def main(self, mex_url=None, bisque_token=None, bq=None):
# Allow for testing by passing an alreay initialized session
if bq is None:
bq = BQSession().init_mex(mex_url, bisque_token)
pars = bq.parameters()
image_url = pars.get('dataset_url', None)
annotation_type = pars.get('annotation_type', None)
annotation_attribute = pars.get('annotation_attribute', None)
value_old = pars.get('value_old', None)
bq.update_mex('Starting')
mex_id = mex_url.split('/')[-1]
if image_url is None or len(image_url) < 2:
datasets = bq.fetchxml('/data_service/dataset')
datasets = [d.get('uri') for d in datasets.xpath('dataset')]
else:
datasets = [image_url]
total = 0
changes = []
# delete annotations for each element in the dataset
for ds_url in datasets:
dataset = bq.fetchxml(ds_url, view='deep')
dataset_name = dataset.get('name')
bq.update_mex('processing "%s"' % dataset_name)
refs = dataset.xpath('value[@type="object"]')
for r in refs:
url = r.text
image = bq.fetchxml(url, view='deep')
uuid = image.get('resource_uniq')
modified = delete(image,
value_old,
ann_type=annotation_type,
ann_attr=annotation_attribute)
if len(modified) > 0:
bq.postxml(url, image, method='PUT')
total = total + len(modified)
changes.append({
'name':
'%s (%s)' %
(image.get('name'), image.get('resource_uniq')),
'value':
'%s' % len(modified)
})
changes.insert(0, {'name': 'Total', 'value': '%s' % total})
bq.finish_mex(tags=[{
'name': 'outputs',
'tag': [{
'name': 'deleted',
'tag': changes,
}]
}])
def main(self, mex_url=None, bisque_token=None, image_url=None, bq=None):
# Allow for testing by passing an alreay initialized session
if bq is None:
bq = BQSession().init_mex(mex_url, bisque_token)
bq.update_mex('Classifying...')
pars = bq.parameters()
image_url = pars.get('data_url', None)
model_url = pars.get('model_url', None)
store_on_image = pars.get('store_on_image', False)
method = pars.get('method', 'points')
points = int(pars.get('number_of_points', 10))
confidence = int(pars.get('confidence', 95))
border = int(pars.get('border', 0))
_, image_uniq = image_url.rsplit('/', 1)
_, model_uniq = model_url.rsplit('/', 1)
url = '%s/connoisseur/%s/classify:%s/method:%s/points:%s/confidence:%s/border:%s' % (
bq.bisque_root, model_uniq, image_uniq, method, points, confidence,
border)
# dima: the moving of the image is not of the best taste, this should be changed later
if method != 'segmentation':
url += '/format:xml'
txt = bq.c.fetch(url,
headers={
'Content-Type': 'text/xml',
'Accept': 'text/xml'
})
gobs = etree.fromstring(txt)
else:
color_mode = 'colors'
url += '/colors:colors'
filename = '%s_%s_conf%.2f_n%s_b%s.png' % (
image_uniq, color_mode, confidence, points, border)
filename = bq.c.fetch(url, path=filename)
image_url = upload_file(bq, filename) or ''
gobs = None
bq.update_mex('Storing results...')
outputs = etree.Element('tag', name="outputs")
img = etree.SubElement(outputs,
'tag',
name="MyImage",
type="image",
value=image_url)
if gobs is not None:
img.append(gobs)
if store_on_image is True:
r = bq.postxml(image_url, xml=txt)
bq.finish_mex(tags=[outputs])
