def init(self, parameters, inputs, outputs):
try:
### data parameters
self.img_size = (parameters.img_size, parameters.img_size)
# Detection probability threshold.
self.conf_thresh = parameters.conf_thresh
# Number of People
self.number_people = 0
# Person Index for Model
self.person_index = parameters.person_index
### Fall parameters
self.min_non_dets = parameters.min_non_dets
self.box_size_thresh = (parameters.box_size_thresh, parameters.box_size_thresh)
self.anks_shdr_thresh = parameters.anks_shdr_thresh
self.dist_hist = parameters.dist_hist
self.dist_count = parameters.dist_count
self.fall_interval = parameters.fall_interval
self.fall_time = -1
# Load model from the specified directory.
print("loading the model...")
self.model = panoramasdk.model()
self.model.open(parameters.object_detector, 1)
print("Detector loaded")
self.pose_model = panoramasdk.model()
self.pose_model.open(parameters.pose_model, 1)
print("Pose model loaded")
# Create input and output arrays.
class_info = self.model.get_output(0)
prob_info = self.model.get_output(1)
rect_info = self.model.get_output(2)
# Create pose output arrays
heatmap_info = self.pose_model.get_output(0)
self.class_array = np.empty(class_info.get_dims(), dtype=class_info.get_type())
self.prob_array = np.empty(prob_info.get_dims(), dtype=prob_info.get_type())
self.rect_array = np.empty(rect_info.get_dims(), dtype=rect_info.get_type())
self.heatmaps_array = np.empty(heatmap_info.get_dims(), dtype=heatmap_info.get_type())
# Fall tracking variables
self.xpart_tracker, self.ypart_tracker = reset_tracker()
self.frame_num, self.frame_prev, self.frame_curr, self.zero_dets = reset_counts()
self.fall_idx = -1
self.master_idx = -1
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
try:
# Frame Number
self.frame_num = 0
# Load model from the specified directory
print("loading model")
self.action_detection_model = panoramasdk.model()
self.action_detection_model.open(parameters.action_detection, 1)
print("model loaded")
# panorama SDK specific declarations
self.class_name_list = []
self.class_prob_list = []
class_info = self.action_detection_model.get_output(0)
self.class_array = np.empty(class_info.get_dims(),
dtype=class_info.get_type())
# Set up Timer
self.time_start = time.time()
self.seconds_to_wash = 120
# Misc
self.list_actions = []
self.message = ''
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
try:
self.frame_num = 0
self.class_name_list = []
self.class_prob_list = []
# Detection probability threshold.
self.threshold = parameters.threshold
# Load model from the specified directory.
print("loading model")
self.model = panoramasdk.model()
self.model.open(parameters.action_detection, 1)
print("model loaded")
class_info = self.model.get_output(0)
self.class_array = np.empty(class_info.get_dims(),
dtype=class_info.get_type())
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
try:
# Frame Number Initialization
self.frame_num = 0
# Index for pokemon from parameters
self.index = parameters.pokemon_index
# Set threshold for model from parameters
self.threshold = parameters.threshold
# set number of pokemon
self.number_pokemon = 0
# Load model from the specified directory.
print("loading the model...")
self.model = panoramasdk.model()
self.model.open(parameters.pokemon_detection, 1)
print("model loaded")
# Create input and output arrays.
class_info = self.model.get_output(0)
prob_info = self.model.get_output(1)
rect_info = self.model.get_output(2)
self.class_array = np.empty(class_info.get_dims(), dtype=class_info.get_type())
self.prob_array = np.empty(prob_info.get_dims(), dtype=prob_info.get_type())
self.rect_array = np.empty(rect_info.get_dims(), dtype=rect_info.get_type())
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
try:
self.threshold = parameters.threshold
self.person_index = parameters.person_index
self.threshold = parameters.threshold
self.frame_num = 0
self.number_people = 0
self.colours = np.random.rand(32, 3)
print("Loading model: " + parameters.people_counter)
self.model = panoramasdk.model()
self.model.open(parameters.people_counter, 1)
print("Creating input and output arrays")
class_info = self.model.get_output(0)
prob_info = self.model.get_output(1)
rect_info = self.model.get_output(2)
self.class_array = np.empty(class_info.get_dims(), dtype=class_info.get_type())
self.prob_array = np.empty(prob_info.get_dims(), dtype=prob_info.get_type())
self.rect_array = np.empty(rect_info.get_dims(), dtype=rect_info.get_type())
self.mjpegserver = PanoramaMJPEGServer()
print("Initialization complete")
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
"""
This is the init method where you can declare variables that will be used in this class and initialize any objects if necessary.
Args:
Input parameters from the application configuration on the console.
Input stream object that is created and passed in from mediapipeline.
Output stream object that send data stream to panoramasdk data sink.
Returns:
Boolean.
"""
try:
# Detection probability threshold.
self.threshold = parameters.threshold
self.person_index = parameters.person_index
self.boxes = []
self.frame_num = 0
self.send_images = True
# SD Code 2
self._batch_frame_count = [0] * len(inputs.video_in)
self._frame_count = [0] * len(inputs.video_in)
self._cam_standing_people = [[]] * len(inputs.video_in)
self._curr_refs = [[]] * len(inputs.video_in)
self._total_size_mask_count = [0] * len(inputs.video_in)
self._cam_left = [-1] * len(inputs.video_in)
self._size_mask = [[]] * len(inputs.video_in)
# Load model from the specified directory.
self.model = panoramasdk.model()
self.model.open(parameters.people_counter, 1)
class_info = self.model.get_output(0)
prob_info = self.model.get_output(1)
rect_info = self.model.get_output(2)
self.class_array = np.empty(class_info.get_dims(),
dtype=class_info.get_type())
self.prob_array = np.empty(prob_info.get_dims(),
dtype=prob_info.get_type())
self.rect_array = np.empty(rect_info.get_dims(),
dtype=rect_info.get_type())
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
print('init()')
try:
self.input_size = parameters.input_size
self.class_names = CLASSES
self.threshold = parameters.threshold
self.keep_ratio = KEEP_RATIO
self.use_cv2_rect = USE_CV2_RECT
self.frame_num = 0
bars = '-\|/'
bar_format = '{} ' + MODEL_DISPLAY_NAME + ' {}'
self.progress_bars = [bar_format.format(c, c) for c in bars]
self.progress_bars_count = len(self.progress_bars)
self.processor = utils.Processor(self.class_names,
self.input_size,
threshold=self.threshold,
keep_ratio=self.keep_ratio)
# Load model from the specified directory.
print(f'Loading model {parameters.model}')
start = time.time()
self.model = panoramasdk.model()
self.model.open(parameters.model, 1)
print(f'Model loaded in {int(time.time() - start)} seconds')
# Create output arrays
info_0 = self.model.get_output(0)
info_1 = self.model.get_output(1)
info_2 = self.model.get_output(2)
self.pred_0 = np.empty(info_0.get_dims(), dtype=info_0.get_type())
self.pred_1 = np.empty(info_1.get_dims(), dtype=info_1.get_type())
self.pred_2 = np.empty(info_2.get_dims(), dtype=info_2.get_type())
# Use all the model outputs
self.predictions = [self.pred_0, self.pred_1, self.pred_2]
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
try:
self.frame_num = 0
# Load model from the specified directory.
print("loading the model...")
self.model = panoramasdk.model()
self.model.open(parameters.classifier, 1)
print("model loaded")
# Create input and output arrays.
prob_info = self.model.get_output(0)
self.prob_array = np.empty(prob_info.get_dims(),
dtype=prob_info.get_type())
self.classes = get_classes()
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
try:
# Detection probability threshold.
self.threshold = parameters.threshold
# Frame Number Initialization
self.frame_num = 0
# Number of People
self.number_people = 0
# Bounding Box Colors
self.colours = np.random.rand(32, 3)
# Person Index for Model from parameters
self.person_index = parameters.person_index
# Set threshold for model from parameters
self.threshold = parameters.threshold
# Create stream buffer.
self.buffered_media = {}
self.buffered_image = {}
# Load model from the specified directory.
print("loading the model...")
self.model = panoramasdk.model()
self.model.open(parameters.people_counter, 1)
print("model loaded")
# Create input and output arrays.
class_info = self.model.get_output(0)
prob_info = self.model.get_output(1)
rect_info = self.model.get_output(2)
self.class_array = np.empty(class_info.get_dims(),
dtype=class_info.get_type())
self.prob_array = np.empty(prob_info.get_dims(),
dtype=prob_info.get_type())
self.rect_array = np.empty(rect_info.get_dims(),
dtype=rect_info.get_type())
return True
except Exception as e:
print("Exception: {}".format(e))
return False
def init(self, parameters, inputs, outputs):
print('init()')
try:
# NOTE: these are specific to the alarm server
self.consecutive_coyote_frames = 0
self.consecutive_coyote_threshold = 5
self.warning_server_url = "http://garage.home.com/trigger"
self.input_size = parameters.input_size
self.class_names = CLASSES
self.processor = utils.Processor(
self.class_names, self.input_size, keep_ratio=True)
self.threshold = parameters.threshold
self.frame_num = 0
# Load model from the specified directory.
print(f'Loading model {parameters.model}')
start = time.time()
self.model = panoramasdk.model()
self.model.open(parameters.model, 1)
print(f'Model loaded in {int(time.time() - start)} seconds')
# Create output arrays
info_0 = self.model.get_output(0)
info_1 = self.model.get_output(1)
info_2 = self.model.get_output(2)
self.pred_0 = np.empty(info_0.get_dims(), dtype=info_0.get_type())
self.pred_1 = np.empty(info_1.get_dims(), dtype=info_1.get_type())
self.pred_2 = np.empty(info_2.get_dims(), dtype=info_2.get_type())
# Use all the model outputs
self.predictions = [self.pred_0, self.pred_1, self.pred_2]
return True
except Exception as e:
print("Exception: {}".format(e))
return False