def make_pickle(x,dir_name,files): print dir_name dataset = np.zeros((320*240,3)) for f in files: img = Image.open(dir_name+'/'+f) d = (np.array(img.getdata())/255) dataset = np.vstack((dataset,d)) output = open(dir_name+'.pkl','wb') cPickle.dump(dataset, output) output.close()
def generate_orignal(x, dir_name, files):
print dir_name
dataset = np.zeros(120*160)
print 'image orignal processing.....-generate_orignal'
for f in files:
imf = Image.open(dir_name + '/' + f)
d = (np.array(imf.getdata())/255)
dataset = np.vstack((dataset,d))
print 'processing fin!'
output = open(dir_name + '_orignal.pkl', 'wb')
cPickle.dump(dataset[1:, :], output)
output.close()
def print_it(x, dir_name, files):
print dir_name
dataset = np.zeros(120*160)
print 'image pkl processing.....-print_it'
#dataset = np.zeros(256*320)
for f in files:
imf = Image.open(dir_name + '/' + f)
d = ((np.array(imf.getdata())/255) > 0.5 ) * 1
dataset = np.vstack((dataset,d))
output = open(dir_name + '.pkl', 'wb')
cPickle.dump(dataset, output)
print 'processing fin!-print_it'
output.close()
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
DIR1 = "../data/changedetection/camerajitter/traffic"
data_train_0,data_train_1,data_train_2, data_test = load_data_BS(DIR1 + "/orignal_color.pkl")
img = Image.open(DIR1 + "/gt001537.png")
x_, y_ = (asarray(img)[:,:]).shape #determine procesing image size
print "Doie : " , x_,y_
groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
#groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
print '[DONE]'
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train_0.shape[0], num_hiddens=20))
dbn1 = DBN(GaussianRBM(num_visibles=data_train_1.shape[0], num_hiddens=20))
dbn2 = DBN(GaussianRBM(num_visibles=data_train_2.shape[0], num_hiddens=20))
dbn[0].learning_rate = 0.001
dbn1[0].learning_rate = 0.001
dbn2[0].learning_rate = 0.001
# train 1st layer
print 'training...\t',
dbn.train(data_train_0, num_epochs=1, batch_size=1,shuffle=False)
dbn1.train(data_train_1, num_epochs=1, batch_size=1,shuffle=False)
dbn2.train(data_train_2, num_epochs=1, batch_size=1,shuffle=False)
print '[DONE]'
data_test_0 = ((data_test.T)[:,::3]).T
data_test_1 = ((data_test.T)[:,1::3]).T
data_test_2 = ((data_test.T)[:,2::3]).T
# global runtime
# sFilename = "data\\weightlogs"
# f = h5.File(sFileName+".hdf5")
# f.create_group(runtime)
# group.create_dataset('parameters',data=par)
#storeWeights(category,"data\\weightlogs", dbn[0].W, dbn[0].vsigma,dbn1[0].W, dbn1[0].vsigma,dbn2[0].W, dbn2[0].vsigma )
Ndat = 20 #data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
dataout = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0[:,-1]).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,subtract(asarray(X),data_test_0[:,-1],asarray(dbn[0].vsigma),point+1)))
print 'evaluating 2...\t',
dataout1 = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1[:,-1]).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1,subtract(asarray(X),data_test_1[:,-1],asarray(dbn1[0].vsigma),point+1)))
print 'evaluating 3...\t',
dataout2 = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_2[:,point]).T
X = asmatrix(data_test_2[:,-1]).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2,subtract(asarray(X),data_test_2[:,-1],asarray(dbn2[0].vsigma),point+1)))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
fmeasureV = zeros(Ndat)
plt.figure(2)
for i in range(Ndat):
plt.subplot(5,5,i+1)
d = multiply(asarray(dataout[i+1,:]),asarray(dataout1[i+1,:]),asarray(dataout2[i+1,:]))
d = mod(d+1,2)
fmeasureV[i] = f_measure(d,groundtruth)
print "Image Example Fmeaure: ",i," : ", fmeasureV[i] * 100
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
# img_s = Image.fromarray(asarray(reshape(d,(x_,y_))*255,dtype="uint8"))
# img_s.save("C:\work\\backgdSubt\dataset\datasets\change detection\\baseline\\baseline\office\grbm" + "\\" + str(i) + ".bmp")
plt.imshow(reshape(d,(x_,y_)), cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
#plt.figure(2)
plt.show()
DIR2 = "../data/changedetection/camerajitter/traffic"
srcDir = DIR2 + "/input"
targetDir = DIR2 + "/grbm"
scalevaraince = input("Enter value: ") #argmax(fmeasureV) + 12
ii = 1
for k in os.listdir(srcDir):
imf = Image.open(srcDir + '/' + k)
print array(imf).shape
data_test = (array(imf)).flatten()
#dataset = np.vstack((dataset,d))
data_test_0 = ((data_test)[::3]).T
data_test_1 = ((data_test)[1::3]).T
data_test_2 = ((data_test)[2::3]).T
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
# #datasub = zeros(x_*y_)
#for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = subtract(asarray(X),data_test_0,asarray(dbn[0].vsigma),scalevaraince + 1 )
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = subtract(asarray(X),data_test_1,asarray(dbn1[0].vsigma),scalevaraince + 1)
X = asmatrix(data_test_2).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
# plt.imshow(reshape(X,(x_,y_)))
# plt.show()
dataout2 = subtract(asarray(X),data_test_2,asarray(dbn2[0].vsigma),scalevaraince + 1)
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
# plt.figure(1)
# for i in range(Ndat):
# plt.subplot(5,5,i+1)
d = multiply(asarray(dataout),asarray(dataout1),asarray(dataout2))
d = mod(d+1,2)
#print type(d[0])
# #plt.savefig(targetDir + "\\" + str(ii) + ".png")
#print
img_s = Image.fromarray(asarray(reshape(d,(x_,y_))*255,dtype="uint8"))
# plt.figure(2)
# plt.imshow(img_s)#,cmap = cm.Greys_r, interpolation ="nearest")
# plt.show()
if (ii <1000):
img_s.save(targetDir + "\\bin000" + str(ii) + ".bmp")
else:
img_s.save(targetDir + "\\bin00" + str(ii) + ".bmp")
ii = ii +1
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
DIR1 = "../data/changedetection/camerajitter/traffic"
data_train_0, data_train_1, data_train_2, data_test = load_data_BS(
DIR1 + "/orignal_color.pkl")
img = Image.open(DIR1 + "/gt001537.png")
x_, y_ = (asarray(img)[:, :]).shape #determine procesing image size
print "Doie : ", x_, y_
groundtruth = (((asarray(img)[:, :]) / 255.0 > 0.5) * 1).flatten()
#groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
print '[DONE]'
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train_0.shape[0], num_hiddens=20))
dbn1 = DBN(GaussianRBM(num_visibles=data_train_1.shape[0], num_hiddens=20))
dbn2 = DBN(GaussianRBM(num_visibles=data_train_2.shape[0], num_hiddens=20))
dbn[0].learning_rate = 0.001
dbn1[0].learning_rate = 0.001
dbn2[0].learning_rate = 0.001
# train 1st layer
print 'training...\t',
dbn.train(data_train_0, num_epochs=1, batch_size=1, shuffle=False)
dbn1.train(data_train_1, num_epochs=1, batch_size=1, shuffle=False)
dbn2.train(data_train_2, num_epochs=1, batch_size=1, shuffle=False)
print '[DONE]'
data_test_0 = ((data_test.T)[:, ::3]).T
data_test_1 = ((data_test.T)[:, 1::3]).T
data_test_2 = ((data_test.T)[:, 2::3]).T
# global runtime
# sFilename = "data\\weightlogs"
# f = h5.File(sFileName+".hdf5")
# f.create_group(runtime)
# group.create_dataset('parameters',data=par)
#storeWeights(category,"data\\weightlogs", dbn[0].W, dbn[0].vsigma,dbn1[0].W, dbn1[0].vsigma,dbn2[0].W, dbn2[0].vsigma )
Ndat = 20 #data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
dataout = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0[:, -1]).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,
subtract(asarray(X), data_test_0[:, -1],
asarray(dbn[0].vsigma), point + 1)))
print 'evaluating 2...\t',
dataout1 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1[:, -1]).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1,
subtract(asarray(X), data_test_1[:, -1],
asarray(dbn1[0].vsigma), point + 1)))
print 'evaluating 3...\t',
dataout2 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_2[:,point]).T
X = asmatrix(data_test_2[:, -1]).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2,
subtract(asarray(X), data_test_2[:, -1],
asarray(dbn2[0].vsigma), point + 1)))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
fmeasureV = zeros(Ndat)
plt.figure(2)
for i in range(Ndat):
plt.subplot(5, 5, i + 1)
d = multiply(asarray(dataout[i + 1, :]), asarray(dataout1[i + 1, :]),
asarray(dataout2[i + 1, :]))
d = mod(d + 1, 2)
fmeasureV[i] = f_measure(d, groundtruth)
print "Image Example Fmeaure: ", i, " : ", fmeasureV[i] * 100
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
# img_s = Image.fromarray(asarray(reshape(d,(x_,y_))*255,dtype="uint8"))
# img_s.save("C:\work\\backgdSubt\dataset\datasets\change detection\\baseline\\baseline\office\grbm" + "\\" + str(i) + ".bmp")
plt.imshow(reshape(d, (x_, y_)),
cmap=cm.Greys_r,
interpolation="nearest")
plt.axis('off')
#plt.figure(2)
plt.show()
DIR2 = "../data/changedetection/camerajitter/traffic"
srcDir = DIR2 + "/input"
targetDir = DIR2 + "/grbm"
scalevaraince = input("Enter value: ") #argmax(fmeasureV) + 12
ii = 1
for k in os.listdir(srcDir):
imf = Image.open(srcDir + '/' + k)
print array(imf).shape
data_test = (array(imf)).flatten()
#dataset = np.vstack((dataset,d))
data_test_0 = ((data_test)[::3]).T
data_test_1 = ((data_test)[1::3]).T
data_test_2 = ((data_test)[2::3]).T
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
# #datasub = zeros(x_*y_)
#for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = subtract(asarray(X), data_test_0, asarray(dbn[0].vsigma),
scalevaraince + 1)
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = subtract(asarray(X), data_test_1, asarray(dbn1[0].vsigma),
scalevaraince + 1)
X = asmatrix(data_test_2).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
# plt.imshow(reshape(X,(x_,y_)))
# plt.show()
dataout2 = subtract(asarray(X), data_test_2, asarray(dbn2[0].vsigma),
scalevaraince + 1)
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
# plt.figure(1)
# for i in range(Ndat):
# plt.subplot(5,5,i+1)
d = multiply(asarray(dataout), asarray(dataout1), asarray(dataout2))
d = mod(d + 1, 2)
#print type(d[0])
# #plt.savefig(targetDir + "\\" + str(ii) + ".png")
#print
img_s = Image.fromarray(
asarray(reshape(d, (x_, y_)) * 255, dtype="uint8"))
# plt.figure(2)
# plt.imshow(img_s)#,cmap = cm.Greys_r, interpolation ="nearest")
# plt.show()
if (ii < 1000):
img_s.save(targetDir + "\\bin000" + str(ii) + ".bmp")
else:
img_s.save(targetDir + "\\bin00" + str(ii) + ".bmp")
ii = ii + 1
