class Player(Service): """Controls buzzer.""" def __init__(self, gpio, bpm): super().__init__() self._toneplayer = TonePlayer(gpio, bpm) def process(self, sound): self._toneplayer.play(*sound) def play(self, sound): self.submit(sound)
class Player(Service): """Controls buzzer.""" def __init__(self, gpio, bpm): super().__init__() self._toneplayer = TonePlayer(gpio, bpm) def process(self, sound): self._toneplayer.play(*sound) def play(self, sound): self.submit(sound)
class Player(Service): """Controls buzzer.""" def __init__(self, gpio, bpm): super().__init__() self._toneplayer = TonePlayer(gpio, bpm) self.JOY_SOUND = ('C5q', 'E5q', 'C6q') self.SAD_SOUND = ('C6q', 'E5q', 'C5q') self.MODEL_LOAD_SOUND = ('C6w', 'c6w', 'C6w') self.BEEP_SOUND = ('E6q', 'C6q') def process(self, sound): self._toneplayer.play(*sound) def play(self, sound): self.submit(sound)
def main(): print("Play tune") player = TonePlayer(gpio=BUZZER_GPIO_PIN, bpm=10) player.play(*START_SOUND) print("Initialize robot") robot = Robot() robot.resetPosition() print("Switch on leds") with Leds() as leds: leds.update(Leds.rgb_on(Color.GREEN)) print("Switch on camera") with PiCamera(sensor_mode=4, resolution=(CAMERA_WIDTH, CAMERA_HEIGHT), framerate=30) as camera: if ENABLE_DISPLAY: camera.start_preview() annotator = Annotator(camera, dimensions=(320, 240)) else: annotator = None print("Load model") with CameraInference(face_detection.model()) as inference: loop(inference=inference, robot=robot, annotator=annotator, leds=leds) if ENABLE_DISPLAY: camera.stop_preview() player.play(*STOP_SOUND) # Give time for the user to remote its finger. sleep(3) robot.resetPosition()
def main(): def face_data(face): x, y, width, height = face.bounding_box x_mean = int(x + width/2) angle = atan2(x_mean - x_center,focal_length) distance = 0 if width > 0: distance = focal_length * real_face_width_inch / width return angle, distance parser = argparse.ArgumentParser() parser.add_argument( '--num_frames', '-n', type=int, dest='num_frames', default=-1, help='Sets the number of frames to run for, otherwise runs forever.') args = parser.parse_args() focal_length = 1320 # focal length in pixels for 1640 x 1232 resolution - found by calibration camera_resolution = (1640, 1232) x_center = int(camera_resolution[0] / 2) real_face_width_inch = 11 # width/height of bounding box of human face in inches min_angle = atan2(-x_center,focal_length) # min angle where face can be detected (leftmost area) in radians max_angle = atan2(x_center,focal_length) face_detected_on_prev_frame = False previous_angle = 0 LOAD_SOUND = ('G5e', 'f5e', 'd5e', 'A5e', 'g5e', 'E5e', 'g5e', 'C6e') BUZZER_GPIO = 22 with PiCamera(sensor_mode=4, resolution=(1640, 1232), framerate=30) as camera,\ Leds() as leds: leds.update(Leds.privacy_on()) myCorrectionMin=0.2 myCorrectionMax=0.2 maxPW=(2.0+myCorrectionMax)/1000 minPW=(1.0-myCorrectionMin)/1000 camera.start_preview() tone_player = TonePlayer(BUZZER_GPIO, bpm=70) tone_player.play(*LOAD_SOUND) #servo = AngularServo(PIN_A, min_pulse_width=minPW, max_pulse_width=maxPW) servo = AngularServo(PIN_A, max_pulse_width = maxPW) #servo = AngularServo(PIN_A) annotator = Annotator(camera, dimensions=(320, 240)) scale_x = 320 / 1640 scale_y = 240 / 1232 def transform(bounding_box): x, y, width, height = bounding_box return (scale_x * x, scale_y * y, scale_x * (x + width), scale_y * (y + height)) with CameraInference(face_detection.model()) as inference: for i, result in enumerate(inference.run()): if i == args.num_frames: break faces = face_detection.get_faces(result) annotator.clear() for face in faces: annotator.bounding_box(transform(face.bounding_box), fill=0) annotator.update() print('Iteration #%d: num_faces=%d' % (i, len(faces))) if faces: previous_angle = 0 leds.update(Leds.rgb_on(Color.BLUE)) if face_detected_on_prev_frame: angle, distance = face_data(face) #if angle < min_angle: # angle = min_angle #if angle > max_angle: # angle = max_angle servo.angle = angle*(-100) previous_angle = angle*(-100) print('Angle:' + str(angle)) sleep(.05) face_detected_on_prev_frame = True else: leds.update(Leds.rgb_on(Color.RED)) if not face_detected_on_prev_frame: servo.angle = previous_angle sleep(.05) pass face_detected_on_prev_frame = False camera.stop_preview()
def main(): model_path = '/opt/aiy/models/retrained_graph.binaryproto' #model_path = '/opt/aiy/models/mobilenet_v1_160res_0.5_imagenet.binaryproto' label_path = '/opt/aiy/models/retrained_labels_new.txt' #label_path = '/opt/aiy/models/mobilenet_v1_160res_0.5_imagenet_labels.txt' model_path = '/opt/aiy/models/rg_v3_new.binaryproto' label_path = '/opt/aiy/models/retrained_labels_new.txt' input_height = 160 input_width = 160 input_layer = 'input' output_layer = 'final_result' threshold = 0.8 # Model & labels model = ModelDescriptor( name='mobilenet_based_classifier', input_shape=(1, input_height, input_width, 3), input_normalizer=(128.0, 128.0), compute_graph=utils.load_compute_graph(model_path)) labels = read_labels(label_path) new_labels = [] for eachLabel in labels: if len(eachLabel)>1: new_labels.append(eachLabel) labels = new_labels #print(labels) s = xmlrpc.client.ServerProxy("http://aiy.mdzz.info:8000/") player = TonePlayer(BUZZER_GPIO, 10) player.play(*MODEL_LOAD_SOUND) while True: while True: if s.camera() == 1: print('vision kit is woken up') with Leds() as leds: leds.pattern = Pattern.blink(100) leds.update(Leds.rgb_pattern(Color.RED)) time.sleep(2.0) start_time = round(time.time()) break time.sleep(0.2) print('no signal, sleeping...') with PiCamera() as camera: # Configure camera camera.sensor_mode = 4 camera.resolution = (1664, 1232) # Full Frame, 16:9 (Camera v2) camera.framerate = 30 camera.start_preview() while True: # Do inference on VisionBonnet #print('Start capturing') with CameraInference(face_detection.model()) as inference: for result in inference.run(): #print(type(result)) faces = face_detection.get_faces(result) if len(faces) >= 1: #print('camera captures...') extension = '.jpg' filename = time.strftime('%Y-%m-%d %H:%M:%S') + extension camera.capture(filename) image_npp = np.empty((1664 * 1232 * 3,), dtype=np.uint8) camera.capture(image_npp, 'rgb') image_npp = image_npp.reshape((1232, 1664, 3)) image_npp = image_npp[:1232, :1640, :] # image = Image.open('jj.jpg') # draw = ImageDraw.Draw(image) faces_data = [] faces_cropped = [] for i, face in enumerate(faces): # print('Face #%d: %s' % (i, face)) x, y, w, h = face.bounding_box #print(x,y,w,h) w_rm = int(0.3 * w / 2) face_cropped = crop_np((x, y, w, h), w_rm, image_npp) if face_cropped is None: continue #print('face_cropped None'); continue # faces_data.append(image[y: y + h, x + w_rm: x + w - w_rm]) # image[y: y + h, x + w_rm: x + w - w_rm].save('1.jpg') face_cropped.save('face_cropped_'+str(i)+'.jpg') faces_cropped.append(face_cropped) #break break # else: # tt = round(time.time()) - start_time # if tt > 10: # break #print('face cutting finishes') #print(type(faces_cropped), len(faces_cropped)) player.play(*BEEP_SOUND) flag = 0 for eachFace in faces_cropped: #print(type(eachFace)) if eachFace is None: flag = 1 if (len(faces_cropped)) <= 0: flag = 1 if flag == 1: continue with ImageInference(model) as img_inference: #with CameraInference(model) as img_inference: print('Entering classify_hand_gestures()') output = classify_hand_gestures(img_inference, faces_cropped, model=model, labels=labels, output_layer=output_layer, threshold=threshold) #print(output) if (output == 3): player.play(*JOY_SOUND) print('Yani face detected') print(s.result("Owner", filename)) else: player.play(*SAD_SOUND) print('Suspicious face detected') print(s.result("Unknown Face", filename)) upload(filename) # Stop preview # #break while (s.camera()==0): print('sleeping') time.sleep(.2) print('Waken up')
def main(): """Face detection camera inference example.""" parser = argparse.ArgumentParser() parser.add_argument( '--num_frames', '-n', type=int, dest='num_frames', default=None, help='Sets the number of frames to run for, otherwise runs forever.') args = parser.parse_args() # Forced sensor mode, 1640x1232, full FoV. See: # https://picamera.readthedocs.io/en/release-1.13/fov.html#sensor-modes # This is the resolution inference run on. with PiCamera(sensor_mode=4, resolution=(1640, 1232), framerate=30) as camera,\ Leds() as leds: leds.update(Leds.privacy_on()) leds.update(Leds.rgb_on(Color.BLUE)) camera.start_preview() tone_player = TonePlayer(BUZZER_GPIO, bpm=70) tone_player.play(*LOAD_SOUND) # Annotator renders in software so use a smaller size and scale results # for increased performace. annotator = Annotator(camera, dimensions=(320, 240)) scale_x = 320 / 1640 scale_y = 240 / 1232 # Incoming boxes are of the form (x, y, width, height). Scale and # transform to the form (x1, y1, x2, y2). def transform(bounding_box): x, y, width, height = bounding_box return (scale_x * x, scale_y * y, scale_x * (x + width), scale_y * (y + height)) with CameraInference(face_detection.model()) as inference: for result in inference.run(args.num_frames): faces = face_detection.get_faces(result) annotator.clear() for face in faces: annotator.bounding_box(transform(face.bounding_box), fill=0) x, y, width, height = face.bounding_box annotator.update() if len(faces) >= 1: print( '#%05d (%5.2f fps): num_faces=%d, avg_joy_score=%.2f, x=%.2f, y=%.2f, width=%.2f, height=%.2f' % (inference.count, inference.rate, len(faces), avg_joy_score(faces), x, y, width, height)) if x > 0: alpha = x / float(1200) else: alpha = .5 try: leds.update( Leds.rgb_on( Color.blend(LEFT_COLOR, Color.GREEN, alpha))) except: pass distance = focal_length * real_face_width_inches / width camera.annotate_text = '%d inches' % distance else: pass camera.stop_preview()