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banknote3.py
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from sklearn import svm
import os
import numpy as np
import scipy
from scipy import ndimage
import pickle
import math
dict={}
with open('svmhogfeatures.pickle','rb') as f:
dict=pickle.load(f)
svmhogfeatures=dict['svmhogfeatures']
train_labels=dict['train_labels']
train_dataset=dict['train_dataset']
per=np.random.permutation(train_labels.shape[0])
svmhogfeatures=svmhogfeatures[per,:]
train_labels=train_labels[per]
classifier=svm.SVC()
classifier.fit(svmhogfeatures,train_labels)
def grad(image):
gradient=np.ndarray((400,800),dtype=np.float32)
gmagnitude=np.ndarray((400,800),dtype=np.float32)
for i in range(1,401):
for j in range(1,801):
gx=image[i-1,j]-image[i+1,j]
gy=image[i,j-1]-image[i,j+1]
if(gx==0 and gy==0):
gmagnitude[i-1,j-1]=0
gradient[i-1,j-1]=0
elif(gx==0):
gmagnitude[i-1,j-1]=abs(gy)
gradient[i-1,j-1]=90
elif(gy==0):
gmagnitude[i-1,j-1]=abs(gx)
gradient[i-1,j-1]=0
else:
gmagnitude[i-1,j-1]=math.sqrt(gx*gx+gy*gy)
a=math.degrees(math.atan(gy/gx))
if(a>0):
gradient[i-1,j-1]=a
else:
gradient[i-1,j-1]=a+180
return gradient,gmagnitude
def histogram(gradient,gmagnitude,j1,j2,k1,k2):
hist= np.zeros((1,9),dtype=np.float32)
for i in range(j1,j2+1):
for j in range(k1,k2+1):
if(gradient[i,j]>=0 and gradient[i,j]<=20):
hist[0,0]+=gmagnitude[i,j]*(20-gradient[i,j])/20
hist[0,1]+=gmagnitude[i,j]*(gradient[i,j]-0)/20
elif(gradient[i,j]>20 and gradient[i,j]<=40):
hist[0,1]+=gmagnitude[i,j]*(40-gradient[i,j])/20
hist[0,2]+=gmagnitude[i,j]*(gradient[i,j]-20)/20
elif(gradient[i,j]>40 and gradient[i,j]<=60):
hist[0,2]+=gmagnitude[i,j]*(60-gradient[i,j])/20
hist[0,3]+=gmagnitude[i,j]*(gradient[i,j]-40)/20
elif(gradient[i,j]>60 and gradient[i,j]<=80):
hist[0,3]+=gmagnitude[i,j]*(80-gradient[i,j])/20
hist[0,4]+=gmagnitude[i,j]*(gradient[i,j]-60)/20
elif(gradient[i,j]>80 and gradient[i,j]<=100):
hist[0,4]+=gmagnitude[i,j]*(100-gradient[i,j])/20
hist[0,5]+=gmagnitude[i,j]*(gradient[i,j]-80)/20
elif(gradient[i,j]>100 and gradient[i,j]<=120):
hist[0,5]+=gmagnitude[i,j]*(120-gradient[i,j])/20
hist[0,6]+=gmagnitude[i,j]*(gradient[i,j]-100)/20
elif(gradient[i,j]>120 and gradient[i,j]<=140):
hist[0,6]+=gmagnitude[i,j]*(140-gradient[i,j])/20
hist[0,7]+=gmagnitude[i,j]*(gradient[i,j]-120)/20
elif(gradient[i,j]>140 and gradient[i,j]<=160):
hist[0,7]+=gmagnitude[i,j]*(160-gradient[i,j])/20
hist[0,8]+=gmagnitude[i,j]*(gradient[i,j]-140)/20
elif(gradient[i,j]>160 and gradient[i,j]<=180):
hist[0,8]+=gmagnitude[i,j]*(180-gradient[i,j])/20
hist[0,0]+=gmagnitude[i,j]*(gradient[i,j]-160)/20
return hist
def normalise(blockhist):
norm=0
for i in range(0,36):
norm+=(blockhist[0,i])*(blockhist[0,i])
norm=math.sqrt(norm)
for i in range(0,36):
blockhist[0,i]=(blockhist[0,i])/norm
return blockhist
'''
def sroot(im):
for i in range(0,258):
for j in range(0,130):
im[i,j]=math.sqrt(im[i,j])
return im
'''
def check(folder):
if(folder=='t10'):
return 10
if(folder=='t20'):
return 20
if(folder=='t50'):
return 50
if(folder=='t100'):
return 100
if(folder=='t500'):
return 500
if(folder=='t2000'):
return 2000
#testing with new image
folders=['t10','t20','t50','t100','t500','t2000',]
counter=0
for folder in folders:
note=check(folder)
images=os.listdir(folder)
images.sort()
for image in images:
imagepath=os.path.join(folder,image)
test_dataset=np.ndarray((1,402,802),dtype=np.float32)
resizedimage=np.zeros((402,802),dtype=np.float32)
imag=ndimage.imread(imagepath,flatten=1).astype(float)
resizedimage=scipy.misc.imresize(imag,(402,802))
test_dataset[0,:,:]=resizedimage
gradient=np.ndarray((400,800),dtype=np.float32)
gmagnitude=np.ndarray((400,800),dtype=np.float32)
imge=np.ndarray((402,802),dtype=np.float32)
imge=test_dataset[0,:,:]
gradient,gmagnitude=grad(imge)
hogfeatures=np.ndarray((1,3780),dtype=np.float32)
j=0
k=0
count=0
while(j<=300):
k=0
while(k<=700):
histcell1=np.ndarray((1,9),dtype=np.float32)
histcell2=np.ndarray((1,9),dtype=np.float32)
histcell3=np.ndarray((1,9),dtype=np.float32)
histcell4=np.ndarray((1,9),dtype=np.float32)
blockhist=np.ndarray((1,36),dtype=np.float32)
histcell1=histogram(gradient,gmagnitude,j,j+49,k,k+49)
histcell2=histogram(gradient,gmagnitude,j,j+49,k+50,k+99)
histcell3=histogram(gradient,gmagnitude,j+50,j+99,k,k+49)
histcell4=histogram(gradient,gmagnitude,j+50,j+99,k+50,k+99)
blockhist[0,:9]=histcell1
blockhist[0,9:18]=histcell2
blockhist[0,18:27]=histcell3
blockhist[0,27:]=histcell4
blockhist=normalise(blockhist)
hogfeatures[0,count:count+36]=blockhist
count+=36
k+=50
j+=50
print 'Actual-',note,' prediction-',classifier.predict(hogfeatures)[0]