# -*- 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)
