def SceneTrain(NumSub, ImagPerClassToRead, DataPath, TrainImgs, ResizeAmt):
# Read Images and Generate Image Features XNow
for i in range(0,NumSub):
for j in range(0, ImagPerClassToRead):
InitAll.tic()
#print DataPath+str(i+1)+'frame'+str(TrainImgs[i][j])+'.png'
XNow = cv2.imread(DataPath+str(i+1)+'frame'+str(TrainImgs[i][j])+'.png',0)
XNow = cv2.resize(XNow, ResizeAmt, interpolation = cv2.INTER_CUBIC)
XNow = InitAll.ComputeGIST(XNow)
#print("Sub " + str(i+1) + " Image " + str(j+1))
if(i==0 and j==0):
X = np.reshape(XNow, (1,np.product(XNow.shape)))
else:
X = np.vstack((X,np.reshape(XNow, (1,np.product(XNow.shape)))))
InitAll.toc()
print "Subject " + str(i+1) + " done...."
# Now Generate Class Labels Y
# Class labels start from 1 and not 0
Y = [i for i in range(1,NumSub+1)]*ImagPerClassToRead
Y = list(np.sort(Y))
SVMModel = svm.SVC()
SVMModel.fit(X, Y)
# Saving the objects:
with open('SceneTrainedSVMModel'+strftime("%Y-%m-%d %H:%M:%S", gmtime())+'.pickle', 'w') as f:
pickle.dump([X, Y, SVMModel], f)
return SVMModel
def SceneTest(TestImg, SVMModel):
# Use SVM to predict class label and return it
return SVMModel.predict(InitAll.ComputeGIST(TestImg))[0]
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.cm as cm
import os
from subprocess import PIPE, Popen
import math
import random
import pickle
from sklearn import svm
import InitAll
from SceneTrain import SceneTrain
from SceneTest import SceneTest
from sklearn.metrics import confusion_matrix
# Initialize everything
DataPath, ImagPerClass, NumSub, ImagPerClassToRead, TrainImgs, TestImgs, ResizeAmt = InitAll.InitAll(
)
# Perform Training
SVMModel = SceneTrain(NumSub, ImagPerClassToRead, DataPath, TrainImgs,
ResizeAmt)
# Perform Testing
k = 0
PredictedLabels = np.zeros(
sum(ImagPerClass) - NumSub * ImagPerClassToRead, int)
TrueLabels = np.zeros(sum(ImagPerClass) - NumSub * ImagPerClassToRead, int)
for i in range(0, NumSub):
for j in TestImgs[i]:
InitAll.tic()
#print "Tested " + "Subject " + str(i+1) + " Image " + str(j)
# Read Images and Generate Image Features XNow
from sklearn.metrics import confusion_matrix
# Initialize everything
DataPath, ImagPerClass, NumSub, ImagPerClassToRead, TrainImgs, TestImgs, ResizeAmt = InitAll.InitAll()
# Perform Training
SVMModel = SceneTrain(NumSub, ImagPerClassToRead, DataPath, TrainImgs, ResizeAmt)
# Perform Testing
k = 0
PredictedLabels = np.zeros(sum(ImagPerClass) - NumSub * ImagPerClassToRead, int)
TrueLabels = np.zeros(sum(ImagPerClass) - NumSub * ImagPerClassToRead, int)
for i in range(0, NumSub):
for j in TestImgs[i]:
InitAll.tic()
# print "Tested " + "Subject " + str(i+1) + " Image " + str(j)
# Read Images and Generate Image Features XNow
XNow = cv2.imread(DataPath + str(i + 1) + "frame" + str(j) + ".png", 0)
XNow = cv2.resize(XNow, ResizeAmt, interpolation=cv2.INTER_CUBIC)
PredictedLabels[k] = SceneTest(XNow, SVMModel)
TrueLabels[k] = i + 1
k += 1
# print "Predicted " + str(PredictedLabels[k-1]) + " Actual " + str(i+1)
InitAll.toc()
print "Testing Subject " + str(i + 1) + " done...."
# Compute confusion matrix
cm = confusion_matrix(TrueLabels, PredictedLabels)
print (cm)
