def main():
# load data
fr = open('pca_params.pkl', 'rb')
pca_params = pickle.load(fr, encoding='latin1')
fr.close()
# read data
train_images, train_labels, test_images, test_labels, class_list = data.import_data(
"0-9")
print('Training image size:', train_images.shape)
print('Testing_image size:', test_images.shape)
feat = {}
# Training
print('--------Training--------')
feature = saab.initialize(train_images, pca_params)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
feat['training_feature'] = feature
print('--------Testing--------')
feature = saab.initialize(test_images, pca_params)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
feat['testing_feature'] = feature
# save data
fw = open('feat.pkl', 'wb')
pickle.dump(feat, fw)
fw.close()
def main():
# load data
fr = open('pca_params_E5S5.pkl', 'rb')
pca_params = pickle.load(fr)
fr.close()
# read data
train_images, train_labels, test_images, test_labels, class_list = data.import_data(
"0-9")
print('Training image size:', train_images.shape)
print('Testing_image size:', test_images.shape)
batch_size = 100
num_samples = int(len(train_images) / batch_size)
# Training
print('--------Training--------')
features = []
for i in range(num_samples):
trn_images = train_images[i * batch_size:i * batch_size +
batch_size, :]
feature = saab.initialize(trn_images, pca_params)
feature = feature.reshape(feature.shape[0], -1)
features.append(feature)
feature = np.vstack(features)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
feat = {}
feat['feature'] = feature
# save data
fw = open('feat_E5S5.pkl', 'wb')
pickle.dump(feat, fw)
fw.close()
def main():
# load data
fr = open('pca_params.pkl', 'rb')
pca_params = pickle.load(fr)
fr.close()
# read data
#train_images, train_labels, test_images, test_labels, class_list = data.import_data("0-9")
n1 = misc.imread('/Users/brinalbheda/Desktop/HW6/1.png').reshape(32, 32, 1)
n2 = misc.imread('/Users/brinalbheda/Desktop/HW6/2.png').reshape(32, 32, 1)
n3 = misc.imread('/Users/brinalbheda/Desktop/HW6/3.png').reshape(32, 32, 1)
n4 = misc.imread('/Users/brinalbheda/Desktop/HW6/4.png').reshape(32, 32, 1)
arr_train_images = np.stack([n1, n2, n3, n4])
arr_train_images = arr_train_images.astype('float32')
train_images = arr_train_images
print('Training image size:', train_images.shape)
#print('Testing_image size:', test_images.shape)
# Training
print('--------Training--------')
feature = saab.initialize(train_images, pca_params)
fs = feature.shape
np.save('fs', fs)
feature = feature.reshape(feature.shape[0], -1)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
feat = {}
feat['feature'] = feature
# save data
fw = open('feat.pkl', 'wb')
pickle.dump(feat, fw)
fw.close()
def main():
# load data
fr = open('pca_params_E5S5.pkl', 'rb')
pca_params = pickle.load(fr, encoding='latin1')
fr.close()
# read data
train_images, train_labels, test_images, test_labels, class_list = data.import_data(
"0-9")
print('Testing_image size:', test_images.shape)
# testing
print('--------Testing--------')
feature = saab.initialize(test_images, pca_params)
feature = feature.reshape(feature.shape[0], -1)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
# feature normalization
# std_var=(np.std(feature, axis=0)).reshape(1,-1)
# feature=feature/std_var
num_clusters = [120, 80, 10]
use_classes = 10
fr = open('llsr_weights_E5S5.pkl', 'rb')
weights = pickle.load(fr, encoding='latin1')
fr.close()
fr = open('llsr_bias_E5S5.pkl', 'rb')
biases = pickle.load(fr, encoding='latin1')
fr.close()
for k in range(len(num_clusters)):
# least square regression
weight = weights['%d LLSR weight' % k]
bias = biases['%d LLSR bias' % k]
feature = np.matmul(feature, weight) + bias
print(k, ' layer LSR weight shape:', weight.shape)
print(k, ' layer LSR bias shape:', bias.shape)
print(k, ' layer LSR output shape:', feature.shape)
if k != len(num_clusters) - 1:
# Relu
for i in range(feature.shape[0]):
for j in range(feature.shape[1]):
if feature[i, j] < 0:
feature[i, j] = 0
else:
pred_labels = np.argmax(feature, axis=1)
acc_test = sklearn.metrics.accuracy_score(test_labels, pred_labels)
print('testing acc is {}'.format(acc_test))
fw = open('test_pred_E5S5.pkl', 'wb')
pickle.dump(feature, fw)
fw.close()
def main():
# load data
fr = open('pca_params_10.pkl', 'rb')
pca_params = pickle.load(fr)
fr.close()
# read data
train_images, train_labels, test_images, test_labels, class_list = data.import_data(
"0-9")
print('Training image size:', train_images.shape)
print('Testing_image size:', test_images.shape)
Edge_filter = np.array([-1, -2, 0, 2, 1])
Level_filter = np.array([1, 4, 6, 4, 1])
Spot_filter = np.array([-1, 0, 2, 0, -1])
Wave_filter = np.array([-1, 2, 0, -2, 1])
Ripple_filter = np.array([1, -4, 6, -4, 1])
Filter = Wave_filter.T * Ripple_filter
#Filter2=Level_filter.T*Edge_filter
#Filter1=Level_filter.T*Level_filter
altered_train = train_images.copy()
for i in range(train_images.shape[0]):
altered_train[i, :, :, 0] = cv2.filter2D(train_images[i, :, :, 0], -1,
Filter)
train_images = altered_train.copy()
# Training
print('--------Training--------')
train_images = train_images[0:3000, :, :, :] #Taking only 3000 samples
train_labels = train_labels[0:3000]
feature = saab.initialize(train_images, pca_params)
feature = feature.reshape(feature.shape[0], -1)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
feat = {}
feat['feature'] = feature
# save data
fw = open('feat_10.pkl', 'wb')
pickle.dump(feat, fw)
fw.close()
def main():
# load data
fr=open('pca_params.pkl','rb')
pca_params=pickle.load(fr)
fr.close()
# read data
image1=misc.imread('1.png').reshape(32,32,1)
image2=misc.imread('2.png').reshape(32,32,1)
image3=misc.imread('3.png').reshape(32,32,1)
image4=misc.imread('4.png').reshape(32,32,1)
image1=image1/255.
image2=image2/255.
image3=image3/255.
image4=image4/255.
image=np.stack([image1,image2,image3,image4]);
image=image.astype('float32')
train_images=image;
# train_images, train_labels, test_images, test_labels, class_list = data.import_data("0-9")
print('Training image size:', train_images.shape)
# print('Testing_image size:', test_images.shape)
# Training
print('--------Training--------')
feature=saab.initialize(train_images, pca_params)
fs=feature.shape
np.save('fs',fs)
feature=feature.reshape(feature.shape[0],-1)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
feat={}
feat['feature']=feature
# save data
fw=open('feat.pkl','wb')
pickle.dump(feat, fw)
fw.close()
def main():
# load data
fr=open('pca_params_10.pkl','rb')
pca_params=pickle.load(fr, encoding='latin1')
fr.close()
# read data
train_images, train_labels, test_images, test_labels, class_list = data.import_data("0-9")
print('Training image size:', train_images.shape)
print('Testing_image size:', test_images.shape)
Level_filter=np.array([1,4,6,4,1])
Edge_filter=np.array([-1,-2, 0 ,2, 1])
#Filter1=Level_filter.T*Level_filter
Spot_filter=np.array([-1, 0, 2, 0, -1])
Wave_filter=np.array([-1, 2, 0, -2, 1])
Ripple_filter=np.array([1 ,-4, 6 ,-4, 1])
#Filter1=Level_filter.T*Level_filter
Filter=Wave_filter.T*Ripple_filter
altered_train=train_images.copy()
altered_test=train_images.copy()
for i in range(test_images.shape[0]):
altered_test[i,:,:,0] = cv2.filter2D(test_images[i,:,:,0],-1,Filter)
test_images = altered_test.copy()
test_images = test_images[3000:6000,:,:,:]
test_labels = test_labels[3000:6000]
# testing
print('--------Testing--------')
feature=saab.initialize(test_images, pca_params)
feature=feature.reshape(feature.shape[0],-1)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
# feature normalization
#std_var=(np.std(feature, axis=0)).reshape(1,-1)
#feature=feature/std_var
num_clusters=[120, 80, 10]
use_classes=10
fr=open('llsr_weights_10.pkl','rb')
weights=pickle.load(fr)
fr.close()
fr=open('llsr_bias_10.pkl','rb')
biases=pickle.load(fr)
fr.close()
for k in range(len(num_clusters)):
# least square regression
weight=weights['%d LLSR weight'%k]
bias=biases['%d LLSR bias'%k]
feature=np.matmul(feature,weight)
feature=feature+bias
print(k,' layer LSR weight shape:', weight.shape)
print(k,' layer LSR bias shape:', bias.shape)
print(k,' layer LSR output shape:', feature.shape)
if k!=len(num_clusters)-1:
pred_labels=np.argmax(feature, axis=1)
num_clas=np.zeros((num_clusters[k],use_classes))
for i in range(num_clusters[k]):
for t in range(use_classes):
for j in range(feature.shape[0]):
if pred_labels[j]==i and train_labels[j]==t:
num_clas[i,t]+=1
acc_train=np.sum(np.amax(num_clas, axis=1))/feature.shape[0]
print(k,' layer LSR testing acc is {}'.format(acc_train))
# Relu
for i in range(feature.shape[0]):
for j in range(feature.shape[1]):
if feature[i,j]<0:
feature[i,j]=0
else:
pred_labels=np.argmax(feature, axis=1)
acc_train=sklearn.metrics.accuracy_score(test_labels,pred_labels)
print('testing acc is {}'.format(acc_train))
fw=open('ffcnn_feature_test10.pkl','wb')
pickle.dump(feature, fw)
fw.close()
def main():
# load data
fr=open('pca_params.pkl','rb')
pca_params=pickle.load(fr)
fr.close()
image = cv2.imread(r'C:\Users\lenovo\Documents\4.png',0)
h,w=img.shape
img= image.reshape(1,h,w,1)
print('--------Training--------')
feature,pca_params=saab.initialize(img, pca_params)
n,h,w,c=feature.shape
feature=feature.reshape(feature.shape[0],-1)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
feat={}
feat['feature']=feature
#print(feat)
print(pca_params)
print(pca_params['Layer_1/kernel'].shape)
# save data
num_channels=[8,16]
#Reconstruction
feature = feature.reshape(n,h,w,c)
transformed=feature.reshape(h*w,-1)
for i in range(1,-1,-1):
feature_expectation=pca_params['Layer_%d/feature_expectation'%i]
dc = pca_params['Layer_%d/dc'%i]
kernels=pca_params['Layer_%d/kernel'%i]
if i==1:
bias=pca_params['Layer_%d/bias'%i]
e=np.zeros((1, kernels.shape[0]))
e[0,0]=1
transformed+=e*bias
sample_patches_centered_with_bias = np.matmul(transformed,np.linalg.pinv(np.transpose(kernels)))
sample_patches_centered = sample_patches_centered_with_bias - np.sqrt(16*num_channels[0])*bias
else:
transformed=transformed.reshape(64,num_channels[0])
sample_patches_centered = np.matmul(transformed,np.linalg.pinv(np.transpose(kernels)))
sample_patches = sample_patches_centered + feature_expectation
ac = int(np.sqrt(sample_patches.shape[0]))
sample_patches = sample_patches.reshape(1,ac,ac,-1)
h,w = ac,ac
if i==1:
sample_patches = sample_patches.reshape(1,2,2,1,1,4,4,num_channels[0])
patches= sample_patches.transpose(0,1,3,5,2,4,6,7).reshape(1,8,8,num_channels[0])
transformed = patches
else:
sample_patches = sample_patches.reshape(1,8,8,1,1,4,4,1)
patches= sample_patches.transpose(0,1,3,5,2,4,6,7).reshape(1,32,32,1)
transformed = patches
transformed = transformed.reshape(32,32)
plt.imshow(transformed,cmap='gray')
PSNR=10*math.log10((255**2/(sum(sum((transformed-image[0,:,:,0])**2))/(32*32))))
print(PSNR)
fw=open('feat.pkl','wb')
pickle.dump(feat, fw)
fw.close()
t2 = t2/255.
test_images.append(t2)
t3 = np.array(t3.getdata())
t3 = t3.reshape(-1,32,32,1)
t3 = t3/255.
test_images.append(t3)
t4 = np.array(t4.getdata())
t4 = t4.reshape(-1,32,32,1)
t4 = t4/255.
test_images.append(t4)
test_images= np.vstack(test_images)
test_labels = [9,0,5,3]
f = saab.initialize(test_images,pca_params)
num_layers=pca_params['num_layers']
kernel_sizes=pca_params['kernel_size']
trans= f
for i in range(num_layers-1,-1,-1):
print('--------stage %d --------'%i)
feature_expectation = pca_params['Layer_%d/feature_expectation'%i]
kernels = pca_params['Layer_%d/kernel'%i]
mean = pca_params['Layer_%d/pca_mean'%i]
if i==0:
# Transform to get data for the previous stage
sample_patches_centered=np.matmul(trans, np.linalg.pinv(np.transpose(kernels))
else:
def main():
# load data
fr = open('pca_params.pkl', 'rb')
pca_params = pickle.load(fr)
fr.close()
# read data
#train_images, train_labels, class_list = data.import_data("0-9")
train_img1 = color.rgb2gray(io.imread('1.png'))
train_img2 = color.rgb2gray(io.imread('2.png'))
train_img3 = color.rgb2gray(io.imread('3.png'))
train_img4 = color.rgb2gray(io.imread('4.png'))
train_images = train_img4
#train_images = train_img1;
train_images = train_images.reshape(1, 32, 32, 1)
print('Training image size: dtype: ', train_images.shape,
train_images.dtype)
#print('Testing_image size:', test_images.shape)
# Training
print('--------Training--------')
train_red = train_images
feature, pca_params = saab.initialize(train_red, pca_params)
n, h, w, c = np.shape(feature)
# 60000x400 (16*5*5)
feature = feature.reshape(feature.shape[0], -1)
print("S4 shape:", feature.shape)
print('--------Finish Feature Extraction subnet--------')
print('feature.dtype: {}'.format(feature.dtype))
feat = {}
feat['feature'] = feature
#Reconstructing the image
feature = np.reshape(feature, (n, h, w, c))
trans = np.reshape(feature, (h * w, -1))
stages = [1, 0]
for i in stages:
DC_comp = pca_params['Layer_%d/dc' % i]
f_expect = pca_params['Layer_%d/feature_expectation' % i]
kernels = pca_params['Layer_%d/kernel' % i]
kernelst_inv = np.linalg.pinv(np.transpose(kernels))
if (i == 0):
trans = np.reshape(trans, (64, 8))
spc = np.matmul(trans, kernelst_inv)
else:
kernels_shape = np.shape(kernels)
e = np.zeros((1, kernels_shape[0]))
e[0, 0] = 1
bias_factor = pca_params['Layer_%d/bias' % i]
trans += e * bias_factor
spcwb = np.matmul(trans, kernelst_inv)
spc = spcwb - ((np.sqrt(128)) * bias_factor)
s_p = spc + f_expect
s_p_shape = np.shape(s_p)
h = int(np.sqrt(s_p_shape[0]))
w = int(np.sqrt(s_p_shape[0]))
s_p = s_p.reshape(1, h, w, -1)
s_p_t = [0, 1, 3, 5, 2, 4, 6, 7]
if (i == 0):
s_p_bef = [1, 8, 8, 1, 1, 4, 4, 1]
s_p = np.reshape(s_p, (s_p_bef))
p = s_p.transpose(s_p_t).reshape(1, 32, 32, 1)
trans = p
else:
s_p_bef = [1, 2, 2, 1, 1, 4, 4, 8]
s_p = np.reshape(s_p, (s_p_bef))
p = s_p.transpose(s_p_t).reshape(1, 8, 8, 8)
trans = p
trans = np.reshape(trans, (32, 32))
plt.imshow(trans, cmap='gray')
#PSNR calculation
def PSNR_cal(I_image, y_image):
den = 0
for i in range(0, 32):
for j in range(0, 32):
den += (y_image[i][j] - I_image[i][j])**2
factor = (1 / (32 * 32))
mse = factor * den
psnr = 10 * np.log10((255 * 255) / mse)
return psnr
psnr = PSNR_cal(train_img4, trans)
print("The psnr is", psnr)
'''#Reconstructing the image
feature = np.reshape(feature,(n,h,w,c))
trans = np.reshape(feature,(h*w,-1))
stages = [1,0];
for i in stages:
DC_comp = pca_params['Layer_%d/dc'%i]
f_expect = pca_params['Layer_%d/feature_expectation'%i]
kernels = pca_params['Layer_%d/kernel'%i]
kernelst_inv = np.linalg.pinv(np.transpose(kernels))
if (i == 0):
trans = np.reshape(trans,( 64, 4 ))
spc = np.matmul(trans,kernelst_inv)
else:
kernels_shape = np.shape(kernels)
e = np.zeros( ( 1, kernels_shape[0] ) )
e[0 ,0 ] = 1
bias_factor = pca_params['Layer_%d/bias'%i]
trans += e*bias_factor;
spcwb = np.matmul(trans,kernelst_inv)
spc = spcwb - ((np.sqrt(64))*bias_factor)
s_p = spc + f_expect
s_p_shape = np.shape(s_p)
h = int(np.sqrt(s_p_shape[0]))
w = int(np.sqrt(s_p_shape[0]))
s_p = s_p.reshape(1, h, w, -1)
s_p_t = [0,1,3,5,2,4,6,7]
if (i == 0):
s_p_bef = [1,8,8,1,1,4,4,1]
s_p = np.reshape(s_p,(1,8,8,1,1,4,4,1))
p = s_p.transpose(s_p_t).reshape(1,32,32,1)
trans = p;
else:
s_p_bef = [1,2,2,1,1,4,4,4]
s_p = np.reshape(s_p,(1,2,2,1,1,4,4,4))
p = s_p.transpose(s_p_t).reshape(1,8,8,4)
trans = p;
trans = np.reshape(trans,(32,32))
plt.imshow(trans,cmap = 'gray')
#PSNR calculation
def PSNR_cal(I_image,y_image):
den = 0;
for i in range(0,32):
for j in range(0,32):
den += (y_image[i][j] - I_image[i][j])**2;
factor = (1/(32*32));
mse = factor*den;
psnr = 10*np.log10((255*255)/mse);
return psnr;
psnr = PSNR_cal(train_img4,trans)
print("The psnr is" ,psnr)'''
# save data
fw = open('feat.pkl', 'wb')
pickle.dump(feat, fw)
fw.close()
