def test_test2(self):
s = os.path.join(self.smile_dir, 'test2.smile')
j = os.path.join(self.json_dir, 'test2.jsn')
a = pysmile.encode(json.load(open(j, 'rb')))
b = open(s, 'rb').read()
self.assertEqual(a, b,
'{}\nExpected:\n{!r}\nGot:\n{!r}'.format(s, b, a))
def callback_on_message(ch, method, properties, body):
time_start = datetime.datetime.now()
# byte array to bitmap
frame = cv2.imdecode(np.asarray(bytearray(body)), cv2.IMREAD_COLOR)
# Resize frame of video to 1/4 size for faster face recognition processing
#small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
small_frame = frame # no resizing
#cv2.imwrite('test.bmp', small_frame)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
# Display the results
for (top, right, bottom, left) in face_locations:
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
# top *= 4
# right *= 4
# bottom *= 4
# left *= 4
# encode face img for transport
retval, buffer = cv2.imencode('.bmp', frame[top:bottom,
left:right].copy())
# build a message to send to RMQ
message = {
'top': top,
'right': right,
'bottom': bottom,
'left': left,
'process_time':
(datetime.datetime.now() - time_start).total_seconds(),
'face': base64.b64encode(buffer) #extract the face
}
print('Processed %s' % message['process_time'])
# push the faces to RMQ - serialize message with smile binary format
rmq_client.publish_exchange(ch, rmq_completed_exchange,
pysmile.encode(message))
def test_test2(self):
s = os.path.join(self.smile_dir, 'test2.smile')
j = os.path.join(self.json_dir, 'test2.jsn')
a = pysmile.encode(json.load(open(j, 'rb')))
b = open(s, 'rb').read()
self.assertEqual(a, b, '{}\nExpected:\n{!r}\nGot:\n{!r}'.format(s, b, a))
