#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
import bob.bio.base
import numpy
import logging
logger = logging.getLogger("bob.bio.video")
[docs]class FrameContainer:
"""A class for reading, manipulating and saving video content.
"""
def __init__(self, hdf5 = None, load_function = bob.bio.base.load):
self._frames = []
if hdf5 is not None:
self.load(hdf5, load_function)
def __len__(self):
return len(self._frames)
def __iter__(self):
"""Generator that returns the 3-tuple (frame_id, data, quality) for each frame."""
# don't sort
for frame in self._frames:
yield frame
def __getitem__(self, i):
"""Indexer (mostly used in tests)."""
return self._frames[i]
[docs] def add(self, frame_id, frame, quality = None):
"""Adds the frame with the given id and the given quality."""
self._frames.append((str(frame_id), frame, quality))
[docs] def load(self, hdf5, load_function = bob.bio.base.load):
self._frames = []
# Read content (frames) from HDF5File
for path in hdf5.sub_groups(relative=True, recursive=False):
# extract frame_id
if path[:6] == 'Frame_':
frame_id = str(path[6:])
hdf5.cd(path)
# Read data
data = load_function(hdf5)
# read quality, if present
quality = hdf5.read("FrameQuality") if hdf5.has_key("FrameQuality") else None
self.add(frame_id, data, quality)
hdf5.cd("..")
if not len(self):
raise IOError("Could not load data as a Frame Container from file %s" % hdf5.filename)
[docs] def save(self, hdf5, save_function = bob.bio.base.save):
""" Save the content to the given HDF5 File.
The contained data will be written using the given save_function."""
if not len(self):
logger.warn("Saving empty FrameContainer '%s'", hdf5.filename)
for frame_id, data, quality in self:
hdf5.create_group("Frame_%s" % frame_id)
hdf5.cd("Frame_%s" % frame_id)
save_function(data, hdf5)
if quality is not None:
hdf5.set("FrameQuality", quality)
hdf5.cd("..")
[docs] def is_similar_to(self, other):
if len(self) != len(other): return False
for a,b in zip(self, other):
if a[0] != b[0]: return False
if abs(a[2] - b[2]) > 1e-8: return False
if not numpy.allclose(a[1], b[1]): return False
return True
[docs] def as_array(self):
"""Returns the data of frames as a numpy array.
Returns
-------
numpy.ndarray
The frames are returned as an array with the shape of (n_frames, ...)
like a video.
"""
def _reader(frame):
# Each frame is assumed to be an image here. We make it a single frame
# video here by expanding its dimensions. This way it can be used with
# the vstack_features function.
return frame[1][None, ...]
return bob.bio.base.vstack_features(_reader, self._frames, same_size=True)
[docs]def save_compressed(frame_container, filename, save_function, create_link=True):
hdf5 = bob.bio.base.open_compressed(filename, 'w')
frame_container.save(hdf5, save_function)
bob.bio.base.close_compressed(filename, hdf5, create_link=create_link)
del hdf5
[docs]def load_compressed(filename, load_function):
hdf5 = bob.bio.base.open_compressed(filename, 'r')
fc = FrameContainer(hdf5, load_function)
bob.bio.base.close_compressed(filename, hdf5)
del hdf5
return fc