def check_embedding_on_detected_person_forSVM_ByDir(current_groupid, embedding,
style, classid,
nrof_classes):
total = 0
found = 0
people = None
if SVM_TRAIN_WITHOUT_CATEGORY is True:
facedir = '{}/data/faces/{}/{}/face_dataset/{}'.format(
BASEDIR, current_groupid, 'front', classid)
else:
facedir = '{}/data/faces/{}/{}/face_dataset/{}'.format(
BASEDIR, current_groupid, style, classid)
print("check embedding: facedir = {}".format(facedir))
embedding_array = []
if os.path.isdir(facedir):
image_paths = []
images = os.listdir(facedir)
if len(images) < 1:
print(
"Check embedding: Empty directory: facedir={}".format(facedir))
return 0, 0
for img in images:
img_path = os.path.join(facedir, img)
emb_path = save_embedding.get_embedding_path(img_path)
emb = save_embedding.read_embedding_string(emb_path)
emb = np.asarray(emb)
embedding_array.append(emb)
if len(embedding_array) > 0:
for emb in embedding_array:
val = compare2(embedding, emb)
total = total + 1
face_accuracy = val
print('face_accuracy={}'.format(face_accuracy))
'''
threshold = 1.0
if nrof_classes <= 5 and nrof_classes > 0:
threshold = 1.0
elif nrof_classes <= 10 and nrof_classes > 0:
threshold = 1.0
'''
threshold = 0.42
if face_accuracy > threshold:
found = found + 1
if total >= 500:
break
return found, total
def down_img_embedding(img_url, group_id, face_id, style='front'):
# 下载图片及embedding
img_path = get_image_path(img_url, group_id, face_id, style)
embedding_path = get_embedding_path(img_path)
embedding_url = img_url + SUFFIX
if not os.path.exists(img_path):
download_img_for_svm(img_url, group_id, face_id, style)
embedding = None
if not os.path.exists(embedding_path):
status = down_embedding(embedding_url, embedding_path)
if not status:
# 下载embedding失败， 开始计算embedding
embedding = featureCalculation(img_path)
if embedding is None:
return False, None
create_embedding_string(embedding, embedding_path)
# 上传一次
key = img_url.rsplit('/', 1)[-1]
embedding_string = ','.join(str(x) for x in embedding)
embedding_bytes = embedding_string.encode('utf-8')
if useAliyun is True:
url = aliyun_upload_data(key, embedding_bytes)
else:
embedding_url = qiniu_upload_data(key, embedding_bytes)
#print('Download embedding file failed, caculate it.')
else:
embedding = txt2embedding(embedding_path)
#print('Download embedding file suc.')
else:
#print('embedding file exist.')
embedding = txt2embedding(embedding_path)
# print('need recover db')
return True, embedding