Merge pull request #55 from menpo/sdm_pickle

Fix pickling SDMs

.travis.yml

@@ -8,12 +8,11 @@ env:
     - PYTHON_VERSION: 3.4
 
 install:
-- wget https://raw.githubusercontent.com/menpo/condaci/v0.3.0/condaci.py -O condaci.py
-- python condaci.py setup $PYTHON_VERSION --channel $BINSTAR_USER
-- export PATH=$HOME/miniconda/bin:$PATH
+  - wget https://raw.githubusercontent.com/menpo/condaci/v0.4.1/condaci.py -O condaci.py
+  - python condaci.py setup
 
 script:
-- python condaci.py auto ./conda --binstaruser $BINSTAR_USER --binstarkey $BINSTAR_KEY
+  - ~/miniconda/bin/python condaci.py build ./conda
 
 notifications:
   slack:

appveyor.yml

@@ -13,15 +13,15 @@ matrix:
    fast_finish: true
 
 platform:
-- x86
-- x64
+  - x86
+  - x64
 
 init:
-- ps: Start-FileDownload 'https://raw.githubusercontent.com/menpo/condaci/v0.3.0/condaci.py' C:\\condaci.py; echo "Done"
-- cmd: python C:\\condaci.py setup %PYTHON_VERSION% --channel %BINSTAR_USER%
+  - ps: Start-FileDownload 'https://raw.githubusercontent.com/menpo/condaci/v0.4.1/condaci.py' C:\\condaci.py; echo "Done"
+  - cmd: python C:\\condaci.py setup
 
 install:
-- cmd: C:\\Miniconda\\python C:\\condaci.py auto ./conda --binstaruser %BINSTAR_USER% --binstarkey %BINSTAR_KEY%
+  - cmd: C:\\Miniconda\\python C:\\condaci.py build ./conda
 
 notifications:
   - provider: Slack

conda/meta.yaml

@@ -16,7 +16,7 @@ test:
   requires:
   - coverage
   - nose
-  - mock
+  - mock 1.0.1
 
   imports:
   - menpofit

menpofit/regression/parametricfeatures.py

@@ -1,3 +1,6 @@
+import numpy as np
+
+
 def extract_parametric_features(appearance_model, warped_image,
                                 rergession_features):
     r"""
@@ -120,3 +123,54 @@ def project_out(appearance_model, warped_image):
     """
     diff = warped_image.as_vector() - appearance_model.mean().as_vector()
     return appearance_model.distance_to_subspace_vector(diff).ravel()
+
+
+class nonparametric_regression_features(object):
+
+    def __init__(self, patch_shape, feature_patch_length, regression_features):
+        self.patch_shape = patch_shape
+        self.feature_patch_length = feature_patch_length
+        self.regression_features = regression_features
+
+    def __call__(self, image, shape):
+        # extract patches
+        patches = image.extract_patches(shape, patch_size=self.patch_shape)
+
+        features = np.zeros((shape.n_points, self.feature_patch_length))
+        for j, patch in enumerate(patches):
+            # compute features
+            features[j, ...] = self.regression_features(patch).as_vector()
+
+        return np.hstack((features.ravel(), 1))
+
+
+class parametric_regression_features(object):
+
+    def __init__(self, transform, template, appearance_model,
+                 regression_features):
+        self.transform = transform
+        self.template = template
+        self.appearance_model = appearance_model
+        self.regression_features = regression_features
+
+    def __call__(self, image, shape):
+        self.transform.set_target(shape)
+        # TODO should the template be a mask or a shape? warp_to_shape here
+        warped_image = image.warp_to_mask(self.template.mask, self.transform,
+                                          warp_landmarks=False)
+        features = extract_parametric_features(
+            self.appearance_model, warped_image, self.regression_features)
+        return np.hstack((features, 1))
+
+
+class semiparametric_classifier_regression_features(object):
+
+    def __init__(self, patch_shape, classifiers):
+        self.patch_shape = patch_shape
+        self.classifiers = classifiers
+
+    def __call__(self, image, shape):
+        patches = image.extract_patches(shape, patch_size=self.patch_shape)
+        features = [clf(patch.as_vector(keep_channels=True))
+                    for (clf, patch) in zip(self.classifiers, patches)]
+        return np.hstack((np.asarray(features).ravel(), 1))

menpofit/regression/regressors.py

@@ -13,9 +13,10 @@ class mlr(object):
     T: numpy.array
         The shapes differential that denote the dependent variable.
     """
-    def __init__(self, X, T):
+    def __init__(self, X, T, lmda=0):
         XX = np.dot(X.T, X)
-        XX = (XX + XX.T) / 2
+        if lmda > 0:
+            np.fill_diagonal(XX, lmda + np.diag(XX))
         XT = np.dot(X.T, T)
         self.R = np.linalg.solve(XX, XT)
 

menpofit/regression/trainer.py

@@ -11,7 +11,9 @@
                                     ParametricFittingResult)
 from .base import (NonParametricRegressor, SemiParametricRegressor,
                    ParametricRegressor)
-from .parametricfeatures import extract_parametric_features, weights
+from .parametricfeatures import extract_parametric_features, weights, \
+    nonparametric_regression_features, parametric_regression_features, \
+    semiparametric_classifier_regression_features
 from .regressors import mlr
 
 
@@ -101,6 +103,22 @@ def features(self, image, shape):
         """
         pass
 
+    @abc.abstractmethod
+    def get_features_function(self):
+        r"""
+        Abstract method to return the function that computes the features for
+        the regression.
+
+        Parameters
+        ----------
+        image : :map:`MaskedImage`
+            The current image.
+
+        shape : :map:`PointCloud`
+            The current shape.
+        """
+        pass
+
     @abc.abstractmethod
     def delta_ps(self, gt_shape, perturbed_shape):
         r"""
@@ -178,7 +196,7 @@ def train(self, images, shapes, perturbed_shapes=None, verbose=False,
                                        axis=1)))
         if verbose:
             print_dynamic('- Regression RMSE is {0:.5f}.\n'.format(error))
-        return self._build_regressor(regressor, self.features)
+        return self._build_regressor(regressor, self.get_features_function())
 
     def perturb_shapes(self, gt_shape):
         r"""
@@ -293,6 +311,11 @@ def _create_fitting(self, image, shapes, gt_shape=None):
         return NonParametricFittingResult(image, self, parameters=[shapes],
                                           gt_shape=gt_shape)
 
+    def get_features_function(self):
+        return nonparametric_regression_features(self.patch_shape,
+                                              self._feature_patch_length,
+                                              self.regression_features)
+
     def features(self, image, shape):
         r"""
         Method that extracts the features for the regression, which in this
@@ -306,15 +329,7 @@ def features(self, image, shape):
         shape : :map:`PointCloud`
             The current shape.
         """
-        # extract patches
-        patches = image.extract_patches(shape, patch_size=self.patch_shape)
-
-        features = np.zeros((shape.n_points, self._feature_patch_length))
-        for j, patch in enumerate(patches):
-            # compute features
-            features[j, ...] = self.regression_features(patch).as_vector()
-
-        return np.hstack((features.ravel(), 1))
+        return self.get_features_function()(image, shape)
 
     def delta_ps(self, gt_shape, perturbed_shape):
         r"""
@@ -520,6 +535,11 @@ def _create_fitting(self, image, shapes, gt_shape=None):
         return ParametricFittingResult(image, self, parameters=[shapes],
                                        gt_shape=gt_shape)
 
+    def get_features_function(self):
+        return parametric_regression_features(self.transform, self.template,
+                                              self.appearance_model,
+                                              self.regression_features)
+
     def features(self, image, shape):
         r"""
         Method that extracts the features for the regression, which in this
@@ -533,13 +553,7 @@ def features(self, image, shape):
         shape : :map:`PointCloud`
             The current shape.
         """
-        self.transform.set_target(shape)
-        # TODO should the template be a mask or a shape? warp_to_shape here
-        warped_image = image.warp_to_mask(self.template.mask, self.transform,
-                                          warp_landmarks=False)
-        features = extract_parametric_features(
-            self.appearance_model, warped_image, self.regression_features)
-        return np.hstack((features, 1))
+        return self.get_features_function()(image, shape)
 
     def delta_ps(self, gt_shape, perturbed_shape):
         r"""
@@ -615,6 +629,10 @@ def _set_up(self):
         # set up sampling grid
         self.sampling_grid = build_sampling_grid(self.patch_shape)
 
+    def get_features_function(self):
+        return semiparametric_classifier_regression_features(self.patch_shape,
+                                                             self.classifiers)
+
     def features(self, image, shape):
         r"""
         Method that extracts the features for the regression, which in this
@@ -628,7 +646,4 @@ def features(self, image, shape):
         shape : :map:`PointCloud`
             The current shape.
         """
-        patches = image.extract_patches(shape, patch_size=self.patch_shape)
-        features = [clf(patch.as_vector(keep_channels=True))
-                    for (clf, patch) in zip(self.classifiers, patches)]
-        return np.hstack((np.asarray(features).ravel(), 1))
+        return self.get_features_function()(image, shape)

setup.py

@@ -12,5 +12,5 @@
       packages=find_packages(),
       install_requires=['menpo>=0.5.1,<0.6',
                         'scikit-learn>=0.16,<0.17'],
-      tests_require=['nose', 'mock']
+      tests_require=['nose', 'mock==1.0.1']
       )