Update tests for new LJSON change

lenna is now an ljson and therefore all the tests that assumed
every image was landmarked with PTS started failing.

Also, unpin menpofit from menpo 0.4.0a3 and let if find the master
branch so that the tests have a chance of passing.

.travis.yml

@@ -20,7 +20,7 @@ install:
 - wget https://raw.githubusercontent.com/jabooth/condaci/v0.2.0/condaci.py -O condaci.py
 - python condaci.py setup $PYTHON_VERSION --channel $BINSTAR_USER
 - export PATH=$HOME/miniconda/bin:$PATH
-#- conda config --add channels $BINSTAR_USER/channel/master
+- conda config --add channels $BINSTAR_USER/channel/master
 
 script:
 - python condaci.py auto ./conda --binstaruser $BINSTAR_USER --binstarkey $BINSTAR_KEY

appveyor.yml

@@ -25,7 +25,7 @@ platform:
 init:
 - ps: Start-FileDownload 'https://raw.githubusercontent.com/jabooth/condaci/v0.2.0/condaci.py' C:\\condaci.py; echo "Done"
 - cmd: python C:\\condaci.py setup %PYTHON_VERSION% --channel %BINSTAR_USER%
-#- cmd: C:\\Miniconda\\Scripts\\conda config --add channels %BINSTAR_USER%/channel/master
+- cmd: C:\\Miniconda\\Scripts\\conda config --add channels %BINSTAR_USER%/channel/master
 
 install:
 - cmd: C:\\Miniconda\\python C:\\condaci.py auto ./conda --binstaruser %BINSTAR_USER% --binstarkey %BINSTAR_KEY%

conda/meta.yaml

@@ -9,7 +9,7 @@ requirements:
 
   run:
     - python
-    - menpo 0.4.0a3
+    - menpo
     - numpy 1.9.0
     - scipy 0.14.0
     - scikit-learn 0.15.2

menpofit/test/aam_builder_test.py

@@ -9,7 +9,7 @@
 from menpo.feature import sparse_hog, igo, lbp, no_op
 
 import menpo.io as mio
-from menpo.landmark import labeller, ibug_face_68_trimesh
+from menpo.landmark import ibug_face_68_trimesh
 from menpofit.aam import AAMBuilder, PatchBasedAAMBuilder
 
 
@@ -19,23 +19,23 @@
 for i in range(4):
     im = mio.import_builtin_asset(filenames[i])
     im.crop_to_landmarks_proportion_inplace(0.1)
-    labeller(im, 'PTS', ibug_face_68_trimesh)
     if im.n_channels == 3:
         im = im.as_greyscale(mode='luminosity')
     training.append(im)
 
 # build aams
+template_trilist_image = training[0].landmarks[None]
+trilist = ibug_face_68_trimesh(template_trilist_image)[1].lms.trilist
 aam1 = AAMBuilder(features=[igo, sparse_hog, no_op],
                   transform=PiecewiseAffine,
-                  trilist=training[0].landmarks['ibug_face_68_trimesh'].
-                  lms.trilist,
+                  trilist=trilist,
                   normalization_diagonal=150,
                   n_levels=3,
                   downscale=2,
                   scaled_shape_models=False,
                   max_shape_components=[1, 2, 3],
                   max_appearance_components=[3, 3, 3],
-                  boundary=3).build(training, group='PTS')
+                  boundary=3).build(training)
 
 aam2 = AAMBuilder(features=[no_op, no_op],
                   transform=ThinPlateSplines,
@@ -46,7 +46,7 @@
                   scaled_shape_models=True,
                   max_shape_components=None,
                   max_appearance_components=1,
-                  boundary=0).build(training, group='PTS')
+                  boundary=0).build(training)
 
 aam3 = AAMBuilder(features=igo,
                   transform=ThinPlateSplines,
@@ -57,7 +57,7 @@
                   scaled_shape_models=True,
                   max_shape_components=[2],
                   max_appearance_components=10,
-                  boundary=2).build(training, group='PTS')
+                  boundary=2).build(training)
 
 aam4 = PatchBasedAAMBuilder(features=lbp,
                             patch_shape=(10, 13),
@@ -67,55 +67,51 @@
                             scaled_shape_models=True,
                             max_shape_components=1,
                             max_appearance_components=None,
-                            boundary=2).build(training, group='PTS')
+                            boundary=2).build(training)
 
 
 @raises(ValueError)
 def test_features_exception():
-    AAMBuilder(features=[igo, sparse_hog]).build(training, group='PTS')
+    AAMBuilder(features=[igo, sparse_hog]).build(training)
 
 
 @raises(ValueError)
 def test_n_levels_exception():
-    AAMBuilder(n_levels=0).build(training, group='PTS')
+    AAMBuilder(n_levels=0).build(training)
 
 
 @raises(ValueError)
 def test_downscale_exception():
-    aam = AAMBuilder(downscale=1).build(training,
-                                        group='PTS')
+    aam = AAMBuilder(downscale=1).build(training)
     assert (aam.downscale == 1)
-    AAMBuilder(downscale=0).build(training, group='PTS')
+    AAMBuilder(downscale=0).build(training)
 
 
 @raises(ValueError)
 def test_normalization_diagonal_exception():
-    aam = AAMBuilder(normalization_diagonal=100).build(training,
-                                                       group='PTS')
+    aam = AAMBuilder(normalization_diagonal=100).build(training)
     assert (aam.appearance_models[0].n_features == 382)
-    AAMBuilder(normalization_diagonal=10).build(training, group='PTS')
+    AAMBuilder(normalization_diagonal=10).build(training)
 
 
 @raises(ValueError)
 def test_max_shape_components_exception():
-    AAMBuilder(max_shape_components=[1, 0.2, 'a']).build(training,
-                                                         group='PTS')
+    AAMBuilder(max_shape_components=[1, 0.2, 'a']).build(training)
 
 
 @raises(ValueError)
 def test_max_appearance_components_exception():
-    AAMBuilder(max_appearance_components=[1, 2]).build(training,
-                                                       group='PTS')
+    AAMBuilder(max_appearance_components=[1, 2]).build(training)
 
 
 @raises(ValueError)
 def test_boundary_exception():
-    AAMBuilder(boundary=-1).build(training, group='PTS')
+    AAMBuilder(boundary=-1).build(training)
 
 
 @patch('sys.stdout', new_callable=StringIO)
 def test_verbose_mock(mock_stdout):
-    AAMBuilder().build(training, group='PTS', verbose=True)
+    AAMBuilder().build(training, verbose=True)
 
 
 @patch('sys.stdout', new_callable=StringIO)

menpofit/test/aam_fitter_test.py

@@ -11,7 +11,7 @@
 
 import menpo.io as mio
 from menpo.shape.pointcloud import PointCloud
-from menpo.landmark import labeller, ibug_face_68_trimesh
+from menpo.landmark import ibug_face_68_trimesh
 from menpofit.aam import AAMBuilder, LucasKanadeAAMFitter
 from menpofit.lucaskanade.appearance import (
     AlternatingForwardAdditive, AlternatingForwardCompositional,
@@ -307,35 +307,34 @@
 for i in range(4):
     im = mio.import_builtin_asset(filenames[i])
     im.crop_to_landmarks_proportion_inplace(0.1)
-    labeller(im, 'PTS', ibug_face_68_trimesh)
     if im.n_channels == 3:
         im = im.as_greyscale(mode='luminosity')
     training_images.append(im)
 
 # build aam
+template_trilist_image = training_images[0].landmarks[None]
+trilist = ibug_face_68_trimesh(template_trilist_image)[1].lms.trilist
 aam = AAMBuilder(features=igo,
                  transform=DifferentiablePiecewiseAffine,
-                 trilist=training_images[0].landmarks['ibug_face_68_trimesh'].
-                 lms.trilist,
+                 trilist=trilist,
                  normalization_diagonal=150,
                  n_levels=3,
                  downscale=2,
                  scaled_shape_models=True,
                  max_shape_components=[1, 2, 3],
                  max_appearance_components=[3, 2, 1],
-                 boundary=3).build(training_images, group='PTS')
+                 boundary=3).build(training_images)
 
 aam2 = AAMBuilder(features=igo,
                   transform=DifferentiablePiecewiseAffine,
-                  trilist=training_images[0].landmarks['ibug_face_68_trimesh'].
-                  lms.trilist,
+                  trilist=trilist,
                   normalization_diagonal=150,
                   n_levels=1,
                   downscale=2,
                   scaled_shape_models=True,
                   max_shape_components=[1],
                   max_appearance_components=[1],
-                  boundary=3).build(training_images, group='PTS')
+                  boundary=3).build(training_images)
 
 
 def test_aam():
@@ -368,7 +367,7 @@ def test_n_appearance_exception():
 
 def test_pertrurb_shape():
     fitter = LucasKanadeAAMFitter(aam)
-    s = fitter.perturb_shape(training_images[0].landmarks['PTS'].lms,
+    s = fitter.perturb_shape(training_images[0].landmarks[None].lms,
                              noise_std=0.08, rotation=False)
     assert (s.n_dims == 2)
     assert (s.n_landmark_groups == 0)
@@ -410,7 +409,7 @@ def aam_helper(aam, algorithm, im_number, max_iters, initial_error,
     fitter = LucasKanadeAAMFitter(aam, algorithm=algorithm)
     fitting_result = fitter.fit(
         training_images[im_number], initial_shape[im_number],
-        gt_shape=training_images[im_number].landmarks['PTS'].lms,
+        gt_shape=training_images[im_number].landmarks[None].lms,
         max_iters=max_iters)
     assert_allclose(
         np.around(fitting_result.initial_error(error_type=error_type), 5),

menpofit/test/atm_builder_test.py

@@ -20,7 +20,7 @@
     im = mio.import_builtin_asset(filenames[i])
     if im.n_channels == 3:
         im = im.as_greyscale(mode='luminosity')
-    training.append(im.landmarks['PTS']['all'])
+    training.append(im.landmarks[None].lms)
     templates.append(im)
 
 # build atms
@@ -31,7 +31,7 @@
                   downscale=2,
                   scaled_shape_models=False,
                   max_shape_components=[1, 2, 3],
-                  boundary=3).build(training, templates[0], group='PTS')
+                  boundary=3).build(training, templates[0])
 
 atm2 = ATMBuilder(features=[no_op, no_op],
                   transform=ThinPlateSplines,
@@ -41,7 +41,7 @@
                   downscale=1.2,
                   scaled_shape_models=True,
                   max_shape_components=None,
-                  boundary=0).build(training, templates[1], group='PTS')
+                  boundary=0).build(training, templates[1])
 
 atm3 = ATMBuilder(features=igo,
                   transform=ThinPlateSplines,
@@ -51,7 +51,7 @@
                   downscale=3,
                   scaled_shape_models=True,
                   max_shape_components=[2],
-                  boundary=2).build(training, templates[2], group='PTS')
+                  boundary=2).build(training, templates[2])
 
 atm4 = PatchBasedATMBuilder(features=lbp,
                             patch_shape=(10, 13),
@@ -60,8 +60,7 @@
                             downscale=1.2,
                             scaled_shape_models=True,
                             max_shape_components=1,
-                            boundary=2).build(training, templates[3],
-                                              group='PTS')
+                            boundary=2).build(training, templates[3])
 
 
 @raises(ValueError)
@@ -76,23 +75,21 @@ def test_n_levels_exception():
 
 @raises(ValueError)
 def test_downscale_exception():
-    atm = ATMBuilder(downscale=1).build(training, templates[2], group='PTS')
+    atm = ATMBuilder(downscale=1).build(training, templates[2])
     assert (atm.downscale == 1)
-    ATMBuilder(downscale=0).build(training, templates[2], group='PTS')
+    ATMBuilder(downscale=0).build(training, templates[2])
 
 
 @raises(ValueError)
 def test_normalization_diagonal_exception():
-    atm = ATMBuilder(normalization_diagonal=100).build(training, templates[3],
-                                                       group='PTS')
+    atm = ATMBuilder(normalization_diagonal=100).build(training, templates[3])
     assert (atm.warped_templates[0].n_true_pixels() == 1246)
     ATMBuilder(normalization_diagonal=10).build(training, templates[3])
 
 
 @raises(ValueError)
 def test_max_shape_components_exception():
-    ATMBuilder(max_shape_components=[1, 0.2, 'a']).build(training, templates[0],
-                                                         group='PTS')
+    ATMBuilder(max_shape_components=[1, 0.2, 'a']).build(training, templates[0])
 
 
 @raises(ValueError)
@@ -102,12 +99,12 @@ def test_max_shape_components_exception_2():
 
 @raises(ValueError)
 def test_boundary_exception():
-    ATMBuilder(boundary=-1).build(training, templates[1], group='PTS')
+    ATMBuilder(boundary=-1).build(training, templates[1])
 
 
 @patch('sys.stdout', new_callable=StringIO)
 def test_verbose_mock(mock_stdout):
-    ATMBuilder().build(training, templates[2], group='PTS', verbose=True)
+    ATMBuilder().build(training, templates[2], verbose=True)
 
 
 @patch('sys.stdout', new_callable=StringIO)

menpofit/test/atm_fitter_test.py

@@ -12,8 +12,8 @@
 from menpo.shape.pointcloud import PointCloud
 from menpofit.atm import ATMBuilder, LucasKanadeATMFitter
 from menpofit.lucaskanade.image import (ImageInverseCompositional,
-                                            ImageForwardAdditive,
-                                            ImageForwardCompositional)
+                                        ImageForwardAdditive,
+                                        ImageForwardCompositional)
 
 
 initial_shape = []
@@ -302,7 +302,7 @@
     im.crop_to_landmarks_proportion_inplace(0.1)
     if im.n_channels == 3:
         im = im.as_greyscale(mode='luminosity')
-    training_shapes.append(im.landmarks['PTS']['all'])
+    training_shapes.append(im.landmarks[None].lms)
     templates.append(im)
 
 
@@ -314,7 +314,7 @@
                   downscale=2,
                   scaled_shape_models=True,
                   max_shape_components=[1, 2, 3],
-                  boundary=3).build(training_shapes, templates[0], group='PTS')
+                  boundary=3).build(training_shapes, templates[0])
 
 atm2 = ATMBuilder(features=igo,
                   transform=DifferentiablePiecewiseAffine,
@@ -323,7 +323,7 @@
                   downscale=2,
                   scaled_shape_models=True,
                   max_shape_components=[1],
-                  boundary=3).build(training_shapes, templates[1], group='PTS')
+                  boundary=3).build(training_shapes, templates[1])
 
 atm3 = ATMBuilder(features=igo,
                   transform=DifferentiablePiecewiseAffine,
@@ -332,7 +332,7 @@
                   downscale=2,
                   scaled_shape_models=True,
                   max_shape_components=[1, 2, 3],
-                  boundary=3).build(training_shapes, templates[2], group='PTS')
+                  boundary=3).build(training_shapes, templates[2])
 
 atm4 = ATMBuilder(features=igo,
                   transform=DifferentiablePiecewiseAffine,
@@ -341,7 +341,7 @@
                   downscale=2,
                   scaled_shape_models=True,
                   max_shape_components=[1],
-                  boundary=3).build(training_shapes, templates[3], group='PTS')
+                  boundary=3).build(training_shapes, templates[3])
 
 
 def test_atm1():
@@ -371,7 +371,7 @@ def test_n_shape_exception_2():
 
 def test_pertrurb_shape():
     fitter = LucasKanadeATMFitter(atm1)
-    s = fitter.perturb_shape(templates[0].landmarks['PTS'].lms,
+    s = fitter.perturb_shape(templates[0].landmarks[None].lms,
                              noise_std=0.08, rotation=False)
     assert (s.n_dims == 2)
     assert (s.n_landmark_groups == 0)
@@ -412,7 +412,7 @@ def atm_helper(atm, algorithm, im_number, max_iters, initial_error,
     fitter = LucasKanadeATMFitter(atm, algorithm=algorithm)
     fitting_result = fitter.fit(
         templates[im_number], initial_shape[im_number],
-        gt_shape=templates[im_number].landmarks['PTS'].lms,
+        gt_shape=templates[im_number].landmarks[None].lms,
         max_iters=max_iters)
     assert_allclose(
         np.around(fitting_result.initial_error(error_type=error_type), 5),