图像特征的提取（gaussian,gabor,frangi,hessian,Morphology...）及将图片保存为txt文件

# -*- coding: utf-8 -*-
#2018-2-19 14:30:30
#Author:Fourmi_gsj
import cv2
import numpy as np
import pylab as pl
from PIL import Image
import skimage.io as io
from skimage import data_dir,data,filters,color,morphology
import matplotlib.pyplot as plt
from math import exp,floor
import os
#"/home/fourmi/桌面/fourmi/DRIVE/test/images"
PICTURE_PATH="/home/fourmi/桌面/fourmi/DRIVE/training/images"
#"/home/fourmi/桌面/fourmi/DRIVE/training/1st_manual"
PICTURE_PATH0 = "/home/fourmi/桌面/fourmi/DRIVE/test/1st_manual"
"*********************第一部分特征提取（图像处理）***************************************"
def load_image():

    cv2.imshow("original",img)
    gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
    return img,gray

def get_Image():#循环读取对应文件的图片
    for i in range(21,41):
            path = PICTURE_PATH+"/"+str(i)+"_"+"training"+".tif"
            path0 = PICTURE_PATH0+"/"+str(i)+"_"+"manual1"+".gif"
	    #Gabor(path)
            #img = cv2.imread(path0)
            img = ImageToMatrix(path0)
            gray = img
            #gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
	    #SIFT(img,gray)
            #SURF(img,gray)
            #cv2.waitKey(0)
    return path,path0,img,gray

def Gabor(img):
#using Gabor
    real,imag = filters.gabor(img,frequency = 1)
    return real
"""
    plt.figure('GABOR')
    plt.subplot(121)
    plt.imshow(img0)
    plt.subplot(122)
    plt.imshow(real,plt.cm.gray)
    #plt.figure('the imag')
    #plt.imshow(imag,plt.cm.gray)
    plt.show()
"""

def Hessian(img):
#using Hessian
#    img0 = io.imread(path)

    real = filters.hessian(img, scale_range=(1, 10), scale_step=0.05, beta1=0.04, beta2=0.04)
    return real
"""
    plt.figure('Hessian')
    plt.imshow(real)
    plt.show()
"""

def SIFT(img,gray):
#using SIFT
    sift = cv2.xfeatures2d.SIFT_create()
    keypoints, descriptor = sift.detectAndCompute(gray,None)

    cv2.drawKeypoints(image = img,
                  outImage = img,
                  keypoints = keypoints,
                  flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS,
                  color = (51,163,236))
    cv2.imshow("SIFT",img)
    cv2.waitKey(0)

def SURF(img,gray):
#using SURF
    surf = cv2.xfeatures2d.SURF_create()
    keypoints, descriptor = surf.detectAndCompute(gray,None)

    cv2.drawKeypoints(image = img,
                  outImage = img,
                  keypoints = keypoints,
                  flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS,
                  color = (51,163,236))
    real = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    cv2.imshow("SURF",real)
    cv2.waitKey(0)
    return real

def Morphology(img):
#using Morphology
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3, 3))
    eroded = cv2.erode(img,kernel)
    dilated = cv2.dilate(img,kernel)

    #NpKernel = np.uint8(np.ones((3,3)))
    #Nperoded = cv2.erode(img,NpKernel)
    #cv2.imshow("Eroded by NumPy kernel",Nperoded);

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(5, 5))
    closed = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
    opened = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
    cv2.imshow("Close Image",closed);
    cv2.imshow("Open Image", opened);
    return eroded ,opened,closed,dilated

def GaussianBlurSize(GaussianBlur_size):
#using GaussianBlurSize
   global KSIZE
   KSIZE = GaussianBlur_size * 2 +3
   print KSIZE, SIGMA
   dst = cv2.GaussianBlur(gray, (KSIZE,KSIZE), SIGMA, KSIZE)
   cv2.imshow(window_name,dst)

def GaussianBlurSigma(GaussianBlur_sigma):
#using GaussianBlurSigma
    global SIGMA
    SIGMA = GaussianBlur_sigma/10.0
    print KSIZE, SIGMA
    dst = cv2.GaussianBlur(gray, (KSIZE,KSIZE), SIGMA, KSIZE)
    cv2.imshow(window_name,dst)

def window_gaussian():
    SIGMA = 1
    KSIZE = 15
    GaussianBlur_size = 1
    GaussianBlur_sigma = 15
    max_value = 300
    max_type = 6
    window_name = "GaussianBlurS Demo"
    trackbar_size = "Size*2+3"
    trackbar_sigema = "Sigma/10"
    cv2.namedWindow(window_name)
    cv2.createTrackbar( trackbar_size, window_name, \
                    GaussianBlur_size, max_type, GaussianBlurSize )
    cv2.createTrackbar( trackbar_sigema, window_name, \
                    GaussianBlur_sigma, max_value, GaussianBlurSigma )
    GaussianBlurSize(1)
    GaussianBlurSigma(15)
    cv2.waitKey(0)

def Frangi(img):
#using Frangi
    real = filters.frangi(img, scale_range=(1, 10), scale_step=0.05, beta1=0.04, beta2=0.04, black_ridges=True)
    return real
    """
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3, 3))
    dilated = cv2.dilate(real,kernel)
    eroded = cv2.erode(img,kernel)
    closed = cv2.morphologyEx(real, cv2.MORPH_CLOSE, kernel)
    opened = cv2.morphologyEx(real, cv2.MORPH_OPEN, kernel)
    plt.figure('FRANGI')
    plt.subplot(131)
    plt.imshow(closed)
    plt.subplot(132)
    plt.imshow(opened,plt.cm.gray)
    plt.subplot(133)
    plt.figure('FRANGI')
    plt.imshow(real)
    plt.show()
"""

def ImageToMatrix(path):
    im = Image.open(path)
    width,height = im.size
    im = im.convert("L")
    data = im.getdata()
    data = np.matrix(data,dtype='float')
    new_data = np.reshape(data,(height,width))
    new_im_data = np.uint8(np.array(new_data))
    return new_im_data

def MatrixToImage(data):
    data = data*255
    new_im = Image.fromarray(data.astype(np.uint8))
    return new_im

def get_imlist(path):   #此函数读取特定文件夹下的tif/gif格式图像
    return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.tif')]

#以下代码看可以读取文件夹下所有文件
# def getAllImages(folder):
#     assert os.path.exists(folder)
#     assert os.path.isdir(folder)
#     imageList = os.listdir(folder)
#     imageList = [os.path.abspath(item) for item in imageList if os.path.isfile(os.path.join(folder, item))]
#     return imageList

# print getAllImages(r"D:\\test")
"***************************第二部分将图片数据保存为txt文件****************************************"
def Img2Txt():
#图片生成txt
    data = np.empty((22,1,565*584))
    for i in range(1,21):#20
        path0 = PICTURE_PATH0+"/"+str(i)+"_"+"manual1"+".gif"
        print path0
        #path = PICTURE_PATH+"/"+str(i)+"_"+"test"+".tif"
        #path = PICTURE_PATH+"/"+str(22)+"_"+"training"+".tif"
        #img = cv2.imread(path)
        img = ImageToMatrix(path0)
        #img[:,:,2] = 0
        #gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        #real = gray
        #real = Morphology(gray)
        #real = Gabor(gray)
        #real = Frangi(gray)
        #real = Hessian(gray)
        img_ndarray=np.asarray(img)
        #img_ndarray=np.asarray(real,dtype='float64')  #将图像转化为数组并将像素转化到0-1之间
        #data[i]=np.ndarray.flatten(img_ndarray*10)    #将图像的矩阵形式转化为一维数组保存到data中
        #A=np.array(data[i]).reshape(565*584,1)
        #pl.savetxt('test_label'+str(i)+'.txt',A,fmt ="%.00f") #将矩阵保存到txt文件中

def ImgSplit(path):
    img =cv2.imread(path)
    b,g,r = cv2.split(img)
    cv2.imshow('Red',r)
    cv2.imshow('Green',g)
    cv2.imshow('Blue',b)
    cv2.waitKey(0)
    return g#同时获得绿色通道图片

def featureExtract(path):
    g = ImgSplit(path)#图片的通道分离
    fra = Frangi(g)
    hes = Hessian(g)
    ga = Gabor(g)
    eroded ,opened,closed,dilated = Morphology(g)
    cv2.imshow("Eroded Image",eroded);
    cv2.imshow("Dilated Image",dilated);
    cv2.imshow("Origin", g)
    cv2.imshow('Frangi',fra)
    cv2.imshow('Hessian',hes)
    cv2.imshow('Gabor',ga)
    cv2.waitKey(0)
if __name__=='__main__':
    path = PICTURE_PATH+"/"+str(22)+"_"+"training"+".tif"
    featureExtract(path)