def draw_pose(R, t, subspace=None, color=None):
if subspace is None:
subspace = eye(3)[:2]
if color is None:
color = 'r'
C = -dot(R.T, t)
Cp = pca.project(C, subspace)
for i in range(3):
ep = pca.project(C + R[:, i], subspace)
plot_points([Cp, ep], '-' + color)
def draw_pose(R, t, subspace=None, color=None):
if subspace is None:
subspace = eye(3)[:2]
if color is None:
color = "r"
C = -dot(R.T, t)
Cp = pca.project(C, subspace)
for i in range(3):
ep = pca.project(C + R[:, i], subspace)
plot_points([Cp, ep], "-" + color)
def main():
parser = argparse.ArgumentParser(description=__doc__, epilog=__epilog__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('-t', '--test', action='store_true', default=False,
help=argparse.SUPPRESS)
parser.add_argument('-p', '--plot', action='store_true', default=False,
help="Visualizes confusion matrices graphically (it is off by default)")
parser.add_argument('machine_file', default='mlp.hdf5',
metavar='PATH', help="Path to the filename where to store the trained machine (defaults to %(default)s)")
args = parser.parse_args()
# loads the machine file
print("Loading Pen-digits training set...")
X_train, labels_train = data.as_mnist('data/train.hdf5', 28)
print("Loading Pen-digits development set...")
X_devel, labels_devel = data.as_mnist('data/devel.hdf5', 28)
# creates a matrix for the MLP output in which only the correct label
# position is set with +1.
y_train = -1*numpy.ones((10, len(labels_train)), dtype=float)
y_train[labels_train, range(len(labels_train))] = 1.0
y_devel = -1*numpy.ones((10, len(labels_devel)), dtype=float)
y_devel[labels_devel, range(len(labels_devel))] = 1.0
# Normalizing input set
#print("Normalizing input data...")
X_train = X_train.astype(float)
X_train /= 255.
X_devel = X_devel.astype(float)
X_devel /= 255.
f = bob.io.base.HDF5File(args.machine_file, 'r')
X_mean = f.read('X_mean')
X_std = f.read('X_std')
pca_comps = f.read('pca_comps')
X_train -= X_mean
X_train = pca.project(X_train, pca_comps)
if X_std != False:
X_train /= X_std
X_devel -= X_mean
X_devel = pca.project(X_devel, pca_comps)
if X_std != False:
X_devel /= X_std
import project as answers
machine = answers.Machine()
machine.load(f)
del f
hidden = machine.w2.shape[0] - 1
print("Number of inputs : %d" % (28*28,))
print("Number of hidden units : %d" % (hidden,))
print("Number of outputs : 10")
print("Total number of free parameters: %d" % \
((28*28+1)*hidden+(hidden+1)*10))
print("** FULL Train set results (%d examples):" % X_train.shape[1])
print(" * cost (J) = %f" % machine.J(X_train, y_train))
cer = machine.CER(X_train, y_train)
print(' * CER = %g%% (%d sample(s))' % (100*cer, X_train.shape[1]*cer))
print("** Development set results (%d examples):" % X_devel.shape[1])
print(" * cost (J) = %f" % machine.J(X_devel, y_devel))
cer = machine.CER(X_devel, y_devel)
print(' * CER = %g%% (%d sample(s))' % (100*cer, X_devel.shape[1]*cer))
if args.test:
print("Loading Pen-digits (writer-dependent) test set...")
X_test, labels_test = data.as_mnist('data/test-dependent.hdf5', 28)
# creates a matrix for the MLP output in which only the correct label
# position is set with +1.
y_test = -1*numpy.ones((10, len(labels_test)), dtype=float)
y_test[labels_test, range(len(labels_test))] = 1.0
X_test = X_test.astype(float)
X_test /= 255.
X_test -= X_mean
X_test = pca.project(X_test, pca_comps)
if X_std != False:
X_test /= X_std
print("** Test set results (%d examples):" % X_test.shape[1])
print(" * cost (J) = %f" % machine.J(X_test, y_test))
cer = machine.CER(X_test, y_test)
print(' * CER = %g%% (%d sample(s))' % (100*cer, X_test.shape[1]*cer))
if args.plot:
print("Plotting confusion matrices...")
# plot confusion matrix
N = 2
if args.test: N = 3
fig = mpl.figure(figsize=(N*6, 6))
def plot_cm(X, y, set_name):
# plot training
cm = data.confusion_matrix(y.argmax(axis=0), machine.forward(X).argmax(axis=0))
res = mpl.imshow(cm, cmap=mpl.cm.summer, interpolation='nearest')
for x in numpy.arange(cm.shape[0]):
for y in numpy.arange(cm.shape[1]):
col = 'white'
if cm[x,y] > 0.5: col = 'black'
mpl.annotate('%.2f' % (100*cm[x,y],), xy=(y,x), color=col,
fontsize=8, horizontalalignment='center', verticalalignment='center')
classes = [str(k) for k in range(10)]
mpl.xticks(numpy.arange(10), classes)
mpl.yticks(numpy.arange(10), classes, rotation=90)
mpl.ylabel("(Your prediction)")
mpl.xlabel("(Real class)")
mpl.title("Confusion Matrix (%s set) - in %%" % set_name)
mpl.subplot(1, N, 1)
plot_cm(X_train, y_train, 'train')
mpl.subplot(1, N, 2)
plot_cm(X_devel, y_devel, 'devel.')
if args.test:
mpl.subplot(1, N, 3)
plot_cm(X_test, y_test, 'test')
print("Close the plot window to terminate.")
mpl.show()
img2 = "data/crans_2_small.jpg"
image1 = np.array(Image.open(img1).convert("L"))
image2 = np.array(Image.open(img2).convert("L"))
locations1, features1 = sift.siftFeature(img1)
locations2, features2 = sift.siftFeature(img2)
# use PCAac to reduce dimensions
numberOfFeaturesOne = locations1.shape[0]
numberOfFeaturesTwo = locations2.shape[0]
features = np.vstack((features1, features2))
V, S, mean = pca.pca(features)
pcaFeatures1 = pca.project(features1, V, 36)
pcaFeatures2 = pca.project(features2, V, 36)
# normalize features
util.normalize(pcaFeatures1)
util.normalize(pcaFeatures2)
np.savetxt("pcafeature1", pcaFeatures1, delimiter="\t")
np.savetxt("pcafeature2", pcaFeatures2, delimiter="\t")
# interface with Java program to do matching
rowX = pcaFeatures1.shape[0]
rowY = pcaFeatures2.shape[0]
column = pcaFeatures1.shape[1]
matchCommand = "java match " + str(rowX) + " " + str(rowY) + " " + str(
def main():
parser = argparse.ArgumentParser(description=__doc__, epilog=__epilog__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('-s', '--seed', type=int, default=0,
metavar='SEED (INT)', help="Random (initialization) seed (defaults to %(default)s)")
parser.add_argument('-H', '--hidden', type=int, default=10,
metavar='INT', help="Number of hidden units (defaults to %(default)s)")
parser.add_argument('-p', '--plot', action='store_true', default=False,
help="Turn-ON plotting **after** training (it is off by default)")
parser.add_argument('-m', '--normalize', action='store_true', default=False, help="Turn-ON normalization of data (it is off by default)")
parser.add_argument('-l', '--regularization', type=float, default=0.,
metavar='FLOAT', help="Regularization parameter (defaults to %(default)s - i.e. no reguralization)")
parser.add_argument('-c', '--components', default=236, type=int,
help="Number of principal components to keep (defaults to %(default)s)")
parser.add_argument('-n', '--projected-gradient-norm', type=float,
default=1e-6, metavar='FLOAT', help='The norm of the projected gradient. Training with LBFGS-B will stop when the surface respects this degree of "flatness" (defaults to %(default)s)')
parser.add_argument('machine_file', default='mlp.hdf5',
metavar='MACHINE', help="Path to the filename where to store the trained machine (defaults to %(default)s)")
args = parser.parse_args()
##### START of program
print("Pen-digit Classification using an MLP with tanh activation")
print("Number of inputs : %d" % (28*28,))
print("Number of hidden units : %d" % (args.hidden,))
print("Number of outputs : 10")
print("Total number of free parameters: %d" % \
((28*28+1)*args.hidden+(args.hidden+1)*10))
print("Loading Pen-digit training set...")
X_train, labels_train = data.as_mnist('data/train.hdf5', 28)
print("Loading Pen-digit development set...")
X_devel, labels_devel = data.as_mnist('data/devel.hdf5', 28)
# creates a matrix for the MLP output in which only the correct label
# position is set with +1.
y_train = -1*numpy.ones((10, len(labels_train)), dtype=float)
y_train[labels_train, range(len(labels_train))] = 1.0
y_devel = -1*numpy.ones((10, len(labels_devel)), dtype=float)
y_devel[labels_devel, range(len(labels_devel))] = 1.0
print("Using %d samples for training..." % len(y_train.T))
# Normalizing input set
#print("Normalizing input data...")
X_train = X_train.astype(float)
X_train /= 255.
X_mean = X_train.mean(axis=1).reshape(-1,1)
#X_std = X_train.std(axis=1, ddof=1).reshape(-1,1)
#X_std[X_std == 0] = 1
#X_train = (X_train - X_mean) / X_std
X_train -= X_mean
# apply PCA for dimensionality reduction prior to MLP
e, U = pca.pca_bob(X_train)
# plot energy loading curve
total_energy = sum(e)
if args.plot:
print("Plotting energy load curve...")
mpl.plot(range(len(e)),
100*numpy.cumsum(e)/total_energy)
mpl.title('Energy loading curve for M-NIST (training set)')
mpl.xlabel('Number of components')
mpl.ylabel('Energy (percentage)')
mpl.grid()
print("Close the plot window to continue.")
mpl.show()
print("With %d components (your choice), you preserve %.2f%% of the energy" % (args.components, 100*sum(e[:args.components])/total_energy))
pca_comps = U[:,0:args.components];
X_train = pca.project(X_train, pca_comps)
X_std = X_train.std(axis = 1, ddof = 1).reshape(-1,1)
if args.normalize:
X_train /= X_std
else:
X_std = False
import project as answers
trainer = answers.Trainer(args.seed, args.hidden, args.regularization,
args.projected_gradient_norm)
start = time.time()
machine = trainer.train(X_train, y_train*0.8)
total = time.time() - start
if machine is None:
print("Training did **NOT** finish. Aborting...")
sys.exit(1)
sys.stdout.write("** Training is over, took %.2f minute(s)\n" % (total/60.))
sys.stdout.flush()
f = bob.io.base.HDF5File(args.machine_file, 'w')
f.set('X_mean', X_mean)
f.set('pca_comps', pca_comps)
f.set('X_std', X_std)
machine.save(f)
del f
X_devel = X_devel.astype(float)
X_devel /= 255.
X_devel -= X_mean
X_devel = pca.project(X_devel, pca_comps)
if args.normalize:
X_devel /= X_std
print("** Development set results (%d examples):" % X_devel.shape[1])
print(" * cost (J) = %f" % machine.J(X_devel, y_devel))
cer = machine.CER(X_devel, y_devel)
print(' * CER = %g%% (%d sample(s))' % (100*cer, X_devel.shape[1]*cer))
def draw_point_cloud(xs, subspace=None):
if subspace is None:
subspace = eye(3)[:2]
plot_points(pca.project(xs, subspace), '.b')
img2 = "data/crans_2_small.jpg"
image1 = np.array(Image.open(img1).convert("L"))
image2 = np.array(Image.open(img2).convert("L"))
locations1, features1 = sift.siftFeature(img1)
locations2, features2 = sift.siftFeature(img2)
# use PCAac to reduce dimensions
numberOfFeaturesOne = locations1.shape[0]
numberOfFeaturesTwo = locations2.shape[0]
features = np.vstack((features1, features2))
V, S, mean = pca.pca(features)
pcaFeatures1 = pca.project(features1, V, 36)
pcaFeatures2 = pca.project(features2, V, 36)
# normalize features
util.normalize(pcaFeatures1)
util.normalize(pcaFeatures2)
np.savetxt("pcafeature1", pcaFeatures1, delimiter="\t")
np.savetxt("pcafeature2", pcaFeatures2, delimiter="\t")
# interface with Java program to do matching
rowX = pcaFeatures1.shape[0]
rowY = pcaFeatures2.shape[0]
column = pcaFeatures1.shape[1]
matchCommand = "java match " +str(rowX)+ " " +str(rowY)+ " " +str(column)
def draw_point_cloud(xs, subspace=None):
if subspace is None:
subspace = eye(3)[:2]
plot_points(pca.project(xs, subspace), ".b")
def identifyKeyFrame(self, SIFTFeatures, indices, threshold = 0.15):
if len(indices) in [1,2]:
lst = []
lst.append(indices[0])
return lst
# build up graph structure
numberOfNodes = len(SIFTFeatures)
graph = Graph(numberOfNodes)
for i in range(numberOfNodes):
for j in range(i+1, numberOfNodes, 1):
one = SIFTFeatures[i]
two = SIFTFeatures[j]
# check whether one and two are near duplicate
pcaFeatures1 = pca.project(one, self.V, 36)
pcaFeatures2 = pca.project(two, self.V, 36)
# normalize features
util.normalize(pcaFeatures1)
util.normalize(pcaFeatures2)
np.savetxt("pcafeature1", pcaFeatures1, delimiter="\t")
np.savetxt("pcafeature2", pcaFeatures2, delimiter="\t")
# interface with Java program to do matching
rowX = pcaFeatures1.shape[0]
rowY = pcaFeatures2.shape[0]
column = pcaFeatures1.shape[1]
matchCommand = "java match " +str(rowX)+ " " +str(rowY)+ " " +str(column)
print matchCommand
os.system(matchCommand)
# plot according to match stored in "data" folder
matchFile = open("data/match", 'r')
lines = matchFile.readlines()
matchSize = len(lines)
oneSize = one.shape[0]
twoSize = two.shape[0]
ratio = matchSize / float(min(oneSize, twoSize))
if ratio > threshold:
graph.connect(i,j)
# Find nodes with largest edges in each connected component
connectedComponents = graph.connectedComponent()
tempkeyFrames = []
for component in connectedComponents:
edges = []
for node in component:
edges.append(graph.getNumOfEdges(node))
maxIndice = 0
maxValue = edges[0]
for i in range(1, len(edges), 1):
if maxValue < edges[i]:
maxValue = edges[i]
maxIndice = i
# random choose one if there are many within one component
maxEdges = []
for i in range(len(edges)):
if edges[i] == maxValue:
maxEdges.append(i)
if len(maxEdges) == 1:
tempkeyFrames.append(component[maxIndice])
else:
maxEdgeSize = len(maxEdges)
randomNumber = randint(0, maxEdgeSize - 1)
tempkeyFrames.append(component[randomNumber])
keyFrames = []
for indice in tempkeyFrames:
keyFrames.append(indices[indice])
return keyFrames