class NormalIntermediateFeature(Filter):
def __init__(self, filter_size, input_size, scales, features, orientations,
folder_name, mode):
self.filter_size = filter_size # size of patch
self.input_size = input_size
self.orientations = orientations # number of orientation
self.scales = scales
self.features = features
self.folder_name = folder_name
self.mode = mode
def compute_s2(self, input_layer, output_layer):
class_number = 1
data_number = 1
exi1 = path.isdir(self.folder_name + "/" + str(class_number) + "_" +
str(data_number) + "_" + str(self.input_size))
print "\n", "normal_s2"
while exi1:
exi2 = path.isdir(self.folder_name + "/" + str(class_number) +
"_" + str(data_number) + "_" +
str(self.input_size))
while exi2:
self.prototype = Layer(self.filter_size, self.scales,
self.orientations, "learning_s2",
self.features, self.filter_size,
input_layer)
self.prototype.set_layer(
self.folder_name,
str(class_number) + "_" + str(data_number) + "_" +
str(self.input_size))
print "feature set " + str(class_number) + "_" + str(
data_number)
self.compute_layer(input_layer, output_layer)
data_number += 1
exi2 = path.isdir(self.folder_name + "/" + str(class_number) +
"_" + str(data_number) + "_" +
str(self.input_size))
data_number = 1
class_number += 1
exi1 = path.isdir(self.folder_name + "/" + str(class_number) +
"_" + str(data_number) + "_" +
str(self.input_size))
def compute_unit(self, input_layer, scale, feature, x, y, orientation):
patch = input_layer.get_array(scale)[0, x:x + self.filter_size,
y:y + self.filter_size,
orientation]
p_patch = self.prototype.get_array(scale)[feature, 0:self.filter_size,
0:self.filter_size,
orientation]
alpha = (self.filter_size / 4)**2.0
res = np.exp(-np.linalg.norm(p_patch - patch) * 1.0 / (2 * alpha))
return res
class NormalIntermediateFeature(Filter):
def __init__(self, filter_size, input_size, scales, features, orientations, folder_name, mode):
self.filter_size = filter_size # size of patch
self.input_size = input_size
self.orientations = orientations # number of orientation
self.scales = scales
self.features = features
self.folder_name = folder_name
self.mode = mode
def compute_s2(self, input_layer, output_layer):
class_number = 1
data_number = 1
exi1 = path.isdir(self.folder_name + "/" + str(class_number) + "_" + str(data_number)
+ "_" + str(self.input_size))
print "\n", "normal_s2"
while exi1:
exi2 = path.isdir(self.folder_name + "/" + str(class_number) + "_" + str(data_number)
+ "_" + str(self.input_size))
while exi2:
self.prototype = Layer(self.filter_size, self.scales, self.orientations, "learning_s2",
self.features, self.filter_size, input_layer)
self.prototype.set_layer(self.folder_name, str(class_number) + "_" + str(data_number)
+ "_" + str(self.input_size))
print "feature set " + str(class_number) + "_" + str(data_number)
self.compute_layer(input_layer, output_layer)
data_number += 1
exi2 = path.isdir(self.folder_name + "/" + str(class_number) + "_" + str(data_number)
+ "_" + str(self.input_size))
data_number = 1
class_number += 1
exi1 = path.isdir(self.folder_name + "/" + str(class_number) + "_" + str(data_number)
+ "_" + str(self.input_size))
def compute_unit(self, input_layer, scale, feature, x, y, orientation):
patch = input_layer.get_array(scale)[0, x:x + self.filter_size, y:y + self.filter_size, orientation]
p_patch = self.prototype.get_array(scale)[feature, 0:self.filter_size, 0:self.filter_size, orientation]
alpha = (self.filter_size / 4) ** 2.0
res = np.exp(-np.linalg.norm(p_patch - patch) * 1.0 / (2 * alpha))
return res
ns2Layer = Layer(INPUT_SIZE, C1_SCALES, ORIENTATIONS, "normal_s2",
FEATURES, PROTOTYPE_SIZE, c1Layer)
#print "\nC2 Layer"
c2Layer = Layer(PROTOTYPE_SIZE, C1_SCALES, ORIENTATIONS, "c2",
FEATURES, 0, ns2Layer)
S1.compute_layer(InputLayer, s1Layer)
C1.compute_layer(s1Layer, c1Layer)
NormalS2.compute_s2(c1Layer, ns2Layer)
C2.compute_layer(ns2Layer, c2Layer)
vector = []
for s in range(C1_SCALES):
#features = c2Layer.get_features(s)
for o in range(ORIENTATIONS):
vector.extend(c2Layer.get_array(s)[:, 0, 0, o])
print "\n"
NormalSVM.predict_class(vector)
print "\n", time.time() - start
elif mode == "l":
start = time.time()
S1 = GaborFilter(11, 0.3, 4.0, 1.0, ORIENTATIONS)
C1 = LocalInvariance(10, ORIENTATIONS)
LearningS2 = LearningIntermediateFeature(PROTOTYPE_SIZE, ORIENTATIONS,
FEATURES_FOLDER_NAME)
NormalS2 = NormalIntermediateFeature(PROTOTYPE_SIZE, INPUT_SIZE,
C1_SCALES, FEATURES, ORIENTATIONS,
