count += 1
if count % 100 == 0:
logger.info('Processing image {}/{}'.format(count, num_imgs))
for cl, cl_boxes in boxes.iteritems():
coords = []
im_name = im_path.split('/')[-1]
ar = []
ar_bin = []
for b in cl_boxes:
coord_box = {}
coord_box['c'] = b['lt'] + b['rb']
coord_box['score'] = float(b['score'])
coords.append(coord_box)
f = b['f']
f_bin = extractor.binarize_fea(f)
im_fea = ImFea()
im_fea_bin = ImFeaBin()
im_fea.f.extend(f)
im_fea_bin.f.extend(f_bin)
ar.append(im_fea)
ar_bin.append(im_fea_bin)
doc = create_doc(im_name, cl, coords, ar, ar_bin)
num_docs += 1
# create index action
action = {"_index": es_index, "_type": es_type, "_source": doc}
actions.append(action)
if len(actions) == 1000:
boxes = extractor.extract_regions_and_feats(im, im_name)
if len(boxes) == 0:
no_faces.append(im_path)
if count % 100 == 0:
logger.info('Processing image {}/{}'.format(count, 300))
for cl, b in boxes.iteritems():
coords = []
ar = []
ar_bin = []
coord_box = {}
coord_box['c'] = b['lt'] + b['rb']
coord_box['score'] = float(b['score'])
coords.append(coord_box)
f = b['f']
f_bin = extractor.binarize_fea(f)
im_fea = ImFea()
im_fea_bin = ImFeaBin()
im_fea.f.extend(f)
im_fea_bin.f.extend(f_bin)
ar.append(im_fea)
ar_bin.append(im_fea_bin)
doc = create_doc(im_name, cl, coords, ar, ar_bin)
num_docs += 1
# create index action
action = {
"_index": es_index,
"_type": es_type,
