波器
def build_filters():
    """ returns a list of kernels in several orientations
    """
    filters = []
    ksize = 31                                                                 #gaborl尺度 这里是一个
    for theta in np.arange(0, np.pi, np.pi / 4):                               #gaborl方向 0 45 90 135 角度尺度的不同会导致滤波后图像不同
        
        params = {'ksize':(ksize, ksize), 'sigma':3.3, 'theta':theta, 'lambd':18.3,    
                  'gamma':4.5, 'psi':0.89, 'ktype':cv2.CV_32F}
                                                                            #gamma越大核函数图像越小，条纹数不变，sigma越大 条纹和图像都越大
                                                                            #psi这里接近0度以白条纹为中心，180度时以黑条纹为中心
                                                                            #theta代表条纹旋转角度
                                                                            #lambd为波长 波长越大 条纹越大
        kern = cv2.getGaborKernel(**params)                                    #创建内核
        kern /= 1.5*kern.sum()
        filters.append((kern,params))
    return filters                                                          

#滤波过程
def process(img, filters):
    """ returns the img filtered by the filter list
    """
    accum = np.zeros_like(img)                                              #初始化img一样大小的矩阵
    for kern,params in filters:
        fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)                         #2D滤波函数  kern为其滤波模板
        np.maximum(accum, fimg, accum)                                      #参数1与参数2逐位比较  取大者存入参数3  这里就是将纹理特征显化更加明显
    return accum

#获取感兴趣区域的top 和 bottom值 用于切割显示图像
def getRoiHCut2(img, p0):
    h, w = img.shape

    maxTop = np.argmax(img[0: h / 2, 0])                                    #在一定区域遍历选取指静脉边缘 具体高宽结合图像
    minTop = np.argmax(img[0: h / 2, w-1])
    if(maxTop < 65):
        maxBottom = np.argmax(img[(13 * h / 16): 40*h/48  , 0]) + 3 * h / 4
        minBottom = np.argmax(img[(13 * h / 16): 40*h/48, w-1]) + 3 * h / 4
    else:
        maxBottom = np.argmax(img[(3 * h / 4): h  , 0]) + 3 * h / 4
        minBottom = np.argmax(img[(3 * h / 4): h, w-1]) + 3 * h / 4
    maxTop = (2*maxTop + minTop) / 3
    maxBottom = (maxBottom + 2*minBottom) / 3

    return img[maxTop:maxBottom,:]

#获取感兴趣区域范围
def getRoi(img):
    height, width = img.shape
    heightDist = height / 4

    w = img.copy()
    w1 = w[heightDist:3 * heightDist,width / 4:]
    p0 = brightestColumn(w1) + heightDist + height / 2                      #将手指边缘的高度加上四分之三原始高度  
    pCol = w[:,p0:p0 + 1]

    pColInv = pCol[::-1]

    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))              #构建一个有限对比适应性直方图均衡化器  

    w1_2 = clahe.apply(w[:, (p0 /20):(p0 + p0 / 2)])                       #截取区域宽度大概是p0高度的一点五倍 apply是获取一个返回值 这里是为了方便参数的传递
    w2 = getRoiHCut2(w1_2, p0)

    res = cv2.resize(w2, (270, 150), interpolation=cv2.INTER_CUBIC)

    return clahe.apply(res)

def logImg(img):
    return img.astype(float) / 255                                          #将图像数据转为０－１存储

mDir=[]
imgs = []
dbDir = os.getcwd() + "/db100/"
people = os.listdir(dbDir)
people.sort()

for person in people:
    personDir = dbDir + person + "/"
    hands = os.listdir(personDir)

    for hand in hands:
        handDir = personDir + hand + "/"
        mDir += [handDir]
        mg = os.listdir(handDir)
        mg.sort()
        imgs = imgs + [handDir + s.split(".")[0] for s in mg if not s.split(".")[0] == "Thumbs"]

p0Imgs = [i.replace('db', 'gab_roi_db') for i in imgs]                         #p0Imgs是每个文件的路径，mDir是需要创建路径所有文件夹存放预处理后图片
mDir = [i.replace('db', 'gab_roi_db') for i in mDir]

#判断路径是否存在   不存在就创建路径
for path in mDir:
    if not os.path.exists(path):
        os.makedirs(path)

filters = build_filters()
for index, imgPath  in enumerate(imgs):
    img = cv2.imread(imgPath + ".bmp", 0)
    res0 = process(getRoi(img), filters)                                        #获取ROI进行直方图均衡化 切割后 在gabor滤波
    cv2.imwrite(p0Imgs[index] + ".png", res0)
    print index


cv2.waitKey(0)
cv2.destroyAllWindows()