Merge pull request #474 from jabooth/hdf5abletopickle

remove HDF5able making room for h5it

conda/meta.yaml

@@ -21,7 +21,6 @@ requirements:
     - pillow  2.5.1
 
     # Menpo Project
-    - hdf5able 0.3.3
     - cyassimp 0.2.0
     - menpo-pyvrml97 2.3.0a4  # [not py3k]
 

menpo/base.py

@@ -1,8 +1,6 @@
 import abc
 import os.path
 
-from hdf5able import HDF5able
-
 # To debug the Copyable interface, simply uncomment lines 11-23 below and the
 # four lines in the copy() method.
 # Then you can call print_copyable_log() to see exactly what types have been
@@ -23,7 +21,7 @@
 #         print('  {:15}|  {}'.format(k, ', '.join(v)))
 
 
-class Copyable(HDF5able):
+class Copyable(object):
     """
     Efficient copying of classes containing numpy arrays.
 

menpo/fit/fittingresult.py

@@ -1,6 +1,5 @@
 from __future__ import division
 import abc
-from hdf5able import HDF5able
 import numpy as np
 
 from menpo.shape.pointcloud import PointCloud
@@ -424,7 +423,7 @@ def error_images(self):
         return error_images
 
 
-class SerializableFittingResult(HDF5able, FittingResult):
+class SerializableFittingResult(FittingResult):
     r"""
     Designed to allow the fitting results to be easily serializable. In
     comparison to the other fitting result objects, the serializable fitting

menpo/fitmultilevel/aam/base.py

@@ -1,14 +1,13 @@
 from __future__ import division
 
 import numpy as np
-from hdf5able import HDF5able, SerializableCallable
 
 from menpo.shape import TriMesh
 from menpo.fitmultilevel.base import DeformableModel, name_of_callable
 from .builder import build_patch_reference_frame, build_reference_frame
 
 
-class AAM(DeformableModel, HDF5able):
+class AAM(DeformableModel):
     r"""
     Active Appearance Model class.
 
@@ -73,22 +72,6 @@ def __init__(self, shape_models, appearance_models, n_training_images,
         self.downscale = downscale
         self.scaled_shape_models = scaled_shape_models
 
-    def h5_dict_to_serializable_dict(self):
-        import menpo.transform
-        d = self.__dict__.copy()
-        transform = d.pop('transform')
-        d['transform'] = SerializableCallable(transform, [menpo.transform])
-
-        features = d.pop('features')
-        if self.pyramid_on_features:
-            # features is a single callable
-            d['features'] = SerializableCallable(features, [menpo.feature])
-        else:
-            # features is a list of callables
-            d['features'] = [SerializableCallable(f, [menpo.feature])
-                             for f in features]
-        return d
-
     @property
     def n_levels(self):
         """

menpo/fitmultilevel/fittingresult.py

@@ -1,7 +1,6 @@
 from __future__ import division
 
 from itertools import chain
-from hdf5able import HDF5able
 
 from menpo.transform import Scale
 from menpo.fit.fittingresult import FittingResult
@@ -268,7 +267,7 @@ def aam_reconstructions(self):
         return aam_reconstructions
 
 
-class SerializableMultilevelFittingResult(HDF5able, FittingResult):
+class SerializableMultilevelFittingResult(FittingResult):
     r"""
     Designed to allow the fitting results to be easily serializable. In
     comparison to the other fitting result objects, the serializable fitting

menpo/io/__init__.py

@@ -1,7 +1,7 @@
-from .input import (load, import_image, import_images,
+from .input import (import_image, import_images,
                     import_mesh, import_meshes, import_builtin_asset,
                     import_landmark_file, import_landmark_files,
                     data_path_to, data_dir_path, ls_builtin_assets,
                     mesh_paths, image_paths, landmark_file_paths)
-from .output import (save, export_image, export_landmark_file, export_mesh,
+from .output import (export_image, export_landmark_file, export_mesh,
                      export_textured_mesh)

menpo/io/input/__init__.py

@@ -1,4 +1,4 @@
-from .base import (load, import_image, import_images,
+from .base import (import_image, import_images,
                    import_mesh, import_meshes, import_builtin_asset,
                    import_landmark_file, import_landmark_files,
                    data_path_to, data_dir_path, ls_builtin_assets,

menpo/io/input/base.py

@@ -1,33 +1,13 @@
 import abc
 import os
 from glob import glob
-
 from pathlib import Path
-import hdf5able
 
 from ..utils import _norm_path
 from menpo import menpo_src_dir_path
 from menpo.visualize import progress_bar_str, print_dynamic
 
 
-def load(path):
-    r"""
-    Load a given HDF5 file of serialized Menpo objects or base types.
-
-    Parameters
-    ----------
-    path : `str`
-        A path to a HDF5 file that conforms to the hdf5able specification.
-
-    Returns
-    -------
-    hdf5able :
-        Any collection of HDF5able objects.
-
-    """
-    return hdf5able.load(_norm_path(path))
-
-
 def data_dir_path():
     r"""A path to the Menpo built in ./data folder on this machine.
 

menpo/io/output/__init__.py

@@ -1,2 +1,2 @@
-from .base import (save, export_landmark_file, export_image, export_mesh,
+from .base import (export_landmark_file, export_image, export_mesh,
                    export_textured_mesh)

menpo/io/output/base.py

@@ -1,28 +1,9 @@
 from pathlib import Path
-from hdf5able import save as h5_save
 
 from .extensions import landmark_types, image_types, mesh_types
 from ..utils import _norm_path
 
 
-def save(path, hdf5able):
-    r"""
-    Serialized a collection of Menpo objects and base types to disk in a
-    HDF5 file container.
-
-    Parameters
-    ----------
-    path : `str`
-        A path to a HDF5 file that conforms to the hdf5able specification.
-
-    hdf5able : `HDF5able` or base types
-        Any collection of base types (dict, list, str, etc) along with any
-        HDG5able Menpo type.
-
-    """
-    h5_save(_norm_path(path), hdf5able)
-
-
 def export_landmark_file(fp, landmark_group, extension=None,
                          overwrite=False):
     r"""

setup.py

@@ -34,8 +34,7 @@
                         'wrapt==1.9.0',
 
                         'menpo-pyvrml97==2.3.0a4',
-                        'cyassimp==0.2.0',
-                        'hdf5able==0.3.3']
+                        'cyassimp==0.2.0']
 
     if sys.version_info.major == 2:
         install_requires.append('pathlib==1.0')