def facepp_detect():
API_KEY = '76f2ed8ab272e8793c990ae6c9e0a5e8'
API_SECRET = 'RdVvpVpLaBYi37eMfGzAa8drJyDtDBpE'
api = API(API_KEY, API_SECRET)
count = 0
for pic in POSITIVE_SAMPLES:
#if pic != "../data/positive_samples/IMG_7207.JPG":
# continue
st = time.time()
res = api.detection.detect(img = File(pic), mode = 'normal')
print pic,":",(time.time()-st)*1000,"ms"
if has_valid_pic(res):
count += 1
print pic,":YES"
else:
print pic,":NO",":",len(res['face'])
print "True Positive:",round(float(count)*100/len(POSITIVE_SAMPLES),2)
print "False Negative:",100 - round(float(count)*100/len(POSITIVE_SAMPLES),2)
count = 0
for pic in NEGTIVE_SAMPLES:
#if pic != "../data/negtive_samples/IMG_7404.JPG":
# continue
res = api.detection.detect(img = File(pic), mode='normal')
if not has_valid_pic(res):
count += 1
print pic,":YES"
else:
print pic,":NO",":",len(res['face'])
print "True Negative:",round(float(count)*100/len(NEGTIVE_SAMPLES),2)
print "False Positive:",100 - round(float(count)*100/len(NEGTIVE_SAMPLES),2)
def faceclone(src_name, dst_name):
src_img = cv2.imread(src_name)
dst_img = cv2.imread(dst_name)
src_rst = api.detection.detect(img=File(src_name), attribute='pose')
src_img_width = src_rst['img_width']
src_img_height = src_rst['img_height']
src_face = src_rst['face'][0]
dst_rst = api.detection.detect(img=File(dst_name), attribute='pose')
dst_img_width = dst_rst['img_width']
dst_img_height = dst_rst['img_height']
dst_face = dst_rst['face'][0]
# here we will change our coordinate system
# by moving the left top point
dw = 100
dh = 100
nsrc_img = copy_image(src_img, dw, dh)
ndst_img = copy_image(dst_img, dw, dh)
ss = np.array(get_feature_points(src_face, src_img_width, src_img_height,
dw, dh),
dtype=np.float32)
ps = np.array(get_feature_points(dst_face, dst_img_width, dst_img_height,
dw, dh),
dtype=np.float32)
map_matrix = cv2.getAffineTransform(ps, ss)
nsrc_img_width = src_img_width + dw * 2
nsrc_img_height = src_img_height + dh * 2
ndst_img_width = dst_img_width + dw * 2
ndst_img_height = dst_img_height + dh * 2
map_result = cv2.warpAffine(ndst_img,
map_matrix,
dsize=(nsrc_img_width, nsrc_img_height))
extract_mask, center = contour.extract_face_mask(src_face['face_id'],
src_img_width,
src_img_height, src_name)
next_mask = copy_image(extract_mask, dw, dh)
center = (map_result.shape[0] / 2, map_result.shape[1] / 2)
map_result = cv2.seamlessClone(nsrc_img,
map_result,
next_mask,
center,
flags=cv2.NORMAL_CLONE)
imap_matrix = cv2.invertAffineTransform(map_matrix)
final = cv2.warpAffine(map_result,
imap_matrix,
dsize=(ndst_img_width, ndst_img_height))
new_final = copy_back_image(final, dw, dh)
return new_final
def callFaceppApi(self, image_byte_content, face_id = None):
for attempts in range(5):
try:
# face_id is a necessary parameter to the landmark api, so if it's passed in here, it's a landmark invoke
if face_id:
print 'Uploading image to facepp api for landmark.'
result = api.detection.landmark(img=File('b.jpg', image_byte_content), face_id=face_id, type='25p')
else:
print 'Uploading image to facepp api for detection.'
result = api.detection.detect(img=File('b.jpg', image_byte_content), mode = 'normal', attribute = 'glass,pose,gender,age,race,smiling')
return result
except:
print 'DEBUG, upload image failed, timeout, retrying...'
else:
print 'ERROR, all attempts to call facepp api failed, timeout.'
def landmarkFromFacepp(imgPath):
infor = api.detection.detect(img=File(imgPath))
try:
faceID = infor[u'face'][0][u'face_id']
except:
print("no face detected")
req_url = "http://api.faceplusplus.com/detection/landmark"
params = urllib.urlencode({
'api_secret': API_SECRET,
'api_key': API_KEY,
'face_id': faceID,
'type': '83p'
})
req_rst = urllib.urlopen(req_url, params).read() # landmark data
req_rst_dict = JSONDecoder().decode(req_rst)
if len(req_rst_dict['result']) == 1:
landmarkDict = req_rst_dict['result'][0]['landmark']
imgInfo = Image.open(imgPath)
xSize = imgInfo.size[0]
ySize = imgInfo.size[1]
landmarkList = sorted(list(landmarkDict))
landmark_array = []
for key in landmarkList:
temp_xy = [landmarkDict[key]['x'], landmarkDict[key]['y']]
landmark_array.append(np.array(temp_xy))
landmarkArray = np.array(landmark_array)
landmarkArray[:, 0] = landmarkArray[:, 0] * xSize / 100
landmarkArray[:, 1] = landmarkArray[:, 1] * ySize / 100
return landmarkArray
def is_person(self, image_file):
path = 'media/image/face.png'
# path = os.path.join('media/image/', image_file.filename)
image_file.save(path)
# import ipdb; ipdb.set_trace()
f = File(path)
api = API(API_KEY, API_SECRET, SERVER)
print("Sending face to api")
response = api.recognition.identify(group_name='Hackathoners', img=f)
print(response)
faces = response['face']
if len(faces) > 0:
face = faces[0]
name = face['candidate'][0]['person_name']
if name in self.people.keys():
print("Found " + name)
self.audio.add_to_playlist(self.people[name])
self.audio.play()
else:
self.audio.add_to_playlist('run.wav')
self.audio.play()
print("Did not match a person.")
def get_pic():
image_string = request.form['data']
timestamp = (datetime.now() - datetime(1970, 1, 1)).total_seconds()
image_file = open(IMAGE_FOLDER + 'temp' + str(timestamp) + '.png', 'wb')
image_file.write(image_string.decode('base64'))
image_file.close()
try:
a = api.detection.detect(img=File('D:\\temp' + str(timestamp) +
'.png'))
if len(a['face']) >= 0:
# print a['face'][0]['face_id']
try:
api.person.create(person_name=current_user.student_id,
face_id=a['face'][0]['face_id'])
# print a['face'][0]['face_id']
os.remove('D:\\temp' + str(timestamp) + '.png')
current_user.update(pic_exist=True)
api.train.verify(person_name=current_user.student_id)
except APIError, e:
print e.body
return "Error.Face++ is so Low."
return url_for('main.check_in')
except APIError, e:
try:
api.person.delete(person_name=current_user.studetn_id)
except:
pass
print e.body
def face_identify(photo_url = '', photo_img = '', defaultGroupName = 'yahoohack') :
if photo_url == '' and photo_img == '':
print 'photo is empty!'
return
if photo_img != '' :
res = face_api.recognition.identify(group_name = defaultGroupName, img = File(photo_img))
img = Image.open(photo_img)
img_width, img_height = img.size
if photo_url != '' :
#???
res = face_api.recognition.identify(group_name = defaultGroupName, url = photo_url)
faces = res['face']
faces_confidence = [ (face['candidate'][0]['confidence'],face) for face in faces]
sorted(faces_confidence, key=lambda q: -q[0])
faces = [item[1] for item in faces_confidence]
#print faces
faces_box = {}
for face in faces :
print face
person_name = face['candidate'][0]['person_name']
box = getFaceBox(face,img_width,img_height)
if faces_box.get(face['face_id'],None) == None :
faces_box[face['face_id']] = person_name, box
return faces_box
def face_compare():
face_search = '/Users/Roy/Documents/Thesis/python-sdk/test_image/faceDetected.jpg'
# 对待比对的图片进行检测，再搜索相似脸
# detect image and search same face
ret = api.detect(image_file=File(face_search))
search_result = api.search(face_token=ret["faces"][0]["face_token"],
outer_id='test1')
# 输出结果
# print result
print_result('search', search_result)
print '=' * 60
for k, v in Face.iteritems():
if v == search_result['results'][0]['face_token']:
print 'The person with highest confidence:', k
break
print k
if k == "chen.jpeg":
person = "chen"
elif k == "gakki.jpeg":
person = "gakki"
elif k == "roy.jpg":
person = "roy"
pass
return person
def face_compare():
face_search = '/Users/Roy/Documents/masterthesis/test_image/faceDetected.jpg'
# detect image and search same face
ret = api.detect(image_file=File(face_search))
search_result = api.search(face_token=ret["faces"][0]["face_token"], outer_id='test1')
# print result
print_result('search', search_result)
print '=' * 60
if search_result['results'][0]['confidence'] > 60:
for k, v in Face.iteritems():
if v == search_result['results'][0]['face_token']:
print 'The person with highest confidence:', k
break
print k
else:
k = "none"
print(k)
if k == "chen.jpeg":
compare_result = "chen"
elif k=="gakki.jpeg":
compare_result="gakki"
elif k=="roy.jpg":
compare_result ="roy"
else:
compare_result = "none"
return compare_result
def face_search(photo_url = '', photo_img = '', defaultFaceSetName = 'yahoohack_faceset') :
if photo_url == '' and photo_img == '':
print 'photo is empty!'
return
if photo_img != '' :
res = face_api.detection.detect(img = File(photo_img))
if photo_url != '' :
res = face_api.detection.detect(url = photo_url)
if not res['face']: return []
face_id = res['face'][0]['face_id']
search_res = face_api.recognition.search(key_face_id = face_id, faceset_name = defaultFaceSetName, count = 10)
faces_res = search_res['candidate']
face_ids = [face['face_id'] for face in faces_res]
face_ids_str = ','.join(face_ids)
face_info = face_api.info.get_face(face_id = face_ids_str)['face_info']
person_names = {}
for face in face_info :
if len(face['person']) > 0 :
person_names[face['face_id']] = face['person'][0]['person_name']
person_name_list = []
for face_id in face_ids :
if person_names.get(face_id,None) != None :
person_name_list.append(person_names[face_id])
print person_name_list
return person_name_list
def proc_image(self, img_name):
print 'proc_image: image file', img_name
rst = api.detection.detect(img=File(img_name), attribute='pose')
for face in rst['face']:
# basically, there will be only one image in the lfw file
self.proc_face(face, rst['img_width'], rst['img_height'], img_name)
print 'proc_image: done'
def processing():
api = API(API_KEY, API_SECRET)
#url = "http://blogs.reuters.com/great-debate/files/2013/07/obama-best.jpg"
result = api.detection.detect(img=File(
'C:/Users/Bhawana/Desktop/bhaw.jpg')) #to detect local images
#result = api.detection.detect(url=url) to detect online images
return print_result(result)
def DetectFace(filepath):
face_detect = api.detection.detect(img=File(filepath))
if face_detect['face'] != []:
return face_detect['face'][0]['face_id']
else:
return -1
def analysisPhoto(imgPath):
face1,face2,results,smile = 0, 0, 0, 0
api = API(APIKEY, APISCRETE)
try:
detect_res = api.detection.detect(img=File(imgPath))
except Exception,e:
print e
detect_res = {}
def test_facepp(pic):
#pic = '../data/negtive_samples/IMG_7404.JPG'
#pic = '../data/positive_samples/IMG_7116.JPG'
API_KEY = '76f2ed8ab272e8793c990ae6c9e0a5e8'
API_SECRET = 'RdVvpVpLaBYi37eMfGzAa8drJyDtDBpE'
api = API(API_KEY, API_SECRET)
res = api.detection.detect(img = File(pic), mode='normal')
print_result('detect result', res)
def afr_test(msg):
subprocess.call('raspistill -o face.jpg -t 300 -w 480 -h 360 ', shell=True)
result = api.recognition.recognize(img=File('face.jpg'),
group_name='ry_robot_001')
#print 'result',result
if result['face']:
return result['face'][0]['candidate'][0]['person_name']
else:
return 'no person'
def getFaceInfo(self, input_file):
ret = self.api.detect(image_file=File(input_file))
self.search_result = self.api.search(
face_token=ret["faces"][0]["face_token"], outer_id=self.id)
for k, v in self.Face.iteritems():
if v == self.search_result['results'][0]['face_token']:
print 'The person with highest confidence:', k
return k
break
def faceclone(src_name, dst_name):
src_img = cv2.imread(src_name)
dst_img = cv2.imread(dst_name)
src_rst = api.detection.detect(img=File(src_name), attribute='pose')
src_img_width = src_rst['img_width']
src_img_height = src_rst['img_height']
src_face = src_rst['face'][0]
dst_rst = api.detection.detect(img=File(dst_name), attribute='pose')
dst_img_width = dst_rst['img_width']
dst_img_height = dst_rst['img_height']
dst_face = dst_rst['face'][0]
ss = np.array(get_feature_points(src_face, src_img_width, src_img_height),
dtype=np.float32)
ps = np.array(get_feature_points(dst_face, dst_img_width, dst_img_height),
dtype=np.float32)
map_matrix = cv2.getAffineTransform(ps, ss)
#dsize = (300,300)
map_result = cv2.warpAffine(dst_img,
map_matrix,
dsize=(src_img_width, src_img_height))
extract_mask, center = contour.extract_face_mask(src_face['face_id'],
src_img_width,
src_img_height, src_name)
# merge
## first blending the border
extract_alpha = contour.extract_face_alpha(src_face['face_id'],
src_img_width, src_img_height,
src_name)
center = (map_result.shape[0] / 2, map_result.shape[1] / 2)
map_result = cv2.seamlessClone(src_img,
map_result,
extract_mask,
center,
flags=cv2.NORMAL_CLONE)
imap_matrix = cv2.invertAffineTransform(map_matrix)
final = cv2.warpAffine(map_result, imap_matrix, dsize=(dst_img.shape[0:2]))
return final
def update_faces_from_camera(self, outer_id='default'):
if not RASPBERRYPI:
return None
image_name = '%s.jpg'%time.time()
cmd = 'sudo raspistill -t 2000 -o %s -p 100,100,300,200 -q 5' % image_name
GPIO.output(BCM_PIN4_SHOW_TAKE_PHOTO, True)
with self.camera_lock:
os.system(cmd)
GPIO.output(BCM_PIN4_SHOW_TAKE_PHOTO, False)
return self.upload_faces(outer_id, image_file=File(image_name), image_path=image_name)
def register(filepath, setid, list):
ret = api.detect(image_file=File(filepath))
while (ret.has_key("error_message") != 0):
ret = api.detect(image_file=File(filepath))
#print_result("detect", ret)
if not len(ret['faces']):
print("相片中没有人脸")
return
search_result = api.search(face_token=ret["faces"][0]["face_token"],
outer_id=setid)
while (search_result.has_key("error_message") != 0):
search_result = api.search(face_token=ret["faces"][0]["face_token"],
outer_id=setid)
if (search_result["results"][0]["confidence"] >=
search_result["thresholds"]["1e-5"]):
print(search_result['results'][0]['user_id'])
list.append(search_result['results'][0]['user_id'])
else:
print("不在名单中")
def recognize_with_facepp(self, path):
# rst = api.recognition.identify(group_name = 'test', url = TARGET_IMAGE)
rst = api.recognition.identify(group_name='saladgroup', img=File(path))
print('recognition result', rst)
print('=' * 60)
if len(rst['face']) > 0 and len(rst['face'][0]['candidate']) > 0:
print('The person with highest confidence:', \
rst['face'][0]['candidate'][0]['person_name'])
return rst
def analyze_user(filepath, id_img):
print filepath
api = API(API_KEY, API_SECRET)
ret = api.faceset.create(outer_id=id_img)
# print_result("faceset create", ret)
Face = {}
res = api.detect(image_file=File(filepath))
if res["faces"]:
Face['person'] = res["faces"][0]["face_token"]
res = api.face.analyze(
image_file=File(filepath),
face_tokens=Face['person'],
return_attributes=
'gender,age,smiling,glass,headpose,facequality,blur')
print_result("person", res)
# res = api.detect(image_file=File(face_two))
# print_result("person_two", res)
# Face['person_two'] = res["faces"][0]["face_token"]
# # 将得到的FaceToken存进Faceset里面
# # save FaceToken in Faceset
# api.faceset.addface(outer_id=id_test, face_tokens=Face.itervalues())
# # 对待比对的图片进行检测，再搜索相似脸
# # detect image and search same face
# ret = api.detect(image_file=File(face_search))
# print_result("detect", ret)
# search_result = api.search(face_token=ret["faces"][0]["face_token"], outer_id=id_test)
# # 输出结果
# # print result
# print_result('search', search_result)
# print '=' * 60
# for k, v in Face.iteritems():
# if v == search_result['results'][0]['face_token']:
# print 'The person with highest confidence:', k
# break
api.faceset.delete(outer_id=id_img, check_empty=0)
return res
def afr_online(msg):
file_path = os.path.join(os.path.dirname(__file__), 'face.jpg')
subprocess.call('raspistill -o %s -t 300 -w 480 -h 360 ' % file_path,
shell=True)
result = api.recognition.recognize(img=File(file_path),
group_name='ry_robot_001')
#print 'result',result
if result['face']:
return result['face'][0]['candidate'][0]['person_name']
else:
return 'no person'
def detect(path):
API_KEY = "3o6_lMDRxcpYalXhuXq9cymJeeN7cHCS"
API_SECRET = "6776wZFWYVfYjwDgS8G_0rmWhtXVyUcW"
from facepp import API, File
api = API(API_KEY, API_SECRET)
result = api.detect(image_file=File(path),return_landmark=1)
landmarks = result['faces'][0]['landmark']
new_dict = {}
for k,v in landmarks.iteritems():
new_dict[k] = np.array([v['y'],v['x']])
return new_dict
def facedetect(path):
API_KEY = "RuF7pDgXRAl0EfZjZrWIYElt_QoO-XhN"
API_SECRET = "lpUhYiPv8dSdFgMyjkaBKvO2X1nbKzv1"
from facepp import API, File
api = API(API_KEY, API_SECRET)
result = api.detect(image_file=File(path), return_landmark=1)
#we only detect face feature points
landmarks = result['faces'][0]['landmark']
feature_dict = {}
for k, v in landmarks.iteritems():
feature_dict[k] = np.array([v['x'], v['y']])
return feature_dict
def create_face_id(pic_folder, result_file):
file_list = os.listdir(pic_folder)
f_result = open(result_file, 'w')
for file_name in file_list:
try:
file_path = os.path.join(pic_folder, file_name)
face = api.detection.detect(img=File(file_path))
face_id = face['face'][0]['face_id']
f_result.write(file_path.decode('gbk') + '\t' + face_id + '\n')
print file_path.decode('gbk')
except:
traceback.print_exc()
continue
def createFace ():
# 您需要先注册一个App，并将得到的API key和API secret写在这里。
# You need to register your App first, and enter you API key/secret.
API_KEY = "o_aUYdJlo0Pl1beu0kXiIQShUheA7pe3"
API_SECRET = "Q0DrLrFybJvD7VP_XCKbOEeVeq7mUdLA"
#get the folder list
name_list=os.listdir('/Users/Roy/Documents/masterthesis/image_set')
print (name_list)
face_list=[]
for i in range(len(name_list)):
face_url = '/Users/Roy/Documents/masterthesis/image_set/'+name_list[i]
face_list.append(face_url)
print face_list
api_server_international = 'https://api-us.faceplusplus.com/facepp/v3/'
# First import the API class from the SDK
# 首先，导入SDK中的API类
from facepp import API, File
#创建一个API对象，如果你是国际版用户，代码为：api = API(API_KEY, API_SECRET, srv=api_server_international)
#Create a API object, if you are an international user,code: api = API(API_KEY, API_SECRET, srv=api_server_international)
api = API(API_KEY, API_SECRET)
api.faceset.delete(outer_id='test1', check_empty=0)
print('deleted')
# 创建一个Faceset用来存储FaceToken
# create a Faceset to save FaceToken
ret = api.faceset.create(outer_id='test1')
# print_result("faceset create", ret)
print('faceset create')
# 对图片进行检测
# detect image
Face = {}
Face2 = {}
for i in range(len(face_list)):
res = api.detect(image_file=File(face_list[i]))
# Face['person'+str(i)] = res["faces"][0]["face_token"]
Face[name_list[i]] = res["faces"][0]["face_token"]
print (Face)
# print (Face2)
# 将得到的FaceToken存进Faceset里面
# save FaceToken in Faceset
my_faceset = api.faceset.addface(outer_id='test1', face_tokens=Face.itervalues())
print('finally create')
return Face
def GenerateSet(path):
filename = os.path.join(path, 'setid.txt')
if not os.path.isdir(path):
os.makedirs(path)
if os.path.exists(filename):
input = open(filename)
setid = input.readlines()[0].strip('\n')
input.close()
return setid
else:
output = open(filename, 'w')
ret = api.faceset.create(outer_id=path.split('/')[-1])
while (ret.has_key("error_message") != 0):
ret = api.faceset.create(outer_id=path.split('/')[-1])
print_result("faceset create", ret)
setid = ret["outer_id"]
output.write(setid)
output.close()
list_file = []
GetPicFile(path, list_file)
Face = {}
print(list_file)
for i in list_file:
res = api.detect(image_file=File(i))
while (res.has_key("error_message") != 0):
res = api.detect(image_file=File(i))
Face[GetFilename(i)] = res["faces"][0]["face_token"]
for k, v in Face.items():
result = api.face.setuserid(face_token=v, user_id=k)
while (result.has_key("error_message") != 0):
result = api.face.setuserid(face_token=v, user_id=k)
print_result("set id", result)
result1 = api.faceset.addface(outer_id=setid, face_tokens=v)
while (result1.has_key("error_message") != 0):
result1 = api.faceset.addface(outer_id=setid, face_tokens=v)
print_result("add face", result1)
return setid
def bidui(image_path):
import sys
reload(sys)
sys.setdefaultencoding( "utf-8" )
#将需要识别的图片和集合对比
#------------------------------------------------------------------------------
#准备阶段
API_KEY = "a4OWmRJTir1XGFx6vZtwPvlf6nsYxErQ"
API_SECRET = "Q1bR1pHjv7SGWa3xSN0tNivJ4lK0K8Tu"
#国际版的服务器地址
api_server_international = 'https://api-us.faceplusplus.com/facepp/v3/'
# 导入系统库并定义辅助函数
from pprint import pformat
def print_result(hit, result):
def encode(obj):
if type(obj) is unicode:
return obj.encode('utf-8')
if type(obj) is dict:
return {encode(v): encode(k) for (v, k) in obj.iteritems()}
if type(obj) is list:
return [encode(i) for i in obj]
return obj
print hit
result = encode(result)
print '\n'.join(" " + i for i in pformat(result, width=75).split('\n'))
#导入SDK中的API类
from facepp import API, File
#创建一个API对象
api = API(API_KEY, API_SECRET)
#-----------------------------------------------------------------------------
# 本地图片的地址
face_search = image_path
# 对待比对的图片进行检测
Face = {}
res = api.detect(image_file=File(face_search))
#print_result("face_search", res)
#搜索相似脸
search_result = api.search(face_token=res["faces"][0]["face_token"], outer_id='finally')
# 输出结果
search_confidence = search_result['results'][0]['confidence']
uers=search_result['results'][0]['user_id']
print '置信度:', search_confidence
if search_confidence >=80:
print '你好!',uers.decode('utf-8')
return 1
else:
return 0
def AddPicToSet():
filename = raw_input("Enter setid file path: ")
path = raw_input("Enter Picture path: ")
if os.path.exists(filename):
input = open(filename)
setid = input.readlines()[0].strip('\n')
list_file = []
GetPicFile(path, list_file)
Face = {}
print(list_file)
for i in list_file:
res = api.detect(image_file=File(i))
while (res.has_key("error_message") != 0):
res = api.detect(image_file=File(i))
Face[GetFilename(i)] = res["faces"][0]["face_token"]
for k, v in Face.items():
result = api.face.setuserid(face_token=v, user_id=k)
while (result.has_key("error_message") != 0):
result = api.face.setuserid(face_token=v, user_id=k)
print_result("set id", result)
result1 = api.faceset.addface(outer_id=setid, face_tokens=v)
while (result1.has_key("error_message") != 0):
result1 = api.faceset.addface(outer_id=setid, face_tokens=v)
print_result("add face", result1)