path = '/home/zhangzimou/Desktop/code_lnk/database/FVC2002/DB1_B' size, = np.shape(os.listdir(path)) filename = [1]*size #ten different fingerprints. eight images for same fingerprint count = 0 for file in os.listdir(path): filename[count] = file count += 1 #search all filenames for same images and acheive correct probability matchedPoints_max = np.zeros([size,1]) score = np.zeros([size,1]) correct = 0 wrong = 0 i = 0 img1 = cv2.imread(path+'/'+filename[i],0) imgE1,imgfore1=enhance(img1) imgB1=basic.binarize(imgE1) imgT1=pre.thinning(imgB1) for j in range(size): print "Image %d & Image %d" % (i,j) #input img2 = cv2.imread(path+'/'+filename[j],0) #decide if they same fingerprints due to index of files if i/8 == j/8: print "They are from same fingerprint" else: print "They are from different fingerprint" #preprocess for images imgE2,imgfore2=enhance(img2) imgB2=basic.binarize(imgE2)
Created on Mon Nov 28 20:52:15 2016
@author: zhangzimou
"""
import cv2
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
from scipy.signal import convolve2d
import time
import sys
sys.path.append("..")
from basic import imshow
import basic
from preprocess import enhance
import preprocess as pre
from minutiaeExtract import minutiaeExtract
from basic import block_view
FVC0 = '/home/zhangzimou/Desktop/code_lnk/database/FVC2000/'
FVC2 = '/home/zhangzimou/Desktop/code_lnk/database/FVC2002/'
FVC4 = '/home/zhangzimou/Desktop/code_lnk/database/FVC2004/'
path = FVC4 + 'DB1_B/'
imgB = basic.binarize(image)
imgT = pre.thinning(imgB)
plt.figure()
plt.imshow(imgB, cmap='gray')
plt.figure()
plt.imshow(imgT, cmap='gray')
from minutiaeExtract import minutiaeExtract
from basic import block_view
FVC0 = '/home/zhangzimou/Desktop/code_lnk/database/FVC2000/'
FVC2 = '/home/zhangzimou/Desktop/code_lnk/database/FVC2002/'
FVC4 = '/home/zhangzimou/Desktop/code_lnk/database/FVC2004/'
path = FVC4 + 'DB4_B/'
blockSize = 8
img = cv2.imread(path + '102_1.tif', 0)
img_seg, imgfore = pre.segmentation(img)
img_en = enhance(img)
imgB = basic.binarize(img_en)
imgT = pre.thinning(imgB)
plt.figure()
imshow(img_seg)
plt.figure()
plt.imshow(imgB, cmap='gray')
plt.figure()
plt.imshow(imgT, cmap='gray')
ending, bifur, theta1, theta2 = minutiaeExtract(imgT, imgfore)
plt.plot(ending.T[1], ending.T[0], 'b.', bifur.T[1], bifur.T[0], 'r.')
plt.quiver(ending.T[1],
ending.T[0],
np.cos(theta1),
np.sin(-theta1),
FVC0='/home/zhangzimou/Desktop/code_lnk/database/FVC2000/' FVC2='/home/zhangzimou/Desktop/code_lnk/database/FVC2002/' FVC4='/home/zhangzimou/Desktop/code_lnk/database/FVC2004/' path=FVC2+'DB1_B/' blockSize=8 img=cv2.imread(path+'108_3.tif',0) imgE,imgfore=enhance(img) imgB=basic.binarize(imgE) imgT=pre.thinning(imgB) #plt.figure() #imshow(img) plt.figure() plt.imshow(imgE,cmap='gray') plt.figure() plt.imshow(imgT,cmap='gray') ending,bifur,theta1,theta2=minutiaeExtract(imgT,imgfore) plt.plot(ending.T[1],ending.T[0],'b.',bifur.T[1],bifur.T[0],'r.') plt.quiver(ending.T[1],ending.T[0],np.cos(theta1),np.sin(-theta1),color='b',width=0.003) plt.quiver(bifur.T[1],bifur.T[0],np.cos(theta2),np.sin(-theta2),color='r',width=0.003)
import basic
from preprocess import enhance
import preprocess as pre
from minutiaeExtract import minutiaeExtract
start = time.clock()
img_origin = cv2.imread('../pic4.tif', 0)
img_origin, imgfore = pre.segmentation(img_origin)
blockSize = 16
theta = pre.calcDirection(img_origin, blockSize)
wl = pre.calcWl(img_origin, blockSize)
#img=ridgeComp2(img,theta,blockSize)
img_enhance = pre.GaborFilter(img_origin, blockSize, wl, np.pi / 2 - theta)
img_enhance[np.where(imgfore == 255)] = 255
img_thin = pre.thinning(basic.binarize(img_enhance))
ending, bifur, theta1, theta2 = minutiaeExtract(img_thin, imgfore)
ending, bifur = ending.T, bifur.T
end = time.clock()
print end - start
plt.figure()
imshow(img_origin)
N, M = img_origin.shape
X, Y = np.mgrid[0:N:blockSize, 0:M:blockSize]
plt.quiver(Y, X, np.cos(theta), np.sin(theta), color='r')
plt.figure()
imshow(img_enhance)
plt.figure()
plt.imshow(img_thin, cmap='gray')
plt.figure()