def _loadEntry(self, index):
image_entry = self.image_entries[index]
sample = utils.buildImageSample(image_entry, self.c, self.imreader,
self.where_object)
sample = _filterKeys(self.used_keys, sample)
sample = utils.applyTransformGroup(self.transform_group, sample)
return sample
def __getitem__(self, index):
'''
Used to train for classification / segmentation of individual objects.
Args:
index: (int) An index of an image in the dataset.
Returns a dict with keys:
image: (np.uint8) The array corresponding to an image.
mask: (np.uint8) The array corresponding to a mask if exists, or None.
objectid: (int) The primary key in the "objects" table.
name: (string) The name field in the "objects" table.
score: (float) The score field in the "objects" table.
imagefile: (string) The image id.
All key-value pairs from the "properties" table for this objectid.
'''
if self.preloaded_samples is not None:
sample = self.preloaded_samples[index]
else:
logging.debug('Querying for %d-th object out of %d', index,
len(self.object_entries))
object_entry = self.object_entries[index]
sample = utils.buildObjectSample(object_entry, self.c,
self.imreader)
if sample is None:
logging.warning('Returning None for index %d', index)
return None
sample = _filterKeys(self.used_keys, sample)
sample = utils.applyTransformGroup(self.transform_group, sample)
return sample
def test_list(self):
transform_group = [lambda x: x['image'], lambda x: x['objectid']]
sample = {'image': np.zeros(3), 'objectid': 1, 'name': 'car'}
sample = utils.applyTransformGroup(transform_group, sample)
self.assertTrue(isinstance(sample, list))
self.assertEqual(len(sample), 2) # only "image" and 'objectid' left.
self.assertTrue(isinstance(sample[0], (np.ndarray, np.generic)))
self.assertTrue(isinstance(sample[1], int))
def test_dict(self):
transform_group = {
'image': lambda x: x[:, :, 0],
'name': lambda _: 'hi',
'dummy': lambda x: x,
}
sample = {'image': np.zeros((10, 10, 3)), 'objectid': 1, 'name': 'car'}
sample = utils.applyTransformGroup(transform_group, sample)
self.assertEqual(len(sample['image'].shape), 2) # Grayscale image.
self.assertEqual(sample['name'], 'hi') # All names replaced to "hi".
self.assertIsNone(sample['dummy']) # Returns None for missing keys.
def _object_dataset_function(db_file,
rootdir='.',
where_object='TRUE',
used_keys=None,
transform_group=None):
'''
Args:
db_file: (string) A path to an sqlite3 database file.
rootdir: (string) A root path, is pre-appended to "imagefile"
entries of "images" table in the sqlite3 database.
where_object: (string) The WHERE part of the SQL query on the
"objects" table, as in:
"SELECT * FROM objects WHERE ${where_object};"
Allows to query only needed objects.
used_keys: (a list of strings or None) If specified, use only
these keys for every sample, and discard the rest.
transform_group: A dict {string: callable}. Each key of this dict
should match a key in each sample, and the callables
are applied to the respective sample dict values.
'''
conn = utils.openConnection(db_file, 'r', copy_to_memory=False)
c = conn.cursor()
utils.checkWhereObject(where_object)
c.execute('SELECT * FROM objects WHERE %s ORDER BY objectid' %
where_object)
object_entries = c.fetchall()
imreader = backendMedia.MediaReader(rootdir=rootdir)
_checkUsedKeys(used_keys)
utils.checkTransformGroup(transform_group)
samples = []
logging.info('Loading samples...')
for index in range(len(object_entries)):
object_entry = object_entries[index]
sample = utils.buildObjectSample(object_entry, c, imreader)
if sample is None:
continue
sample = _filterKeys(used_keys, sample)
sample = utils.applyTransformGroup(transform_group, sample)
samples.append(sample)
logging.info('Loaded %d samples.', len(object_entries))
return samples
def __getitem__(self, index):
'''
Used to train for the detection/segmentation task.
Args:
index: An index of an image in the dataset.
Returns a dict with keys:
image: np.uint8 array corresponding to an image.
mask: np.uint8 array corresponding to a mask if exists, or None.
objects: np.int32 array of shape [Nx5].
Each row is (x1, y1, width, height, classname)
imagefile: The image id.
'''
image_entry = self.image_entries[index]
sample = utils.buildImageSample(image_entry, self.c, self.imreader,
self.where_object)
if sample is None:
logging.warning('Returning None for index %d', index)
return None
sample = _filterKeys(self.used_keys, sample)
sample = utils.applyTransformGroup(self.transform_group, sample)
return sample
def _loadEntry(self, index):
object_entry = self.object_entries[index]
sample = utils.buildObjectSample(object_entry, self.c, self.imreader)
sample = _filterKeys(self.used_keys, sample)
sample = utils.applyTransformGroup(self.transform_group, sample)
return sample
def test_callable(self):
transform_group = lambda x: x['image']
sample = {'image': np.zeros(3), 'objectid': 1}
sample = utils.applyTransformGroup(transform_group, sample)
self.assertTrue(isinstance(sample, (np.ndarray, np.generic)))