def set_target(self, img, rect, landmarks=None):
# rect: [x0, y0, x1, y1]
self.template = self.crop(img, rect)
self.rect = list(rect)
m, n, nchannel = self.template.shape
if landmarks is not None:
self.landmarks = landmarks.copy()
else:
self.landmarks = distortion_util.create_landmarks(m, n, self.nSide)
self.landmarks_global = self.landmarks.copy()
self.landmarks_global[0, :, :] += rect[0]
self.landmarks_global[1, :, :] += rect[1]
def_spec = distortion_util.get_def_spec(self.template, self.landmarks,
"landmark")
alg_spec = distortion_util.get_hddd_parameters_fixed_layer(
self.template, self.sigma, self.landmarks, 7, 0.7)
# alg_spec = distortion_util.get_hddd_parameters(self.template, self.sigma, self.landmarks);
deformation_pylib.InitializeDeformation(def_spec.SerializeToString())
print "GenerateDDDSamples"
deformation_pylib.GenerateDDDSamples(self.template,
alg_spec.SerializeToString())
def set_target(self, img, rect, landmarks=None):
# rect: [x0, y0, x1, y1]
self.template = self.crop(img, rect);
self.rect = list(rect);
m, n, nchannel = self.template.shape;
if landmarks is not None:
self.landmarks = landmarks.copy();
else:
self.landmarks = distortion_util.create_landmarks(m, n, self.nSide)
self.landmarks_global = self.landmarks.copy();
self.landmarks_global[0,:,:] += rect[0]
self.landmarks_global[1,:,:] += rect[1]
def_spec = distortion_util.get_def_spec(self.template, self.landmarks, "landmark");
alg_spec = distortion_util.get_hddd_parameters_fixed_layer(self.template, self.sigma, self.landmarks, 7, 0.7)
# alg_spec = distortion_util.get_hddd_parameters(self.template, self.sigma, self.landmarks);
deformation_pylib.InitializeDeformation(def_spec.SerializeToString());
print "GenerateDDDSamples"
deformation_pylib.GenerateDDDSamples(self.template, alg_spec.SerializeToString());
def test_data_driven_descent(img, landmarks, distortion_sigma):
# Landmark is set
def_spec = distortion_util.get_def_spec(img, landmarks, "landmark")
#alg_spec = get_ddd_parameters(def_spec.dof, distortion_sigma, landmarks)
alg_spec = distortion_util.get_hddd_parameters_fixed_layer(
img, distortion_sigma, landmarks, 7, 0.7)
alg_spec.dump_intermediate = True
deformation_pylib.InitializeDeformation(def_spec.SerializeToString())
print "GenerateDDDSamples"
deformation_pylib.GenerateDDDSamples(img, alg_spec.SerializeToString())
while True:
deform = numpy.zeros(img.shape, dtype='f4')
p = numpy.random.normal(0, distortion_sigma,
(1, def_spec.dof)).astype('f4')
#p = numpy.array([5.0] * def_spec.dof).astype('f4');
deformation_pylib.SetParameters(p)
deformation_pylib.DeformImage(img, deform)
import pdb
pdb.set_trace()
print "Run Data-Driven Descent"
#cProfile.runctx("result = run_ddd(deform)", {"deform" : deform, "run_ddd" : run_ddd}, {});
result = run_ddd(deform)
# print the result
print "Gt:", p
print "Estimated:", result.estimates
# show the debug images
rows = 4
cols = 4
fig, axes = plt.subplots(rows, cols, sharex='col', sharey='row')
for frame in result.frames:
count = frame.t
# if frame.t % 2 == 1: continue;
# if c >= 25: break;
rectified_img = numpy.array(frame.representation)
rectified_img.shape = (img.shape[0], img.shape[1], 3)
row = count / cols
col = count % cols
#axes[row][col].imshow(rectified_img, interpolation='nearest')
axes[row][col].imshow(deform, interpolation='nearest')
axes[row][col].set_title("iteration = %d" % frame.t)
p = numpy.array(frame.estimates).reshape(landmarks.shape)
plot_landmarks(axes[row][col], landmarks, delta=p)
axes[-1][-1].imshow(img, interpolation='nearest')
axes[-1][-1].set_title("Template")
plot_landmarks(axes[-1][-1], landmarks)
plt.show()
key = raw_input("Press Enter to continue...")
print int(key)
if key != '\n': break
def test_data_driven_descent(img, landmarks, distortion_sigma):
# Landmark is set
def_spec = distortion_util.get_def_spec(img, landmarks, "landmark")
#alg_spec = get_ddd_parameters(def_spec.dof, distortion_sigma, landmarks)
alg_spec = distortion_util.get_hddd_parameters_fixed_layer(img, distortion_sigma, landmarks, 7, 0.7);
alg_spec.dump_intermediate = True;
deformation_pylib.InitializeDeformation(def_spec.SerializeToString());
print "GenerateDDDSamples"
deformation_pylib.GenerateDDDSamples(img, alg_spec.SerializeToString());
while True:
deform = numpy.zeros(img.shape, dtype='f4');
p = numpy.random.normal(0, distortion_sigma, (1, def_spec.dof)).astype('f4');
#p = numpy.array([5.0] * def_spec.dof).astype('f4');
deformation_pylib.SetParameters(p);
deformation_pylib.DeformImage(img, deform);
import pdb;
pdb.set_trace();
print "Run Data-Driven Descent"
#cProfile.runctx("result = run_ddd(deform)", {"deform" : deform, "run_ddd" : run_ddd}, {});
result = run_ddd(deform);
# print the result
print "Gt:", p
print "Estimated:", result.estimates
# show the debug images
rows = 4;
cols = 4;
fig, axes = plt.subplots(rows, cols, sharex='col', sharey='row')
for frame in result.frames:
count = frame.t;
# if frame.t % 2 == 1: continue;
# if c >= 25: break;
rectified_img = numpy.array(frame.representation);
rectified_img.shape = (img.shape[0], img.shape[1], 3);
row = count / cols;
col = count % cols;
#axes[row][col].imshow(rectified_img, interpolation='nearest')
axes[row][col].imshow(deform, interpolation='nearest')
axes[row][col].set_title("iteration = %d" % frame.t);
p = numpy.array(frame.estimates).reshape(landmarks.shape);
plot_landmarks(axes[row][col], landmarks, delta=p)
axes[-1][-1].imshow(img, interpolation='nearest');
axes[-1][-1].set_title("Template");
plot_landmarks(axes[-1][-1], landmarks)
plt.show();
key = raw_input("Press Enter to continue...")
print int(key)
if key != '\n': break;
