def _search(self, params):
assert hasattr(self, 'search_url')
classifications = json.loads(params['classifications']) if 'classifications' in params else []
cmu_images = requests.post(self.search_url,
data=params['url'],
headers={
'Content-type': 'text',
'Content-length': str(len(params['url']))
},
verify=False).json()
cmu_images = [[image.replace(os.environ['IMAGE_SPACE_CMU_PREFIX'],
os.environ['IMAGE_SPACE_SOLR_PREFIX']), score]
for (image, score) in cmu_images]
cmu_scores = {image.lower(): score for image, score in cmu_images}
documents = solr_documents_from_paths([x[0] for x in cmu_images], classifications)
# Augment original scores from response into solr documents
for document in documents:
document['im_score'] = cmu_scores[document['id'].lower()]
return {
'numFound': len(documents),
'docs': documents
}
def runImageSimilaritySearch(self, params):
assert hasattr(self, 'search_url')
classifications = json.loads(params['classifications']) if 'classifications' in params else []
params['n'] = params['n'] if 'n' in params else str(DEFAULT_PAGE_SIZE)
smqtk_r = requests.get(self.search_url + '/n=' + params['n'] + '/' + params['url']).json()
neighbors_to_distances = dict(zip(smqtk_r['neighbors'], smqtk_r['distances']))
db = getDbConnection().get_default_database()
mapped_paths = db[os.environ['IMAGE_SPACE_SMQTK_MAP_COLLECTION']].find({
'sha': {
'$in': smqtk_r['neighbors']
}
})
solr_id_to_shas = {os.environ['IMAGE_SPACE_SOLR_PREFIX'] + '/' + x['path']: x['sha'] for x in mapped_paths}
documents = solr_documents_from_paths(solr_id_to_shas.keys(), classifications)
for document in documents:
document['im_distance'] = neighbors_to_distances[solr_id_to_shas[document['id']]]
if 'near_duplicates' in params and int(params['near_duplicates']) == 1:
documents = [x for x in documents if x['im_distance'] <= NEAR_DUPLICATES_THRESHOLD]
return {
'numFound': len(documents),
'docs': documents
}
