def get_current_iqr_session(self):
"""
Get the current IQR Session instance.
:rtype: smqtk.IQR.iqr_session.IqrSession
"""
with self._iqr_controller:
sid = flask.session.sid
if not self._iqr_controller.has_session_uuid(sid):
sid_work_dir = osp.join(self.work_dir, sid)
descriptor = ContentDescriptorConfiguration.new_inst(self._fd_type_str)
indexer = IndexerConfiguration.new_inst(self._idxr_type_str)
iqr_sess = IqrSession(sid_work_dir, descriptor, indexer, sid)
self._iqr_controller.add_session(iqr_sess, sid)
# If there are things already in our extension ingest, extend
# the base indexer
feat_map = \
descriptor.compute_descriptor_async(iqr_sess.extension_ds)
indexer.extend_model(feat_map)
return self._iqr_controller.get_session(sid)
def get_descriptor_inst(self, label):
"""
Get the cached content descriptor instance for a configuration label
:type label: str
:rtype: smqtk.content_description.ContentDescriptor
"""
with self.descriptor_cache_lock:
if label not in self.descriptor_cache:
self.log.debug("Caching descriptor '%s'", label)
self.descriptor_cache[label] = \
ContentDescriptorConfiguration.new_inst(label)
return self.descriptor_cache[label]
def all_content_types():
"""
Of available descriptors, what content types are processable, and
what types are associated to which available descriptor generator.
"""
r = {}
all_types = set()
for l in ContentDescriptorConfiguration.available_labels():
d = self.get_descriptor_inst(l)
all_types.update(d.valid_content_types())
r[l] = sorted(d.valid_content_types())
return flask.jsonify({
"all": sorted(all_types),
"labels": r
})
def main():
import optparse
description = \
"Generate the model for the given indexer type, using features " \
"from the given feature descriptor type. We use configured valued in " \
"the smqtk_config module and from the system configuration JSON file " \
"(etc/system_config.json) unless otherwise specified by options to " \
"this script. Specific ingest used is determined by the ingest type " \
"provided (-t/--type)."
parser = bin_utils.SMQTKOptParser(description=description)
group_required = optparse.OptionGroup(parser, "Required Options")
group_optional = optparse.OptionGroup(parser, "Optional")
group_required.add_option('-d', '--data-set',
help="Data set to use for model generation.")
group_required.add_option('-c', '--content-descriptor',
help="Feature descriptor type for model and "
"feature generation.")
group_required.add_option('-i', '--indexer',
help="(Optional) Indexer type for model "
"generation.")
group_optional.add_option('--sys-json',
help="Custom system configuration JSON file to "
"use. Otherwise we use the one specified in "
"the smqtk_config module.")
group_optional.add_option('-l', '--list',
action='store_true', default=False,
help="List available ingest configurations. If "
"a valid ingest configuration has been "
"specified, we list available "
"FeatureDetector and Indexer configurations "
"available.")
group_optional.add_option('-t', '--threads', type=int, default=None,
help='Number of threads/processes to use for '
'processing. By default we use all '
'available cores/threads.')
group_optional.add_option('-v', '--verbose', action='store_true',
default=False,
help='Add debug messaged to output logging.')
parser.add_option_group(group_required)
parser.add_option_group(group_optional)
opts, args = parser.parse_args()
bin_utils.initialize_logging(logging.getLogger(),
logging.INFO - (10*opts.verbose))
log = logging.getLogger("main")
dset_label = opts.data_set
cd_label = opts.content_descriptor
idxr_label = opts.indexer
parallel = opts.threads
# Prep custom JSON configuration if one was given
if opts.sys_json:
with open(opts.sys_json) as json_file:
json_config = json.loads(jsmin(json_file.read()))
ConfigurationInterface.BASE_CONFIG = json_config['Ingests']
if opts.list:
log.info("")
log.info("Available Data Sets:")
log.info("")
for l in DataSetConfiguration.available_labels():
log.info("\t%s" % l)
log.info("")
log.info("Available ContentDescriptor types:")
log.info("")
for l in ContentDescriptorConfiguration.available_labels():
log.info("\t%s" % l)
log.info("")
log.info("Available Indexer types:")
log.info("")
for l in IndexerConfiguration.available_labels():
log.info("\t%s", l)
log.info("")
exit(0)
# Check given labels
fail = False
if dset_label and dset_label not in DataSetConfiguration.available_labels():
log.error("Given label '%s' is NOT associated to an existing "
"data set configuration!", dset_label)
fail = True
if cd_label and cd_label not in ContentDescriptorConfiguration.available_labels():
log.error("Given label '%s' is NOT associated to an existing "
"content descriptor configuration!", cd_label)
fail = True
if idxr_label and idxr_label not in IndexerConfiguration.available_labels():
log.error("Given label '%s' is NOT associated to an existing "
"indexer configuration!", idxr_label)
fail = True
if fail:
exit(1)
del fail
log.info("Loading data-set instance...")
#: :type: DataIngest or VideoIngest
dset = DataSetConfiguration.new_inst(dset_label)
log.info("Loading descriptor instance...")
#: :type: smqtk.content_description.ContentDescriptor
descriptor = ContentDescriptorConfiguration.new_inst(cd_label)
# Generate any model files needed by the chosen descriptor
descriptor.PARALLEL = parallel
descriptor.generate_model(dset)
# Don't do indexer model generation if a type was not provided
if idxr_label:
log.info("Loading indexer instance...")
#: :type: smqtk.indexing.Indexer
indexer = IndexerConfiguration.new_inst(idxr_label)
# It is not guaranteed that the feature computation method is doing
# anything in parallel, but if it is, request that it perform serially
# in order to allow multiple high-level feature computation jobs, else
# we could be overrun with threads.
descriptor.PARALLEL = 1
# Using NonDaemonicPool because content_description that might to
# parallel processing might use multiprocessing.Pool instances, too.
# Pools don't usually allow daemonic processes, so this custom top-level
# pool allows worker processes to spawn pools themselves.
fmap = descriptor.compute_descriptor_async(
dset,
parallel=parallel,
pool_type=NonDaemonicPool
)
indexer.generate_model(fmap, parallel=parallel)
# Chuck files listing image files to process
CHUNK_FILES_GLOB = "/home/mattmann/data/exp5/image_catalog/deploy/data/archive/chunks/*/filelist_chunk_*.txt"
# Directory where we will detail what stages have already been completed
STAMP_FILES_DIR = "/data/local/memex/kitware/smqtk/stage_markers"
# Working directory to clean after completing every chunk for disk space conservation
CLEAN_WORK_DIR = "/data/local/memex/kitware/smqtk/work/ContentDescriptors"
# Parallel processes to run
PARALLEL = 4
# Descriptor Generator configuration type
DESCR_GENERATOR_CONFIG = "CD_CSIFT_RoxyImages_spatial"
# Descritpor Factory configuration to use
DESCR_FACTORY_CONFIG = "LocalDiskFactory"
# Descriptor generator to use
DESCR_GENERATOR = ContentDescriptorConfiguration.new_inst(
DESCR_GENERATOR_CONFIG)
DESCR_GENERATOR.PARALLEL = 1
# Descriptor Factory to use
DESCR_FACTORY = DescriptorFactoryConfiguration.new_inst(DESCR_FACTORY_CONFIG)
def check_stage(label):
"""
Check if a stage has been completed.
:return: True if the given stage label has been marked complete.
"""
return osp.isfile(osp.join(STAMP_FILES_DIR, label))
def mark_stage(label):
"""
def __init__(self, name, parent_app, data_set,
descriptor_type, indexer_type,
url_prefix=None):
"""
Initialize a generic IQR Search module with a single descriptor and
indexer.
:param name: Name of this blueprint instance
:type name: str
:param parent_app: Parent containing flask app instance
:type parent_app: smqtk.web.search_app.app.search_app
:param data_set: Data set to work over
:type data_set: SMQTK.data_rep.DataSet
:param descriptor_type: Feature Descriptor type string
:type descriptor_type: str
:param indexer_type: indexer type string
:type indexer_type: str
:param url_prefix: Web address prefix for this blueprint.
:type url_prefix: str
:raises ValueError: Invalid Descriptor or indexer type
"""
super(IQRSearch, self).__init__(
name, import_name=__name__,
static_folder=os.path.join(SCRIPT_DIR, "static"),
template_folder=os.path.join(SCRIPT_DIR, "templates"),
url_prefix=url_prefix
)
# Make sure that the configured descriptor/indexer types exist, as
# we as their system configuration sections
if descriptor_type not in ContentDescriptorConfiguration.available_labels():
raise ValueError("'%s' not a valid descriptor type" % descriptor_type)
if indexer_type not in IndexerConfiguration.available_labels():
raise ValueError("'%s' not a valid indexer type" % indexer_type)
self._parent_app = parent_app
self._data_set = data_set
self._fd_type_str = descriptor_type
self._idxr_type_str = indexer_type
self._explicit_uids = set()
self._explicit_uids_lock = multiprocessing.RLock()
# TODO: Read in dict from save file
# Uploader Sub-Module
self.upload_work_dir = os.path.join(self.work_dir, "uploads")
self.mod_upload = FileUploadMod('%s_uploader' % self.name, parent_app,
self.upload_work_dir,
url_prefix='/uploader')
self.register_blueprint(self.mod_upload)
# IQR Session control
# TODO: Move session management to database. Create web-specific
# IqrSession class that stores/gets its state directly from
# database.
self._iqr_controller = IqrController()
# structures for session ingest progress
# Two levels: SID -> FID
self._ingest_progress_locks = {}
self._ingest_progress = {}
# Preview Image Caching
# TODO: Initialize this into static directory that is being served.
self._preview_cache = PreviewCache(osp.join(self.work_dir, "Previews"))
# Directory to write data for static viewing
self._static_data_dir = os.path.join(self.static_folder, 'tmp_data')
# Cache mapping of written static files for data elements
self._static_cache = {}
#
# Routing
#
@self.route("/")
@self._parent_app.module_login.login_required
def index():
r = {
"module_name": self.name,
"uploader_url": self.mod_upload.url_prefix,
"uploader_post_url": self.mod_upload.upload_post_url(),
}
r.update(parent_app.nav_bar_content())
# noinspection PyUnresolvedReferences
return flask.render_template("iqr_search_index.html", **r)
@self.route('/iqr_session_info', methods=["GET"])
@self._parent_app.module_login.login_required
def iqr_session_info():
"""
Get information about the current IRQ session
"""
with self.get_current_iqr_session() as iqrs:
# noinspection PyProtectedMember
return flask.jsonify({
"uuid": iqrs.uuid,
"positive_uids": tuple(iqrs.positive_ids),
"negative_uids": tuple(iqrs.negative_ids),
"extension_ingest_contents":
dict((uid, str(df))
for uid, df in iqrs.extension_ds.iteritems()),
"FeatureMemory": {
}
})
@self.route("/check_current_iqr_session")
@self._parent_app.module_login.login_required
def check_current_iqr_session():
"""
Check that the current IQR session exists and is initialized.
:rtype: {
success: bool
}
"""
# Getting the current IQR session ensures that one has been
# constructed for the current session.
with self.get_current_iqr_session():
return flask.jsonify({
"success": True
})
@self.route('/iqr_ingest_file', methods=['POST'])
@self._parent_app.module_login.login_required
def iqr_ingest_file():
"""
Ingest the file with the given UID, getting the path from the
uploader.
:return: status message
:rtype: str
"""
# TODO: Add status dict with a "GET" method branch for getting that
# status information.
# Start the ingest of a FID when POST
if flask.request.method == "POST":
iqr_sess = self.get_current_iqr_session()
fid = flask.request.form['fid']
self.log.debug("[%s::%s] Getting temporary filepath from "
"uploader module", iqr_sess.uuid, fid)
upload_filepath = self.mod_upload.get_path_for_id(fid)
self.mod_upload.clear_completed(fid)
# Extend session ingest -- modifying
with iqr_sess:
self.log.debug("[%s::%s] Adding new file to extension "
"ingest", iqr_sess.uuid, fid)
sess_upload = osp.join(iqr_sess.work_dir,
osp.basename(upload_filepath))
os.rename(upload_filepath, sess_upload)
upload_data = DataFileElement(sess_upload)
iqr_sess.extension_ds.add_data(upload_data)
# Compute feature for data -- non-modifying
self.log.debug("[%s::%s] Computing feature for file",
iqr_sess.uuid, fid)
feat = iqr_sess.descriptor.compute_descriptor(upload_data)
# Extend indexer model with feature data -- modifying
with iqr_sess:
self.log.debug("[%s::%s] Extending indexer model with "
"feature", iqr_sess.uuid, fid)
iqr_sess.indexer.extend_model({upload_data.uuid(): feat})
# of course, add the new data element as a positive
iqr_sess.adjudicate((upload_data.uuid(),))
return "Finished Ingestion"
@self.route("/adjudicate", methods=["POST", "GET"])
@self._parent_app.module_login.login_required
def adjudicate():
"""
Update adjudication for this session
:return: {
success: <bool>,
message: <str>
}
"""
if flask.request.method == "POST":
fetch = flask.request.form
elif flask.request.method == "GET":
fetch = flask.request.args
else:
raise RuntimeError("Invalid request method '%s'"
% flask.request.method)
pos_to_add = json.loads(fetch.get('add_pos', '[]'))
pos_to_remove = json.loads(fetch.get('remove_pos', '[]'))
neg_to_add = json.loads(fetch.get('add_neg', '[]'))
neg_to_remove = json.loads(fetch.get('remove_neg', '[]'))
self.log.debug("Adjudicated Positive{+%s, -%s}, Negative{+%s, -%s} "
% (pos_to_add, pos_to_remove,
neg_to_add, neg_to_remove))
with self.get_current_iqr_session() as iqrs:
iqrs.adjudicate(pos_to_add, neg_to_add,
pos_to_remove, neg_to_remove)
return flask.jsonify({
"success": True,
"message": "Adjudicated Positive{+%s, -%s}, Negative{+%s, -%s} "
% (pos_to_add, pos_to_remove,
neg_to_add, neg_to_remove)
})
@self.route("/get_item_adjudication", methods=["GET"])
@self._parent_app.module_login.login_required
def get_adjudication():
"""
Get the adjudication status of a particular result by ingest ID.
This should only ever return a dict where one of the two, or
neither, are labeled True.
:return: {
is_pos: <bool>,
is_neg: <bool>
}
"""
ingest_uid = flask.request.args['uid']
with self.get_current_iqr_session() as iqrs:
return flask.jsonify({
"is_pos": ingest_uid in iqrs.positive_ids,
"is_neg": ingest_uid in iqrs.negative_ids
})
@self.route("/get_positive_uids", methods=["GET"])
@self._parent_app.module_login.login_required
def get_positive_uids():
"""
Get a list of the positive ingest UIDs
:return: {
uids: list of <int>
}
"""
with self.get_current_iqr_session() as iqrs:
return flask.jsonify({
"uids": list(iqrs.positive_ids)
})
@self.route("/get_random_uids")
@self._parent_app.module_login.login_required
def get_random_uids():
"""
Return to the client a list of all known dataset IDs but in a random
order. If there is currently an active IQR session with elements in
its extension ingest, then those IDs are included in the random
list.
:return: {
uids: list of int
}
"""
all_ids = self._data_set.uuids()
with self.get_current_iqr_session() as iqrs:
all_ids.update(iqrs.extension_ds.uuids())
all_ids = list(all_ids)
random.shuffle(all_ids)
return flask.jsonify({
"uids": all_ids
})
@self.route("/get_ingest_image_preview_data", methods=["GET"])
@self._parent_app.module_login.login_required
def get_ingest_item_image_rep():
"""
Return the base64 preview image data for the data file associated
with the give UID.
"""
uid = flask.request.args['uid']
info = {
"success": True,
"message": None,
"is_explicit": None,
"shape": None, # (width, height)
"data": None,
"ext": None,
"static_file_link": None,
}
#: :type: smqtk.data_rep.DataElement
de = None
if self._data_set.has_uuid(uid):
de = self._data_set.get_data(uid)
with self._explicit_uids_lock:
info["is_explicit"] = uid in self._explicit_uids
else:
with self.get_current_iqr_session() as iqrs:
if iqrs.extension_ds.has_uuid(uid):
de = iqrs.extension_ds.get_data(uid)
info["is_explicit"] = uid in self._explicit_uids
if not de:
info["success"] = False
info["message"] = "UUID not part of the active data set!"
else:
# TODO: Have data-file return an HTML chunk for implementation
# defined visualization?
img_path = self._preview_cache.get_preview_image(de)
img = PIL.Image.open(img_path)
info["shape"] = img.size
with open(img_path, 'rb') as img_file:
info["data"] = base64.encodestring(img_file.read())
info["ext"] = osp.splitext(img_path)[1].lstrip('.')
if de.uuid() not in self._static_cache:
self._static_cache[de.uuid()] = \
de.write_temp(self._static_data_dir)
info['static_file_link'] = 'static/' \
+ os.path.relpath(self._static_cache[de.uuid()],
self.static_folder)
return flask.jsonify(info)
@self.route("/mark_uid_explicit", methods=["POST"])
@self._parent_app.module_login.login_required
def mark_uid_explicit():
"""
Mark a given UID as explicit in its containing ingest.
:return: Success value of True if the given UID was valid and set
as explicit in its containing ingest.
:rtype: {
"success": bool
}
"""
uid = flask.request.form['uid']
self._explicit_uids.add(uid)
# TODO: Save out dict
return flask.jsonify({'success': True})
@self.route("/iqr_refine", methods=["POST"])
@self._parent_app.module_login.login_required
def iqr_refine():
"""
Classify current IQR session indexer, updating ranking for
display.
Fails gracefully if there are no positive[/negative] adjudications.
Expected Args:
"""
pos_to_add = json.loads(flask.request.form.get('add_pos', '[]'))
pos_to_remove = json.loads(flask.request.form.get('remove_pos', '[]'))
neg_to_add = json.loads(flask.request.form.get('add_neg', '[]'))
neg_to_remove = json.loads(flask.request.form.get('remove_neg', '[]'))
with self.get_current_iqr_session() as iqrs:
try:
iqrs.refine(pos_to_add, neg_to_add,
pos_to_remove, neg_to_remove)
return flask.jsonify({
"success": True,
"message": "Completed refinement"
})
except Exception, ex:
return flask.jsonify({
"success": False,
"message": "ERROR: %s: %s" % (type(ex).__name__,
ex.message)
})
# Chuck files listing image files to process
CHUNK_FILES_GLOB = "/home/mattmann/data/exp5/image_catalog/deploy/data/archive/chunks/*/filelist_chunk_*.txt"
# Directory where we will detail what stages have already been completed
STAMP_FILES_DIR = "/data/local/memex/kitware/smqtk/stage_markers"
# Working directory to clean after completing every chunk for disk space conservation
CLEAN_WORK_DIR = "/data/local/memex/kitware/smqtk/work/ContentDescriptors"
# Parallel processes to run
PARALLEL = 4
# Descriptor Generator configuration type
DESCR_GENERATOR_CONFIG = "CD_CSIFT_RoxyImages_spatial"
# Descritpor Factory configuration to use
DESCR_FACTORY_CONFIG = "LocalDiskFactory"
# Descriptor generator to use
DESCR_GENERATOR = ContentDescriptorConfiguration.new_inst(DESCR_GENERATOR_CONFIG)
DESCR_GENERATOR.PARALLEL = 1
# Descriptor Factory to use
DESCR_FACTORY = DescriptorFactoryConfiguration.new_inst(DESCR_FACTORY_CONFIG)
def check_stage(label):
"""
Check if a stage has been completed.
:return: True if the given stage label has been marked complete.
"""
return osp.isfile(osp.join(STAMP_FILES_DIR, label))
def mark_stage(label):
"""
def main():
usage = "%prog [OPTIONS] INPUT_FILE"
description = "Compute a feature vector for a given data file, outputting " \
"the generated feature vector to standard out, or to an " \
"output file if one was specified.\n" \
"\n" \
"An ingest " \
"configuration must be specified for the purpose of " \
"identifying which model files to use (assuming a given " \
"descriptor has/uses model files). The ingest configuration " \
"also informs where to put temporary working files. "
parser = bin_utils.SMQTKOptParser(usage, description=description)
parser.add_option('-c', '--content-descriptor',
help='The descriptor type to use. This must be a type '
'available in system configuration')
parser.add_option('-o', '--output-filepath',
help='Optional path to a file to output feature vector '
'to. Otherwise the feature vector is printed to '
'standard out. Output is saved in numpy binary '
'format (.npy suffix recommended).')
parser.add_option('-l', '--list', action='store_true', default=False,
help='List available descriptor types.')
parser.add_option('-v', '--verbose', action='store_true', default=False,
help='Print additional debugging messages. All logging '
'goes to standard error.')
opts, args = parser.parse_args()
output_filepath = opts.output_filepath
descriptor_type = opts.content_descriptor
verbose = opts.verbose
llevel = logging.DEBUG if verbose else logging.INFO
bin_utils.initialize_logging(logging.getLogger(), llevel)
log = logging.getLogger("main")
if opts.list:
log.info("")
log.info("Available ContentDescriptor types:")
log.info("")
for dl in ContentDescriptorConfiguration.available_labels():
log.info("\t%s", dl)
log.info("")
exit(0)
if len(args) == 0:
log.error("Failed to provide an input file path")
exit(1)
if len(args) > 1:
log.warning("More than one filepath provided as an argument. Only "
"computing for the first one.")
input_filepath = args[0]
data_element = DataFileElement(input_filepath)
fd = ContentDescriptorConfiguration.new_inst(descriptor_type)
feat = fd.compute_descriptor(data_element)
if output_filepath:
numpy.save(output_filepath, feat)
else:
# Construct string, because numpy
s = []
for f in feat:
s.append('%15f' % f)
print ' '.join(s)
def __init__(self, config_filepath=None):
super(DescriptorServiceServer, self).__init__(
self.__class__.__name__,
static_folder=os.path.join(SCRIPT_DIR, 'static'),
template_folder=os.path.join(SCRIPT_DIR, 'templates')
)
#
# Configuration setup
#
config_env_loaded = config_file_loaded = None
# Load default -- This should always be present, aka base defaults
self.config.from_object('smqtk_config')
config_default_loaded = True
# Load from env var if present
if self.ENV_CONFIG in os.environ:
self.log.info("Loading config from env var (%s)...",
self.ENV_CONFIG)
self.config.from_envvar(self.ENV_CONFIG)
config_env_loaded = True
# Load from configuration file if given
if config_filepath and os.path.isfile(config_filepath):
config_file_path = \
os.path.expanduser(os.path.abspath(config_filepath))
self.log.info("Loading config from file (%s)...", config_file_path)
self.config.from_pyfile(config_file_path)
config_file_loaded = True
self.log.debug("Config defaults loaded : %s", config_default_loaded)
self.log.debug("Config from env loaded : %s", config_env_loaded)
self.log.debug("Config from file loaded: %s", config_file_loaded)
if not (config_default_loaded or config_env_loaded
or config_file_loaded):
raise RuntimeError("No configuration file specified for loading. "
"(%s=%s) (file=%s)"
% (self.ENV_CONFIG,
os.environ.get(self.ENV_CONFIG, None),
config_filepath))
# Descriptor factory setup
if self.ENV_DSS_DE_FACTORY not in os.environ:
raise RuntimeError("Missing environment configuration variable "
"`%s`, which should be set to the configuration "
"label of the DescriptorElementFactory to use."
% self.ENV_DSS_DE_FACTORY)
self.de_factory_label = os.environ.get(self.ENV_DSS_DE_FACTORY,
"MemoryDescriptorFactory")
self.log.info("Using Descriptor factory: \"%s\"", self.de_factory_label)
try:
self.descr_elem_factory = \
DescriptorFactoryConfiguration.new_inst(self.de_factory_label)
except KeyError:
raise ValueError("Invalid factory label set to %s: \"%s\""
% (self.ENV_DSS_DE_FACTORY, self.de_factory_label))
# Cache of ContentDescriptor instances
self.descriptor_cache = {}
self.descriptor_cache_lock = multiprocessing.RLock()
#
# Security
#
self.secret_key = self.config['SECRET_KEY']
@self.route("/")
def list_ingest_labels():
return flask.jsonify({
"labels": sorted(ContentDescriptorConfiguration
.available_labels())
})
@self.route("/all/content_types")
def all_content_types():
"""
Of available descriptors, what content types are processable, and
what types are associated to which available descriptor generator.
"""
r = {}
all_types = set()
for l in ContentDescriptorConfiguration.available_labels():
d = self.get_descriptor_inst(l)
all_types.update(d.valid_content_types())
r[l] = sorted(d.valid_content_types())
return flask.jsonify({
"all": sorted(all_types),
"labels": r
})
@self.route("/all/compute/<path:uri>")
def all_compute(uri):
"""
Compute descriptors over the specified content for all generators
that function over the data's content type.
# JSON Return format
{
"success": <bool>
"content_type": <str>
"message": <str>
"descriptors": { "<label>": <list[float]>, ... } | None
"reference_uri": <str>
}
"""
message = "execution nominal"
data_elem = None
try:
data_elem = self.resolve_data_element(uri)
except ValueError, ex:
message = "Failed URI resolution: %s" % str(ex)
descriptors = {}
finished_loop = False
if data_elem:
for l in ContentDescriptorConfiguration.available_labels():
if data_elem.content_type() \
in self.get_descriptor_inst(l).valid_content_types():
d = None
try:
d = self.generate_descriptor(data_elem, l)
except RuntimeError, ex:
message = "Descriptor extraction failure: %s" \
% str(ex)
except ValueError, ex:
message = "Data content type issue: %s" % str(ex)
descriptors[l] = d and d.vector().tolist()
def main():
usage = "%prog [OPTIONS] INPUT_FILE"
description = """\
Compute a descriptor vector for a given data file, outputting the generated
feature vector to standard out, or to an output file if one was specified (in
numpy format).
"""
parser = bin_utils.SMQTKOptParser(usage, description=description)
group_labels = optparse.OptionGroup(parser, "Configuration Labels")
group_labels.add_option('-c', '--content-descriptor',
help='The descriptor type to use. This must be a '
'type available in the system configuration')
group_labels.add_option('-f', '--factory-type',
help='The DescriptorElementFactory configuration '
'to use when computing the descriptor. This '
'must be a type available in the system '
'configuration.')
parser.add_option_group(group_labels)
group_optional = optparse.OptionGroup(parser, "Optional Parameters")
group_optional.add_option('-l', '--list',
action='store_true', default=False,
help='List available descriptor types.')
group_optional.add_option('--overwrite',
action='store_true', default=False,
help="Force descriptor computation even if an "
"existing descriptor vector was discovered "
"based on the given content descriptor type "
"and data combination.")
group_optional.add_option('-o', '--output-filepath',
help='Optional path to a file to output feature '
'vector to. Otherwise the feature vector is '
'printed to standard out. Output is saved '
'in numpy binary format (.npy suffix '
'recommended).')
group_optional.add_option('-v', '--verbose',
action='store_true', default=False,
help='Print additional debugging messages. All '
'logging goes to standard error.')
parser.add_option_group(group_optional)
opts, args = parser.parse_args()
output_filepath = opts.output_filepath
descriptor_label = opts.content_descriptor
factory_label = opts.factory_type
overwrite = opts.overwrite
verbose = opts.verbose
llevel = logging.DEBUG if verbose else logging.INFO
bin_utils.initialize_logging(logging.getLogger(), llevel)
log = logging.getLogger("main")
if opts.list:
log.info("")
log.info("Available ContentDescriptor types:")
log.info("")
for dl in ContentDescriptorConfiguration.available_labels():
log.info("\t%s", dl)
log.info("")
log.info("Available DescriptorElementFactory types:")
log.info("")
for df in DescriptorFactoryConfiguration.available_labels():
log.info("\t%s", df)
log.info("")
exit(0)
if len(args) == 0:
log.error("Failed to provide an input file path")
exit(1)
if len(args) > 1:
log.warning("More than one filepath provided as an argument. Only "
"computing for the first one.")
input_filepath = args[0]
data_element = DataFileElement(input_filepath)
cd = ContentDescriptorConfiguration.new_inst(descriptor_label)
factory = DescriptorFactoryConfiguration.new_inst(factory_label)
descr_elem = cd.compute_descriptor(data_element, factory, overwrite)
vec = descr_elem.vector()
if vec is None:
log.error("Failed to generate a descriptor vector for the input data!")
if output_filepath:
numpy.save(output_filepath, vec)
else:
# Construct string, because numpy
s = []
# noinspection PyTypeChecker
for f in vec:
s.append('%15f' % f)
print ' '.join(s)