def __init__(self):
logging.info("starting camera")
cam_props = {
"width": 640,
"height": 512,
"cv_image_factor": config.Config.get().get("cv_image_factor"),
"exposure_mode": config.Config.get().get("camera_exposure_mode"),
"framerate": config.Config.get().get("camera_framerate"),
"bitrate": config.Config.get().get("camera_jpeg_bitrate"),
"jpeg_quality": int(config.Config.get().get("camera_jpeg_quality"))
}
self._camera = camera.Camera(props=cam_props)
self.recording = False
self.video_start_time = time.time() + 8640000
self._image_time = 0
self._cv_image_factor = int(config.Config.get().get(
"cv_image_factor", 4))
self._image_refresh_timeout = float(config.Config.get().get(
"camera_refresh_timeout", 0.1))
self._color_object_size_min = int(config.Config.get().get(
"camera_color_object_size_min",
80)) / (self._cv_image_factor * self._cv_image_factor)
self._color_object_size_max = int(config.Config.get().get(
"camera_color_object_size_max",
32000)) / (self._cv_image_factor * self._cv_image_factor)
self._path_object_size_min = int(config.Config.get().get(
"camera_path_object_size_min",
80)) / (self._cv_image_factor * self._cv_image_factor)
self._path_object_size_max = int(config.Config.get().get(
"camera_path_object_size_max",
32000)) / (self._cv_image_factor * self._cv_image_factor)
self._photos = []
self.load_photo_metadata()
if not self._photos:
self._photos = []
for filenames in os.walk(PHOTO_PATH):
for filename in filenames[2]:
if (PHOTO_PREFIX in filename or VIDEO_PREFIX in filename
) and PHOTO_THUMB_SUFFIX not in filename:
self._photos.append({'name': filename})
self.save_photo_metadata()
self._cnn_classifiers = {}
cnn_model = config.Config.get().get("cnn_default_model", "")
if cnn_model != "":
try:
self._cnn_classifiers[cnn_model] = CNNManager.get_instance(
).load_model(cnn_model)
self._cnn_classifier_default = self._cnn_classifiers[cnn_model]
logging.info("loaded: " + cnn_model + " " +
str(self._cnn_classifier_default))
except Exception:
logging.warning("model not found: %s", cnn_model)
self._camera.grab_start()
self._image_cv = self.get_image()
super(Camera, self).__init__()
def run_server():
bot = None
cam = None
try:
try:
app.bot_config = Config.read()
bot = CoderBot.get_instance(
servo=(app.bot_config.get("move_motor_mode") == "servo"),
motor_trim_factor=float(
app.bot_config.get('move_motor_trim', 1.0)))
audio = Audio.get_instance()
audio.say(app.bot_config.get("sound_start"))
try:
cam = Camera.get_instance()
Motion.get_instance()
except picamera.exc.PiCameraError:
logging.error("Camera not present")
CNNManager.get_instance()
EventManager.get_instance("coderbot")
if app.bot_config.get('load_at_start') and app.bot_config.get(
'load_at_start'):
app.prog = app.prog_engine.load(
app.bot_config.get('load_at_start'))
app.prog.execute()
except ValueError as e:
app.bot_config = {}
logging.error(e)
bot.set_callback(PIN_PUSHBUTTON, button_pushed, 100)
remove_doreset_file()
app.run(host="0.0.0.0",
port=5000,
debug=True,
use_reloader=False,
threaded=True)
finally:
if cam:
cam.exit()
if bot:
bot.exit()
app.shutdown_requested = True
def serve_legacy():
"""
Serve the the legacy web application
"""
return render_template('main.html',
host=request.host[:request.host.find(':')],
locale=get_locale(),
config=app.bot_config,
program_level=app.bot_config.get("prog_level", "std"),
cam=Camera.get_instance() != None,
cnn_model_names=json.dumps([[name, name] for name in CNNManager.get_instance().get_models().keys()]))
def serve_legacy():
"""
Serve the the legacy web application
"""
return render_template('main.html',
host=request.host[:request.host.find(':')],
locale=get_locale(),
config=app.bot_config,
program_level=app.bot_config.get("prog_level", "std"),
cam=Camera.get_instance() != None,
cnn_model_names=json.dumps([[name] for name in CNNManager.get_instance().get_models().keys()]))
def handle_cnn_models_new():
cam = Camera.get_instance()
cnn = CNNManager.get_instance()
logging.info("cnn_models_new")
data = json.loads(request.get_data(as_text=True))
cnn.train_new_model(model_name=data["model_name"],
architecture=data["architecture"],
image_tags=data["image_tags"],
photos_meta=cam.get_photo_list(),
training_steps=data["training_steps"],
learning_rate=data["learning_rate"])
return json.dumps({"name": data["model_name"], "status": 0})
def handle_cnn_models_new():
cam = Camera.get_instance()
cnn = CNNManager.get_instance()
logging.info("cnn_models_new")
data = json.loads(request.get_data(as_text=True))
cnn.train_new_model(model_name=data["model_name"],
architecture=data["architecture"],
image_tags=data["image_tags"],
photos_meta=cam.get_photo_list(),
training_steps=data["training_steps"],
learning_rate=data["learning_rate"])
return json.dumps({"name": data["model_name"], "status": 0})
def cnn_classify(self, model_name=None):
classifier = None
if model_name:
classifier = self._cnn_classifiers.get(model_name)
if classifier is None:
classifier = CNNManager.get_instance().load_model(model_name)
self._cnn_classifiers[model_name] = classifier
else:
classifier = self._cnn_classifier_default
img = self.get_image()
classes = classifier.classify_image(img.mat())
s_classes = sorted(classes.items(), key=lambda x: x[1], reverse=True)
return s_classes
def run_server():
bot = None
cam = None
try:
try:
app.bot_config = Config.read()
bot = CoderBot.get_instance(servo=(app.bot_config.get("move_motor_mode") == "servo"),
motor_trim_factor=float(app.bot_config.get('move_motor_trim', 1.0)))
audio = Audio.get_instance()
audio.say(app.bot_config.get("sound_start"))
try:
cam = Camera.get_instance()
Motion.get_instance()
except picamera.exc.PiCameraError:
logging.error("Camera not present")
CNNManager.get_instance()
EventManager.get_instance("coderbot")
if app.bot_config.get('load_at_start') and app.bot_config.get('load_at_start'):
app.prog = app.prog_engine.load(app.bot_config.get('load_at_start'))
app.prog.execute()
except ValueError as e:
app.bot_config = {}
logging.error(e)
bot.set_callback(PIN_PUSHBUTTON, button_pushed, 100)
remove_doreset_file()
app.run(host="0.0.0.0", port=5000, debug=True, use_reloader=False, threaded=True)
finally:
if cam:
cam.exit()
if bot:
bot.exit()
app.shutdown_requested = True
def cnn_classify(self, model_name=None, top_results=3):
classifier = None
if model_name:
classifier = self._cnn_classifiers.get(model_name)
if classifier is None:
classifier = CNNManager.get_instance().load_model(model_name)
self._cnn_classifiers[model_name] = classifier
else:
classifier = self._cnn_classifier_default
classes = None
try:
img = self.get_image()
classes = classifier.classify_image(img.mat(), top_results=top_results)
except Exception:
logging.warning("classifier not available")
classes = [("None", 1.0)]
return classes
def cnn_classify(self, model_name=None, top_results=3):
classifier = None
if model_name:
classifier = self._cnn_classifiers.get(model_name)
if classifier is None:
classifier = CNNManager.get_instance().load_model(model_name)
self._cnn_classifiers[model_name] = classifier
else:
classifier = self._cnn_classifier_default
classes = None
try:
img = self.get_image()
classes = classifier.classify_image(img.mat(),
top_results=top_results)
except Exception:
logging.warning("classifier not available")
classes = [("None", 1.0)]
return classes
def __init__(self):
logging.info("starting camera")
cam_props = {"width":640, "height":512,
"cv_image_factor": config.Config.get().get("cv_image_factor"),
"exposure_mode": config.Config.get().get("camera_exposure_mode"),
"framerate": config.Config.get().get("camera_framerate"),
"bitrate": config.Config.get().get("camera_jpeg_bitrate"),
"jpeg_quality": int(config.Config.get().get("camera_jpeg_quality"))}
self._camera = camera.Camera(props=cam_props)
self.recording = False
self.video_start_time = time.time() + 8640000
self._image_time = 0
self._cv_image_factor = int(config.Config.get().get("cv_image_factor", 4))
self._image_refresh_timeout = float(config.Config.get().get("camera_refresh_timeout", 0.1))
self._color_object_size_min = int(config.Config.get().get("camera_color_object_size_min", 80)) / (self._cv_image_factor * self._cv_image_factor)
self._color_object_size_max = int(config.Config.get().get("camera_color_object_size_max", 32000)) / (self._cv_image_factor * self._cv_image_factor)
self._path_object_size_min = int(config.Config.get().get("camera_path_object_size_min", 80)) / (self._cv_image_factor * self._cv_image_factor)
self._path_object_size_max = int(config.Config.get().get("camera_path_object_size_max", 32000)) / (self._cv_image_factor * self._cv_image_factor)
self._photos = []
self.load_photo_metadata()
if not self._photos:
self._photos = []
for filenames in os.walk(PHOTO_PATH):
for filename in filenames[2]:
if (PHOTO_PREFIX in filename or VIDEO_PREFIX in filename) and PHOTO_THUMB_SUFFIX not in filename:
self._photos.append({'name': filename})
self.save_photo_metadata()
self._cnn_classifiers = {}
cnn_model = config.Config.get().get("cnn_default_model", "")
if cnn_model != "":
try:
self._cnn_classifiers[cnn_model] = CNNManager.get_instance().load_model(cnn_model)
self._cnn_classifier_default = self._cnn_classifiers[cnn_model]
logging.info("loaded: " + cnn_model + " " + str(self._cnn_classifier_default))
except Exception:
logging.warning("model not found: %s", cnn_model)
self._camera.grab_start()
self._image_cv = self.get_image()
super(Camera, self).__init__()
def cnn_detect_objects(self, model_name=None, top_results=3):
classifier = None
if model_name:
classifier = self._cnn_classifiers.get(model_name)
if classifier is None:
classifier = CNNManager.get_instance().load_model(model_name)
self._cnn_classifiers[model_name] = classifier
else:
classifier = self._cnn_classifier_default
t0 = time.time()
classes = None
try:
img = self.get_image()
classes = classifier.detect_objects(img.mat(), top_results=top_results)
except Exception:
logging.warning("classifier not available")
classes = [("None", 100)]
raise
logging.info("fps: %f", 1.0/(time.time()-t0))
return classes
def handle_cnn_models_list():
cnn = CNNManager.get_instance()
logging.info("cnn_models_list")
return json.dumps(cnn.get_models())
def handle_cnn_models_status(model_name):
cnn = CNNManager.get_instance()
logging.info("cnn_models_status")
model_status = cnn.get_models().get(model_name)
return json.dumps(model_status)
def handle_cnn_models_delete(model_name):
cnn = CNNManager.get_instance()
logging.info("cnn_models_delete")
model_status = cnn.delete_model(model_name=model_name)
return json.dumps(model_status)
def handle_cnn_models_delete(model_name):
cnn = CNNManager.get_instance()
logging.info("cnn_models_delete")
model_status = cnn.delete_model(model_name=model_name)
return json.dumps(model_status)
def handle_cnn_models_status(model_name):
cnn = CNNManager.get_instance()
logging.info("cnn_models_status")
model_status = cnn.get_models().get(model_name)
return json.dumps(model_status)
def handle_cnn_models_list():
cnn = CNNManager.get_instance()
logging.info("cnn_models_list")
return json.dumps(cnn.get_models())