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
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)
# Show confusion matrix in a separate window
plt.matshow(cm)
plt.title('Confusion matrix')
plt.colorbar()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
# 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)
# Show confusion matrix in a separate window
plt.matshow(cm)
plt.title("Confusion matrix")
plt.colorbar()
plt.ylabel("True label")
plt.xlabel("Predicted label")
plt.show()
