def load_chunk(tarfile, size=None):
"""Load a number of images from a single imagenet .tar file.
This function also converts the image from grayscale to RGB if necessary.
Args:
tarfile (tarfile.TarFile): The archive from which the files get loaded.
size (Optional[Tuple[int, int]]): Resize the image to this size if provided.
Returns:
numpy.ndarray: Contains the image data in format [batch, w, h, c]
"""
result = []
filenames = []
for member in tarfile.getmembers():
filename = member.path
content = tarfile.extractfile(member)
img = Image.open(content)
rgbimg = Image.new("RGB", img.size)
rgbimg.paste(img)
if size != None:
rgbimg = rgbimg.resize(size, Image.ANTIALIAS)
result.append(np.array(rgbimg).reshape(1, rgbimg.size[0], rgbimg.size[1], 3))
filenames.append(filename)
return np.concatenate(result), filenames
def createArchive(self, cfgOutName , name = '', mode = 'w:gz'):
"""
create the archive to upload
"""
if not name:
import uuid
name = os.path.join(os.getcwd(), str(uuid.uuid4()) +'default.tgz')
import tarfile
print 'opening tar file'
tarfile = tarfile.open(name=name , mode=mode, dereference=True)
print 'adding %s to the tarball' % cfgOutName
tarfile.add(cfgOutName, arcname='PSet.py')
#checkSum
print 'calculating the checksum'
lsl = [(x.name, int(x.size), int(x.mtime), x.uname) for x in tarfile.getmembers()]
# hasher = hashlib.md5(str(lsl))
hasher = hashlib.sha256(str(lsl))
checksum = hasher.hexdigest()
#end
tarfile.close()
return name, checksum
def get_categories(self, tarfile):
catname = re.compile("\/(.+)\.rules$")
for member in tarfile.getmembers():
if member.name.endswith('.rules'):
match = catname.search(member.name)
name = match.groups()[0]
category = Category.objects.filter(source = self, name = name)
if not category:
category = Category.objects.create(source = self,
name = name, created_date = timezone.now(),
filename = member.name)
category.get_rules(self)
else:
category[0].get_rules(self)
def _extract_tar_info(tarfile, class_to_idx=None, sort=True):
files = []
labels = []
for ti in tarfile.getmembers():
if not ti.isfile():
continue
dirname, basename = os.path.split(ti.path)
label = os.path.basename(dirname)
ext = os.path.splitext(basename)[1]
if ext.lower() in IMG_EXTENSIONS:
files.append(ti)
labels.append(label)
if class_to_idx is None:
unique_labels = set(labels)
sorted_labels = list(sorted(unique_labels, key=natural_key))
class_to_idx = {c: idx for idx, c in enumerate(sorted_labels)}
tarinfo_and_targets = [(f, class_to_idx[l]) for f, l in zip(files, labels)
if l in class_to_idx]
if sort:
tarinfo_and_targets = sorted(tarinfo_and_targets,
key=lambda k: natural_key(k[0].path))
return tarinfo_and_targets, class_to_idx
def _analyze_tarfile_for_import(tarfile, project, schema, tmpdir):
"""Validate paths in tarfile.
Parameters
----------
tarfile : :class:`tarfile.TarFile`
tarfile to analyze.
project : :class:`~signac.Project`
The project to import the data into.
schema : str or callable
An optional schema function, which is either a string or a function that accepts a
path as its first and only argument and returns the corresponding state point as dict.
(Default value = None).
tmpdir : :class:`tempfile.TemporaryDirectory`
Temporary directory, an instance of ``TemporaryDirectory``.
Yields
------
src : str
Source path.
copy_executor : callable
A callable that uses a provided function to copy to a destination.
Raises
------
TypeError
If the schema given is not None, callable, or a string.
:class:`~signac.errors.DestinationExistsError`
If a job is already initialized.
:class:`~signac.errors.StatepointParsingError`
If the jobs identified with the given schema function are not unique.
AssertionError
If ``tmpdir`` given is not a directory.
"""
def read_sp_manifest_file(path):
"""Read state point from the manifest file.
Parameters
----------
path : str
Path to manifest file.
Returns
-------
dict
state point.
"""
# Must use forward slashes, not os.path.sep.
fn_manifest = _tarfile_path_join(path, project.Job.FN_MANIFEST)
try:
with closing(tarfile.extractfile(fn_manifest)) as file:
return json.loads(file.read())
except KeyError:
pass
if schema is None:
schema_function = read_sp_manifest_file
elif callable(schema):
schema_function = _with_consistency_check(schema, read_sp_manifest_file)
elif isinstance(schema, str):
schema_function = _with_consistency_check(
_make_path_based_schema_function(schema), read_sp_manifest_file
)
else:
raise TypeError("The schema variable must be None, callable, or a string.")
mappings = {}
skip_subdirs = set()
dirs = [member.name for member in tarfile.getmembers() if member.isdir()]
for name in sorted(dirs):
if (
os.path.dirname(name) in skip_subdirs
): # skip all sub-dirs of identified dirs
skip_subdirs.add(name)
continue
sp = schema_function(name)
if sp is not None:
job = project.open_job(sp)
if os.path.exists(job.workspace()):
raise DestinationExistsError(job)
mappings[name] = job
skip_subdirs.add(name)
# Check uniqueness
if len(set(mappings.values())) != len(mappings):
raise StatepointParsingError(
"The jobs identified with the given schema function are not unique!"
)
tarfile.extractall(path=tmpdir)
for path, job in mappings.items():
assert os.path.isdir(tmpdir)
src = os.path.join(tmpdir, path)
assert os.path.isdir(src)
copy_executor = _CopyFromTarFileExecutor(src, job)
yield src, copy_executor
# Path to frozen detection graph. This is the actual model that is used for the object detection.
path_to_model = model + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
#number of classes to be identified
NUM_CLASSES = 90
# Model gets downloaded
opener_web = urllib.request.URLopener()
opener_web.retrieve(download_url + model_tar, model_tar)
tarfile = tarfile.open(model_tar)
for file in tarfile.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tarfile.extract(file, os.getcwd())
# ## Loading this Tensorflow model into the memory
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(path_to_model, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# ## Loading label map
def _package_chart(self, tarfile, version=None, **kwargs):
'''Internal Helper
Internal method to make it easier to hanle closing
the tarfile passed here automatically on exit.
'''
def get_data(filename):
membername = os.path.join(self.name, filename)
yaml = tarfile.extractfile(membername)
return membername, ruamel.yaml.load(
yaml, Loader=ruamel.yaml.RoundTripLoader)
chart_file, chart_data = get_data('Chart.yaml')
chart_data['version'] = version
values_file, values_data = get_data('values.yaml')
values = self.data.get('values', None)
if values:
# TODO(kerrin) expand the amount of data available
# for users to control
data = {
'version': version,
'name': self.name,
}
data.update(kwargs)
def expand_values(source, expanded):
for key, value in source.items():
if isinstance(value, dict):
try:
expand_values(value, expanded[key])
except KeyError as e:
raise windlass.exc.MissingEntryInChartValues(
expected_source=source,
missing_key=e.args[0],
values_filename=values_file,
chart_name=self.name)
else:
newvalue = value.format(**data)
expanded[key] = newvalue
# Update by reference the values_data dictionary based on
# the format of the supplied values field.
expand_values(values, values_data)
with tempfile.NamedTemporaryFile() as tmp_file:
with tarfile.open(tmp_file.name, 'w:gz') as out:
for member in tarfile.getmembers():
if member.name == chart_file:
# Override the size of the file
datastr = ruamel.yaml.dump(
chart_data, Dumper=ruamel.yaml.RoundTripDumper)
databytes = datastr.encode('utf-8')
member.size = len(databytes)
out.addfile(member, io.BytesIO(databytes))
elif member.name == values_file:
# Override the size of the file
datastr = ruamel.yaml.dump(
values_data, Dumper=ruamel.yaml.RoundTripDumper)
databytes = datastr.encode('utf-8')
member.size = len(databytes)
out.addfile(member, io.BytesIO(databytes))
else:
out.addfile(member, tarfile.extractfile(member.name))
with open(tmp_file.name, 'rb') as fp:
return fp.read()