def start_self_driving():
global on
model = inference.ModelDescriptor(
name='mobilenet_160',
input_shape=(1, 160, 160, 3),
input_normalizer=(128.0, 128.0),
compute_graph=utils.load_compute_graph(MODEL_NAME))
with PiCamera(sensor_mode=4, resolution=(160, 160),
framerate=30) as camera:
camera_thread = threading.Thread(target=capture, args=(camera, ))
camera_thread.daemon = True
camera_thread.start()
with inference.CameraInference(model) as inf:
print('Model is ready. Type on/off to start/stop self-driving')
sys.stdout.flush()
on_off_thread = threading.Thread(target=on_off, args=())
on_off_thread.daemon = True
on_off_thread.start()
for result in inf.run():
if on:
direction, probability = process(result)
print('prediction: {:.2f} {} {:.2f}'.format(
time.time(), direction, probability))
sys.stdout.flush()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input_layer',
default='map/TensorArrayStack/TensorArrayGatherV3',
help='Name of input layer.')
parser.add_argument('--output_layer',
default="prediction",
help='Name of output layer.')
parser.add_argument(
'--num_frames',
type=int,
default=-1,
help='Sets the number of frames to run for, otherwise runs forever.')
parser.add_argument('--input_mean',
type=float,
default=128.0,
help='Input mean.')
parser.add_argument('--input_std',
type=float,
default=128.0,
help='Input std.')
parser.add_argument(
'--threshold',
type=float,
default=0.1,
help='Threshold for classification score (from output tensor).')
parser.add_argument('--top_k',
type=int,
default=3,
help='Keep at most top_k labels.')
parser.add_argument('--detecting_list',
type=list,
default=[
'Biston betularia (Peppered Moth)',
'Spodoptera litura (Oriental Leafworm Moth)'
],
help='Input a list of bugs that you want to keep.')
parser.add_argument('--message_threshold',
type=int,
default=1,
help='Input detection threshold for sending sms')
args = parser.parse_args()
model = inference.ModelDescriptor(
name='mobilenet_based_classifier',
input_shape=(1, 192, 192, 3),
input_normalizer=(128.0, 128.0),
compute_graph=utils.load_compute_graph(
'mobilenet_v2_192res_1.0_inat_insect.binaryproto'))
labels = read_labels(
"/home/pi/models/mobilenet_v2_192res_1.0_inat_insect_labels.txt")
detector = FawDetector()
detector.run(args.input_layer, args.output_layer, args.num_frames,
args.input_mean, args.input_std, args.threshold, args.top_k,
args.detecting_list, args.message_threshold, model, labels)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', required=True,
help='Path to converted model file that can run on VisionKit.')
parser.add_argument('--label_path', required=True,
help='Path to label file that corresponds to the model.')
parser.add_argument('--input_height', type=int, required=True, help='Input height.')
parser.add_argument('--input_width', type=int, required=True, help='Input width.')
parser.add_argument('--input_layer', required=True, help='Name of input layer.')
parser.add_argument('--output_layer', required=True, help='Name of output layer.')
parser.add_argument('--num_frames', type=int, default=None,
help='Sets the number of frames to run for, otherwise runs forever.')
parser.add_argument('--input_mean', type=float, default=128.0, help='Input mean.')
parser.add_argument('--input_std', type=float, default=128.0, help='Input std.')
parser.add_argument('--input_depth', type=int, default=3, help='Input depth.')
parser.add_argument('--threshold', type=float, default=0.1,
help='Threshold for classification score (from output tensor).')
parser.add_argument('--top_k', type=int, default=3, help='Keep at most top_k labels.')
parser.add_argument('--preview', action='store_true', default=False,
help='Enables camera preview in addition to printing result to terminal.')
parser.add_argument('--show_fps', action='store_true', default=False,
help='Shows end to end FPS.')
args = parser.parse_args()
model = inference.ModelDescriptor(
name='mobilenet_based_classifier',
input_shape=(1, args.input_height, args.input_width, args.input_depth),
input_normalizer=(args.input_mean, args.input_std),
compute_graph=utils.load_compute_graph(args.model_path))
labels = read_labels(args.label_path)
with PiCamera(sensor_mode=4, resolution=(1640, 1232), framerate=30) as camera:
if args.preview:
camera.start_preview()
with inference.CameraInference(model) as camera_inference:
for result in camera_inference.run(args.num_frames):
processed_result = process(result, labels, args.output_layer,
args.threshold, args.top_k)
send_signal_to_servos(processed_result[0])
message = get_message(processed_result, args.threshold, args.top_k)
if args.show_fps:
message += '\nWith %.1f FPS.' % camera_inference.rate
print(message)
if args.preview:
camera.annotate_foreground = Color('black')
camera.annotate_background = Color('white')
# PiCamera text annotation only supports ascii.
camera.annotate_text = '\n %s' % message.encode(
'ascii', 'backslashreplace').decode('ascii')
if args.preview:
camera.stop_preview()
def start_self_driving():
model = inference.ModelDescriptor(
name='mobilenet_160',
input_shape=(1, 160, 160, 3),
input_normalizer=(128.0, 128.0),
compute_graph=utils.load_compute_graph(MODEL_NAME))
print('Model loaded')
with PiCamera(sensor_mode=4, resolution=(160, 160), framerate=30) as camera:
print('Connected to the Pi Camera')
with inference.CameraInference(model) as inf:
for result in inf.run():
direction, probability = process(result)
RCool_drive.drive(direction)
print('{:.2f} {} {:.2f}'.format(time.time(), direction, probability))
def main():
model = inference.ModelDescriptor(
name='mobilenet_160',
input_shape=(1, 160, 160, 3),
input_normalizer=(128.0, 128.0),
compute_graph=utils.load_compute_graph('dumb.binaryproto'))
with inference.ImageInference(model) as inf:
print('Waiting for input...')
sys.stdout.flush()
for _ in sys.stdin:
now = time.time()
img = Image.open('{}/current.jpg'.format(os.getcwd()))
result = inf.run(img)
label, probability = process(result)
print('prediction: {} {} {}'.format(now, label, probability))
sys.stdout.flush()
def initialize(self):
if Model.car is None:
car = Car()
if Car.connected:
Model.car = car
self.log('INFO', 'Car for self-driving is connected')
else:
self.log('ERROR', 'Car is not connected for self-driving')
return False
if Model.model is None:
try:
from aiy.vision import inference
from aiy.vision.models import utils
Model.model = inference.ModelDescriptor(
name='mobilenet_160',
input_shape=(1, 160, 160, 3),
input_normalizer=(128.0, 128.0),
compute_graph=utils.load_compute_graph(MODEL_NAME))
self.log('INFO', 'Self-driving model is loaded')
except Exception as e:
self.log(
'ERROR',
'Self-driving model cannot be loaded: {}'.format(str(e)))
return False
if Model.inference_engine is None:
try:
from aiy.vision import inference
Model.inference_engine = inference.InferenceEngine()
try:
Model.inference_engine.unload_model('mobilenet_160')
except:
pass
Model.model_name = Model.inference_engine.load_model(
Model.model)
Model.good = True
self.log('INFO', 'Image inference has started')
except Exception as e:
self.log(
'ERROR',
'Image inference cannot be started: {}'.format(str(e)))
return False
return True
def main():
# Loading the model and label
model = inference.ModelDescriptor(
name='mobilenet_based_classifier',
input_shape=(1, 160, 160, 3),
input_normalizer=(128.0, 128.0),
compute_graph=utils.load_compute_graph('CrackClassification_graph.binaryproto'))
print("Model loaded.")
labels = read_labels(label_path + 'crack_label.txt')
print("Labels loaded")
# Classifier parameters
top_k = 3
threshold = 0.4
num_frame = None
show_fps = False
# LED setup
ledRED = LED(PIN_B)
ledGREEN = LED(PIN_A)
ledRED.off()
ledGREEN.on()
with PiCamera(sensor_mode=4, resolution=(1640, 1232), framerate=30) as camera:
with inference.CameraInference(model) as camera_inference:
for result in camera_inference.run(num_frame):
processed_result = process(result, labels, 'final_result',threshold, top_k)
if processed_result[0][0] == 'positive':
print("CRACK")
ledGREEN.off()
ledRED.on()
else:
print("CLEAR")
ledRED.off()
ledGREEN.on()
print("Camera inference rate: " + str(camera_inference.rate))
with open(filename) as fp:
fp = open(filename)
tmp_shutter_numb = fp.readlines()
tmp_shutter_numb = tmp_shutter_numb[0].rstrip()
shutter_numb = int(tmp_shutter_numb)
def read_labels(label_path):
with open(label_path) as label_file:
return [label.strip() for label in label_file.readlines()]
model = inference.ModelDescriptor(
name='mobilenet_based_classifier',
input_shape=(1, args.input_height, args.input_width, args.input_depth),
input_normalizer=(args.input_mean, args.input_std),
compute_graph=utils.load_compute_graph(args.model_path))
labels = read_labels(args.label_path)
def get_message(result, threshold, top_k):
if result:
return '%s' % '\n'.join(result)
else:
return 'Nothing detected when threshold=%.2f, top_k=%d' % (threshold,
top_k)
def process(result, labels, tensor_name, threshold, top_k):
"""Processes inference result and returns labels sorted by confidence."""
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dog_park_model_path',
help='Path to the model file for the dog park.')
parser.add_argument('--vb1_model_path',
help='Path to the model file for volley ball court 1.')
parser.add_argument('--vb2_model_path',
help='Path to the model file for volley ball court 1.')
parser.add_argument(
'--label_path',
required=True,
help='Path to label file that corresponds to the model.')
parser.add_argument('--input_mean',
type=float,
default=128.0,
help='Input mean.')
parser.add_argument('--input_std',
type=float,
default=128.0,
help='Input std.')
parser.add_argument('--input_depth',
type=int,
default=3,
help='Input depth.')
parser.add_argument('--enable_streaming',
default=False,
action='store_true',
help='Enable streaming server')
parser.add_argument('--streaming_bitrate',
type=int,
default=1000000,
help='Streaming server video bitrate (kbps)')
parser.add_argument('--mdns_name',
default='',
help='Streaming server mDNS name')
parser.add_argument(
'--preview',
action='store_true',
default=False,
help=
'Enables camera preview in addition to printing result to terminal.')
parser.add_argument(
'--time_interval',
type=int,
default=10,
help='Time interval at which to store data in seconds.')
parser.add_argument(
'--gather_data',
action='store_true',
default=False,
help='Also save images according to the assigned category.')
parser.add_argument(
'--timelapse',
action='store_true',
default=False,
help='Also save some timelapses of the entire scene, every 120 seconds.'
)
parser.add_argument('--image_folder',
default='/home/pi/Pictures/Data',
help='Folder to save captured images')
args = parser.parse_args()
labels = read_labels(args.label_path)
# At least one model needs to be passed in.
assert args.dog_park_model_path or args.vb1_model_path or args.vb2_model_path
# Check that the folder exists
if args.gather_data:
expected_subfolders = ['dog_park', 'court_one', 'court_two']
subfolders = os.listdir(args.image_folder)
for folder in expected_subfolders:
assert folder in subfolders
with ExitStack() as stack:
dog_park = {
'location_name': 'dog_park',
'path': args.dog_park_model_path,
} if args.dog_park_model_path else None
vb1 = {
'location_name': 'court_one',
'path': args.vb1_model_path,
} if args.vb1_model_path else None
vb2 = {
'location_name': 'court_two',
'path': args.vb2_model_path,
} if args.vb2_model_path else None
# Get the list of models, filter to only the ones that were passed in.
models = [dog_park, vb1, vb2]
models = list(filter(lambda model: model, models))
# Initialize models and add them to the context
for model in models:
print('Initializing {model_name}...'.format(
model_name=model["location_name"]))
descriptor = inference.ModelDescriptor(
name='mobilenet_based_classifier',
input_shape=(1, 160, 160, args.input_depth),
input_normalizer=(args.input_mean, args.input_std),
compute_graph=utils.load_compute_graph(model['path']))
model['descriptor'] = descriptor
if dog_park:
dog_park['image_inference'] = stack.enter_context(
inference.ImageInference(dog_park['descriptor']))
if vb1:
vb1['image_inference'] = stack.enter_context(
inference.ImageInference(vb1['descriptor']))
if vb2:
vb2['image_inference'] = stack.enter_context(
inference.ImageInference(vb2['descriptor']))
camera = stack.enter_context(
PiCamera(sensor_mode=4, resolution=(820, 616), framerate=30))
server = None
if args.enable_streaming:
server = stack.enter_context(
StreamingServer(camera,
bitrate=args.streaming_bitrate,
mdns_name=args.mdns_name))
if args.preview:
# Draw bounding boxes around locations
# Load the arbitrarily sized image
img = Image.new('RGB', (820, 616))
draw = ImageDraw.Draw(img)
for location in LOCATIONS.values():
x1, y1, x2, y2 = location
draw_rectangle(draw, x1, y1, x2, y2, 3, outline='white')
# Create an image padded to the required size with
# mode 'RGB'
pad = Image.new('RGB', (
((img.size[0] + 31) // 32) * 32,
((img.size[1] + 15) // 16) * 16,
))
# Paste the original image into the padded one
pad.paste(img, (0, 0))
# Add the overlay with the padded image as the source,
# but the original image's dimensions
camera.add_overlay(pad.tobytes(), alpha=64, layer=3, size=img.size)
camera.start_preview()
data_filename = _make_filename(args.image_folder, 'data', None, 'json')
data_generator = commit_data_to_long_term(args.time_interval,
data_filename)
data_generator.send(None)
# Capture one picture of entire scene each time it's started again.
time.sleep(2)
date = time.strftime('%Y-%m-%d')
scene_filename = _make_filename(args.image_folder, date, None)
camera.capture(scene_filename)
# Draw bounding box on image showing the crop locations
with Image.open(scene_filename) as scene:
draw = ImageDraw.Draw(scene)
for location in LOCATIONS.values():
x1, y1, x2, y2 = location
draw_rectangle(draw, x1, y1, x2, y2, 3, outline='white')
scene.save(scene_filename)
# Constantly get cropped images
for cropped_images in get_cropped_images(camera, args.timelapse):
svg_doc = None
if args.enable_streaming:
width = 820 * SVG_SCALE_FACTOR
height = 616 * SVG_SCALE_FACTOR
svg_doc = svg.Svg(width=width, height=height)
for location in LOCATIONS.values():
x, y, x2, y2 = location
w = (x2 - x) * SVG_SCALE_FACTOR
h = (y2 - y) * SVG_SCALE_FACTOR
x = x * SVG_SCALE_FACTOR
y = y * SVG_SCALE_FACTOR
svg_doc.add(
svg.Rect(
x=int(x),
y=int(y),
width=int(w),
height=int(h),
rx=10,
ry=10,
fill_opacity=0.3,
style='fill:none;stroke:white;stroke-width:4px'))
# For each inference model, crop and process a different thing.
for model in models:
location_name = model['location_name']
image_inference = model['image_inference']
cropped_image = cropped_images[location_name]
# TODO: (Image Comparison) If False,return no activity.
if cropped_image:
# then run image_inference on them.
result = image_inference.run(cropped_image)
processed_result = process(result, labels, 'final_result')
data_generator.send(
(location_name, processed_result, svg_doc))
message = get_message(processed_result)
# Print the message
# print('\n')
# print('{location_name}:'.format(location_name=location_name))
# print(message)
else:
# Fake processed_result
processed_result = [('inactive', 1.00), ('active', 0.00)]
data_generator.send(
(location_name, processed_result, svg_doc))
label = processed_result[0][0]
timestamp = time.strftime('%Y-%m-%d_%H.%M.%S')
# print(timestamp)
# print('\n')
if args.gather_data and cropped_image:
# Gather 1% data on 'no activity' since it's biased against that.
# Gather 0.1% of all images.
if (
# (label == 'no activity' and random.random() > 0.99) or
# (random.random() > 0.999)
# (location_name != 'dog_park' and random.random() > 0.99) or
(random.random() > 0.9)):
subdir = '{location_name}/{label}'.format(
location_name=location_name, label=label)
filename = _make_filename(args.image_folder, timestamp,
subdir)
cropped_image.save(filename)
# if svg_doc:
# ## Plot points out
# ## 160 x 80 grid
# ## 16px width
# ## 20, 40, 60 for 0, 1, 2
# lines = message.split('\n')
# y_correction = len(lines) * 20
# for line in lines:
# svg_doc.add(svg.Text(line,
# x=(LOCATIONS[location_name][0]) * SVG_SCALE_FACTOR,
# y=(LOCATIONS[location_name][1] - y_correction) * SVG_SCALE_FACTOR,
# fill='white', font_size=20))
# y_correction = y_correction - 20
# TODO: Figure out how to annotate at specific locations.
# if args.preview:
# camera.annotate_foreground = Color('black')
# camera.annotate_background = Color('white')
# # PiCamera text annotation only supports ascii.
# camera.annotate_text = '\n %s' % message.encode(
# 'ascii', 'backslashreplace').decode('ascii')
if server:
server.send_overlay(str(svg_doc))
if args.preview:
camera.stop_preview()
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
required=True,
help='Path to converted model file that can run on VisionKit.')
parser.add_argument(
'--label_path',
required=True,
help='Path to label file that corresponds to the model.')
parser.add_argument('--input_height',
type=int,
required=True,
help='Input height.')
parser.add_argument('--input_width',
type=int,
required=True,
help='Input width.')
parser.add_argument('--input_layer',
required=True,
help='Name of input layer.')
parser.add_argument('--output_layer',
required=True,
help='Name of output layer.')
parser.add_argument('--input_mean',
type=float,
default=128.0,
help='Input mean.')
parser.add_argument('--input_std',
type=float,
default=128.0,
help='Input std.')
parser.add_argument('--input_depth',
type=int,
default=3,
help='Input depth.')
parser.add_argument(
'--threshold',
type=float,
default=0.1,
help='Threshold for classification score (from output tensor).')
parser.add_argument('--top_k',
type=int,
default=1,
help='Keep at most top_k labels.')
args = parser.parse_args()
model = inference.ModelDescriptor(
name='mobilenet_based_classifier',
input_shape=(1, args.input_height, args.input_width, args.input_depth),
input_normalizer=(args.input_mean, args.input_std),
compute_graph=utils.load_compute_graph(args.model_path))
labels = read_labels(args.label_path)
print("Taking photo")
with PiCamera() as camera:
camera.resolution = (640, 480)
camera.start_preview()
sleep(3.000)
camera.capture(photo_filename)
with inference.ImageInference(model) as image_inference:
image = Image.open(photo_filename)
result = image_inference.run(image)
processed_result = process(result, labels, args.output_layer,
args.threshold, args.top_k)
message = get_message(processed_result, args.threshold, args.top_k)
return message
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input_layer',
default='map/TensorArrayStack/TensorArrayGatherV3',
help='Name of input layer.')
parser.add_argument('--output_layer',
default="prediction",
help='Name of output layer.')
parser.add_argument(
'--num_frames',
type=int,
default=-1,
help='Sets the number of frames to run for, otherwise runs forever.')
parser.add_argument('--input_mean',
type=float,
default=128.0,
help='Input mean.')
parser.add_argument('--input_std',
type=float,
default=128.0,
help='Input std.')
parser.add_argument(
'--threshold',
type=float,
default=0.6,
help='Threshold for classification score (from output tensor).')
parser.add_argument('--top_k',
type=int,
default=3,
help='Keep at most top_k labels.')
parser.add_argument(
'--detecting_list',
type=list,
default=[
'Biston betularia (Peppered Moth)',
'Spodoptera litura (Oriental Leafworm Moth)',
'Utetheisa ornatrix (Ornate Bella Moth)',
'Polygrammate hebraeicum (Hebrew Moth)',
'Palpita magniferalis (Splendid Palpita Moth) (0.14)',
'Hyles lineata (White-lined Sphinx Moth)',
'Hemileuca eglanterina (Western Sheep Moth)',
'Ceratomia undulosa (Waved Sphinx Moth)',
'Nadata gibbosa (White-dotted Prominent Moth)',
'Lophocampa caryae (Hickory Tussock Moth)',
'Spodoptera ornithogalli (Yellow-striped Armyworm Moth)',
'Spodoptera litura (Oriental Leafworm Moth)',
'Charadra deridens (Laugher Moth)'
],
help='Input a list of bugs that you want to keep.')
parser.add_argument('--message_threshold',
type=int,
default=4,
help='Input detection threshold for sending sms')
args = parser.parse_args()
model = inference.ModelDescriptor(
name='mobilenet_based_classifier',
input_shape=(1, 192, 192, 3),
input_normalizer=(128.0, 128.0),
compute_graph=utils.load_compute_graph(
'mobilenet_v2_192res_1.0_inat_insect.binaryproto'))
labels = read_labels(
"/home/pi/models/mobilenet_v2_192res_1.0_inat_insect_labels.txt")
detector = FawDetector()
detector.run(args.input_layer, args.output_layer, args.num_frames,
args.input_mean, args.input_std, args.threshold, args.top_k,
args.detecting_list, args.message_threshold, model, labels)
