def main():
print('Initializing HAT')
hat.servo_enable(1, False)
hat.servo_enable(2, False)
hat.light_mode(hat.WS2812)
hat.light_type(hat.GRBW)
print('Initializing camera')
with picamera.PiCamera() as camera:
camera.resolution = (WIDTH, HEIGHT)
camera.framerate = FRAMERATE
camera.vflip = VFLIP # flips image rightside up, as needed
camera.hflip = HFLIP # flips image left-right, as needed
sleep(1) # camera warm-up time
print('Initializing websockets server on port %d' % WS_PORT)
WebSocketWSGIHandler.http_version = '1.1'
websocket_server = make_server(
'',
WS_PORT,
server_class=WSGIServer,
handler_class=WebSocketWSGIRequestHandler,
app=WebSocketWSGIApplication(handler_cls=StreamingWebSocket))
websocket_server.initialize_websockets_manager()
websocket_thread = Thread(target=websocket_server.serve_forever)
print('Initializing HTTP server on port %d' % HTTP_PORT)
http_server = StreamingHttpServer()
http_thread = Thread(target=http_server.serve_forever)
print('Initializing broadcast thread')
output = BroadcastOutput(camera)
broadcast_thread = BroadcastThread(output.converter, websocket_server)
print('Starting recording')
camera.start_recording(output, 'yuv')
try:
print('Starting websockets thread')
websocket_thread.start()
print('Starting HTTP server thread')
http_thread.start()
print('Starting broadcast thread')
broadcast_thread.start()
while True:
camera.wait_recording(1)
except KeyboardInterrupt:
pass
finally:
print('Stopping recording')
camera.stop_recording()
print('Waiting for broadcast thread to finish')
broadcast_thread.join()
print('Shutting down HTTP server')
http_server.shutdown()
print('Shutting down websockets server')
websocket_server.shutdown()
print('Waiting for HTTP server thread to finish')
http_thread.join()
print('Disabling servos')
hat.servo_enable(1, False)
hat.servo_enable(2, False)
hat.clear()
hat.show()
print('Waiting for websockets thread to finish')
websocket_thread.join()
def test_lights():
pantilthat.light_mode(pantilthat.WS2812)
pantilthat.light_type(pantilthat.GRBW)
r, g, b, w = 0, 0, 0, 0
try:
while True:
pantilthat.set_all(0, 0, 0, 0)
pantilthat.show()
time.sleep(0.5)
pantilthat.set_all(100, 0, 0, 0)
pantilthat.show()
time.sleep(0.5)
pantilthat.set_all(0, 100, 0, 0)
pantilthat.show()
time.sleep(0.5)
pantilthat.set_all(0, 0, 100, 0)
pantilthat.show()
time.sleep(0.5)
pantilthat.set_all(100, 100, 0, 0)
pantilthat.show()
time.sleep(0.5)
pantilthat.set_all(0, 100, 100, 0)
pantilthat.show()
time.sleep(0.5)
pantilthat.set_all(100, 0, 100, 0)
pantilthat.show()
time.sleep(0.5)
pantilthat.set_all(100, 100, 100, 0)
time.sleep(0.5)
pantilthat.show()
except KeyboardInterrupt:
pantilthat.clear()
pantilthat.show()
def update(self, next_frame, frameCenter):
initial_h, initial_w, depth = next_frame.shape
in_frame = cv2.resize(next_frame, (self.w, self.h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))
self.exec_net.start_async(request_id=self.next_request_id,
inputs={self.input_blob: in_frame})
rects = []
if self.exec_net.requests[self.cur_request_id].wait(-1) == 0:
# Parse detection results of the current request
res = self.exec_net.requests[self.cur_request_id].outputs[
self.out_blob]
for obj in res[0][0]:
# Draw only objects when probability more than specified threshold
if obj[2] > 0.5:
xmin = int(obj[3] * initial_w)
ymin = int(obj[4] * initial_h)
xmax = int(obj[5] * initial_w)
ymax = int(obj[6] * initial_h)
rects.append([xmin, ymin, xmax - xmin, ymax - ymin])
self.cur_request_id, self.next_request_id = self.next_request_id, self.cur_request_id
# check to see if a face was found
if len(rects) > 0:
# extract the bounding box coordinates of the face and
# use the coordinates to determine the center of the
# face
(x, y, w, h) = rects[0]
faceX = int(x + (w / 2))
faceY = int(y + (h / 2))
# color the error
pth.set_all(255, 0, 0)
if (faceX - frameCenter[0]) > 10:
pth.set_pixel(0, 255, 255, 255)
if (faceX - frameCenter[0]) > 30:
pth.set_pixel(1, 255, 255, 255)
if (faceX - frameCenter[0]) > 50:
pth.set_pixel(2, 255, 255, 255)
if (faceX - frameCenter[0]) < -10:
pth.set_pixel(7, 255, 255, 255)
if (faceX - frameCenter[0]) < -30:
pth.set_pixel(6, 255, 255, 255)
if (faceX - frameCenter[0]) < -50:
pth.set_pixel(5, 255, 255, 255)
pth.show()
# print("face detected centroid", faceX, faceY)
# return the center (x, y)-coordinates of the face
return ((faceX, faceY), rects[0])
# otherwise no faces were found, so return the center of the
# frame
pth.clear()
pth.show()
return (frameCenter, None)
def signal_handler(sig, frame):
# print a status message
print("[INFO] You pressed `ctrl + c`! Exiting...")
# clear lights
pth.clear()
pth.show()
# exit
sys.exit()
def set_light():
getvar_dict = request.query.decode()
set = request.query.set
if (set == "on"):
pantilthat.set_all(0, 0, 0, 255)
pantilthat.show()
return ("Light On")
else:
pantilthat.clear()
pantilthat.show()
return ("Light Off")
def clear():
pantilthat.clear()
def start_camera(self, face_position_X, face_position_Y):
"""
1. Begin video stream
2. Extract faces from frames
3. Display frames with bounding boxes
4. Update global variables with:
-> pixel coordinates of the center of the frame
-> pixel coordinates of the center of the faces
"""
# signal trap to handle keyboard interrupt
signal.signal(signal.SIGINT, signal_handler)
# start the video stream and wait for the camera to warm up
# vs = VideoStream(usePiCamera=self.rpi, resolution=self.resolution).start()
print('Starting Camera')
if self.rpi:
vs = VideoStream(usePiCamera=self.rpi,
resolution=self.resolution).start()
else:
vs = VideoStream(src=1, resolution=self.resolution).start()
time.sleep(2.0)
# initialize the object center finder
face_detector = HaarFaceDetector(
os.path.join(MODELS_DIRECTORY,
'haarcascade_frontalface_default.xml'))
# initialise the recogniser
# fr = PiFaceRecognition()
# start recording
filename = os.path.join(DATA_DIRECTORY, 'recordings',
'{}.avi'.format(time.time()))
cv2.VideoWriter_fourcc(*'MJPG')
print(filename)
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter(filename, fourcc, 20.0, self.resolution)
while True:
# grab the frame from the threaded video stream and flip it
# vertically (since our camera was upside down)
frame = vs.read()
frame = cv2.flip(frame, 0)
# (H, W) = frame.shape[:2]
# print('H', H)
# find the object's location
object_locations = face_detector.extract_faces(frame)
people = [] # for setting colour
# get first face for now
if object_locations:
# print('{} faces found.'.format(len(object_locations)))
(face_position_X.value,
face_position_Y.value) = object_locations[0][0]
# print(object_locations[0][0])
# extract the bounding box and draw it
for pos, rect, neighbour, weight in object_locations:
(x, y, w, h) = rect
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0),
2)
# recogniser part
gray_frame = cv2.cvtColor(
frame, cv2.COLOR_BGR2GRAY) # convert to gray
person, confidence = PiFaceRecognition.infer_lbph_face_recogniser(
gray_frame[y:y + h, x:x + w])
people.append(person)
cv2.putText(frame, person, (x + 5, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255),
2)
cv2.putText(frame, str(confidence), (x + 5, y + h - 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 1)
else:
print('No faces found.')
face_position_X.value, face_position_Y.value = (
self.frame_center_X, self.frame_center_Y)
if len(people) > 1:
# set to orange
pth.set_all(255, 127, 80, 50)
pth.show()
elif 'jai' in people:
# set to green
pth.set_all(173, 255, 47, 50)
pth.show()
elif 'alleeya' in people:
# set to purple
pth.set_all(221, 160, 221, 50)
pth.show()
else:
pth.clear()
pth.show()
# display the frame to the screen
cv2.imshow("Pan-Tilt Face Tracking", frame)
out.write(frame)
cv2.waitKey(1)
out.release()
def update(self, frame, frameCenter):
# convert the frame to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# self.flip += 1
# if self.flip < 100:
# return ((320,240), None)
# elif self.flip < 200:
# return (((160,120), None))
# elif self.flip < 300:
# self.flip = 0
# return (((160,120), None))
# if self.flip:
# self.testcoord[0] -= 1
# else:
# self.testcoord[0] += 1
# if self.testcoord[0] > 170:
# self.flip = True
# if self.testcoord[0] < 150:
# self.flip = False
# print(self.testcoord)
# return (self.testcoord, (self.testcoord[0], self.testcoord[1], 10, 10))
# detect all faces in the input frame
rects = self.detector.detectMultiScale(gray,
scaleFactor=1.05,
minNeighbors=9,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
# check to see if a face was found
if len(rects) > 0:
# extract the bounding box coordinates of the face and
# use the coordinates to determine the center of the
# face
(x, y, w, h) = rects[0]
faceX = int(x + (w / 2))
faceY = int(y + (h / 2))
# color the error
pth.set_all(255, 0, 0)
if (faceX - frameCenter[0]) > 10:
pth.set_pixel(0, 255, 255, 255)
if (faceX - frameCenter[0]) > 30:
pth.set_pixel(1, 255, 255, 255)
if (faceX - frameCenter[0]) > 50:
pth.set_pixel(2, 255, 255, 255)
if (faceX - frameCenter[0]) < -10:
pth.set_pixel(7, 255, 255, 255)
if (faceX - frameCenter[0]) < -30:
pth.set_pixel(6, 255, 255, 255)
if (faceX - frameCenter[0]) < -50:
pth.set_pixel(5, 255, 255, 255)
pth.show()
# print("face detected centroid", faceX, faceY)
# return the center (x, y)-coordinates of the face
return ((faceX, faceY), rects[0])
# otherwise no faces were found, so return the center of the
# frame
pth.clear()
pth.show()
return (frameCenter, None)
def do_LIGHT( self, value = 0 ) :
if value < 0 : value = 0
if value > 255 : value = 255
pantilthat.clear()
pantilthat.set_all( value, value, value )
pantilthat.show()
def do_LIGHT( self, value = 0 ) :
if value < 0 : value = 0
if value > 255 : value = 255
pantilthat.clear()
pantilthat.set_all( value, value, value )
pantilthat.show()
# http://docs.pimoroni.com/pantilthat/
if __name__ == '__main__' :
pantilthat.light_mode( pantilthat.WS2812 )
pantilthat.light_type( pantilthat.GRBW )
pantilthat.pan( 0 )
pantilthat.tilt( 0 )
pantilthat.clear()
pantilthat.show()
if __name__ == '__main__' :
time.sleep( 13 )
# resolution = ( 1920, 1080 )
resolution = ( 1024, 768 )
with picamera.PiCamera( resolution = resolution, framerate = 25 ) as camera:
camera.rotation = 180
camera.led = True
output = StreamingOutput()
camera.start_recording( output, format = 'mjpeg' )
try : StreamingServer( ( '', 80 ), WebcamHandler ).serve_forever()
except : print >>sys.stderr, sys.exc_info()[ 1 ]
finally: camera.stop_recording()