def main(stream_index):
camera = cv2.VideoCapture(stream_index)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
if not camera.isOpened():
print("Cannot open stream of index {}".format(stream_index))
exit(1)
print("Input stream is of resolution: {} x {}".format(
camera.get(3), camera.get(4)))
conn = angus.connect()
service = conn.services.get_service("face_recognition", version=1)
### Choose here the appropriate pictures.
### Pictures given as samples for the album should only contain 1 visible face.
### You can provide the API with more than 1 photo for a given person.
w1_s1 = conn.blobs.create(open("./images/gwenn.jpg", 'rb'))
w2_s1 = conn.blobs.create(open("./images/aurelien.jpg", 'rb'))
w3_s1 = conn.blobs.create(open("./images/sylvain.jpg", 'rb'))
album = {'gwenn': [w1_s1], 'aurelien': [w2_s1], 'sylvain': [w3_s1]}
service.enable_session({"album": album})
while camera.isOpened():
ret, frame = camera.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
job = service.process({"image": buff})
res = job.result
for face in res['faces']:
x, y, dx, dy = face['roi']
cv2.rectangle(frame, (x, y), (x + dx, y + dy), (0, 255, 0))
if len(face['names']) > 0:
name = face['names'][0]['key']
cv2.putText(frame, "Name = {}".format(name), (x, y),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255))
cv2.imshow('original', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
service.disable_session()
camera.release()
cv2.destroyAllWindows()
def AngusGender(self, imageString, image):
path = imageORimageString(imageString, image) #path of saved image.
conn = angus.connect() #connection
service = conn.services.get_service(
"age_and_gender_estimation",
version=1) # determine which service to call
job = service.process({'image': open(path, 'rb')}) #call service
response = job.result
return response
def main(stream_index):
camera = cv2.VideoCapture(stream_index)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
if not camera.isOpened():
print("Cannot open stream of index {}".format(stream_index))
exit(1)
print("Input stream is of resolution: {} x {}".format(
camera.get(3), camera.get(4)))
conn = angus.connect()
service = conn.services.get_service('face_expression_estimation', 1)
service.enable_session()
while camera.isOpened():
ret, frame = camera.read()
if not ret:
break
### angus.ai computer vision services require gray images right now.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", gray, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
job = service.process({"image": buff})
res = job.result
for face in res['faces']:
x, y, dx, dy = face['roi']
cv2.rectangle(frame, (x, y), (x + dx, y + dy), (0, 255, 0))
### Sorting of the 5 expressions measures
### to display the most likely on the screen
exps = [(face[exp], exp) for exp in
['sadness', 'happiness', 'neutral', 'surprise', 'anger']]
exps.sort()
max_exp = exps[-1]
cv2.putText(frame, str(max_exp[1]), (x, y),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255))
cv2.imshow('original', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
### Disabling session on the server
service.disable_session()
camera.release()
cv2.destroyAllWindows()
def main():
os.environ[
'LD_LIBRARY_PATH'] = "../ARSDKBuildUtils/Targets/Unix/Install/lib"
sub = subprocess.Popen(["./JumpingSumoInterface"],
stdin=subprocess.PIPE,
stdout=None,
stderr=subprocess.STDOUT)
time.sleep(2)
conn = angus.connect()
service = conn.services.get_service('face_detection', 1)
launch("./video_fifo", sub, service)
def main():
os.environ[
'LD_LIBRARY_PATH'] = "../ARSDKBuildUtils/Targets/Unix/Install/lib"
sub = subprocess.Popen(
["./JumpingSumoInterface"],
stdin=subprocess.PIPE,
stdout=None,
stderr=subprocess.STDOUT)
time.sleep(2)
conn = angus.connect()
service = conn.services.get_service('face_detection', 1)
launch("./video_fifo", sub, service)
def AngusText2Sound(self, text, lang):
def decode_output(sound, filename):
sound = base64.b64decode(sound)
sound = zlib.decompress(sound)
with open(filename, "wb") as f:
f.write(sound)
conn = angus.connect()
service = conn.services.get_service('text_to_speech', version=1)
job = service.process({'text': text, 'lang': lang})
decode_output(job.result["sound"], "angusSound.mp3")
f1 = open("angusSound.mp3", "r")
return f1
def main(stream_index):
camera = cv2.VideoCapture(stream_index)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 320)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 240)
camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
if not camera.isOpened():
print("Cannot open stream of index {}".format(stream_index))
exit(1)
print("Video stream is of resolution {} x {}".format(
camera.get(3), camera.get(4)))
conn = angus.connect()
service = conn.services.get_service("age_and_gender_estimation", version=1)
service.enable_session()
while camera.isOpened():
ret, frame = camera.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", gray, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
job = service.process({"image": buff})
res = job.result
for face in res['faces']:
print('in face')
x, y, dx, dy = face['roi']
age = face['age']
gender = face['gender']
cv2.rectangle(frame, (x, y), (x + dx, y + dy), (0, 255, 0))
cv2.putText(frame,
"(age, gender) = ({:.1f}, {})".format(age, gender),
(x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255))
cv2.imshow('original', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
service.disable_session()
camera.release()
cv2.destroyAllWindows()
def main(stream_index):
camera = cv2.VideoCapture(stream_index)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
if not camera.isOpened():
print("Cannot open stream of index {}".format(stream_index))
exit(1)
print("Input stream is of resolution: {} x {}".format(
camera.get(3), camera.get(4)))
conn = angus.connect()
service = conn.services.get_service("upper_body_detection", version=1)
service.enable_session()
while camera.isOpened():
ret, frame = camera.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", gray, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
job = service.process({"image": buff})
res = job.result
pprint(res)
for body in res['upper_bodies']:
x, y, dx, dy = body['upper_body_roi']
cv2.rectangle(frame, (x, y), (x + dx, y + dy), (0, 255, 0))
cv2.imshow('original', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
service.disable_session()
camera.release()
cv2.destroyAllWindows()
def init_angus(self):
# Try max 5 times angus-server connexion
conn = angus.connect()
service = None
for count in range(5):
try:
service = conn.services.get_service("scene_analysis",
version=1)
service.enable_session()
break
except Exception as e:
LOGGER.error(e)
LOGGER.warning(
"Scene Analysis not ready (attempt #%s/5), wait 1s and try again",
count + 1)
time.sleep(2)
continue
if service is None:
LOGGER.error("Scene analysis is not available, shutdown")
return False
LOGGER.info("Scene Analysis service connected")
self.service = service
return True
from math import sin, cos
import cv2
import numpy as np
import angus
LINETH = 3
if __name__ == '__main__':
### To grab the host computer web cam instead of a given file, try:
cap = cv2.VideoCapture(0)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
print "Input stream is of resolution: " + str(cap.get(3)) + " x " + str(cap.get(4))
conn = angus.connect()
service = conn.services.get_service('gaze_analysis', 1)
service.enable_session()
while cap.isOpened():
ret, frame = cap.read()
if frame != None:
### angus.ai computer vision services require gray images right now.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".png", gray)
buff = StringIO.StringIO(np.array(buff).tostring())
job = service.process({"image": buff})
res = job.result
print "---------- Raw answer from Angus.ai -----------"
if inputs and outputs:
msg = "i/o"
elif inputs:
msg = "input"
elif outputs:
msg = "output"
d = int(raw_input("Select your %s: "%msg))
return d
if __name__ == "__main__":
########
# Angus
########
root = angus.connect()
################
# Input / Output
################
if len(sys.argv) == 3:
audio_in = int(sys.argv[1])
audio_out = int(sys.argv[2])
else:
audio_in = choose_io(inputs=True)
audio_out = choose_io(outputs=True)
###########
# Directory
###########
directory = dict()
import numpy as np
import angus
if __name__ == '__main__':
### To grab the host computer web cam instead of a given file, try:
### cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture(0)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
print "Input stream is of resolution: " + str(cap.get(3)) + " x " + str(
cap.get(4))
conn = angus.connect()
service = conn.services.get_service('gaze_analysis', 1)
service.enable_session()
while (cap.isOpened()):
ret, frame = cap.read()
if (frame != None):
### angus.ai computer vision services require gray images right now.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".png", gray)
buff = StringIO.StringIO(np.array(buff).tostring())
job = service.process({"image": buff})
res = job.result
print "---------- Raw answer from Angus.ai -----------"
def get_frame_proxy(self, test):
conn = angus.connect("https://gate.angus.ai", )
service = conn.services.get_service("scene_analysis", version=1)
service.enable_session()
return self.get_frame(service)
def main(stream_index):
camera = cv2.VideoCapture(0)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
if not camera.isOpened():
print("Cannot open stream of index {}".format(stream_index))
exit(1)
print("Input stream is of resolution: {} x {}".format(
camera.get(3), camera.get(4)))
conn = angus.connect()
service = conn.services.get_service('gaze_analysis', 1)
service.enable_session()
while camera.isOpened():
ret, frame = camera.read()
if not ret:
break
### angus.ai computer vision services require gray images right now.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", gray, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
job = service.process({"image": buff})
res = job.result
for face in res['faces']:
x, y, dx, dy = map(int, face['roi'])
nose = face['nose']
nose = (nose[0], nose[1])
eyel = face['eye_left']
eyel = (eyel[0], eyel[1])
eyer = face['eye_right']
eyer = (eyer[0], eyer[1])
psi = face['head_roll']
theta = -face['head_yaw']
phi = face['head_pitch']
### head orientation
length = 150
xvec = int(
length *
(sin(phi) * sin(psi) - cos(phi) * sin(theta) * cos(psi)))
yvec = int(
-length *
(sin(phi) * cos(psi) - cos(phi) * sin(theta) * sin(psi)))
cv2.line(frame, nose, (nose[0] + xvec, nose[1] + yvec),
(0, 140, 255), 3)
psi = 0
theta = -face['gaze_yaw']
phi = face['gaze_pitch']
### gaze orientation
length = 150
xvec = int(
length *
(sin(phi) * sin(psi) - cos(phi) * sin(theta) * cos(psi)))
yvec = int(
-length *
(sin(phi) * cos(psi) - cos(phi) * sin(theta) * sin(psi)))
cv2.line(frame, eyel, (eyel[0] + xvec, eyel[1] + yvec),
(0, 140, 0), 3)
xvec = int(
length *
(sin(phi) * sin(psi) - cos(phi) * sin(theta) * cos(psi)))
yvec = int(
-length *
(sin(phi) * cos(psi) - cos(phi) * sin(theta) * sin(psi)))
cv2.line(frame, eyer, (eyer[0] + xvec, eyer[1] + yvec),
(0, 140, 0), 3)
cv2.imshow('original', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
### Disabling session on the server
service.disable_session()
camera.release()
cv2.destroyAllWindows()
def main(stream_index):
p = pyaudio.PyAudio()
# Device configuration
conf = p.get_device_info_by_index(stream_index)
channels = int(conf['maxInputChannels'])
if channels < TARGET_CHANNELS:
raise RuntimeException("Bad device, no input channel")
rate = int(conf['defaultSampleRate'])
# Angus
conn = angus.connect()
service = conn.services.get_service('sound_localization', version=1)
service.enable_session()
# Record Process
stream_queue = Queue.Queue()
def chunk_callback(in_data, frame_count, time_info, status):
in_data = prepare(in_data, channels, rate)
stream_queue.put(in_data)
return (in_data, pyaudio.paContinue)
stream = p.open(format=PYAUDIO_FORMAT,
channels=channels,
rate=rate,
input=True,
frames_per_buffer=CHUNK,
input_device_index=stream_index,
stream_callback=chunk_callback)
stream.start_stream()
while True:
nb_buffer_available = stream_queue.qsize()
if nb_buffer_available > 0:
print("nb buffer available = {}".format(nb_buffer_available))
if nb_buffer_available == 0:
time.sleep(0.01)
continue
data = stream_queue.get()
buff = StringIO.StringIO()
wf = wave.open(buff, 'wb')
wf.setnchannels(TARGET_CHANNELS)
wf.setsampwidth(p.get_sample_size(PYAUDIO_FORMAT))
wf.setframerate(TARGET_RATE)
wf.writeframes(data)
wf.close()
job = service.process({
'sound': StringIO.StringIO(buff.getvalue()),
'baseline': 0.14,
'sensitivity': 0.7
})
pprint(job.result['sources'])
stream.stop_stream()
stream.close()
p.terminate()
def main():
#angus.con
conn = angus.connect("https://gate.angus.ai", )
service = conn.services.get_service("scene_analysis", version=1)
service.enable_session()
try:
camera = cv2.VideoCapture("/Users/rafiahmed/Downloads/GOPR1379.MP4")
print("Video stream is of resolution {} x {}".format(camera.get(3), camera.get(4)))
#camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
#camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
#camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
except:
print "CV2 Bombed"
return
#video = skvideo.io.VideoCapture("/Users/rafiahmed/Downloads/GOPR1379.mp4")
ret, frame = camera.read()
i = 0
while(ret):
ret, frame = camera.read()
cv2.imshow("Frame", frame)
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", gray, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
t = datetime.datetime.now(pytz.utc)
#print("Calling service")
#job = service.process({"image": buff,
# "timestamp": t.isoformat()
# })
job = service.process({"image": buff,
"timestamp": t.isoformat(),
"camera_position": "ceiling",
"sensitivity": {
"appearance": 0.7,
"disappearance": 0.7,
"age_estimated": 0.4,
"gender_estimated": 0.5,
"focus_locked": 0.9,
"emotion_detected": 0.4,
"direction_estimated": 0.8
}
})
#print("Called service")
res = job.result
pprint(res)
if "error" in res:
print("Bomb")
print(res["error"])
else:
# This parses the entities data
print("No Bomb")
for key, val in res["entities"].iteritems():
print("Iterating")
# display only gaze vectors
# retrieving eyes points
eyel, eyer = val["face_eye"]
eyel = tuple(eyel)
eyer = tuple(eyer)
# retrieving gaze vectors
psi = 0
g_yaw, g_pitch = val["gaze"]
theta = - g_yaw
phi = g_pitch
# Computing projection on screen
# and drawing vectors on current frame
length = 150
xvec = int(length * (sin(phi) * sin(psi) - cos(phi) * sin(theta) * cos(psi)))
yvec = int(- length * (sin(phi) * cos(psi) - cos(phi) * sin(theta) * sin(psi)))
cv2.line(frame, eyel, (eyel[0] + xvec, eyel[1] + yvec), (0, 140, 0), 3)
xvec = int(length * (sin(phi) * sin(psi) - cos(phi) * sin(theta) * cos(psi)))
yvec = int(- length * (sin(phi) * cos(psi) - cos(phi) * sin(theta) * sin(psi)))
cv2.line(frame, eyer, (eyer[0] + xvec, eyer[1] + yvec), (0, 140, 0), 3)
i = i+1
print (str("Frame: ") + str(i))
cv2.imshow('original', frame)
service.disable_session()
RATE = 48000
RECORD_SECONDS = 2
def decode_output(sound, filename):
sound = base64.b64decode(sound)
sound = zlib.decompress(sound)
with open(filename, "wb") as f:
f.write(sound)
### Index will differ depending on your system
INDEX = 4 # USB Cam
p = pyaudio.PyAudio()
conn = angus.connect()
service1 = conn.services.get_service('sound_detection', version=1)
service1.enable_session()
stream_queue = Queue.Queue()
conn1 = angus.connect()
service2 = conn.services.get_service('text_to_speech', version=1)
def callback(in_data, frame_count, time_info, status):
stream_queue.put(in_data)
return (in_data, pyaudio.paContinue)
stream = p.open(format=FORMAT,
channels=CHANNELS,
def main(stream_index):
camera = cv2.VideoCapture(stream_index)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
if not camera.isOpened():
print("Cannot open stream of index {}".format(stream_index))
exit(1)
print("Input stream is of resolution: {} x {}".format(camera.get(3), camera.get(4)))
conn = angus.connect()
service = conn.services.get_service("scene_analysis", version=1)
service.enable_session()
while camera.isOpened():
ret, frame = camera.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", gray, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
t = datetime.datetime.now(pytz.utc)
job = service.process({"image": buff,
"timestamp" : t.isoformat(),
"camera_position": "facing",
"sensitivity": {
"appearance": 0.7,
"disappearance": 0.7,
"age_estimated": 0.4,
"gender_estimated": 0.5,
"focus_locked": 0.9,
"emotion_detected": 0.4,
"direction_estimated": 0.8
}
})
res = job.result
if "error" in res:
print(res["error"])
else:
# This parses the events
if "events" in res:
for event in res["events"]:
value = res["entities"][event["entity_id"]][event["key"]]
print("{}| {}, {}".format(event["type"],
event["key"],
value))
# This parses the entities data
for key, val in res["entities"].iteritems():
x, y, dx, dy = map(int, val["face_roi"])
cv2.rectangle(frame, (x, y), (x+dx, y+dy), (0, 255, 0), 2)
cv2.imshow("original", frame)
if cv2.waitKey(1) & 0xFF == 27:
break
service.disable_session()
camera.release()
cv2.destroyAllWindows()
def main(stream_index):
camera = cv2.VideoCapture(stream_index)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
if not camera.isOpened():
print("Cannot open stream of index {}".format(stream_index))
exit(1)
print("Video stream is of resolution {} x {}".format(
camera.get(3), camera.get(4)))
conn = angus.connect()
service = conn.services.get_service("scene_analysis", version=1)
service.enable_session()
while camera.isOpened():
ret, frame = camera.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", gray, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
t = datetime.datetime.now(pytz.utc)
job = service.process({"image": buff, "timestamp": t.isoformat()})
res = job.result
if "error" in res:
print(res["error"])
else:
# This parses the entities data
for key, val in res["entities"].iteritems():
# display only gaze vectors
# retrieving eyes points
eyel, eyer = val["face_eye"]
eyel = tuple(eyel)
eyer = tuple(eyer)
# retrieving gaze vectors
psi = 0
g_yaw, g_pitch = val["gaze"]
theta = -g_yaw
phi = g_pitch
# Computing projection on screen
# and drawing vectors on current frame
length = 150
xvec = int(
length *
(sin(phi) * sin(psi) - cos(phi) * sin(theta) * cos(psi)))
yvec = int(
-length *
(sin(phi) * cos(psi) - cos(phi) * sin(theta) * sin(psi)))
cv2.line(frame, eyel, (eyel[0] + xvec, eyel[1] + yvec),
(0, 140, 0), 3)
xvec = int(
length *
(sin(phi) * sin(psi) - cos(phi) * sin(theta) * cos(psi)))
yvec = int(
-length *
(sin(phi) * cos(psi) - cos(phi) * sin(theta) * sin(psi)))
cv2.line(frame, eyer, (eyer[0] + xvec, eyer[1] + yvec),
(0, 140, 0), 3)
cv2.imshow('original', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
service.disable_session()
camera.release()
cv2.destroyAllWindows()
import angus
from pprint import pprint
conn = angus.connect("https://gate.angus.ai")
service = conn.services.get_service("scene_analysis", version=1)
service.enable_session()
images = ["./G0061126.JPG", "./G0061128.JPG", "./G0061053.JPG"]
for i in images:
job = service.process({"image": open(i, 'rb'),
"timestamp" : "2016-10-26T16:21:01.136287+00:00",
"camera_position": "ceiling",
"sensitivity": {
"appearance": 0.7,
"disappearance": 0.7,
"age_estimated": 0.4,
"gender_estimated": 0.5,
"focus_locked": 0.9,
"emotion_detected": 0.4,
"direction_estimated" : 0.8
}
})
print str("***************************************************************")
print "Results for: " + str(i)
pprint(job.result)
service.disable_session()
def analyze(self):
try:
services = ('age_and_gender_estimation', 'face_expression_estimation', 'gaze_analysis')
url = "https://gate.angus.ai"
c_id = "1269cac4-3537-11e6-819d-0242ac110002"
a_tk = "b407f040-fca7-4dfa-80de-f7399deed597"
conf = angus.get_default_configuration()
conf.set_credential(client_id=c_id, access_token=a_tk)
conn = angus.connect(url=url, conf=conf)
s = conn.services.get_services(services)
job = s.process({'image': open(self.photo.path)})
res = job.result
res_age_gender = res['age_and_gender_estimation']
res_expr = res['face_expression_estimation']
res_gaze = res['gaze_analysis']
self.faces = int(res_age_gender['nb_faces'])
except Exception:
return
tags = []
for face in res_age_gender['faces']:
tags.append(face['gender'])
if face['age'] < 10: tags.append("baby")
elif face['age'] < 40: tags.append("young")
else: tags.append("old")
size = res_age_gender['input_size']
roi = face['roi']
center = [roi[0] + roi[2]/2, roi[1] + roi[3]/2]
c = False
if center[0] < size[0]/2 - size[0]/10: tags.append("left")
elif center[0] > size[0]/2 + size[0]/10: tags.append("right")
else: c = True
if center[1] < size[1]/2 - size[1]/10: tags.append("up")
elif center[1] > size[1]/2 + size[1]/10: tags.append("down")
else: c = True
if c:
tags.append("center")
for face in res_expr['faces']:
if face['sadness'] > 0.5: tags.append("sad")
if face['neutral'] > 0.5: tags.append("neutral")
if face['anger'] > 0.5: tags.append("anger")
if face['surprise'] > 0.5: tags.append("surprise")
if face['happiness'] > 0.5: tags.append("happiness")
for face in res_gaze['faces']:
if face['head_pitch'] < -0.2: tags.append("head_down")
elif face['head_pitch'] > 0.2: tags.append("head_up")
else: tags.append("head_zero")
if face['head_yaw'] < -0.2: tags.append("head_right")
elif face['head_yaw'] > 0.2: tags.append("head_left")
else: tags.append("head_zero")
if face['head_roll'] < -0.2: tags.append("head_roll_right")
elif face['head_roll'] > 0.2: tags.append("head_roll_left")
else: tags.append("head_zero")
if face['gaze_pitch'] < -0.2: tags.append("eye_down")
elif face['gaze_pitch'] > 0.2: tags.append("eye_up")
else: tags.append("eye_zero")
if face['gaze_yaw'] < -0.2: tags.append("eye_right")
elif face['gaze_yaw'] > 0.2: tags.append("eye_left")
else: tags.append("eye_zero")
Tag.init_tags()
tags = set(tags)
for t in tags:
print "get", t
tag = Tag.objects.get(tag=t)
self.tags.add(tag)
self.save()
return self.faces
def main(stream_index):
p = pyaudio.PyAudio()
# Device configuration
conf = p.get_device_info_by_index(stream_index)
channels = int(conf['maxInputChannels'])
if channels < 1:
raise RuntimeException("Bad device, no input channel")
rate = int(conf['defaultSampleRate'])
if rate < 16000:
raise RuntimeException("Bad device, sample rate is too low")
# Angus
conn = angus.connect()
service = conn.services.get_service('voice_detection', version=1)
service.enable_session()
# Record Process
stream_queue = Queue.Queue()
def chunk_callback(in_data, frame_count, time_info, status):
in_data = prepare(in_data, channels, rate)
stream_queue.put(in_data.tostring())
return (in_data, pyaudio.paContinue)
stream = p.open(format=PYAUDIO_FORMAT,
channels=channels,
rate=rate,
input=True,
frames_per_buffer=CHUNK,
input_device_index=stream_index,
stream_callback=chunk_callback)
stream.start_stream()
# Get data and send to Angus.ai
while True:
nb_buffer_available = stream_queue.qsize()
if nb_buffer_available == 0:
time.sleep(0.01)
continue
data = stream_queue.get()
buff = StringIO.StringIO()
wf = wave.open(buff, 'wb')
wf.setnchannels(1)
wf.setsampwidth(p.get_sample_size(PYAUDIO_FORMAT))
wf.setframerate(16000)
wf.writeframes(data)
wf.close()
job = service.process({
'sound': StringIO.StringIO(buff.getvalue()),
'sensitivity': 0.2
})
res = job.result["voice_activity"]
if res == "VOICE":
print "\033[A \033[A"
print "***************************"
print "***** VOICE !!!! ******"
print "***************************"
stream.stop_stream()
stream.close()
p.terminate()
def f(stream_index, width, height):
camera = cv2.VideoCapture(stream_index)
camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, int(width))
camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, int(height))
camera.set(cv2.cv.CV_CAP_PROP_FPS, 10)
if not camera.isOpened():
print("Cannot open stream of index {}".format(stream_index))
exit(1)
print("Video stream is of resolution {} x {}".format(camera.get(3), camera.get(4)))
stats = st.Stats("stats.json")
animation = []
engaged = []
conn = angus.connect()
service = conn.services.get_service("scene_analysis", version=1)
service.enable_session()
while camera.isOpened():
ret, frame = camera.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, buff = cv2.imencode(".jpg", gray, [cv2.IMWRITE_JPEG_QUALITY, 80])
buff = StringIO.StringIO(np.array(buff).tostring())
t = datetime.datetime.now(pytz.utc)
job = service.process({"image": buff,
"timestamp" : t.isoformat(),
"camera_position": "facing",
"sensitivity": {
"appearance": 0.7,
"disappearance": 0.7,
"age_estimated": 0.4,
"gender_estimated": 0.5,
"focus_locked": 0.9,
"emotion_detected": 0.4,
"direction_estimated": 0.8
}
})
res = job.result
events = res["events"]
entities = res["entities"]
for idx, h in entities.iteritems():
pt = ad.displayAge(frame, idx, h, 0.50, 0.35)
ch = ad.displayHair(frame, idx, h)
ad.displayAvatar(frame, h, pt, ch)
ad.displayEmotion(frame, h, pt)
ad.displayGender(frame, h, pt)
ad.displayGaze(frame, idx, h, pt, 0.50)
panel = ((width - 180, 40), (width-20, height - 40))
ad.blur(frame, panel[0], panel[1], (255, 255, 255), 2)
ad.computeConversion(res, events, entities, engaged, stats, animation, 0.5, 500)
ad.displayConversion(frame, stats, (width - 100, int(0.3*height)))
ad.displayAnimation(frame, animation)
ad.display_logo(frame, 20, height - 60)
cv2.imshow('window', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
stats.save()
break
service.disable_session()
camera.release()
cv2.destroyAllWindows()
def __init__(self):
conn = angus.connect()
self.service = conn.services.get_service('age_and_gender_estimation',
version=1)
self.ageDist = self.parseAgeDict("ageDist.txt")
