def __init__(self, name, age, father, mother):
Human.__init__(self, name, age)
if isinstance(father, Father):
self.father = father
else:
raise ValueError('Father must be of a type Father')
if isinstance(mother, Mother):
self.mother = mother
else:
raise ValueError('Mother must be of a type Mother')
def __init__(self, model, boxsize):
"""
Class constructor.
@param model: caffe models
@param weights: caffe models weights
"""
Human.__init__(self, boxsize)
# Reshapes the models input accordingly
self.model, self.weights = model
self.net = None
def __init__(self, *args, **kwargs):
# Cách điển hình để thừa kế thuộc tính là gọi super
# super(Batman, self).__init__(*args, **kwargs)
# Tuy nhiên với đa thừa kế, super() sẽ chỉ gọi lớp cơ sở tiếp theo trong danh sách MRO.
# Vì thế, ta sẽ gọi cụ thể hàm __init__ của các lớp chả.
# Sử dụng *args và **kwargs cho phép việc truyền đối số gọn gàng hơn,
# trong đó mỗi lớp cha sẽ chịu trách nhiệm cho những phần thuộc về nó
Human.__init__(self, 'anonymous', *args, **kwargs)
Bat.__init__(self, *args, can_fly=False, **kwargs)
# ghi đè giá trị của thuộc tính name
self.name = 'Sad Affleck'
def __init__(self, *args, **kwargs):
# Typically to inherit attributes you have to call super:
#super(Batman, self).__init__(*args, **kwargs)
# However we are dealing with multiple inheritance here, and super()
# only works with the next base class in the MRO list.
# So instead we explicitly call __init__ for all ancestors.
# The use of *args and **kwargs allows for a clean way to pass arguments,
# with each parent "peeling a layer of the onion".
Human.__init__(self, 'anonymous', *args, **kwargs)
Bat.__init__(self, *args, can_fly=False, **kwargs)
# override the value for the name attribute
self.name = 'Sad Affleck'
def __init__(self, model, boxsize):
"""
Class constructor.
@param model: tf models
@param weights: tf models weights
"""
Human.__init__(self, boxsize)
self.config = tf.ConfigProto(device_count={"GPU": 1},
allow_soft_placement=True,
log_device_placement=False)
self.config.gpu_options.per_process_gpu_memory_fraction = 0.5
self.sess = None
self.weights = model
self.first_detection = True
self.image_in = None
self.map_human_large = None
def __init__(self, age=0, name="", topic=""):
Human.__init__(self, age, name)
## This is the topic the student is currently working on
self.topic = topic
def __init__(self, model, boxsize=192, confidence_threshold=0.75):
"""
Class constructor.
@param model: tf models
@param weights: tf models weights
"""
Human.__init__(self, boxsize) # boxsize will not be used in this case!
self.config = tf.ConfigProto(device_count={"GPU": 1},
allow_soft_placement=True,
log_device_placement=False)
self.config.gpu_options.per_process_gpu_memory_fraction = 0.5
labels_file = LABELS_DICT[DB]
lbl_map = label_map_util.load_labelmap(
labels_file) # loads the labels map.
categories = label_map_util.convert_label_map_to_categories(
lbl_map, 9999)
category_index = label_map_util.create_category_index(categories)
self.classes = {}
# We build is as a dict because of gaps on the labels definitions
for cat in category_index:
self.classes[cat] = str(category_index[cat]['name'])
# Frozen inference graph, written on the file
CKPT = model
detection_graph = tf.Graph() # new graph instance.
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=detection_graph, config=self.config)
self.image_tensor = detection_graph.get_tensor_by_name(
'image_tensor:0')
# NCHW conversion. not possible
#self.image_tensor = tf.transpose(self.image_tensor, [0, 3, 1, 2])
self.detection_boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
self.detection_scores = detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
self.boxes = []
self.scores = []
self.predictions = []
# Dummy initialization (otherwise it takes longer then)
dummy_tensor = np.zeros((1, 1, 1, 3), dtype=np.int32)
self.sess.run([
self.detection_boxes, self.detection_scores,
self.detection_classes, self.num_detections
],
feed_dict={self.image_tensor: dummy_tensor})
self.confidence_threshold = confidence_threshold
def __init__(self, fname, lname, school):
Human.__init__(self, fname, lname)
self.school = school
self.debt = 0
def __init__(self, age=0, name=None):
Human.__init__(self, age, name)
def __init__(self, name, age, skill):
self.skill = skill
Human.__init__(self, name, age)
def __init__(self, name, age, children):
Human.__init__(self, name, age)
self.children = children
def __init__(self, *args, **kwargs):
# super(Batman, self).__init__(*args, **kwargs) # doesn't work for multiple inheritance
Human.__init__(self, 'anonymous', *args, **kwargs)
Bat.__init__(self, *args, can_fly=False, **kwargs)
self.name = 'Sad Affleck'
