def test_clm():
assert (clm.n_training_images == 4)
assert (clm.n_levels == 2)
assert (clm.downscale == 1.1)
#assert (clm.features[0] == sparse_hog and len(clm.features) == 1)
assert_allclose(np.around(clm.reference_shape.range()), (72., 69.))
assert clm.scaled_shape_models
assert clm.pyramid_on_features
assert_allclose(clm.patch_shape, (8, 8))
assert_allclose(
[clm.shape_models[j].n_components for j in range(clm.n_levels)],
(2, 2))
assert_allclose(clm.n_classifiers_per_level, [68, 68])
ran_0 = np.random.randint(0, clm.n_classifiers_per_level[0])
ran_1 = np.random.randint(0, clm.n_classifiers_per_level[1])
assert (name_of_callable(clm.classifiers[0][ran_0]) == 'linear_svm_lr')
assert (name_of_callable(clm.classifiers[1][ran_1]) == 'linear_svm_lr')
def test_clm_2():
assert (clm2.n_training_images == 4)
assert (clm2.n_levels == 2)
assert (clm2.downscale == 1.2)
#assert (clm2.features[0] is no_op and clm2.features[1] is no_op)
assert_allclose(np.around(clm2.reference_shape.range()), (169., 161.))
assert clm2.scaled_shape_models
assert (not clm2.pyramid_on_features)
assert_allclose(clm2.patch_shape, (3, 10))
assert (np.all([
clm2.shape_models[j].n_components == 3 for j in range(clm2.n_levels)
]))
assert_allclose(clm2.n_classifiers_per_level, [68, 68])
ran_0 = np.random.randint(0, clm2.n_classifiers_per_level[0])
ran_1 = np.random.randint(0, clm2.n_classifiers_per_level[1])
assert (name_of_callable(
clm2.classifiers[0][ran_0]) == 'random_forest_predict')
assert (name_of_callable(clm2.classifiers[1][ran_1]) == 'linear_svm_lr')
def test_clm_2():
assert (clm2.n_training_images == 4)
assert (clm2.n_levels == 2)
assert (clm2.downscale == 1.2)
#assert (clm2.features[0] is no_op and clm2.features[1] is no_op)
assert_allclose(np.around(clm2.reference_shape.range()), (169., 161.))
assert clm2.scaled_shape_models
assert (not clm2.pyramid_on_features)
assert_allclose(clm2.patch_shape, (3, 10))
assert (np.all([clm2.shape_models[j].n_components == 3
for j in range(clm2.n_levels)]))
assert_allclose(clm2.n_classifiers_per_level, [68, 68])
ran_0 = np.random.randint(0, clm2.n_classifiers_per_level[0])
ran_1 = np.random.randint(0, clm2.n_classifiers_per_level[1])
assert (name_of_callable(clm2.classifiers[0][ran_0])
== 'random_forest_predict')
assert (name_of_callable(clm2.classifiers[1][ran_1])
== 'linear_svm_lr')
def test_clm():
assert (clm.n_training_images == 4)
assert (clm.n_levels == 2)
assert (clm.downscale == 1.1)
#assert (clm.features[0] == sparse_hog and len(clm.features) == 1)
assert_allclose(np.around(clm.reference_shape.range()), (72., 69.))
assert clm.scaled_shape_models
assert clm.pyramid_on_features
assert_allclose(clm.patch_shape, (8, 8))
assert_allclose([clm.shape_models[j].n_components
for j in range(clm.n_levels)], (2, 2))
assert_allclose(clm.n_classifiers_per_level, [68, 68])
ran_0 = np.random.randint(0, clm.n_classifiers_per_level[0])
ran_1 = np.random.randint(0, clm.n_classifiers_per_level[1])
assert (name_of_callable(clm.classifiers[0][ran_0])
== 'linear_svm_lr')
assert (name_of_callable(clm.classifiers[1][ran_1])
== 'linear_svm_lr')
def __str__(self):
if self.fitter.pyramid_on_features:
feat_str = name_of_callable(self.fitter.features)
else:
feat_str = []
for j in range(self.n_levels):
if isinstance(self.fitter.features[j], str):
feat_str.append(self.fitter.features[j])
elif self.fitter.features[j] is None:
feat_str.append("none")
else:
feat_str.append(name_of_callable(self.fitter.features[j]))
out = "Fitting Result\n" \
" - Initial error: {0:.4f}\n" \
" - Final error: {1:.4f}\n" \
" - {2} method with {3} pyramid levels, {4} iterations " \
"and using {5} features.".format(
self.initial_error(), self.final_error(), self.fitter.algorithm,
self.n_levels, self.n_iters, feat_str)
return out
def __str__(self):
if self.diagonal is not None:
diagonal = self.diagonal
else:
y, x = self.reference_shape.range()
diagonal = np.sqrt(x ** 2 + y ** 2)
is_custom_perturb_func = (self._perturb_from_gt_bounding_box !=
noisy_shape_from_bounding_box)
regressor_cls = self.algorithms[0]._regressor_cls
# Compute scale info strings
scales_info = []
lvl_str_tmplt = r""" - Scale {}
- {} iterations
- Patch shape: {}
- Holistic feature: {}
- Patch feature: {}"""
for k, s in enumerate(self.scales):
scales_info.append(lvl_str_tmplt.format(
s, self.n_iterations[k], self.patch_shape[k],
name_of_callable(self.holistic_features[k]),
name_of_callable(self.patch_features[k])))
scales_info = '\n'.join(scales_info)
cls_str = r"""Supervised Descent Method
- Regression performed using the {reg_alg} algorithm
- Regression class: {reg_cls}
- Perturbations generated per shape: {n_perturbations}
- Images scaled to diagonal: {diagonal:.2f}
- Custom perturbation scheme used: {is_custom_perturb_func}
- Scales: {scales}
{scales_info}
""".format(
reg_alg=name_of_callable(self._sd_algorithm_cls),
reg_cls=name_of_callable(regressor_cls),
n_perturbations=self.n_perturbations,
diagonal=diagonal,
is_custom_perturb_func=is_custom_perturb_func,
scales=self.scales,
scales_info=scales_info)
return cls_str
def test_clm_1():
assert (clm1.n_training_images == 4)
assert (clm1.n_levels == 3)
assert (clm1.downscale == 2)
#assert (clm1.features[0] == igo and clm1.features[2] is no_op)
assert_allclose(np.around(clm1.reference_shape.range()), (109., 103.))
assert (not clm1.scaled_shape_models)
assert (not clm1.pyramid_on_features)
assert_allclose(clm1.patch_shape, (5, 5))
assert_allclose(
[clm1.shape_models[j].n_components for j in range(clm1.n_levels)],
(1, 2, 3))
assert_allclose(clm1.n_classifiers_per_level, [68, 68, 68])
ran_0 = np.random.randint(0, clm1.n_classifiers_per_level[0])
ran_1 = np.random.randint(0, clm1.n_classifiers_per_level[1])
ran_2 = np.random.randint(0, clm1.n_classifiers_per_level[2])
assert (name_of_callable(clm1.classifiers[0][ran_0]) == 'linear_svm_lr')
assert (name_of_callable(clm1.classifiers[1][ran_1]) == 'linear_svm_lr')
assert (name_of_callable(clm1.classifiers[2][ran_2]) == 'linear_svm_lr')
def test_clm_1():
assert (clm1.n_training_images == 4)
assert (clm1.n_levels == 3)
assert (clm1.downscale == 2)
#assert (clm1.features[0] == igo and clm1.features[2] is no_op)
assert_allclose(np.around(clm1.reference_shape.range()), (109., 103.))
assert (not clm1.scaled_shape_models)
assert (not clm1.pyramid_on_features)
assert_allclose(clm1.patch_shape, (5, 5))
assert_allclose([clm1.shape_models[j].n_components
for j in range(clm1.n_levels)], (1, 2, 3))
assert_allclose(clm1.n_classifiers_per_level, [68, 68, 68])
ran_0 = np.random.randint(0, clm1.n_classifiers_per_level[0])
ran_1 = np.random.randint(0, clm1.n_classifiers_per_level[1])
ran_2 = np.random.randint(0, clm1.n_classifiers_per_level[2])
assert (name_of_callable(clm1.classifiers[0][ran_0])
== 'linear_svm_lr')
assert (name_of_callable(clm1.classifiers[1][ran_1])
== 'linear_svm_lr')
assert (name_of_callable(clm1.classifiers[2][ran_2])
== 'linear_svm_lr')
def _atm_str(atm):
if atm.diagonal is not None:
diagonal = atm.diagonal
else:
y, x = atm.reference_shape.range()
diagonal = np.sqrt(x ** 2 + y ** 2)
# Compute scale info strings
scales_info = []
lvl_str_tmplt = r""" - Scale {}
- Holistic feature: {}
- Template shape: {}
- {} shape components"""
for k, s in enumerate(atm.scales):
scales_info.append(lvl_str_tmplt.format(
s, name_of_callable(atm.holistic_features[k]),
atm.warped_templates[k].shape,
atm.shape_models[k].n_components))
# Patch based ATM
if hasattr(atm, 'patch_shape'):
for k in range(len(scales_info)):
scales_info[k] += '\n - Patch shape: {}'.format(
atm.patch_shape[k])
scales_info = '\n'.join(scales_info)
cls_str = r"""{class_title}
- Images warped with {transform} transform
- Images scaled to diagonal: {diagonal:.2f}
- Scales: {scales}
{scales_info}
""".format(class_title=atm._str_title,
transform=name_of_callable(atm.transform),
diagonal=diagonal,
scales=atm.scales,
scales_info=scales_info)
return cls_str
def test_clm_3():
assert (clm3.n_training_images == 4)
assert (clm3.n_levels == 1)
assert (clm3.downscale == 3)
#assert (clm3.features[0] == igo and len(clm3.features) == 1)
assert_allclose(np.around(clm3.reference_shape.range()), (169., 161.))
assert clm3.scaled_shape_models
assert clm3.pyramid_on_features
assert_allclose(clm3.patch_shape, (2, 3))
assert (np.all([
clm3.shape_models[j].n_components == 1 for j in range(clm3.n_levels)
]))
assert_allclose(clm3.n_classifiers_per_level, [68])
ran_0 = np.random.randint(0, clm3.n_classifiers_per_level[0])
assert (name_of_callable(clm3.classifiers[0][ran_0]) == 'linear_svm_lr')
def test_clm_3():
assert (clm3.n_training_images == 4)
assert (clm3.n_levels == 1)
assert (clm3.downscale == 3)
#assert (clm3.features[0] == igo and len(clm3.features) == 1)
assert_allclose(np.around(clm3.reference_shape.range()), (169., 161.))
assert clm3.scaled_shape_models
assert clm3.pyramid_on_features
assert_allclose(clm3.patch_shape, (2, 3))
assert (np.all([clm3.shape_models[j].n_components == 1
for j in range(clm3.n_levels)]))
assert_allclose(clm3.n_classifiers_per_level, [68])
ran_0 = np.random.randint(0, clm3.n_classifiers_per_level[0])
assert (name_of_callable(clm3.classifiers[0][ran_0])
== 'linear_svm_lr')
def test_name_of_callable_partial():
assert name_of_callable(sparse_hog) == 'sparse_hog'
def test_name_of_callable_object_with_call():
assert name_of_callable(Foo()) == 'Foo'
def __str__(self):
from menpofit.base import name_of_callable
out = "{}\n - {} training images.\n".format(self._str_title,
self.n_training_images)
# small strings about number of channels, channels string and downscale
down_str = []
for j in range(self.n_levels):
if j == self.n_levels - 1:
down_str.append('(no downscale)')
else:
down_str.append('(downscale by {})'.format(
self.downscale**(self.n_levels - j - 1)))
temp_img = Image(image_data=np.random.rand(50, 50))
if self.pyramid_on_features:
temp = self.features(temp_img)
n_channels = [temp.n_channels] * self.n_levels
else:
n_channels = []
for j in range(self.n_levels):
temp = self.features[j](temp_img)
n_channels.append(temp.n_channels)
# string about features and channels
if self.pyramid_on_features:
feat_str = "- Feature is {} with ".format(
name_of_callable(self.features))
if n_channels[0] == 1:
ch_str = ["channel"]
else:
ch_str = ["channels"]
else:
feat_str = []
ch_str = []
for j in range(self.n_levels):
feat_str.append("- Feature is {} with ".format(
name_of_callable(self.features[j])))
if n_channels[j] == 1:
ch_str.append("channel")
else:
ch_str.append("channels")
if self.n_levels > 1:
if self.scaled_shape_models:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}.\n - Each level has a scaled shape " \
"model (reference frame).\n - Patch size is {}W x " \
"{}H.\n".format(out, self.n_levels, self.downscale,
self.patch_shape[1], self.patch_shape[0])
else:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}:\n - Shape models (reference frames) " \
"are not scaled.\n - Patch size is {}W x " \
"{}H.\n".format(out, self.n_levels, self.downscale,
self.patch_shape[1], self.patch_shape[0])
if self.pyramid_on_features:
out = "{} - Pyramid was applied on feature space.\n " \
"{}{} {} per image.\n".format(out, feat_str,
n_channels[0], ch_str[0])
else:
out = "{} - Features were extracted at each pyramid " \
"level.\n".format(out)
for i in range(self.n_levels - 1, -1, -1):
out = "{} - Level {} {}: \n".format(out, self.n_levels - i,
down_str[i])
if not self.pyramid_on_features:
out = "{} {}{} {} per image.\n".format(
out, feat_str[i], n_channels[i], ch_str[i])
out = "{0} - {1} shape components ({2:.2f}% of " \
"variance)\n - {3} {4} classifiers.\n".format(
out, self.shape_models[i].n_components,
self.shape_models[i].variance_ratio() * 100,
self.n_classifiers_per_level[i],
name_of_callable(self.classifiers[i][0]))
else:
if self.pyramid_on_features:
feat_str = [feat_str]
out = "{0} - No pyramid used:\n {1}{2} {3} per image.\n" \
" - {4} shape components ({5:.2f}% of " \
"variance)\n - {6} {7} classifiers.".format(
out, feat_str[0], n_channels[0], ch_str[0],
self.shape_models[0].n_components,
self.shape_models[0].variance_ratio() * 100,
self.n_classifiers_per_level[0],
name_of_callable(self.classifiers[0][0]))
return out
def __str__(self):
out = "Supervised Descent Method\n" \
" - Non-Parametric '{}' Regressor\n" \
" - {} training images.\n".format(
name_of_callable(self._fitters[0].regressor),
self._n_training_images)
# small strings about number of channels, channels string and downscale
down_str = []
for j in range(self.n_levels):
if j == self.n_levels - 1:
down_str.append('(no downscale)')
else:
down_str.append('(downscale by {})'.format(
self.downscale**(self.n_levels - j - 1)))
temp_img = Image(image_data=np.random.rand(40, 40))
if self.pyramid_on_features:
temp = self.features(temp_img)
n_channels = [temp.n_channels] * self.n_levels
else:
n_channels = []
for j in range(self.n_levels):
temp = self.features[j](temp_img)
n_channels.append(temp.n_channels)
# string about features and channels
if self.pyramid_on_features:
feat_str = "- Feature is {} with ".format(
name_of_callable(self.features))
if n_channels[0] == 1:
ch_str = ["channel"]
else:
ch_str = ["channels"]
else:
feat_str = []
ch_str = []
for j in range(self.n_levels):
if isinstance(self.features[j], str):
feat_str.append("- Feature is {} with ".format(
self.features[j]))
elif self.features[j] is None:
feat_str.append("- No features extracted. ")
else:
feat_str.append("- Feature is {} with ".format(
self.features[j].__name__))
if n_channels[j] == 1:
ch_str.append("channel")
else:
ch_str.append("channels")
if self.n_levels > 1:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}.\n".format(out, self.n_levels,
self.downscale)
if self.pyramid_on_features:
out = "{} - Pyramid was applied on feature space.\n " \
"{}{} {} per image.\n".format(out, feat_str,
n_channels[0], ch_str[0])
else:
out = "{} - Features were extracted at each pyramid " \
"level.\n".format(out)
for i in range(self.n_levels - 1, -1, -1):
out = "{} - Level {} {}: \n {}{} {} per " \
"image.\n".format(
out, self.n_levels - i, down_str[i], feat_str[i],
n_channels[i], ch_str[i])
else:
if self.pyramid_on_features:
feat_str = [feat_str]
out = "{0} - No pyramid used:\n {1}{2} {3} per image.\n".format(
out, feat_str[0], n_channels[0], ch_str[0])
return out
def __str__(self):
out = "{0} Fitter\n" \
" - Lucas-Kanade {1}\n" \
" - Transform is {2} and residual is {3}.\n" \
" - {4} training images.\n".format(
self.aam._str_title, self._fitters[0].algorithm,
self._fitters[0].transform.__class__.__name__,
self._fitters[0].residual.type, self.aam.n_training_images)
# small strings about number of channels, channels string and downscale
n_channels = []
down_str = []
for j in range(self.n_levels):
n_channels.append(
self._fitters[j].appearance_model.template_instance.n_channels)
if j == self.n_levels - 1:
down_str.append('(no downscale)')
else:
down_str.append('(downscale by {})'.format(
self.downscale**(self.n_levels - j - 1)))
# string about features and channels
if self.pyramid_on_features:
feat_str = "- Feature is {} with ".format(
name_of_callable(self.features))
if n_channels[0] == 1:
ch_str = ["channel"]
else:
ch_str = ["channels"]
else:
feat_str = []
ch_str = []
for j in range(self.n_levels):
if isinstance(self.features[j], str):
feat_str.append("- Feature is {} with ".format(
self.features[j]))
elif self.features[j] is None:
feat_str.append("- No features extracted. ")
else:
feat_str.append("- Feature is {} with ".format(
self.features[j].__name__))
if n_channels[j] == 1:
ch_str.append("channel")
else:
ch_str.append("channels")
if self.n_levels > 1:
if self.aam.scaled_shape_models:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}.\n - Each level has a scaled shape " \
"model (reference frame).\n".format(out, self.n_levels,
self.downscale)
else:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}:\n - Shape models (reference frames) " \
"are not scaled.\n".format(out, self.n_levels,
self.downscale)
if self.pyramid_on_features:
out = "{} - Pyramid was applied on feature space.\n " \
"{}{} {} per image.\n".format(out, feat_str,
n_channels[0], ch_str[0])
if not self.aam.scaled_shape_models:
out = "{} - Reference frames of length {} " \
"({} x {}C, {} x {}C)\n".format(
out, self._fitters[0].appearance_model.n_features,
self._fitters[0].template.n_true_pixels(),
n_channels[0], self._fitters[0].template._str_shape,
n_channels[0])
else:
out = "{} - Features were extracted at each pyramid " \
"level.\n".format(out)
for i in range(self.n_levels - 1, -1, -1):
out = "{} - Level {} {}: \n".format(out, self.n_levels - i,
down_str[i])
if not self.pyramid_on_features:
out = "{} {}{} {} per image.\n".format(
out, feat_str[i], n_channels[i], ch_str[i])
if (self.aam.scaled_shape_models
or (not self.pyramid_on_features)):
out = "{} - Reference frame of length {} " \
"({} x {}C, {} x {}C)\n".format(
out, self._fitters[i].appearance_model.n_features,
self._fitters[i].template.n_true_pixels(),
n_channels[i], self._fitters[i].template._str_shape,
n_channels[i])
out = "{0} - {1} motion components\n - {2} active " \
"appearance components ({3:.2f}% of original " \
"variance)\n".format(
out, self._fitters[i].transform.n_parameters,
self._fitters[i].appearance_model.n_active_components,
self._fitters[i].appearance_model.variance_ratio() * 100)
else:
if self.pyramid_on_features:
feat_str = [feat_str]
out = "{0} - No pyramid used:\n {1}{2} {3} per image.\n" \
" - Reference frame of length {4} ({5} x {6}C, " \
"{7} x {8}C)\n - {9} motion parameters\n" \
" - {10} appearance components ({11:.2f}% of original " \
"variance)\n".format(
out, feat_str[0], n_channels[0], ch_str[0],
self._fitters[0].appearance_model.n_features,
self._fitters[0].template.n_true_pixels(),
n_channels[0], self._fitters[0].template._str_shape,
n_channels[0], self._fitters[0].transform.n_parameters,
self._fitters[0].appearance_model.n_active_components,
self._fitters[0].appearance_model.variance_ratio() * 100)
return out
def __str__(self):
out = "{0} Fitter\n" \
" - Lucas-Kanade {1}\n" \
" - Transform is {2} and residual is {3}.\n" \
" - {4} training images.\n".format(
self.atm._str_title, self._fitters[0].algorithm,
self._fitters[0].transform.__class__.__name__,
self._fitters[0].residual.type, self.atm.n_training_shapes)
# small strings about number of channels, channels string and downscale
n_channels = []
down_str = []
for j in range(self.n_levels):
n_channels.append(
self._fitters[j].template.n_channels)
if j == self.n_levels - 1:
down_str.append('(no downscale)')
else:
down_str.append('(downscale by {})'.format(
self.downscale**(self.n_levels - j - 1)))
# string about features and channels
if self.pyramid_on_features:
feat_str = "- Feature is {} with ".format(name_of_callable(
self.features))
if n_channels[0] == 1:
ch_str = ["channel"]
else:
ch_str = ["channels"]
else:
feat_str = []
ch_str = []
for j in range(self.n_levels):
if isinstance(self.features[j], str):
feat_str.append("- Feature is {} with ".format(
self.features[j]))
elif self.features[j] is None:
feat_str.append("- No features extracted. ")
else:
feat_str.append("- Feature is {} with ".format(
self.features[j].__name__))
if n_channels[j] == 1:
ch_str.append("channel")
else:
ch_str.append("channels")
if self.n_levels > 1:
if self.atm.scaled_shape_models:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}.\n - Each level has a scaled shape " \
"model (reference frame).\n".format(out, self.n_levels,
self.downscale)
else:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}:\n - Shape models (reference frames) " \
"are not scaled.\n".format(out, self.n_levels,
self.downscale)
if self.pyramid_on_features:
out = "{} - Pyramid was applied on feature space.\n " \
"{}{} {} per image.\n".format(out, feat_str,
n_channels[0], ch_str[0])
if not self.atm.scaled_shape_models:
out = "{} - Reference frames of length {} " \
"({} x {}C, {} x {}C)\n".format(
out,
self._fitters[0].template.n_true_pixels() *
n_channels[0],
self._fitters[0].template.n_true_pixels(),
n_channels[0], self._fitters[0].template._str_shape,
n_channels[0])
else:
out = "{} - Features were extracted at each pyramid " \
"level.\n".format(out)
for i in range(self.n_levels - 1, -1, -1):
out = "{} - Level {} {}: \n".format(out, self.n_levels - i,
down_str[i])
if not self.pyramid_on_features:
out = "{} {}{} {} per image.\n".format(
out, feat_str[i], n_channels[i], ch_str[i])
if (self.atm.scaled_shape_models or
(not self.pyramid_on_features)):
out = "{} - Reference frame of length {} " \
"({} x {}C, {} x {}C)\n".format(
out,
self._fitters[i].template.n_true_pixels() *
n_channels[i],
self._fitters[i].template.n_true_pixels(),
n_channels[i], self._fitters[i].template._str_shape,
n_channels[i])
out = "{0} - {1} motion components\n\n".format(
out, self._fitters[i].transform.n_parameters)
else:
if self.pyramid_on_features:
feat_str = [feat_str]
out = "{0} - No pyramid used:\n {1}{2} {3} per image.\n" \
" - Reference frame of length {4} ({5} x {6}C, " \
"{7} x {8}C)\n - {9} motion parameters\n".format(
out, feat_str[0], n_channels[0], ch_str[0],
self._fitters[0].template.n_true_pixels() * n_channels[0],
self._fitters[0].template.n_true_pixels(),
n_channels[0], self._fitters[0].template._str_shape,
n_channels[0], self._fitters[0].transform.n_parameters)
return out
def __str__(self):
return "{}Supervised Descent Method for CLMs:\n" \
" - Parametric '{}' Regressor\n" \
" - {} training images.\n".format(
self.clm.__str__(), name_of_callable(self._fitters[0].regressor),
self._n_training_images)
def __str__(self):
out = "{}\n - {} training images.\n".format(self._str_title,
self.n_training_images)
# small strings about number of channels, channels string and downscale
n_channels = []
down_str = []
for j in range(self.n_levels):
n_channels.append(
self.appearance_models[j].template_instance.n_channels)
if j == self.n_levels - 1:
down_str.append('(no downscale)')
else:
down_str.append('(downscale by {})'.format(
self.downscale**(self.n_levels - j - 1)))
# string about features and channels
if self.pyramid_on_features:
feat_str = "- Feature is {} with ".format(
name_of_callable(self.features))
if n_channels[0] == 1:
ch_str = ["channel"]
else:
ch_str = ["channels"]
else:
feat_str = []
ch_str = []
for j in range(self.n_levels):
feat_str.append("- Feature is {} with ".format(
name_of_callable(self.features[j])))
if n_channels[j] == 1:
ch_str.append("channel")
else:
ch_str.append("channels")
out = "{} - {} Warp.\n".format(out, name_of_callable(self.transform))
if self.n_levels > 1:
if self.scaled_shape_models:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}.\n - Each level has a scaled shape " \
"model (reference frame).\n".format(out, self.n_levels,
self.downscale)
else:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}:\n - Shape models (reference frames) " \
"are not scaled.\n".format(out, self.n_levels,
self.downscale)
if self.pyramid_on_features:
out = "{} - Pyramid was applied on feature space.\n " \
"{}{} {} per image.\n".format(out, feat_str,
n_channels[0], ch_str[0])
if not self.scaled_shape_models:
out = "{} - Reference frames of length {} " \
"({} x {}C, {} x {}C)\n".format(
out,
self.appearance_models[0].n_features,
self.appearance_models[0].template_instance.n_true_pixels(),
n_channels[0],
self.appearance_models[0].template_instance._str_shape,
n_channels[0])
else:
out = "{} - Features were extracted at each pyramid " \
"level.\n".format(out)
for i in range(self.n_levels - 1, -1, -1):
out = "{} - Level {} {}: \n".format(out, self.n_levels - i,
down_str[i])
if not self.pyramid_on_features:
out = "{} {}{} {} per image.\n".format(
out, feat_str[i], n_channels[i], ch_str[i])
if (self.scaled_shape_models or
(not self.pyramid_on_features)):
out = "{} - Reference frame of length {} " \
"({} x {}C, {} x {}C)\n".format(
out, self.appearance_models[i].n_features,
self.appearance_models[i].template_instance.n_true_pixels(),
n_channels[i],
self.appearance_models[i].template_instance._str_shape,
n_channels[i])
out = "{0} - {1} shape components ({2:.2f}% of " \
"variance)\n - {3} appearance components " \
"({4:.2f}% of variance)\n".format(
out, self.shape_models[i].n_components,
self.shape_models[i].variance_ratio() * 100,
self.appearance_models[i].n_components,
self.appearance_models[i].variance_ratio() * 100)
else:
if self.pyramid_on_features:
feat_str = [feat_str]
out = "{0} - No pyramid used:\n {1}{2} {3} per image.\n" \
" - Reference frame of length {4} ({5} x {6}C, " \
"{7} x {8}C)\n - {9} shape components ({10:.2f}% of " \
"variance)\n - {11} appearance components ({12:.2f}% of " \
"variance)\n".format(
out, feat_str[0], n_channels[0], ch_str[0],
self.appearance_models[0].n_features,
self.appearance_models[0].template_instance.n_true_pixels(),
n_channels[0],
self.appearance_models[0].template_instance._str_shape,
n_channels[0], self.shape_models[0].n_components,
self.shape_models[0].variance_ratio() * 100,
self.appearance_models[0].n_components,
self.appearance_models[0].variance_ratio() * 100)
return out
def __str__(self):
out = "{}\n - {} training images.\n".format(self._str_title,
self.n_training_images)
# small strings about number of channels, channels string and downscale
n_channels = []
down_str = []
for j in range(self.n_levels):
n_channels.append(
self.appearance_models[j].template_instance.n_channels)
if j == self.n_levels - 1:
down_str.append('(no downscale)')
else:
down_str.append('(downscale by {})'.format(
self.downscale**(self.n_levels - j - 1)))
# string about features and channels
if self.pyramid_on_features:
feat_str = "- Feature is {} with ".format(
name_of_callable(self.features))
if n_channels[0] == 1:
ch_str = ["channel"]
else:
ch_str = ["channels"]
else:
feat_str = []
ch_str = []
for j in range(self.n_levels):
feat_str.append("- Feature is {} with ".format(
name_of_callable(self.features[j])))
if n_channels[j] == 1:
ch_str.append("channel")
else:
ch_str.append("channels")
out = "{} - {} Warp.\n".format(out, name_of_callable(self.transform))
if self.n_levels > 1:
if self.scaled_shape_models:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}.\n - Each level has a scaled shape " \
"model (reference frame).\n".format(out, self.n_levels,
self.downscale)
else:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}:\n - Shape models (reference frames) " \
"are not scaled.\n".format(out, self.n_levels,
self.downscale)
if self.pyramid_on_features:
out = "{} - Pyramid was applied on feature space.\n " \
"{}{} {} per image.\n".format(out, feat_str,
n_channels[0], ch_str[0])
if not self.scaled_shape_models:
out = "{} - Reference frames of length {} " \
"({} x {}C, {} x {}C)\n".format(
out,
self.appearance_models[0].n_features,
self.appearance_models[0].template_instance.n_true_pixels(),
n_channels[0],
self.appearance_models[0].template_instance._str_shape,
n_channels[0])
else:
out = "{} - Features were extracted at each pyramid " \
"level.\n".format(out)
for i in range(self.n_levels - 1, -1, -1):
out = "{} - Level {} {}: \n".format(out, self.n_levels - i,
down_str[i])
if not self.pyramid_on_features:
out = "{} {}{} {} per image.\n".format(
out, feat_str[i], n_channels[i], ch_str[i])
if (self.scaled_shape_models
or (not self.pyramid_on_features)):
out = "{} - Reference frame of length {} " \
"({} x {}C, {} x {}C)\n".format(
out, self.appearance_models[i].n_features,
self.appearance_models[i].template_instance.n_true_pixels(),
n_channels[i],
self.appearance_models[i].template_instance._str_shape,
n_channels[i])
out = "{0} - {1} shape components ({2:.2f}% of " \
"variance)\n - {3} appearance components " \
"({4:.2f}% of variance)\n".format(
out, self.shape_models[i].n_components,
self.shape_models[i].variance_ratio() * 100,
self.appearance_models[i].n_components,
self.appearance_models[i].variance_ratio() * 100)
else:
if self.pyramid_on_features:
feat_str = [feat_str]
out = "{0} - No pyramid used:\n {1}{2} {3} per image.\n" \
" - Reference frame of length {4} ({5} x {6}C, " \
"{7} x {8}C)\n - {9} shape components ({10:.2f}% of " \
"variance)\n - {11} appearance components ({12:.2f}% of " \
"variance)\n".format(
out, feat_str[0], n_channels[0], ch_str[0],
self.appearance_models[0].n_features,
self.appearance_models[0].template_instance.n_true_pixels(),
n_channels[0],
self.appearance_models[0].template_instance._str_shape,
n_channels[0], self.shape_models[0].n_components,
self.shape_models[0].variance_ratio() * 100,
self.appearance_models[0].n_components,
self.appearance_models[0].variance_ratio() * 100)
return out
def __str__(self):
from menpofit.base import name_of_callable
out = "{0} Fitter\n" \
" - Gradient-Descent {1}\n" \
" - Transform is {2}.\n" \
" - {3} training images.\n".format(
self.clm._str_title, self._fitters[0].algorithm,
self._fitters[0].transform.__class__.__name__,
self.clm.n_training_images)
# small strings about number of channels, channels string and downscale
down_str = []
for j in range(self.n_levels):
if j == self.n_levels - 1:
down_str.append('(no downscale)')
else:
down_str.append('(downscale by {})'.format(
self.downscale**(self.n_levels - j - 1)))
temp_img = Image(image_data=np.random.rand(50, 50))
if self.pyramid_on_features:
temp = self.features(temp_img)
n_channels = [temp.n_channels] * self.n_levels
else:
n_channels = []
for j in range(self.n_levels):
temp = self.features[j](temp_img)
n_channels.append(temp.n_channels)
# string about features and channels
if self.pyramid_on_features:
feat_str = "- Feature is {} with ".format(
name_of_callable(self.features))
if n_channels[0] == 1:
ch_str = ["channel"]
else:
ch_str = ["channels"]
else:
feat_str = []
ch_str = []
for j in range(self.n_levels):
if isinstance(self.features[j], str):
feat_str.append("- Feature is {} with ".format(
self.features[j]))
elif self.features[j] is None:
feat_str.append("- No features extracted. ")
else:
feat_str.append("- Feature is {} with ".format(
name_of_callable(self.features[j])))
if n_channels[j] == 1:
ch_str.append("channel")
else:
ch_str.append("channels")
if self.n_levels > 1:
if self.clm.scaled_shape_models:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}.\n - Each level has a scaled shape " \
"model (reference frame).\n - Patch size is {}W x " \
"{}H.\n".format(out, self.n_levels, self.downscale,
self.clm.patch_shape[1],
self.clm.patch_shape[0])
else:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}:\n - Shape models (reference frames) " \
"are not scaled.\n - Patch size is {}W x " \
"{}H.\n".format(out, self.n_levels, self.downscale,
self.clm.patch_shape[1],
self.clm.patch_shape[0])
if self.pyramid_on_features:
out = "{} - Pyramid was applied on feature space.\n " \
"{}{} {} per image.\n".format(out, feat_str,
n_channels[0], ch_str[0])
else:
out = "{} - Features were extracted at each pyramid " \
"level.\n".format(out)
for i in range(self.n_levels - 1, -1, -1):
out = "{} - Level {} {}: \n".format(out, self.n_levels - i,
down_str[i])
if not self.pyramid_on_features:
out = "{} {}{} {} per image.\n".format(
out, feat_str[i], n_channels[i], ch_str[i])
out = "{0} - {1} motion components\n - {2} {3} " \
"classifiers.\n".format(
out, self._fitters[i].transform.n_parameters,
len(self._fitters[i].classifiers),
name_of_callable(self._fitters[i].classifiers[0]))
else:
if self.pyramid_on_features:
feat_str = [feat_str]
out = "{0} - No pyramid used:\n {1}{2} {3} per image.\n" \
" - {4} motion components\n - {5} {6} " \
"classifiers.".format(
out, feat_str[0], n_channels[0], ch_str[0],
out, self._fitters[0].transform.n_parameters,
len(self._fitters[0].classifiers),
name_of_callable(self._fitters[0].classifiers[0]))
return out
def __str__(self):
from menpofit.base import name_of_callable
out = "{0} Fitter\n" \
" - Gradient-Descent {1}\n" \
" - Transform is {2}.\n" \
" - {3} training images.\n".format(
self.clm._str_title, self._fitters[0].algorithm,
self._fitters[0].transform.__class__.__name__,
self.clm.n_training_images)
# small strings about number of channels, channels string and downscale
down_str = []
for j in range(self.n_levels):
if j == self.n_levels - 1:
down_str.append('(no downscale)')
else:
down_str.append('(downscale by {})'.format(
self.downscale**(self.n_levels - j - 1)))
temp_img = Image(image_data=np.random.rand(50, 50))
if self.pyramid_on_features:
temp = self.features(temp_img)
n_channels = [temp.n_channels] * self.n_levels
else:
n_channels = []
for j in range(self.n_levels):
temp = self.features[j](temp_img)
n_channels.append(temp.n_channels)
# string about features and channels
if self.pyramid_on_features:
feat_str = "- Feature is {} with ".format(
name_of_callable(self.features))
if n_channels[0] == 1:
ch_str = ["channel"]
else:
ch_str = ["channels"]
else:
feat_str = []
ch_str = []
for j in range(self.n_levels):
if isinstance(self.features[j], str):
feat_str.append("- Feature is {} with ".format(
self.features[j]))
elif self.features[j] is None:
feat_str.append("- No features extracted. ")
else:
feat_str.append("- Feature is {} with ".format(
name_of_callable(self.features[j])))
if n_channels[j] == 1:
ch_str.append("channel")
else:
ch_str.append("channels")
if self.n_levels > 1:
if self.clm.scaled_shape_models:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}.\n - Each level has a scaled shape " \
"model (reference frame).\n - Patch size is {}W x " \
"{}H.\n".format(out, self.n_levels, self.downscale,
self.clm.patch_shape[1],
self.clm.patch_shape[0])
else:
out = "{} - Gaussian pyramid with {} levels and downscale " \
"factor of {}:\n - Shape models (reference frames) " \
"are not scaled.\n - Patch size is {}W x " \
"{}H.\n".format(out, self.n_levels, self.downscale,
self.clm.patch_shape[1],
self.clm.patch_shape[0])
if self.pyramid_on_features:
out = "{} - Pyramid was applied on feature space.\n " \
"{}{} {} per image.\n".format(out, feat_str,
n_channels[0], ch_str[0])
else:
out = "{} - Features were extracted at each pyramid " \
"level.\n".format(out)
for i in range(self.n_levels - 1, -1, -1):
out = "{} - Level {} {}: \n".format(out, self.n_levels - i,
down_str[i])
if not self.pyramid_on_features:
out = "{} {}{} {} per image.\n".format(
out, feat_str[i], n_channels[i], ch_str[i])
out = "{0} - {1} motion components\n - {2} {3} " \
"classifiers.\n".format(
out, self._fitters[i].transform.n_parameters,
len(self._fitters[i].classifiers),
name_of_callable(self._fitters[i].classifiers[0]))
else:
if self.pyramid_on_features:
feat_str = [feat_str]
out = "{0} - No pyramid used:\n {1}{2} {3} per image.\n" \
" - {4} motion components\n - {5} {6} " \
"classifiers.".format(
out, feat_str[0], n_channels[0], ch_str[0],
out, self._fitters[0].transform.n_parameters,
len(self._fitters[0].classifiers),
name_of_callable(self._fitters[0].classifiers[0]))
return out
def test_name_of_callable_function():
assert name_of_callable(igo) == 'igo'
def test_name_of_callable_partial():
assert name_of_callable(sparse_hog) == 'sparse_hog'
def test_name_of_callable_function():
assert name_of_callable(igo) == 'igo'
def test_name_of_callable_object_with_call():
assert name_of_callable(Foo()) == 'Foo'
