def retrieve_face_emotion_att(clientId):
global global_vars
global_var = (item for item in global_vars if item["clientId"] == str(clientId)).next()
data = global_var['binary_data']
chrome_server2client(clientId, 'Veuillez patienter pendant quelques secondes...')
time.sleep(0.5)
chrome_server2client(clientId, 'START')
# Face API
faceResult = face_api.faceDetect(None, None, data)
# Emotion API
emoResult = emotion_api.recognizeEmotion(None, None, data)
# Results
print 'Found {} '.format(len(faceResult)) + ('faces' if len(faceResult)!=1 else 'face')
nb_faces = len(faceResult)
tb_face_rect = [{} for ind in range(nb_faces)]
tb_age = ['' for ind in range(nb_faces)]
tb_gender = ['' for ind in range(nb_faces)]
tb_glasses = ['' for ind in range(nb_faces)]
tb_emo = ['' for ind in range(len(emoResult))]
if (len(faceResult)>0 and len(emoResult)>0):
ind = 0
for currFace in faceResult:
faceRectangle = currFace['faceRectangle']
faceAttributes = currFace['faceAttributes']
tb_face_rect[ind] = faceRectangle
tb_age[ind] = str(faceAttributes['age'])
tb_gender[ind] = faceAttributes['gender']
tb_glasses[ind] = faceAttributes['glasses']
ind += 1
ind = 0
for currFace in emoResult:
tb_emo[ind] = max(currFace['scores'].iteritems(), key=operator.itemgetter(1))[0]
ind += 1
faceWidth = np.zeros(shape=(nb_faces))
faceHeight = np.zeros(shape=(nb_faces))
for ind in range(nb_faces):
faceWidth[ind] = tb_face_rect[ind]['width']
faceHeight[ind] = tb_face_rect[ind]['height']
ind_max = np.argmax(faceWidth*faceHeight.T)
global_var['age'] = tb_age[ind_max]
global_var['gender'] = tb_gender[ind_max]
global_var['emo'] = tb_emo[ind_max]
chrome_server2client(clientId, 'DONE')
time.sleep(0.5)
return tb_age, tb_gender, tb_glasses, tb_emo
else:
return 'N/A','N/A','N/A','N/A'
def retrieve_face_emotion_att(clientId):
global global_vars
global_var = (item for item in global_vars if item["clientId"] == str(clientId)).next()
data = global_var['binary_data']
# Face API
faceResult = face_api.faceDetect(None, None, data)
# Emotion API
emoResult = emotion_api.recognizeEmotion(None, None, data)
# Results
print 'Found {} '.format(len(faceResult)) + ('faces' if len(faceResult)!=1 else 'face')
nb_faces = len(faceResult)
tb_face_rect = [{} for ind in range(nb_faces)]
tb_age = ['' for ind in range(nb_faces)]
tb_gender = ['' for ind in range(nb_faces)]
tb_glasses = ['' for ind in range(nb_faces)]
tb_emo = ['' for ind in range(len(emoResult))]
if (len(faceResult)>0 and len(emoResult)>0):
ind = 0
for currFace in faceResult:
faceRectangle = currFace['faceRectangle']
faceAttributes = currFace['faceAttributes']
tb_face_rect[ind] = faceRectangle
tb_age[ind] = str(faceAttributes['age'])
tb_gender[ind] = faceAttributes['gender']
tb_glasses[ind] = faceAttributes['glasses']
ind += 1
ind = 0
for currFace in emoResult:
tb_emo[ind] = max(currFace['scores'].iteritems(), key=operator.itemgetter(1))[0]
ind += 1
faceWidth = np.zeros(shape=(nb_faces))
faceHeight = np.zeros(shape=(nb_faces))
for ind in range(nb_faces):
faceWidth[ind] = tb_face_rect[ind]['width']
faceHeight[ind] = tb_face_rect[ind]['height']
ind_max = np.argmax(faceWidth*faceHeight.T)
global_var['age'] = tb_age[ind_max]
global_var['gender'] = tb_gender[ind_max]
global_var['emo'] = tb_emo[ind_max]
# global_var['age'] = tb_age[0] # TODO: replace the first face by the biggest face (Done)
# global_var['gender'] = tb_gender[0]
# global_var['emo'] = tb_emo[0]
return tb_age, tb_gender, tb_glasses, tb_emo
else:
return 'N/A','N/A','N/A','N/A'
def retrieve_face_emotion_att(clientId):
global global_vars
global_var = (item for item in global_vars if item["clientId"] == str(clientId)).next()
# Face API
# faceResult = face_api.faceDetect(None, None, data)
faceResult = face_api.faceDetect(None, 'output.png', None)
# Emotion API
# emoResult = emotion_api.recognizeEmotion(None, None, data)
emoResult = emotion_api.recognizeEmotion(None, 'output.png', None)
# Results
print 'Found {} '.format(len(faceResult)) + ('faces' if len(faceResult)!=1 else 'face')
nb_faces = len(faceResult)
tb_face_rect = [{} for ind in range(nb_faces)]
tb_age = ['' for ind in range(nb_faces)]
tb_gender = ['' for ind in range(nb_faces)]
tb_glasses = ['' for ind in range(nb_faces)]
tb_emo = ['' for ind in range(len(emoResult))]
if (len(faceResult)>0 and len(emoResult)>0):
ind = 0
for currFace in faceResult:
faceRectangle = currFace['faceRectangle']
faceAttributes = currFace['faceAttributes']
tb_face_rect[ind] = faceRectangle
tb_age[ind] = str(faceAttributes['age'])
tb_gender[ind] = faceAttributes['gender']
tb_glasses[ind] = faceAttributes['glasses']
ind += 1
ind = 0
for currFace in emoResult:
tb_emo[ind] = max(currFace['scores'].iteritems(), key=operator.itemgetter(1))[0]
ind += 1
faceWidth = np.zeros(shape=(nb_faces))
faceHeight = np.zeros(shape=(nb_faces))
for ind in range(nb_faces):
faceWidth[ind] = tb_face_rect[ind]['width']
faceHeight[ind] = tb_face_rect[ind]['height']
ind_max = np.argmax(faceWidth*faceHeight.T)
global_var['age'] = tb_age[ind_max]
global_var['gender'] = tb_gender[ind_max]
global_var['emo'] = tb_emo[ind_max]
return tb_age, tb_gender, tb_glasses, tb_emo
else:
return 'N/A','N/A','N/A','N/A'
def recognize_from_video():
wait_time = 4 # Identify after each 4 seconds
image_path = 'video.jpg'
start_time = time.time() # For recognition timer (will reset after each 3 secs)
time_origine = time.time() # For counting using time
thread_video = Thread(target = streaming_video, name = 'thread_video')
thread_video.start()
while (time.time() - time_origine < 20) and (key != 27):
elapsed_time = time.time() - start_time
if (elapsed_time > wait_time): # Identify after each 4 seconds
faceDetectResult = face_api.faceDetect(None, image_path, None)
faceRecongize(faceDetectResult)
start_time = time.time() # reset timer
def retrieve_face_emotion_att(filename):
# Face API
faceResult = face_api.faceDetect(None, filename, None)
# Emotion API
emoResult = emotion_api.recognizeEmotion(None, filename, None)
# Results
print 'Found {} '.format(len(faceResult)) + ('faces' if len(faceResult)!=1 else 'face')
nb_faces = len(faceResult)
tb_face_rect = [{} for ind in range(nb_faces)]
tb_age = ['' for ind in range(nb_faces)]
tb_gender = ['' for ind in range(nb_faces)]
tb_glasses = ['' for ind in range(nb_faces)]
tb_emo = ['' for ind in range(len(emoResult))]
if (len(faceResult)>0 and len(emoResult)>0):
ind = 0
for currFace in faceResult:
faceRectangle = currFace['faceRectangle']
faceAttributes = currFace['faceAttributes']
tb_face_rect[ind] = faceRectangle
tb_age[ind] = str(faceAttributes['age'])
tb_gender[ind] = faceAttributes['gender']
tb_glasses[ind] = faceAttributes['glasses']
ind += 1
ind = 0
for currFace in emoResult:
tb_emo[ind] = max(currFace['scores'].iteritems(), key=operator.itemgetter(1))[0]
ind += 1
print 'Face index:', '\t', 'Age', '\t', 'Gender', '\t', 'Glasses', '\t', 'Emotion'
for ind in range(nb_faces):
print 'Face '+str(ind)+': ', '\t', tb_age[ind], '\t', tb_gender[ind], '\t', tb_glasses[ind], '\t', tb_emo[ind]
return tb_age, tb_gender, tb_glasses, tb_emo
def run_program(clientId):
global global_vars
global_var = (item for item in global_vars if item["clientId"] == str(clientId)).next()
# Autorisation to begin Streaming Video
optin0 = allow_streaming_video(clientId)
if (optin0 == 1):
global_var['key'] = 0
start_time = time.time() # For recognition timer (will reset after each 3 secs)
time_origine = time.time() # For display (unchanged)
"""
Permanent loop
"""
i = 0
j = 0
while (time.time() - time_origine < 120):
data = global_var['binary_data']
"""
Decision part
"""
if not (global_var['flag_quit']): #TODO: new
elapsed_time = time.time() - start_time
if ((elapsed_time > wait_time) and global_var['flag_enable_recog']): # Identify after each 3 seconds
faceDetectResult = face_api.faceDetect(None, None, data)
# print faceDetectResult
if (len(faceDetectResult)>=1):
new_faceId = faceDetectResult[0]['faceId']
resultIdentify = face_api.faceIdentify(groupId, [new_faceId], maxNbOfCandidates)
if (len(resultIdentify[0]['candidates'])>=1): # If the number of times recognized is big enough
global_var['flag_recog'] = 1 # Known Person
global_var['flag_ask'] = 0
recognizedPersonId = resultIdentify[0]['candidates'][0]['personId']
conf = resultIdentify[0]['candidates'][0]['confidence']
recognizedPerson = face_api.getPerson(groupId, recognizedPersonId)
recognizedPerson = recognizedPerson.replace('null','None')
recognizedPerson = eval(recognizedPerson)
global_var['nom'] = recognizedPerson['name']
global_var['text'] = 'Reconnu : ' + global_var['nom'] + ' (confidence={})'.format(conf)
print global_var['text']
if (not global_var['flag_reidentify']):
global_var['text2'] = "Appuyez [Y] si c'est bien vous"
global_var['text3'] = "Appuyez [N] si ce n'est pas vous"
res_verify_recog = verify_recog(clientId, global_var['nom'])
if (res_verify_recog==1):
global_var['key'] = ord('y')
elif (res_verify_recog==0):
global_var['key'] = ord('n')
else: # If the number of times recognized anyone from database is too low
global_var['flag_recog'] = 0 # Unknown Person
global_var['nom'] = '@' # '@' is for unknown person
global_var['text'] = 'Personne inconnue'
global_var['text2'] = ''
global_var['text3'] = ''
if (not global_var['flag_reidentify']):
global_var['flag_ask'] = 1
simple_message(clientId, u'Désolé, je ne vous reconnaît pas')
time.sleep(0.25)
else:
global_var['flag_recog'] = -1
global_var['text'] = 'Aucune personne'
global_var['text2'] = ''
global_var['text3'] = ''
start_time = time.time() # reset timer
"""
Redirecting user based on recognition result and user's status (already took photos or not) in database
"""
count_time = time.time() - time_origine
if (count_time <= wait_time):
global_var['text3'] = 'Initialisation (pret dans ' + str(wait_time-count_time)[0:4] + ' secondes)...'
if i==0:
chrome_server2client(clientId, 'START')
i=1
if (global_var['flag_quit']):
break
else:
"""
Start Redirecting after the first 1.5 seconds
"""
if j==0:
chrome_server2client(clientId, 'DONE')
j=1
if (global_var['flag_quit']):
break
if (global_var['flag_recog']==1):
if (global_var['key']==ord('y') or global_var['key']==ord('Y')): # User chooses Y to go to Formation page
global_var['flag_wrong_recog'] = 0
get_face_emotion_api_results(clientId)
go_to_formation(clientId, xls_filename, global_var['nom'])
global_var['key'] = 0
if (global_var['key']==ord('n') or global_var['key']==ord('N')): # User confirms that the recognition result is wrong by choosing N
global_var['flag_wrong_recog'] = 1
global_var['flag_ask'] = 1
global_var['key'] = 0
if ((global_var['flag_recog']==1 and global_var['flag_wrong_recog']==1) or (global_var['flag_recog']==0)): # Not recognized or not correctly recognized
if (global_var['flag_ask']==1):# and (not flag_quit)):
resp_deja_photos = deja_photos(clientId) # Ask user if he has already had a database of face photos
print 'resp_deja_photos = ', resp_deja_photos
# if (resp_deja_photos==-1):
# global_var['flag_ask'] = 0
if (resp_deja_photos==1): # User has a database of photos
global_var['flag_enable_recog'] = 0 # Disable recognition in order not to recognize while re-identifying
global_var['flag_ask'] = 0
name0 = global_var['nom'] # Save the recognition result, which is wrong, in order to compare later
nb_time_max = 2 # Number of times to retry recognize
thread_reidentification = Thread(target = re_identification, args = (clientId, nb_time_max, name0), name = 'thread_reidentification_'+clientId)
thread_reidentification.start()
elif (resp_deja_photos == 0): # User doesnt have a database of photos
global_var['flag_enable_recog'] = 0 # Disable recognition in order not to recognize while taking photos
resp_allow_take_photos = allow_take_photos(clientId)
if (resp_allow_take_photos==1): # User allows to take photos
global_var['flag_take_photo'] = 1 # Enable photo taking
else: # User doesnt want to take photos
global_var['flag_take_photo'] = 0
res = allow_go_to_formation_by_id(clientId)
if (res==1): # User agrees to go to Formation in providing his id manually
name = ask_name(clientId, 1)
go_to_formation(clientId, xls_filename, name)
else: # Quit if user refuses to provide manually his id (after all other functionalities)
break
resp_allow_take_photos = 0
resp_deja_photos = 0
global_var['flag_ask'] = 0
if (global_var['flag_take_photo']==1):# and (not flag_quit)):
step_time = 1 # Interval of time (in second) between two times of taking photo
thread_take_photo = Thread(target = take_photos, args = (clientId, step_time, 1), name = 'thread_take_photo_'+clientId)
thread_take_photo.start()
global_var['flag_take_photo'] = 0
"""
Call Face API and Emotion API, and display
"""
if (global_var['key']==ord('i') or global_var['key']==ord('I')):
retrieve_face_emotion_att(clientId)
global_var['key'] = 0
"""
End of While-loop
"""
"""
Exit the program
"""
quit_program(clientId)
def run_program(clientId):
global global_vars
global_var = (item for item in global_vars if item["clientId"] == str(clientId)).next()
# Autorisation to begin Streaming Video
optin0 = allow_streaming_video(clientId)
if (optin0 == 1):
global_var['key'] = 0
start_time = time.time() # For recognition timer (will reset after each 3 secs)
time_origine = time.time() # For display (unchanged)
"""
Permanent loop
"""
i = 0
j = 0
while True:
data = global_var['binary_data']
"""
Decision part
"""
if not (global_var['flag_quit']):
elapsed_time = time.time() - start_time
if ((elapsed_time > wait_time) and global_var['flag_enable_recog']): # Identify after each 3 seconds
faceDetectResult = face_api.faceDetect(None, None, data)
# print faceDetectResult
if (len(faceDetectResult)>=1):
new_faceId = faceDetectResult[0]['faceId']
resultIdentify = face_api.faceIdentify(groupId, [new_faceId], maxNbOfCandidates)
if (len(resultIdentify[0]['candidates'])>=1): # If the number of times recognized is big enough
global_var['flag_recog'] = 1 # Known Person
global_var['flag_ask'] = 0
recognizedPersonId = resultIdentify[0]['candidates'][0]['personId']
conf = resultIdentify[0]['candidates'][0]['confidence']
recognizedPerson = face_api.getPerson(groupId, recognizedPersonId)
recognizedPerson = recognizedPerson.replace('null','None')
recognizedPerson = eval(recognizedPerson)
global_var['nom'] = recognizedPerson['name']
global_var['text'] = 'Reconnu : ' + global_var['nom'] + ' (confidence={})'.format(conf)
print global_var['text']
if (not global_var['flag_reidentify']):
global_var['text2'] = "Appuyez [Y] si c'est bien vous"
global_var['text3'] = "Appuyez [N] si ce n'est pas vous"
res_verify_recog = verify_recog(clientId, global_var['nom'])
if (res_verify_recog==1):
global_var['key'] = ord('y')
elif (res_verify_recog==0):
global_var['key'] = ord('n')
else: # If the number of times recognized anyone from database is too low
global_var['flag_recog'] = 0 # Unknown Person
global_var['nom'] = '@' # '@' is for unknown person
global_var['text'] = 'Personne inconnue'
global_var['text2'] = ''
global_var['text3'] = ''
if (not global_var['flag_reidentify']):
global_var['flag_ask'] = 1
simple_message(clientId, u'Désolé, je ne vous reconnaît pas')
time.sleep(0.25)
else:
global_var['flag_recog'] = -1
global_var['text'] = 'Aucune personne'
global_var['text2'] = ''
global_var['text3'] = ''
start_time = time.time() # reset timer
"""
Redirecting user based on recognition result and user's status (already took photos or not) in database
"""
count_time = time.time() - time_origine
if (count_time <= wait_time):
global_var['text3'] = 'Initialisation (pret dans ' + str(wait_time-count_time)[0:4] + ' secondes)...'
if i==0:
chrome_server2client(clientId, 'START')
i=1
if (global_var['flag_quit']):
break
else:
"""
Start Redirecting after the first 1.5 seconds
"""
if j==0:
chrome_server2client(clientId, 'DONE')
j=1
if (global_var['flag_quit']):
break
if (global_var['flag_recog']==1):
if (global_var['key']==ord('y') or global_var['key']==ord('Y')): # User chooses Y to go to Formation page
global_var['flag_wrong_recog'] = 0
get_face_emotion_api_results(clientId)
go_to_formation(clientId, xls_filename, global_var['nom'])
global_var['key'] = 0
if (global_var['key']==ord('n') or global_var['key']==ord('N')): # User confirms that the recognition result is wrong by choosing N
global_var['flag_wrong_recog'] = 1
global_var['flag_ask'] = 1
global_var['key'] = 0
if ((global_var['flag_recog']==1 and global_var['flag_wrong_recog']==1) or (global_var['flag_recog']==0)): # Not recognized or not correctly recognized
if (global_var['flag_ask']==1):# and (not flag_quit)):
resp_deja_photos = deja_photos(clientId) # Ask user if he has already had a database of face photos
print 'resp_deja_photos = ', resp_deja_photos
# if (resp_deja_photos==-1):
# global_var['flag_ask'] = 0
if (resp_deja_photos==1): # User has a database of photos
global_var['flag_enable_recog'] = 0 # Disable recognition in order not to recognize while re-identifying
global_var['flag_ask'] = 0
name0 = global_var['nom'] # Save the recognition result, which is wrong, in order to compare later
nb_time_max = 2 # Number of times to retry recognize
thread_reidentification = Thread(target = re_identification, args = (clientId, nb_time_max, name0), name = 'thread_reidentification_'+clientId)
thread_reidentification.start()
elif (resp_deja_photos == 0): # User doesnt have a database of photos
global_var['flag_enable_recog'] = 0 # Disable recognition in order not to recognize while taking photos
resp_allow_take_photos = allow_take_photos(clientId)
if (resp_allow_take_photos==1): # User allows to take photos
global_var['flag_take_photo'] = 1 # Enable photo taking
else: # User doesnt want to take photos
global_var['flag_take_photo'] = 0
res = allow_go_to_formation_by_id(clientId)
if (res==1): # User agrees to go to Formation in providing his id manually
name = ask_name(clientId, 1)
go_to_formation(clientId, xls_filename, name)
else: # Quit if user refuses to provide manually his id (after all other functionalities)
break
resp_allow_take_photos = 0
resp_deja_photos = 0
global_var['flag_ask'] = 0
if (global_var['flag_take_photo']==1):# and (not flag_quit)):
step_time = 1 # Interval of time (in second) between two times of taking photo
thread_take_photo = Thread(target = take_photos, args = (clientId, step_time, 1), name = 'thread_take_photo_'+clientId)
thread_take_photo.start()
global_var['flag_take_photo'] = 0
"""
Call Face API and Emotion API, and display
"""
if (global_var['key']==ord('i') or global_var['key']==ord('I')):
retrieve_face_emotion_att(clientId)
global_var['key'] = 0
"""
End of While-loop
"""
"""
Exit the program
"""
quit_program(clientId)
imgPath = "face_database_for_oxford/" # path to database of faces
imgTest = 'test.png'
flag_video = False
# Parameters
maxNbOfCandidates = 1 # Maximum number of candidates for the identification
# Training Phase
groupId = "group_all"
groupName = "employeurs"
list_nom = []
list_personId = []
nbr = 0
create_group_add_person(groupId, groupName)
result = train_person_group(groupId)
if (not flag_video):
faceDetectResult = face_api.faceDetect(None, imgTest, None)
faceRecongize(faceDetectResult)
else:
text, x0, y0, w0, h0 = '', 0, 0, 0, 0
key = 0
recognize_from_video()