class MusicDBArtwork(object):
"""
Args:
config: MusicDB configuration object
database: MusicDB database
Raises:
TypeError: if config or database are not of the correct type
ValueError: If one of the working-paths set in the config file does not exist
"""
def __init__(self, config, database):
if type(config) != MusicDBConfig:
raise TypeError("Config-class of unknown type")
if type(database) != MusicDatabase:
raise TypeError("Database-class of unknown type")
self.db = database
self.cfg = config
self.fs = Filesystem()
self.musicroot = Filesystem(self.cfg.music.path)
self.artworkroot = Filesystem(self.cfg.artwork.path)
# Define the prefix that must be used by the WebUI and server to access the artwork files
# -> $PREFIX/$Artworkname.jpg
self.manifestawprefix = "artwork"
# Check if all paths exist that have to exist
pathlist = []
pathlist.append(self.cfg.music.path)
pathlist.append(self.cfg.artwork.path)
pathlist.append(self.cfg.artwork.manifesttemplate)
for path in pathlist:
if not self.fs.Exists(path):
raise ValueError("Path \"" + path + "\" does not exist.")
# Instantiate dependent classes
self.meta = MetaTags(self.cfg.music.path)
self.awcache = ArtworkCache(self.cfg.artwork.path)
def GetArtworkFromFile(self, album, tmpawfile):
"""
This method tries to get an artwork from the metadata of the first song of an album.
With the first song, the first one in the database related to the album is meant.
The metadata gets loaded and the artwork stored to a temporary file using the method
:meth:`lib.metatags.MetaTags.StoreArtwork`.
Args:
album: Album entry from the MusicDB Database
tmpawfile (str): Temporary artwork path (incl filename) to which the artwork shall be written
Returns:
``True`` on success, otherwise ``False``
"""
# Load the first files metadata
songs = self.db.GetSongsByAlbumId(album["id"])
firstsong = songs[0]
self.meta.Load(firstsong["path"])
retval = self.meta.StoreArtwork(tmpawfile)
return retval
def SetArtwork(self, albumid, artworkpath, artworkname):
"""
This method sets a new artwork for an album.
It does the following things:
#. Copy the artwork from *artworkpath* to the artwork root directory under the name *artworkname*
#. Create scaled Versions of the artwork by calling :meth:`lib.cache.ArtworkCache.GetArtwork` for each resolution.
#. Update entry in the database
All new creates files ownership will be set to ``[music]->owner:[music]->group`` and gets the permission ``rw-rw-r--``
Args:
albumid: ID of the Album that artwork shall be set
artworkpath (str, NoneType): The absolute path of an artwork that shall be added to the database. If ``None`` the method assumes that the default artwork shall be set. *artworkname* will be ignored in this case.
artworkname (str): The relative path of the final artwork.
Returns:
``True`` on success, otherwise ``False``
Examples:
.. code-block:: python
# Copy from metadata extracted temporary artwork to the artwork directory
self.SetArtwork(albumid, "/tmp/musicdbtmpartwork.jpg", "Artist - Album.jpg")
# Copy a user-defined artwork to the artwork directory
self.SetArtwork(albumid, "/home/username/downloads/fromzeintanetz.jpg", "Artist - Album.jpg")
# Set the default artwork
self.SetArtwork(albumid, None, any)
"""
if artworkpath:
abssrcpath = self.fs.AbsolutePath(artworkpath)
absdstpath = self.artworkroot.AbsolutePath(artworkname)
# Copy file
logging.debug("Copying file from \"%s\" to \"%s\"", abssrcpath,
absdstpath)
self.artworkroot.CopyFile(abssrcpath, absdstpath)
# Set permissions to -rw-rw-r--
try:
self.artworkroot.SetAttributes(
artworkname, self.cfg.music.owner, self.cfg.music.group,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IRGRP | stat.S_IWGRP
| stat.S_IROTH)
except Exception as e:
logging.warning(
"Setting artwork file attributes failed with error %s. \033[1;30m(Leaving them as they are)",
str(e))
if not self.artworkroot.Exists(artworkname):
logging.error(
"Artwork \"%s\" does not exist but was expected to exist!",
artworkname)
return False
# Scale file
# convert edge-size to resolution
# [10, 20, 30] -> ["10x10", "20x20", "30x30"]
resolutions = [str(s) + "x" + str(s) for s in self.cfg.artwork.scales]
for resolution in resolutions:
relpath = self.awcache.GetArtwork(artworkname, resolution)
if not self.artworkroot.Exists(relpath):
logging.error(
"Artwork \"%s\" does not exist but was expected to exist!",
relpath)
return False
# Set permissions to -rw-rw-r--
try:
self.artworkroot.SetAttributes(
relpath, self.cfg.music.owner, self.cfg.music.group,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IRGRP | stat.S_IWGRP
| stat.S_IROTH)
except Exception as e:
logging.warning(
"Setting artwork file attributes failed with error %s. \033[1;30m(Leaving them as they are)",
str(e))
# Update database entry
self.db.SetArtwork(albumid, artworkname)
return True
@staticmethod
def CreateArtworkName(artistname, albumname):
"""
This method creates the name for an artwork regarding the following schema:
``$Artistname - $Albumname.jpg``.
If there is a ``/`` in the name, it gets replaced by ``∕`` (U+2215, DIVISION SLASH)
Args:
artistname (str): Name of an artist
albumname (str): Name of an album
Returns:
valid artwork filename
"""
artistname = artistname.replace("/", "∕")
albumname = albumname.replace("/", "∕")
imagename = artistname + " - " + albumname + ".jpg"
return imagename
def UpdateAlbumArtwork(self, album, artworkpath=None):
"""
This method updates the artwork path entry of an album and the artwork files in the artwork directory.
If a specific artwork shall be forced to use, *artworkpath* can be set to this artwork file.
Following the concept *The Filesystem Is Always Right* and *Do Not Trust Metadata*, the user specified artwork path has higher priority.
Metadata will only be processed if *artworkpath* is ``None``
So an update takes place if *at least one* of the following condition is true:
#. The database entry points to ``default.jpg``
#. *artworkpath* is not ``None``
#. If the database entry points to a nonexistent file
Args:
album: An Album Entry from the MusicDB Database
artworkpath (str, NoneType): Absolute path of an artwork that shall be used as album artwork. If ``None`` the Method tries to extract the artwork from the meta data of an albums song.
Returns:
``True`` If either the update was successful or there was no update necessary.
``False`` If the update failed. Reasons can be an invalid *artworkpath*-Argument
"""
# Create relative artwork path
artist = self.db.GetArtistById(album["artistid"])
imagename = self.CreateArtworkName(artist["name"], album["name"])
# Check if there is no update necessary
dbentry = album["artworkpath"]
if dbentry != "default.jpg" and artworkpath == None:
if self.artworkroot.IsFile(
dbentry
): # If the file does not extist, it must be updated!
return True
# Check if the user given artworkpath is valid
if artworkpath and not self.fs.IsFile(artworkpath):
logging.error(
"The artworkpath that shall be forces is invalid (\"%s\")! \033[1;30m(Artwork update will be canceled)",
str(artworkpath))
return False
# If there is no suggested artwork, try to get one from the meta data
# In case this failes, use the default artwork
if not artworkpath:
artworkpath = "/tmp/musicdbtmpartwork.jpg" # FIXME: Hardcoded usually sucks
retval = self.GetArtworkFromFile(album, artworkpath)
if not retval:
imagename = "default.jpg"
artworkpath = None
# Set new artwork
logging.info("Updating artwork for album \"%s\" to \"%s\" at \"%s\".",
album["name"], imagename, artworkpath)
retval = self.SetArtwork(album["id"], artworkpath, imagename)
return retval
def GenerateAppCacheManifest(self):
"""
This method creates a manifest file for web browsers.
Creating is done in two steps.
First the template given in the configuration gets copied.
Second the paths of all artworks get append to the file.
Also, those of the scaled versions (as given in the config file).
Returns:
*Nothing*
Raises:
PermissonError: When there is no write access to the manifest file
"""
# copy manifest template
template = open(self.cfg.artwork.manifesttemplate, "r")
manifest = open(self.cfg.artwork.manifest, "w")
for line in template:
manifest.write(line)
template.close()
# and append all artworkd
albums = self.db.GetAllAlbums()
awpaths = [album["artworkpath"] for album in albums]
resolutions = [str(s) + "x" + str(s) for s in self.cfg.artwork.scales]
resolutions.append(".")
for resolution in resolutions:
for awpath in awpaths:
path = os.path.join(self.manifestawprefix, resolution)
path = os.path.join(path, awpath)
manifest.write(path + "\n")
manifest.close()
class ArtworkCache(object):
"""
This class handles the artwork cache.
Its main job is to scale an image if a special resolution is requested.
Args:
artworkdir: Absolute path to the artwork root directory
"""
def __init__(self, artworkdir):
self.artworkroot = Filesystem(artworkdir)
def GetArtwork(self, artworkname, resolution):
"""
This method returns a valid path to an artwork with the specified resolution.
The final path will consist of the *artworkdir* given as class parameter, the *resolution* as subdirectory and the *artworkname* as filename. (``{artworkdir}/{resolution}/{artworkname}``)
If the artwork does not exist for this resolution it will be generated.
If the directory for that scale does not exist, it will be created.
In case an error occcurs, an exception gets raised.
The resolution is given as string in the format ``{X}x{Y}`` (For example: ``100x100``).
*X* and *Y* must have the same value.
This method expects an aspect ratio of 1:1.
Beside scaling the JPEG, it will be made progressive.
Args:
artworkname (str): filename of the source artwork (Usually ``$Artist - $Album.jpg``)
resolution (str): resolution of the requested artwork
Returns:
Relative path to the artwork in the specified resolution.
Raises:
ValueError: When the source file does not exist
Example:
.. code-block:: python
cache = ArtworkCache("/data/artwork")
path = cache.GetArtwork("example.jpg", "150x150")
# returned path: "150x150/example.jpg"
# absolute path: "/data/artwork/150x150/example.jpg"
"""
logging.debug("GetArtwork(%s, %s)", artworkname, resolution)
# Check if source exists
if not self.artworkroot.Exists(artworkname):
logging.error("Source file %s does not exist in the artwork root directory!", artworkname)
raise ValueError("Source file %s does not exist in the artwork root directory!",
artworkname)
# Check if already scaled. If yes, our job is done
scaledfile = os.path.join(resolution, artworkname)
if self.artworkroot.Exists(scaledfile):
return scaledfile
# Check if the scale-directory already exist. If not, create one
if not self.artworkroot.IsDirectory(resolution):
logging.debug("Creating subdirectory: %s", resolution)
self.artworkroot.CreateSubdirectory(resolution)
# Scale image
logging.debug("Converting image to %s", resolution)
abssrcpath = self.artworkroot.AbsolutePath(artworkname)
absdstpath = self.artworkroot.AbsolutePath(scaledfile)
# "10x10" -> (10, 10)
length = int(resolution.split("x")[0])
size = (length, length)
im = Image.open(abssrcpath)
im.thumbnail(size, Image.BICUBIC)
im.save(absdstpath, "JPEG", optimize=True, progressive=True)
return scaledfile
class MusicAI(object):
"""
Args:
config: Instance of :class:`lib.cfg.musicdb.MusicDBConfig`.
Raises:
TypeError: If *config* is of an invalid type
"""
def __init__(self, config):
if type(config) != MusicDBConfig:
logging.critical(
"FATAL ERROR: Config-class of unknown type \"%s\"!",
str(type(config)))
raise TypeError()
self.config = config
self.fs = Filesystem(self.config.music.path)
self.genrelist = self.config.musicai.genrelist
self.modelname = self.config.musicai.modelname
self.modelfile = self.config.musicai.modelpath + "/" + self.modelname + ".DCNN.tfl"
def LoadModel(self, modelfilepath):
"""
This method creates a neural network model and loads the trained data from the model in ``modelfilepath`` if the file ``modelfilepath + ".index"`` exists.
The model will be created to run on the GPU if the related configuration is set to ``True``, otherwise the model will run on the CPU.
Currently only ``/gpu:0`` and ``/cpu:0`` are supported.
This method also resets *TensorFlows* and *TFLearn* internals by calling ``tensorflow.reset_default_graph()`` and ``tflearn.config.init_graph()``.
The model can then be saved by :meth:`~mdbapi.musicai.MusicAI.SaveModel`
If model creation fails, an exception gets raised.
.. warning::
The model data can become invalid if the code for creating the model (:meth:`~mdbapi.musicai.MusicAI.CreateModel`) changes the architecture of the model.
Args:
modelfilepath (str): Path where the models data are stored
Returns:
The TFLearn-model
Raises:
tensorflow.errors.InternalError: When creating the model failes.
"""
# Reset tensorflow
tensorflow.reset_default_graph()
tflearn.config.init_graph()
model = None
# Create Model
if self.config.musicai.usegpu:
device = "/gpu:0"
else:
device = "/cpu:0"
logging.debug("Create Model for device %s" % (device))
try:
with tensorflow.device(device):
model = self.CreateModel()
except tensorflow.errors.InternalError as e:
ename = str(type(e).__name__)
logging.exception(
"Creating Tensorflow Model for device \"%s\" failed with error \"%s\""
% (str(device), ename))
print(
"\033[1;31mCreating Tensorflow Model for device \"%s\" failed with error \"%s\" \033[1;30m(My be related to Out-Of-Memory error for GPU usage)\033[0m"
% (str(device), ename))
raise (e)
# Load weights/biases if available
if self.fs.Exists(modelfilepath + ".index"):
model.load(modelfilepath)
return model
def SaveModel(self, model, modelfilepath):
"""
This method stores the configuration (weights, biases, …) in ``modelfilepath``.
*TFLearn* creates multiple files with ``modelfilepath`` as suffix.
The model can then be loaded by :meth:`~mdbapi.musicai.MusicAI.LoadModel`
.. warning::
The model data can become invalid if the code for creating the model (:meth:`~mdbapi.musicai.MusicAI.CreateModel`) changes the architecture of the model.
Args:
model: The *TFLearn* model of the neural network
modelfilepath (str): The path and filename-(prefix) where the data of the model will be saved
Returns:
*Nothing*
"""
model.save(modelfilepath)
def GetStatistics(self):
"""
This method returns statistics of the sizes of the training and feature sets.
Returns:
A dictionary with statistics or ``None`` if there is no training set
Example:
.. code-block:: python
stats = musicai.GetStatistics()
print("Set Size: ", stats["setsize"])
print("Number of Songs in Set: ", stats["numofsongs"])
"""
trainingfilepath = self.config.musicai.modelpath + "/" + self.modelname + ".h5"
if not self.fs.Exists(trainingfilepath):
return None
trainingfile = HDF5Storage(trainingfilepath)
# Check if the song was already added to the trainingset
try:
songset = trainingfile.ReadDataset("songids")
outputset = trainingfile.ReadDataset("outputs")
except AssertionError:
trainingfile.Close()
return None
stats = {}
stats["setsize"] = outputset.shape[0]
stats["numofsongs"] = songset.shape[0]
return stats
# This MUST BE THREADSAFE to process multiple files at once
# Accessing own database is allowed
# returns file to spetrogram on success or None if something failed
def CreateSpectrogram(self, songid, songpath):
"""
This method creates the spectrograms used for the feature set.
If the spectrogram already exists, it will skip the process.
The spectrogram is a png-image.
.. warning::
This method must be thread safe to process multiple files at once
This method represents the following shell commands:
.. code-block:: bash
# decode song to wave-file
ffmpeg -v quiet -y -i $songpath "$songid-stereo.wav"
# make mono wave file out of the stereo wave file
sox "$songid-stereo.wav" "$songid-mono.wav" remix 1,2
# create spectrogram out of the mono wave file
sox "$songid-mono.wav" -n spectrogram -y $slizesize -X 50 -m -r -o "$songid.png"
The temporary wave files are stored in the *musicai-tmppath*.
They will be removed if the spectrogram was created successfully.
Otherwise they will be kept.
The spectrogram in the *musicai-specpath*.
The slicesize denotes the height of the spectrogram which basically means the granularity of frequencies.
This value gets increased by 1 to cover the offset (f=0).
One column in the spectrogram represents 50ms of the song.
All those variables can be set in the MusicDB Configuration.
.. warning::
Before changing the slice-size read the ``sox`` manpages for the ``-y`` parameter carefully.
Args:
songid: The song ID is used to create temporary files
songpath (str): The path to the song that shall be analyzed
Returns:
path to the spectrogram
"""
# prepare pathes
sourcefile = self.fs.AbsolutePath(songpath)
wavefile = self.config.musicai.tmppath + "/" + str(
songid) + "-stereo.wav"
monofile = self.config.musicai.tmppath + "/" + str(
songid) + "-mono.wav"
spectrogram = self.config.musicai.specpath + "/" + str(songid) + ".png"
# we are already done
if self.fs.Exists(spectrogram):
return spectrogram
# create wave-file
# ffmpeg -i audio.aac stereo.wav
if not self.fs.Exists(wavefile):
process = [
"ffmpeg",
"-v",
"quiet", # don't be verbose
"-y", # overwrite existing file
"-i",
sourcefile,
wavefile
]
try:
self.fs.Execute(process)
except Exception as e:
logging.error("Error \"%s\" while executing: %s", str(e),
str(process))
return None
# create mono-file
# sox stereo.wav mono.wav remix 1,2
if not self.fs.Exists(monofile):
process = ["sox", wavefile, monofile, "remix", "1,2"]
try:
self.fs.Execute(process)
except Exception as e:
logging.error("Error \"%s\" while executing: %s", str(e),
str(process))
return None
# create spectrogram
# sox mono.wav -n spectrogram -Y 200 -X 50 -m -r -o mono.png
if not self.fs.Exists(spectrogram):
process = [
"sox", monofile, "-n", "spectrogram", "-y",
str(self.config.musicai.slicesize + 1), "-X", "50", "-m", "-r",
"-o", spectrogram
]
try:
self.fs.Execute(process)
except Exception as e:
logging.error("Error \"%s\" while executing: %s", str(e),
str(process))
return None
# remove tempfiles - Keep spectrogram because it looks so cool. Maybe it finds its place in the UI
self.fs.RemoveFile(wavefile)
self.fs.RemoveFile(monofile)
return spectrogram
# This MUST BE THREADSAFE to process multiple files at once
# Accessing own database is allowed
# returns True on success
def CreateFeatureset(self, songid, songpath):
"""
This function generates the feature set for the MusicAI.
The features are generated by the following steps:
#. First step is to create the spectrogram calling :meth:`~mdbapi.musicai.MusicAI.CreateSpectrogram`.
#. Next it takes slices from the resulting image and converts it into a normalized *numpy* array.
#. The begin and end of a song will be chopped of and gets ignored.
A slicesize can be defind in the MusicDB Configuration under musicai->slicesize. **Be careful with this configuration and check the influence to other methods in this class!**
The first 10 and the last 10 slices will be skipped to avoid unnecessary much intro/outro-data-noise.
The resulting data (a feature) is a *numpy* 3D (slicesize, slicesize, 1) matrix of type float in range of 0.0 to 1.0.
This matrix of all features of a song gets stored in a HDF5 file under ``$spectrograms/$SongID.h5``
Args:
songid: ID of the song the feature set belongs to
songpath (str): path to the song that shall be analyzed
Returns:
``True`` on success, otherwise ``False``
Example:
.. code-block:: python
musicai = MusicAI("./musicdb.ini")
musicai.CreateFeatureset(mdbsong["id"], mdbsong["path"]):
"""
# Create Spectrogram
spectrogram = self.CreateSpectrogram(songid, songpath)
if not spectrogram:
return False
# Open it and make raw data out of the image
image = Image.open(spectrogram)
width, height = image.size
slicesize = self.config.musicai.slicesize
numslices = int(width / slicesize) - 20 # \_ skip slices (intro+outro)
startoffset = 10 * slicesize # /
if numslices <= 0:
logging.warning(
"song %s too small! \033[1;30m(do not use for featureset)" %
(songpath))
return False
# create data
dataset = numpy.zeros((numslices, slicesize, slicesize, 1))
for i in range(numslices):
# Crop into slices
startpixel = i * slicesize + startoffset
imageslice = image.crop(
(startpixel, 0, startpixel + slicesize, slicesize))
# Make numpy-arrays out of it
data = numpy.asarray(imageslice,
dtype=numpy.uint8) # image -> ndarray
data = data / 255.0 # [0 … 255] -> [0.0 … 1.0] (and makes float)
data = data.reshape((slicesize, slicesize, 1)) # X² -> X³
# Store the feature dataset
dataset[i] = data
# Open storage for the features
featurefilepath = self.config.musicai.specpath + "/" + str(
songid) + ".h5"
featurefile = HDF5Storage(featurefilepath)
featurefile.WriteDataset("featureset", dataset)
featurefile.Close()
return True
def HasFeatureset(self, songid):
"""
This method checks if a song has already a feature set.
If there is a HDF5 file in the spectrogram directors the method assumes that this file contains the feature set.
The file gets not read.
The png encoded spectrogram is not the final feature set and so its existence is not relevant for this method.
Args:
songid: ID of the song that shall be checked
Returns:
``True`` if the songs feature set is available, otherwise ``False``
"""
featurefilepath = self.config.musicai.specpath + "/" + str(
songid) + ".h5"
if self.fs.Exists(featurefilepath):
return True
return False
def AddSongToTrainingset(self, songid, genre):
"""
This method can be used to add a song to the available training sets.
To do so, first the feature set must be created.
The feature set file gets read and a genre vector generated.
The resultuing two sets (*inputs* = feature set and *outputs* = genre vector) will be stored in the training file.
The **inputs** are a HDF5 dataset handler shaping a numpy-array of size *n* of input matrices:
``(n, slicesize, slicesize, 1)``
The **outputs** are a HDF5 dataset handler shaping a numpy-array of size *n* of genre-vectors formatted as shown in :meth:`mdbapi.musicai.MusicAI.GetGenreMatrix`:
``(n, 4, 1)``
The genre name should be the same as the related genre tag is named in the database.
It also must be part of the list of genres this AI works with.
Args:
songid: ID of the song that shall be used for training
genre (str): Lower case name of the genre (as stored in the database) the song belongs to
Returns:
``True`` if the song has a feature set so that it was added to the database. Otherwise ``False``
Example:
.. code-block:: python
musicai = MusicAI("./musicdb.ini")
musicai.CreateFeatureset(mdbsong["id"], mdbsong["path"]):
musicai.AddSongToTrainingset(mdbsong["id"], "metal")
"""
if not genre in self.genrelist:
logging.error("The genre \"%s\" is not in the genrelist: \"%s\"!",
genre, str(self.genrelist))
return False
if not self.HasFeatureset(songid):
logging.waring("Song with id %s does not have a featureset",
str(songid))
return False
featurefilepath = self.config.musicai.specpath + "/" + str(
songid) + ".h5"
trainingfilepath = self.config.musicai.modelpath + "/" + self.modelname + ".h5"
featurefile = HDF5Storage(featurefilepath)
trainingfile = HDF5Storage(trainingfilepath)
# Check if the song was already added to the trainingset
try:
songids = trainingfile.ReadDataset("songids")
except AssertionError:
songids = []
if songid in songids:
logging.waring("Song with id %s does already exist in trainingset",
str(songid))
featurefile.Close()
trainingfile.Close()
return False
# Read Featureset
featureset = featurefile.ReadDataset("featureset") # read features
setsize = featureset.shape[0] # determin size of the workingset
genrevector = self.GetGenreMatrix()[genre] # get genre vector
genrevector = numpy.array(genrevector) # \_ create outputs-set
genreset = numpy.tile(genrevector, (setsize, 1)) # /
songidset = numpy.tile(numpy.array(songid), (1, 1))
# Write trainingset
trainingfile.WriteDataset("inputs", featureset)
trainingfile.WriteDataset("outputs", genreset)
trainingfile.WriteDataset("songids", songidset)
featurefile.Close()
trainingfile.Close()
return True
def CreateModel(self):
"""
This method creates and returns the following deep convolutional neural network.
The architecture of the network is close to the one provided the following blog article:
Julien Despois, *"Finding the genre of a song with Deep Learning -- A.I. Odyyssey part. 1"*, Nov. 2016, internet (non-scientific source), https://chatbotslife.com/finding-the-genre-of-a-song-with-deep-learning-da8f59a61194
Inside the method there are some configurable variables to easily change some basic settings of the network:
* ``useflatten = True``: Create a flatten-layer between the convolutional part and the fully connected end
* ``keepprob = 0.5``: Probability of an input gets propagated through the dropout-layer
* ``activation = "elu"``: Name of the activation function used for the convolutional networks as used by *tensorflow*
* ``weightinit = "Xavier"``: Initialisation function for the convolutional layers
* ``weightdecay = 0.001``
* ``learningrate = 0.001``
The reason for using ``"elu"`` as activation function can be found in the following paper:
Djork-Arné Clevert, Thomas Unterthiner and Sepp Hochreiter, *"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)"*, Nov. 2015, CoRR (scientific source), https://arxiv.org/abs/1511.07289
Returns:
The created TFLearn-model
"""
slicesize = self.config.musicai.slicesize # the slice size of the spectrograms define the size of the input layer
genrecount = len(
self.config.musicai.genrelist
) # number of genres and so the number of output neurons
# Some general architechture configuration
useflatten = True # Use a flatten layer afrer the convolutional layers
keepprob = 0.5 # configures the dropout layer
activation = "elu" # elu? warum nicht das übliche relu? -> Weil besser: https://arxiv.org/abs/1511.07289
weightinit = "Xavier" # Initialisation function for the convolutional layers
weightdecay = 0.001
learningrate = 0.001
convnet = input_data(shape=[None, slicesize, slicesize, 1],
name='input')
convnet = conv_2d(convnet,
64,
2,
activation=activation,
weights_init=weightinit,
weight_decay=weightdecay)
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet,
128,
2,
activation=activation,
weights_init=weightinit,
weight_decay=weightdecay)
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet,
256,
2,
activation=activation,
weights_init=weightinit,
weight_decay=weightdecay)
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet,
512,
2,
activation=activation,
weights_init=weightinit,
weight_decay=weightdecay)
convnet = max_pool_2d(convnet, 2)
if useflatten:
convnet = flatten(convnet)
convnet = fully_connected(convnet,
1024,
activation=activation,
weight_decay=weightdecay)
convnet = dropout(convnet, keepprob)
convnet = fully_connected(convnet,
genrecount,
activation='softmax',
weight_decay=weightdecay)
convnet = regression(convnet,
optimizer='rmsprop',
loss='categorical_crossentropy',
learning_rate=learningrate)
model = tflearn.DNN(
convnet,
tensorboard_verbose=3, # many nice graphs inside tensorboard
tensorboard_dir=self.config.musicai.logpath)
return model
def GetGenreMatrix(self):
r"""
This method returns a dictionary with the categorization vector for each genre.
When training a neural network for categorizing, each element of the result-vector is mapped to one category.
This static mapping is returned by this method.
The matrix gets dynamic generated out of the configuration of MusicDB.
The configured genre list gets just transformed into the matirx.
For example, the mapping for the genrelist ``Metal, NDH, Gothic, Elector`` is the following:
.. math::
\begin{pmatrix}
\text{Metal}\\
\text{NDH}\\
\text{Gothic}\\
\text{Electro}
\end{pmatrix}
The format of the mapping is a dictionary with each genre as key.
Each entry in the dictionary is a vector with the related cell set to 1.0 and the other cells to 0.0. For *Gothic* music, the vector would look like this:
.. math::
\text{Gothic} = \begin{pmatrix}
0.0\\
0.0\\
1.0\\
0.0
\end{pmatrix}
Returns:
A dictionary with expected-prediction-vectors for training
"""
matrix = {}
for index, genre in enumerate(self.genrelist):
matrix[genre] = [0.0] * len(self.genrelist)
matrix[genre][index] = 1.0
return matrix
def HasTrainingset(self, songid):
"""
This method checks if a song is used for training - if there exists a training set.
The check is done in two steps:
#. Does the training set file for the model exist.
#. Is the song ID listed in the set of training songs in the training set file.
Args:
songid: ID of the song that shall be checked
Returns:
``True`` if there exists a training set, otherwise ``False``
"""
trainingfilepath = self.config.musicai.modelpath + "/" + self.modelname + ".h5"
if not self.fs.Exists(trainingfilepath):
return False
trainingfile = HDF5Storage(trainingfilepath)
# Check if the song was already added to the trainingset
try:
songids = trainingfile.ReadDataset("songids")
except AssertionError:
trainingfile.Close()
return False
# if the song is listed in this list, it exists in the trainingset
if songid in songids:
trainingfile.Close()
return True
return False
def PerformTraining(self):
"""
This method performs the training of the neural network.
The training is done in several steps:
#. Loading the model using :meth:`~mdbapi.musicai.MusicAI.LoadModel`
#. Loading the training set from file
#. Performing the training itself. This can be canceled by pressing ctrl-c.
#. Saving new model state by calling :meth:`~mdbapi.musicai.MusicAI.SaveModel`
The size of the training set is determined by the MusicDB Configuration.
Each run gets logged in the log-directory that can be configured in the MusicDB Configuration.
The name of each run is the module-name extended with the date (YY-MM-DD) and time (HH:MM) of the training.
These logs can be used for visualization using TensorBoard.
Returns:
``True`` on success, otherwise ``False``
"""
# shorten config
runid = self.modelname + " " + datetime.datetime.now().strftime(
"%Y-%m-%d %H:%M")
batchsize = self.config.musicai.batchsize
epoch = self.config.musicai.epoch
# load model
print("\033[1;34mLoading model … \033[0m")
model = self.LoadModel(self.modelfile)
# getting features
print("\033[1;34mLoading training data … \033[0;36m")
trainingfilepath = self.config.musicai.modelpath + "/" + self.modelname + ".h5"
trainingfile = HDF5Storage(trainingfilepath)
try:
inputset = trainingfile.ReadDataset("inputs")
outputset = trainingfile.ReadDataset("outputs")
except AssertionError as e:
logging.exception("Reading the Trainingset failed!")
print("\033[1;31m … failed!\033[0m")
trainingfile.Close()
return False
# run training
print("\033[1;34mRunning training process … \033[0m")
try:
model.fit(inputset,
outputset,
n_epoch=epoch,
batch_size=batchsize,
shuffle=True,
validation_set=0.01,
snapshot_step=100,
show_metric=True,
run_id=runid)
except KeyboardInterrupt as e:
logging.info(
"Training canceled by user. \033[1;30m(Progress will not be saved)\033[0m"
)
print(
"\033[1;33m … canceled by user. \033[1;30m(Progress will not be saved)\033[0m"
)
trainingfile.Close()
return False
# save model
print("\033[1;34mSaving Model … \033[0m")
self.SaveModel(model, self.modelfile)
trainingfile.Close()
return True
def PerformPrediction(self, songid):
r"""
This method starts a prediction of the genre for a song addressed by its song ID.
It returns a vector of confidence for the genres as described in :meth:`~mdbapi.musicai.MusicAI.GetGenreMatrix`.
The confidence is the average of all features in the set.
The featureset gets loaded from the related HDF5 file in the spectrogram directory configured
in the MusicDB Configuration.
A feature set can be created using the :meth:`~mdbapi.musicai.MusicAI.CreateFeatureset`
If the file does not exist ``None`` gets returned.
The result is calculated the following way from the set *P* of predictions based on a feature
for each genre in the set of genres *G*.
.. math::
p_{g} = \frac{1}{\lvert P \rvert}\sum_{i}^{\lvert P \rvert}{p_{i}} \qquad p_{i} \in P; \; g \in G
Args:
songid: The ID of the song that shall be categorized
Returns:
A confidence-vector for the genres that were predicted, or ``None`` if an error occurs
Example:
.. code-block:: python
# Create a feature set if there is none yet
if not musicai.HasFeatureset(mdbsong["id"]):
musicai.CreateFeatureset(mdbsong["id"], mdbsong["path"])
# Perform prediction
confidence = musicai.PerformPrediction(mdbsong["id"])
if confidence == None:
print("Prediction Failed! No featureset available?")
return
# Print results
# The order of the Genre-List is important! The mapping (index, genre) must be correct!
for index, genre in enumerate(config.musicai.genrelist):
print("%.2f: %s" % (confidence[index], genre))
"""
if not self.HasFeatureset(songid):
logging.warning("Song with id %s does not have a featureset",
str(songid))
return None
# load features
featurefilepath = self.config.musicai.specpath + "/" + str(
songid) + ".h5"
featurefile = HDF5Storage(featurefilepath)
try:
featureset = featurefile.ReadDataset("featureset")
except AssertionError as e:
logging.error("Reading Featureset failed with error %s!", str(e))
featureset = None
featurefile.Close()
if featureset == None:
return None
# load model
model = self.LoadModel(self.modelfile)
# run prediction
predictionset = model.predict(featureset)
featurefile.Close()
# accumulate results
numofgenres = len(self.genrelist)
prediction = [0.0] * numofgenres
for entry in predictionset:
for i in range(numofgenres):
prediction[i] += entry[i]
for i in range(numofgenres):
prediction[i] /= len(predictionset)
return prediction
class UploadManager(object):
"""
This class manages uploading content to the server MusicDB runs on.
All data is stored in the uploads-directory configured in the MusicDB configuration.
Args:
config: :class:`~lib.cfg.musicdb.MusicDBConfig` object holding the MusicDB Configuration
database: (optional) A :class:`~lib.db.musicdb.MusicDatabase` instance
Raises:
TypeError: When the arguments are not of the correct type.
"""
def __init__(self, config, database):
if type(config) != MusicDBConfig:
raise TypeError("config argument not of type MusicDBConfig")
if database != None and type(database) != MusicDatabase:
raise TypeError(
"database argument not of type MusicDatabase or None")
self.db = database
self.cfg = config
self.uploadfs = Filesystem(self.cfg.uploads.path)
self.musicfs = Filesystem(self.cfg.music.path)
self.artworkfs = Filesystem(self.cfg.artwork.path)
# TODO: check write permission of all directories
self.fileprocessing = Fileprocessing(self.cfg.uploads.path)
self.dbmanager = MusicDBDatabase(config, database)
global Tasks
if Tasks == None:
self.LoadTasks()
#####################################################################
# Callback Function Management #
#####################################################################
def RegisterCallback(self, function):
"""
Register a callback function that reacts on Upload related events.
For more details see the module description at the top of this document.
Args:
function: A function that shall be called on an event.
Returns:
*Nothing*
"""
global Callbacks
Callbacks.append(function)
def RemoveCallback(self, function):
"""
Removes a function from the list of callback functions.
Args:
function: A function that shall be called removed.
Returns:
*Nothing*
"""
global Callbacks
# Not registered? Then do nothing.
if not function in Callbacks:
logging.warning(
"A Streaming Thread callback function should be removed, but did not exist in the list of callback functions!"
)
return
Callbacks.remove(function)
def NotifyClient(self, notification, task, message=None):
"""
This method triggers a client-notification.
There are three kind of notifications:
* ``"ChunkRequest"``: A new chunk of data is requested
* ``"StateUpdate"``: The state or annotations of an upload-task has been changed. See ``"state"`` value.
* ``"InternalError"``: There is an internal error occurred during. See ``"message"`` value.
The notification comes with the current status of the upload process.
This includes the following keys - independent of the state of the upload:
* uploadid: ID of the upload the notification is associated with
* offset: Offset of the requested data in the source file
* chunksize: The maximum chunk size
* state: The current state of the upload task
* message: ``null``/``None`` or a message from the server
* uploadtask: The task dictionary itself
* uploadslist: Except for ``ChunkRequest`` events, the WebSocket server append the result of :meth:`lib.ws.mdbwsi.MusicDBWebSocketInterface.GetUploads` to the notification
*task* can be ``None`` in case the notification is meant to be an information that a given upload ID is invalid.
Args:
notification (str): Name of the notification
task (dict): Task structure
message (str): (optional) text message (like an error message) to the client
Returns:
*Nothing*
Raises:
ValueError: When notification has an unknown notification name
"""
if not notification in [
"ChunkRequest", "StateUpdate", "InternalError"
]:
raise ValueError("Unknown notification \"%s\"" % (notification))
status = {}
if task != None:
status["uploadid"] = task["id"]
status["offset"] = task["offset"] # offset of the data to request
status[
"chunksize"] = 4096 * 100 # Upload 400KiB (TODO: Make configurable)
status["state"] = task["state"]
status["uploadtask"] = task
else:
status["uploadid"] = None
status["offset"] = None
status["chunksize"] = None
status["state"] = "notexisting"
status["uploadtask"] = None
status["message"] = message
global Callbacks
for callback in Callbacks:
try:
callback(notification, status)
except Exception as e:
logging.exception(
"A Upload Management event callback function crashed!")
#####################################################################
# State management #
#####################################################################
def SaveTask(self, task):
"""
This method saves a task in the uploads directory under ``tasks/${Task ID}.json``
Args:
task (dict): The task to save
Returns:
*Nothing*
"""
taskid = task["id"]
data = json.dumps(task)
path = self.cfg.uploads.path + "/tasks/" + taskid + ".json"
if not self.uploadfs.IsDirectory("tasks"):
logging.debug("tasks directory missing. Creating \"%s\"",
self.cfg.uploads.path + "/tasks")
self.uploadfs.CreateSubdirectory("tasks")
with open(path, "w+") as fd:
fd.write(data)
return
def LoadTasks(self):
"""
Loads all task from the JSON files inside the tasks-directory.
The list of active tasks will be replaced by the loaded tasks.
Returns:
*Nothing*
"""
logging.debug("Loading Upload-Tasks…")
taskfilenames = self.uploadfs.ListDirectory("tasks")
global Tasks
Tasks = {}
for taskfilename in taskfilenames:
taskpath = self.cfg.uploads.path + "/tasks/" + taskfilename
if self.uploadfs.GetFileExtension(taskpath) != "json":
continue
try:
with open(taskpath) as fd:
task = json.load(fd)
except Exception as e:
logging.warning(
"Loading task file \"%s\" failed with error \"%s\". \033[1;30m(File will be ignored)",
str(taskpath), str(e))
continue
if "id" not in task:
logging.warning(
"File \"%s\" is not a valid task (ID missing). \033[1;30m(File will be ignored)",
str(taskpath), str(e))
continue
Tasks[task["id"]] = task
return
#####################################################################
# Management Functions #
#####################################################################
def InitiateUpload(self, uploadid, mimetype, contenttype, filesize,
checksum, sourcefilename):
"""
Initiates an upload of a file into a MusicDB managed file space.
After calling this method, a notification gets triggered to request the first chunk of data from the clients.
In case uploads are deactivated in the MusicDB Configuration, an ``"InternalError"`` Notification gets sent to the clients.
Args:
uploadid (str): Unique ID to identify the upload task
mimetype (str): MIME-Type of the file (example: ``"image/png"``)
contenttype (str): Type of the content: (``"video"``, ``"album"``, ``"artwork"``)
filesize (int): Size of the complete file in bytes
checksum (str): SHA-1 check sum of the source file
sourcefilename (str): File name (example: ``"test.png"``)
Raises:
TypeError: When one of the arguments has not the expected type
ValueError: When *contenttype* does not have the expected values
"""
if type(uploadid) != str:
raise TypeError("Upload ID must be of type string")
if type(mimetype) != str:
raise TypeError("mime type must be of type string")
if type(contenttype) != str:
raise TypeError("content type must be of type string")
if contenttype not in ["video", "album", "artwork"]:
raise ValueError(
"content type \"%s\" not valid. \"video\", \"album\" or \"artwork\" expected."
% (str(contenttype)))
if type(filesize) != int:
raise TypeError("file size must be of type int")
if filesize <= 0:
raise ValueError("file size must be greater than 0")
if type(checksum) != str:
raise TypeError("Checksum must be of type string")
if type(sourcefilename) != str:
raise TypeError("Source file name must be of type string")
if not self.cfg.uploads.allow:
self.NotifyClient("InternalError", None, "Uploads deactivated")
logging.warning(
"Uploads not allowed! \033[1;30m(See MusicDB Configuration: [uploads]->allow)"
)
return
fileextension = self.uploadfs.GetFileExtension(sourcefilename)
destinationname = contenttype + "-" + checksum + "." + fileextension
destinationpath = self.cfg.uploads.path + "/" + destinationname
# TODO: Check if there is already a task with the given ID.
# If this task is in waitforchunk state, the upload can be continued instead of restarting it.
# Remove existing upload if destination path exists
self.uploadfs.RemoveFile(
destinationpath) # Removes file when it exists
# Create File
with open(destinationpath, "w+b"):
pass
task = {}
task["id"] = uploadid
task["filesize"] = filesize
task["offset"] = 0
task["contenttype"] = contenttype
task["mimetype"] = mimetype
task["sourcefilename"] = sourcefilename
task["sourcechecksum"] = checksum
task["destinationpath"] = destinationpath
task[
"videofile"] = None # Path to the video file in the music directory
task["state"] = "waitforchunk"
task["annotations"] = {}
self.SaveTask(task)
global Tasks
Tasks[uploadid] = task
self.NotifyClient("ChunkRequest", task)
return
def RequestRemoveUpload(self, uploadid):
"""
This method triggers removing a specific upload.
This includes the uploaded file as well as the upload task information and annotations.
The upload task can be in any state.
When the remove-operation is triggered, its state gets changed to ``"remove"``.
Only the ``"remove"`` state gets set. Removing will be done by the Management Thread.
Args:
uploadid (str): ID of the upload-task
Returns:
``True`` on success
"""
try:
task = self.GetTaskByID(uploadid)
except Exception as e:
logging.error(
"Internal error while requesting a new chunk of data: %s",
str(e))
return False
self.UpdateTaskState(task, "remove")
return True
def GetTaskByID(self, uploadid):
"""
This method returns an existing task from the tasklist.
The task gets identified by its ID aka Upload ID
When the task does not exits, the clients get an ``"InternalError"`` notification.
The tasks state is then ``"notexisting"``.
Args:
uploadid (str): ID of the upload-task
Returns:
A task dictionary
Raises:
TypeError: When *uploadid* is not a string
ValueError: When *uploadid* is not a valid key in the Tasks-dictionary
"""
if type(uploadid) != str:
raise TypeError("Upload ID must be a string. Type was \"%s\"." %
(str(type(uploadid))))
global Tasks
if uploadid not in Tasks:
self.NotifiyClient("InternalError", None, "Invalid Upload ID")
raise ValueError("Upload ID \"%s\" not in Task Queue.",
str(uploadid))
return Tasks[uploadid]
def UpdateTaskState(self, task, state, errormessage=None):
"""
This method updates and saves the state of an task.
An ``"StateUpdate"`` notification gets send as well.
If *errormessage* is not ``None``, the notification gets send as ``"InternalError"`` with the message
Args:
task (dict): Task object to update
state (str): New state
message (str): Optional message
Returns:
*Nothing*
"""
task["state"] = state
self.SaveTask(task)
if errormessage:
self.NotifyClient("InternalError", task, errormessage)
else:
self.NotifyClient("StateUpdate", task)
return
def NewChunk(self, uploadid, rawdata):
"""
This method processes a new chunk received from the uploading client.
Args:
uploadid (str): Unique ID to identify the upload task
rawdata (bytes): Raw data to append to the uploaded data
Returns:
``False`` in case an error occurs. Otherwise ``True``.
Raises:
TypeError: When *rawdata* is not of type ``bytes``
"""
if type(rawdata) != bytes:
raise TypeError("raw data must be of type bytes. Type was \"%s\"" %
(str(type(rawdata))))
try:
task = self.GetTaskByID(uploadid)
except Exception as e:
logging.error(
"Internal error while requesting a new chunk of data: %s",
str(e))
return False
chunksize = len(rawdata)
filepath = task["destinationpath"]
try:
with open(filepath, "ab") as fd:
fd.write(rawdata)
except Exception as e:
logging.warning(
"Writing chunk of uploaded data into \"%s\" failed: %s \033[1;30m(Upload canceled)",
filepath, str(e))
self.UpdateTaskState(
task, "uploadfailed",
"Writing data failed with error: \"%s\"" % (str(e)))
return False
task["offset"] += chunksize
self.SaveTask(task)
if task["offset"] >= task["filesize"]:
# Upload complete
self.UploadCompleted(task)
else:
# Get next chunk of data
self.NotifyClient("ChunkRequest", task)
return True
def UploadCompleted(self, task):
"""
This method continues the file management after an upload was completed.
The following tasks were performed:
* Checking the checksum of the destination file (SHA1) and compares it with the ``"sourcechecksum"`` from the *task*-dict.
When the upload was successful, it notifies the clients with a ``"UploadComplete"`` notification.
Otherwise with a ``"UploadFailed"`` one.
Args:
task (dict): The task that upload was completed
Returns:
``True`` When the upload was successfully complete, otherwise ``False``
"""
# Check checksum
destchecksum = self.fileprocessing.Checksum(task["destinationpath"],
"sha1")
if destchecksum != task["sourcechecksum"]:
logging.error(
"Upload Failed: \033[0;36m%s \e[1;30m(Checksum mismatch)",
task["destinationpath"])
self.UpdateTaskState(task, "uploadfailed", "Checksum mismatch")
return False
logging.info("Upload Complete: \033[0;36m%s", task["destinationpath"])
self.UpdateTaskState(task, "uploadcomplete")
# Now, the Management Thread takes care about post processing or removing no longer needed content
return True
def GetTasks(self):
"""
Returns:
The dictionary with all upload tasks
"""
global Tasks
return Tasks
def PreProcessUploadedFile(self, task):
"""
This method initiates pre-processing of an uploaded file.
Depending on the *contenttype* different post processing methods are called:
* ``"video"``: :meth:`~PreProcessVideo`
* ``"artwork"``: :meth:`~PreProcessArtwork`
The task must be in ``"uploadcomplete"`` state, otherwise nothing happens but printing an error message.
If post processing was successful, the task state gets updated to ``"preprocessed"``.
When an error occurred, the state will become ``"invalidcontent"``.
Args:
task (dict): the task object of an upload-task
Returns:
``True`` on success, otherwise ``False``
"""
if task["state"] != "uploadcomplete":
logging.error(
"task must be in \"uploadcomplete\" state for post processing. Actual state was \"%s\". \033[1;30m(Such a mistake should not happen. Anyway, the task won\'t be post process and nothing bad will happen.)",
task["state"])
return False
# Perform post processing
logging.debug("Preprocessing upload %s -> %s",
str(task["sourcefilename"]),
str(task["destinationpath"]))
success = False
if task["contenttype"] == "video":
success = self.PreProcessVideo(task)
elif task["contenttype"] == "artwork":
success = self.PreProcessArtwork(task)
else:
logging.warning(
"Unsupported content type of upload: \"%s\" \033[1;30m(Upload will be ignored)",
str(task["contenttype"]))
self.UpdateTaskState(task, "invalidcontent",
"Unsupported content type")
return False
# Update task state
if success == True:
newstate = "preprocessed"
else:
newstate = "invalidcontent"
self.UpdateTaskState(task, newstate)
return success
def PreProcessVideo(self, task):
"""
Args:
task (dict): the task object of an upload-task
"""
meta = MetaTags()
try:
meta.Load(task["destinationpath"])
except ValueError:
logging.error(
"The file \"%s\" uploaded as video to %s is not a valid video or the file format is not supported. \033[1;30m(File will be not further processed.)",
task["sourcefilename"], task["destinationpath"])
return False
# Get all meta infos (for videos, this does not include any interesting information.
# Maybe the only useful part is the Load-method to check if the file is supported by MusicDB
#tags = meta.GetAllMetadata()
#logging.debug(tags)
return True
def PreProcessArtwork(self, task):
"""
Args:
task (dict): the task object of an upload-task
"""
origfile = task["destinationpath"]
extension = self.uploadfs.GetFileExtension(origfile)
jpegfile = origfile[:-len(extension)] + "jpg"
if extension != "jpg":
logging.debug(
"Transcoding artwork file form %s (\"%s\") to JPEG (\"%s\")",
extension, origfile, jpegfile)
im = Image.open(origfile)
im = im.convert("RGB")
im.save(jpegfile, "JPEG", optimize=True, progressive=True)
task["artworkfile"] = jpegfile
return True
def AnnotateUpload(self, uploadid, annotations):
"""
This method can be used to add additional information to an upload.
This can be done during or after the upload process.
Args:
uploadid (str): ID to identify the upload
Returns:
``True`` on success, otherwise ``False``
Raises:
TypeError: When *uploadid* is not of type ``str``
ValueError: When *uploadid* is not included in the Task Queue
"""
try:
task = self.GetTaskByID(uploadid)
except Exception as e:
logging.error(
"Internal error while requesting a new chunk of data: %s",
str(e))
return False
for key, item in annotations.items():
task["annotations"][key] = item
self.SaveTask(task)
self.NotifyClient("StateUpdate", task)
return True
def IntegrateUploadedFile(self, uploadid, triggerimport):
"""
This method integrated the uploaded files into the music directory.
The whole file tree will be created following the MusicDB naming scheme.
The upload task must be in ``preprocesses`` state. If not, nothing happens.
When *triggerimport* is ``true``, the upload manager start importing the music.
This happens asynchronously inside the Upload Manager Thread.
Args:
uploadid (str): ID to identify the upload
Returns:
``True`` on success, otherwise ``False``
Raises:
TypeError: When *uploadid* is not of type ``str``
ValueError: When *uploadid* is not included in the Task Queue
"""
try:
task = self.GetTaskByID(uploadid)
except Exception as e:
logging.error(
"Internal error while requesting a new chunk of data: %s",
str(e))
return False
if task["state"] != "preprocessed":
logging.warning(
"Cannot integrate an upload that is not in \"preprocessed\" state. Upload with ID \"%s\" was in \"%s\" state! \033[1;30m(Nothing will be done)",
str(task["id"]), str(task["state"]))
return
# Perform post processing
success = False
if task["contenttype"] == "video":
success = self.IntegrateVideo(task)
elif task["contenttype"] == "artwork":
success = True # Importing artwork does not require the file at any specific place
else:
logging.warning(
"Unsupported content type of upload: \"%s\" \033[1;30m(Upload will be ignored)",
str(task["contenttype"]))
self.UpdateTaskState(task, "integrationfailed",
"Unsupported content type")
return
# Update task state
if success == True:
newstate = "integrated"
else:
newstate = "integrationfailed"
self.UpdateTaskState(task, newstate)
# Trigger import
if success == False or triggerimport == False:
return # … but only if wanted, and previous step was successful
self.UpdateTaskState(
task,
"startimport") # The upload management thread will do the rest
return
def IntegrateVideo(self, task):
"""
When an annotation needed for creating the video file path in the music directory is missing, ``False`` gets returned and an error message written into the log
"""
uploadedfile = task["destinationpath"] # uploaded file
try:
artistname = task["annotations"]["artistname"]
releasedate = task["annotations"]["release"]
videoname = task["annotations"]["name"]
except KeyError as e:
logging.error(
"Collection video information for creating its path name failed with key-error for: %s \033[1;30m(Make sure all important annotations are given to that upload: name, artistname, release)",
str(e))
return False
fileextension = self.uploadfs.GetFileExtension(uploadedfile)
videofile = artistname + "/" + releasedate + " - " + videoname + "." + fileextension
task["videofile"] = videofile
logging.debug("Integrating upload %s -> %s", str(uploadedfile),
str(videofile))
# Check if video file already exists
if self.musicfs.Exists(videofile):
logging.warning(
"File \"%s\" already exists in the music directory! \033[1;30m(It will NOT be overwritten)",
str(videofile))
self.NotifyClient("InternalError", task,
"File already exists in the music directory")
return False
# Check if artist directory exists
if not self.musicfs.IsDirectory(artistname):
logging.info(
"Artist directory for \"%s\" does not exist and will be created.",
str(artistname))
try:
self.musicfs.CreateSubdirectory(artistname)
except PermissionError:
logging.error(
"Creating artist sub-directory \"%s\" failed! - Permission denied! \033[1;30m(MusicDB requires write permission to the music file tree)",
str(artistname))
self.NotifyClient(
"InternalError", task,
"Creating artist directory failed - Permission denied")
return False
# Copy file, create Artist directory if not existing
try:
success = self.musicfs.CopyFile(uploadedfile, videofile)
except PermissionError:
logging.error(
"Copying video file to \"%s\" failed! - Permission denied! \033[1;30m(MusicDB requires write permission to the music file tree)",
str(videofile))
self.NotifyClient("InternalError", task,
"Copying failed - Permission denied")
return False
if (success):
logging.info("New video file at %s", str(videofile))
else:
logging.warning("Copying video file to \"%s\" failed!",
str(videofile))
return success
def ImportVideo(self, task):
"""
Task state must be ``"startimport"`` and content type must be ``"video"``
Returns:
``True`` on success.
"""
# Check task state and type
if task["state"] != "startimport":
logging.warning(
"Cannot import an upload that is not in \"startimport\" state. Upload with ID \"%s\" was in \"%s\" state! \033[1;30m(Nothing will be done)",
str(task["id"]), str(task["state"]))
return False
success = False
if task["contenttype"] != "video":
logging.warning(
"Video expected. Actual type of upload: \"%s\" \033[1;30m(No video will be imported)",
str(task["contenttype"]))
return False
# Get important information
try:
artistname = task["annotations"]["artistname"]
videopath = task["videofile"]
except KeyError as e:
logging.error(
"Collecting video information for importing failed with key-error for: %s \033[1;30m(Make sure the artist name is annotated to the upload)",
str(e))
return False
# Check if the artist already exists in the database - if not, add it
artist = self.db.GetArtistByPath(artistname)
if artist == None:
logging.info("Importing new artist: \"%s\"", artistname)
try:
self.dbmanager.AddArtist(artistname)
except Exception as e:
logging.error(
"Importing artist \"%s\" failed with error: %s \033[1;30m(Video upload canceled)",
str(artistname), str(e))
self.NotifyClient("InternalError", task,
"Importing artist failed")
return False
artist = self.db.GetArtistByPath(artistname)
# Import video
try:
success = self.dbmanager.AddVideo(videopath, artist["id"])
except Exception as e:
logging.error(
"Importing video \"%s\" failed with error: %s \033[1;30m(Video upload canceled)",
str(videopath), str(e))
self.NotifyClient("InternalError", task, "Importing video failed")
return False
if not success:
logging.error(
"Importing video \"%s\" failed. \033[1;30m(Video upload canceled)",
str(videopath), str(e))
self.NotifyClient("InternalError", task, "Importing video failed")
return False
# Add origin information to database if annotated
try:
origin = task["annotations"]["origin"]
except KeyError as e:
pass
else:
video = self.db.GetVideoByPath(videopath)
video["origin"] = origin
self.db.WriteVideo(video)
logging.info("Importing Video succeeded")
return True
def ImportVideoArtwork(self, task):
"""
Returns:
``True`` on success
"""
# Check task state and type
if task["state"] != "importartwork":
logging.warning(
"Cannot import artwork that is not in \"importartwork\" state. Upload with ID \"%s\" was in \"%s\" state! \033[1;30m(Nothing will be done)",
str(task["id"]), str(task["state"]))
return False
if task["contenttype"] != "video":
logging.warning(
"Video expected. Actual type of upload: \"%s\" \033[1;30m(No video will be imported)",
str(task["contenttype"]))
return False
# Start generating the artworks
videopath = task["videofile"]
framemanager = VideoFrames(self.cfg, self.db)
video = self.db.GetVideoByPath(videopath)
if not video:
logging.error(
"Getting video \"%s\" from database failed. \033[1;30m(Artwork import canceled)",
str(videopath), str(e))
self.NotifyClient("InternalError", task,
"Video artwork import failed")
return False
retval = framemanager.UpdateVideoFrames(video)
if retval == False:
logging.error(
"Generating video frames and preview failed for video \"%s\". \033[1;30m(Artwork import canceled)",
str(videopath))
self.NotifyClient("InternalError", task,
"Video artwork import failed")
return False
logging.info("Importing Video thumbnails and previews succeeded")
return True
def ImportArtwork(self, task):
"""
Task state must be ``"startimport"`` and content type must be ``"artwork"``
Returns:
``True`` on success.
"""
# Check task state and type
if task["state"] != "startimport":
logging.warning(
"Cannot import an upload that is not in \"startimport\" state. Upload with ID \"%s\" was in \"%s\" state! \033[1;30m(Nothing will be done)",
str(task["id"]), str(task["state"]))
return False
success = False
if task["contenttype"] != "artwork":
logging.warning(
"Album artwork expected. Actual type of upload: \"%s\" \033[1;30m(No artwork will be imported)",
str(task["contenttype"]))
return False
# Get important information
try:
artistname = task["annotations"]["artistname"]
albumname = task["annotations"]["albumname"]
albumid = task["annotations"]["albumid"]
sourcepath = task["artworkfile"]
except KeyError as e:
logging.error(
"Collecting artwork information for importing failed with key-error for: %s \033[1;30m(Make sure the artist and album name is annotated as well as the album ID.)",
str(e))
return False
# Import artwork
awmanager = MusicDBArtwork(self.cfg, self.db)
artworkname = awmanager.CreateArtworkName(artistname, albumname)
success = awmanager.SetArtwork(albumid, sourcepath, artworkname)
if not success:
logging.error(
"Importing artwork \"%s\" failed. \033[1;30m(Artwork upload canceled)",
str(sourcepath))
self.NotifyClient("InternalError", task,
"Importing artwork failed")
return False
# Add origin information to database if annotated
try:
origin = task["annotations"]["origin"]
except KeyError as e:
pass
else:
video = self.db.GetVideoByPath(videopath)
video["origin"] = origin
self.db.WriteVideo(video)
logging.info("Importing Artwork succeeded")
return True
def RemoveTask(self, task):
"""
``tasks/${Task ID}.json``
"""
logging.info(
"Removing uploaded \"%s\" file and task \"%s\" information.",
task["sourcefilename"], task["id"])
datapath = task["destinationpath"]
taskpath = "tasks/" + task["id"] + ".json"
# if artwork, remove artworkfile as well
if task["contenttype"] == "artwork":
artworkfile = task["artworkfile"]
logging.debug("Removing %s",
self.uploadfs.AbsolutePath(artworkfile))
self.uploadfs.RemoveFile(artworkfile)
logging.debug("Removing %s", self.uploadfs.AbsolutePath(datapath))
self.uploadfs.RemoveFile(datapath)
logging.debug("Removing %s", self.uploadfs.AbsolutePath(taskpath))
self.uploadfs.RemoveFile(taskpath)
return True
class VideoFrames(object):
"""
This class implements the concept described above.
The most important method is :meth:`~UpdateVideoFrames` that generates all frames and previews for a given video.
Args:
config: MusicDB configuration object
database: MusicDB database
Raises:
TypeError: if config or database are not of the correct type
ValueError: If one of the working-paths set in the config file does not exist
"""
def __init__(self, config, database):
if type(config) != MusicDBConfig:
raise TypeError("Config-class of unknown type")
if type(database) != MusicDatabase:
raise TypeError("Database-class of unknown type")
self.db = database
self.cfg = config
self.fs = Filesystem()
self.musicroot = Filesystem(self.cfg.music.path)
self.framesroot = Filesystem(self.cfg.videoframes.path)
self.metadata = MetaTags(self.cfg.music.path)
self.maxframes = self.cfg.videoframes.frames
self.previewlength = self.cfg.videoframes.previewlength
self.scales = self.cfg.videoframes.scales
# Check if all paths exist that have to exist
pathlist = []
pathlist.append(self.cfg.music.path)
pathlist.append(self.cfg.videoframes.path)
for path in pathlist:
if not self.fs.Exists(path):
raise ValueError("Path \"" + path + "\" does not exist.")
def CreateFramesDirectoryName(self, artistname, videoname):
"""
This method creates the name for a frames directory regarding the following schema:
``$Artistname/$Videoname``.
If there is a ``/`` in the name, it gets replaced by ``∕`` (U+2215, DIVISION SLASH)
Args:
artistname (str): Name of an artist
videoname (str): Name of a video
Returns:
valid frames sub directory name for a video
"""
artistname = artistname.replace("/", "∕")
videoname = videoname.replace("/", "∕")
dirname = artistname + "/" + videoname
return dirname
def CreateFramesDirectory(self, artistname, videoname):
"""
This method creates the directory that contains all frames and previews for a video.
The ownership of the created directory will be the music user and music group set in the configuration file.
The permissions will be set to ``rwxrwxr-x``.
If the directory already exists, only the attributes will be updated.
Args:
artistname (str): Name of an artist
videoname (str): Name of a video
Returns:
The name of the directory.
"""
# Determine directory name
dirname = self.CreateFramesDirectoryName(artistname, videoname)
# Create directory if it does not yet exist
if self.framesroot.IsDirectory(dirname):
logging.debug("Frame directory \"%s\" already exists.", dirname)
else:
self.framesroot.CreateSubdirectory(dirname)
# Set permissions to -rwxrwxr-x
try:
self.framesroot.SetAttributes(
dirname, self.cfg.music.owner, self.cfg.music.group,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP
| stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH)
except Exception as e:
logging.warning(
"Setting frames sub directory attributes failed with error %s. \033[1;30m(Leaving them as they are)",
str(e))
return dirname
def GenerateFrames(self, dirname, videopath):
"""
This method creates all frame files, including scaled frames, from a video.
After generating the frames, animations can be generated via :meth:`~GeneratePreviews`.
To generate the frames, ``ffmpeg`` is used in the following way:
.. code-block:: bash
ffmpeg -ss $time -i $videopath -vf scale=iw*sar:ih -vframes 1 $videoframes/$dirname/frame-xx.jpg
``videopath`` and ``dirname`` are the parameters of this method.
``videoframes`` is the root directory for the video frames as configured in the MusicDB Configuration file.
``time`` is a moment in time in the video at which the frame gets selected.
This value gets calculated depending of the videos length and amount of frames that shall be generated.
The file name of the frames will be ``frame-xx.jpg`` where ``xx`` represents the frame number.
The number is decimal, has two digits and starts with 01.
The scale solves the differences between the Display Aspect Ratio (DAR) and the Sample Aspect Ratio (SAR).
By using a scale of image width multiplied by the SAR, the resulting frame has the same ratio as the video in the video player.
The total length of the video gets determined by :meth:`~lib.metatags.MetaTags.GetPlaytime`
When there are already frames existing, nothing will be done.
This implies that it is mandatory to remove existing frames manually when there are changes in the configuration.
For example when increasing or decreasing the amount of frames to consider for the animation.
The method will return ``True`` in this case, because there are frames existing.
Args:
dirname (str): Name/Path of the directory to store the generated frames
videopath (str): Path to the video that gets processed
Returns:
``True`` on success, otherwise ``False``
"""
# Determine length of the video in seconds
try:
self.metadata.Load(videopath)
videolength = self.metadata.GetPlaytime()
except Exception as e:
logging.exception(
"Generating frames for video \"%s\" failed with error: %s",
videopath, str(e))
return False
slicelength = videolength / (self.maxframes + 1)
sliceoffset = slicelength / 2
for framenumber in range(self.maxframes):
# Calculate time point of the frame in seconds
#moment = (videolength / self.maxframes) * framenumber
moment = sliceoffset + slicelength * framenumber
# Define destination path
framename = "frame-%02d.jpg" % (framenumber + 1)
framepath = dirname + "/" + framename
# Only create frame if it does not yet exist
if not self.framesroot.Exists(framepath):
# create absolute paths for FFMPEG
absframepath = self.framesroot.AbsolutePath(framepath)
absvideopath = self.musicroot.AbsolutePath(videopath)
# Run FFMPEG - use absolute paths
process = [
"ffmpeg",
"-ss",
str(moment),
"-i",
absvideopath,
"-vf",
"scale=iw*sar:ih", # Make sure the aspect ration is correct
"-vframes",
"1",
absframepath
]
logging.debug("Getting frame via %s", str(process))
try:
self.fs.Execute(process)
except Exception as e:
logging.exception(
"Generating frame for video \"%s\" failed with error: %s",
videopath, str(e))
return False
# Scale down the frame
self.ScaleFrame(dirname, framenumber + 1)
return True
def ScaleFrame(self, dirname, framenumber):
"""
This method creates a scaled version of the existing frames for a video.
The aspect ration of the frame will be maintained.
In case the resulting aspect ratio differs from the source file,
the borders of the source frame will be cropped in the scaled version.
If a scaled version exist, it will be skipped.
The scaled JPEG will be stored with optimized and progressive settings.
Args:
dirname (str): Name of the directory where the frames are stored at (relative)
framenumber (int): Number of the frame that will be scaled
Returns:
*Nothing*
"""
sourcename = "frame-%02d.jpg" % (framenumber)
sourcepath = dirname + "/" + sourcename
abssourcepath = self.framesroot.AbsolutePath(sourcepath)
for scale in self.scales:
width, height = map(int, scale.split("x"))
scaledframename = "frame-%02d (%d×%d).jpg" % (framenumber, width,
height)
scaledframepath = dirname + "/" + scaledframename
# In case the scaled version already exists, nothing will be done
if self.framesroot.Exists(scaledframepath):
continue
absscaledframepath = self.framesroot.AbsolutePath(scaledframepath)
size = (width, height)
frame = Image.open(abssourcepath)
frame.thumbnail(size, Image.BICUBIC)
frame.save(absscaledframepath,
"JPEG",
optimize=True,
progressive=True)
return
def GeneratePreviews(self, dirname):
"""
This method creates all preview animations (.webp), including scaled versions, from frames.
The frames can be generated via :meth:`~GenerateFrames`.
In case there is already a preview file, the method returns ``True`` without doing anything.
Args:
dirname (str): Name/Path of the directory to store the generated frames
Returns:
``True`` on success, otherwise ``False``
"""
# Create original sized preview
framepaths = []
for framenumber in range(self.maxframes):
framename = "frame-%02d.jpg" % (framenumber + 1)
framepath = dirname + "/" + framename
framepaths.append(framepath)
previewpath = dirname + "/preview.webp"
success = True
success &= self.CreateAnimation(framepaths, previewpath)
# Create scaled down previews
for scale in self.scales:
framepaths = []
width, height = map(int, scale.split("x"))
for framenumber in range(self.maxframes):
scaledframename = "frame-%02d (%d×%d).jpg" % (framenumber + 1,
width, height)
scaledframepath = dirname + "/" + scaledframename
framepaths.append(scaledframepath)
previewpath = dirname + "/preview (%d×%d).webp" % (width, height)
success &= self.CreateAnimation(framepaths, previewpath)
return success
def CreateAnimation(self, framepaths, animationpath):
"""
This method creates a WebP animation from frames that are addresses by a sorted list of paths.
Frame paths that do not exists or cannot be opened will be ignored.
If there already exists an animation addressed by animation path, nothing will be done.
The list of frame paths must at least contain 2 entries.
Args:
framepaths (list(str)): A list of relative frame paths that will be used to create an animation
animationpath (str): A relative path where the animation shall be stored at.
Returns:
``True`` when an animation has been created or exists, otherwise ``False``
"""
if self.framesroot.IsFile(animationpath):
logging.debug(
"There is already an animation \"%s\" (Skipping frame generation process)",
animationpath)
return True
# Load all frames
frames = []
for framepath in framepaths:
absframepath = self.framesroot.AbsolutePath(framepath)
try:
frame = Image.open(absframepath)
except FileNotFoundError as e:
logging.warning(
"Unable to load frame \"$s\": %s \033[1;30m(Frame will be ignored)",
absframepath, str(e))
continue
frames.append(frame)
# Check if enough frames for a preview have been loaded
if len(frames) < 2:
logging.error(
"Not enough frames were loaded. Cannot create a preview animation. \033[1;30m(%d < 2)",
len(frames))
return False
# Create absolute animation file path
absanimationpath = self.framesroot.AbsolutePath(animationpath)
# Calculate time for each frame in ms being visible
duration = int((self.previewlength * 1000) / self.maxframes)
# Store as WebP animation
preview = frames[0] # Start with frame 0
preview.save(
absanimationpath,
save_all=True, # Save all frames
append_images=frames[1:], # Save these frames
duration=duration, # Display time for each frame
loop=0, # Show in infinite loop
method=6) # Slower but better method [1]
# [1] https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html#webp
return True
def SetVideoFrames(self,
videoid,
framesdir,
thumbnailfile=None,
previewfile=None):
"""
Set Database entry for the video with video ID ``videoid``.
Using this method defines the frames directory to which all further paths are relative to.
The thumbnail file addresses a static source frame (like ``frame-01.jpg``),
the preview file addresses the preview animation (usually ``preview.webp``).
If ``thumbnailfile`` or ``previewfile`` is ``None``, it will not be changed in the database.
This method checks if the files exists.
If not, ``False`` gets returned an *no* changes will be done in the database.
Example:
.. code-block:: python
retval = vf.SetVideoFrames(1000, "Fleshgod Apocalypse/Monnalisa", "frame-02.jpg", "preview.webp")
if retval == False:
print("Updating video entry failed.")
Args:
videoid (int): ID of the video that database entry shall be updated
framesdir (str): Path of the video specific sub directory containing all frames/preview files. Relative to the video frames root directory
thumbnailfile (str, NoneType): File name of the frame that shall be used as thumbnail, relative to ``framesdir``
previewfile (str, NoneType): File name of the preview animation, relative to ``framesdir``
Returns:
``True`` on success, otherwise ``False``
"""
# Check if all files are valid
if not self.framesroot.IsDirectory(framesdir):
logging.error(
"The frames directory \"%s\" does not exist in the video frames root directory.",
framesdir)
return False
if thumbnailfile and not self.framesroot.IsFile(framesdir + "/" +
thumbnailfile):
logging.error(
"The thumbnail file \"%s\" does not exits in the frames directory \"%s\".",
thumbnailfile, framesdir)
return False
if previewfile and not self.framesroot.IsFile(framesdir + "/" +
previewfile):
logging.error(
"The preview file \"%s\" does not exits in the frames directory \"%s\".",
previewfile, framesdir)
return False
# Change paths in the database
retval = self.db.SetVideoFrames(videoid, framesdir, thumbnailfile,
previewfile)
return retval
def UpdateVideoFrames(self, video):
"""
#. Create frames directory (:meth:`~CreateFramesDirectory`)
#. Generate frames (:meth:`~GenerateFrames`)
#. Generate previews (:meth:`~GeneratePreviews`)
Args:
video: Database entry for the video for that the frames and preview animation shall be updated
Returns:
``True`` on success, otherwise ``False``
"""
logging.info("Updating frames and previews for %s", video["path"])
artist = self.db.GetArtistById(video["artistid"])
artistname = artist["name"]
videopath = video["path"]
videoname = video["name"]
videoid = video["id"]
# Prepare everything to start generating frames and previews
framesdir = self.CreateFramesDirectory(artistname, videoname)
# Generate Frames
retval = self.GenerateFrames(framesdir, videopath)
if retval == False:
return False
# Generate Preview
retval = self.GeneratePreviews(framesdir)
if retval == False:
return False
# Update database
retval = self.SetVideoFrames(videoid, framesdir, "frame-01.jpg",
"preview.webp")
return retval
def ChangeThumbnail(self, video, timestamp):
"""
This method creates a thumbnail image files, including scaled a version, from a video.
The image will be generated from a frame addressed by the ``timestamp`` argument.
To generate the thumbnail, ``ffmpeg`` is used in the following way:
.. code-block:: bash
ffmpeg -y -ss $timestamp -i $video["path"] -vf scale=iw*sar:ih -vframes 1 $videoframes/$video["framesdirectory"]/thumbnail.jpg
``video`` and ``timestamp`` are the parameters of this method.
``videoframes`` is the root directory for the video frames as configured in the MusicDB Configuration file.
The scale solves the differences between the Display Aspect Ratio (DAR) and the Sample Aspect Ratio (SAR).
By using a scale of image width multiplied by the SAR, the resulting frame has the same ratio as the video in the video player.
The total length of the video gets determined by :meth:`~lib.metatags.MetaTags.GetPlaytime`
If the time stamp is not between 0 and the total length, the method returns ``False`` and does nothing.
When there is already a thumbnail existing it will be overwritten.
Args:
video: A video entry that shall be updated
timestamp (int): Time stamp of the frame to select in seconds
Returns:
``True`` on success, otherwise ``False``
"""
dirname = video["framesdirectory"]
videopath = video["path"]
videoid = video["id"]
# Determine length of the video in seconds
try:
self.metadata.Load(videopath)
videolength = self.metadata.GetPlaytime()
except Exception as e:
logging.exception(
"Generating a thumbnail for video \"%s\" failed with error: %s",
videopath, str(e))
return False
if timestamp < 0:
logging.warning(
"Generating a thumbnail for video \"%s\" requires a time stamp > 0. Given was: %s",
videopath, str(timestamp))
return False
if timestamp > videolength:
logging.warning(
"Generating a thumbnail for video \"%s\" requires a time stamp smaller than the video play time (%s). Given was: %s",
videopath, str(videolength), str(timestamp))
return False
# Define destination path
framename = "thumbnail.jpg"
framepath = dirname + "/" + framename
# create absolute paths for FFMPEG
absframepath = self.framesroot.AbsolutePath(framepath)
absvideopath = self.musicroot.AbsolutePath(videopath)
# Run FFMPEG - use absolute paths
process = [
"ffmpeg",
"-y", # Yes, overwrite existing frame
"-ss",
str(timestamp),
"-i",
absvideopath,
"-vf",
"scale=iw*sar:ih", # Make sure the aspect ration is correct
"-vframes",
"1",
absframepath
]
logging.debug("Getting thumbnail via %s", str(process))
try:
self.fs.Execute(process)
except Exception as e:
logging.exception(
"Generating a thumbnail for video \"%s\" failed with error: %s",
videopath, str(e))
return False
# Scale down the frame
self.ScaleThumbnail(dirname)
# Set new Thumbnail
retval = self.SetVideoFrames(videoid,
dirname,
thumbnailfile="thumbnail.jpg",
previewfile=None)
if not retval:
return False
return True
def ScaleThumbnail(self, dirname):
"""
This method creates a scaled version of the existing thumbnail for a video.
The aspect ration of the frame will be maintained.
In case the resulting aspect ratio differs from the source file,
the borders of the source frame will be cropped in the scaled version.
If a scaled version exist, it will be overwritten.
The scaled JPEG will be stored with optimized and progressive settings.
Args:
dirname (str): Name of the directory where the frames are stored at (relative)
Returns:
*Nothing*
"""
sourcename = "thumbnail.jpg"
sourcepath = dirname + "/" + sourcename
abssourcepath = self.framesroot.AbsolutePath(sourcepath)
for scale in self.scales:
width, height = map(int, scale.split("x"))
scaledframename = "thumbnail (%d×%d).jpg" % (width, height)
scaledframepath = dirname + "/" + scaledframename
absscaledframepath = self.framesroot.AbsolutePath(scaledframepath)
size = (width, height)
frame = Image.open(abssourcepath)
frame.thumbnail(size, Image.BICUBIC)
frame.save(absscaledframepath,
"JPEG",
optimize=True,
progressive=True)
return