# -*- 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)