def face_grouping(uid, face_image_url):
api = API(constant.FACEPP_API_KEY, constant.FACEPP_API_SECRET)
rst = api.faceset.create()
faceset_id = rst['faceset_id']
for url in face_image_url:
for face in face_image_url[url]['face']:
res = api.faceset.add_face(faceset_id=faceset_id, face_id = face['face_id'])
session_id = api.grouping.grouping(faceset_id = faceset_id)
session_id = session_id['session_id']
rst = api.wait_async(session_id)
api.faceset.delete(faceset_id=faceset_id)
if not rst.has_key('result'):
raise Exception('API return error')
return rst['result']
class ImageDetector:
''' Image Detector class demo'''
file_path = None
api = None
def __init__(self, file_path):
self.file_path = file_path
self.api = API(API_KEY, API_SECRET)
def detect(self):
file = File(self.file_path)
middle_result = self.api.detection.detect(img=file)
result = self.api.wait_async(middle_result["session_id"])
return len(result['result']['face'])
print_result('create person {}'.format(name), rst)
# Step 3: create a new group and add those persons in it
# 步骤3：.创建Group，将之前创建的Person加入这个Group
rst = api.group.create(group_name='test')
print_result('create group', rst)
rst = api.group.add_person(group_name='test', person_name=FACES.iterkeys())
print_result('add these persons to group', rst)
# Step 4: train the model
# 步骤4：训练模型
rst = api.train.identify(group_name='test')
print_result('train', rst)
# wait for training to complete
# 等待训练完成
rst = api.wait_async(rst['session_id'])
print_result('wait async', rst)
# Step 5: recognize face in a new image
# 步骤5：识别新图中的Face
rst = api.recognition.identify(group_name='test', url=TARGET_IMAGE)
print_result('recognition result', rst)
print '=' * 60
print 'The person with highest confidence:', \
rst['face'][0]['candidate'][0]['person_name']
# Finally, delete the persons and group because they are no longer needed
# 最终，删除无用的person和group
api.group.delete(group_name='test')
api.person.delete(person_name=FACES.iterkeys())
person_name = name, face_id = faces)
# Step 3: create a new group and add those persons in it
# 步骤3：.创建Group，将之前创建的Person加入这个Group
#rst = api.group.create(group_name = 'test')
#print_result('create group', rst)
rst = api.group.add_person(group_name = 'test', person_name = FACES.iterkeys())
print_result('add these persons to group', rst)
# Step 4: train the model
# 步骤4：训练模型
rst = api.train.identify(group_name = 'test')
print_result('train', rst)
# wait for training to complete
# 等待训练完成
rst = api.wait_async(rst['session_id'])
print_result('wait async', rst)
# Step 5: recognize face in a new image
# 步骤5：识别新图中的Face
rst = api.recognition.identify(group_name = 'test', img = File(TARGET_IMAGE))
print_result('recognition result', rst)
print '=' * 60
print 'The person with highest confidence:', \
rst['face'][0]['candidate'][0]['person_name']
# Finally, delete the persons and group because they are no longer needed
# 最终，删除无用的person和group
#api.group.delete(group_name = 'test')
#api.person.delete(person_name = FACES.iterkeys())
print_result("create person {}".format(name), rst)
# Step 3: create a new group and add those persons in it
# 步骤3：.创建Group，将之前创建的Person加入这个Group
rst = api.group.create(group_name="test")
print_result("create group", rst)
rst = api.group.add_person(group_name="test", person_name=FACES.iterkeys())
print_result("add these persons to group", rst)
# Step 4: train the model
# 步骤4：训练模型
rst = api.train.identify(group_name="test")
print_result("train", rst)
# wait for training to complete
# 等待训练完成
rst = api.wait_async(rst["session_id"])
print_result("wait async", rst)
# Step 5: recognize face in a new image
# 步骤5：识别新图中的Face
rst = api.recognition.identify(group_name="test", url=TARGET_IMAGE)
print_result("recognition result", rst)
print "=" * 60
print "The person with highest confidence:", rst["face"][0]["candidate"][0]["person_name"]
# Finally, delete the persons and group because they are no longer needed
# 最终，删除无用的person和group
api.group.delete(group_name="test")
api.person.delete(person_name=FACES.iterkeys())
# Congratulations! You have finished this tutorial, and you can continue
