def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# 计算总的种类
self.num_classes = len(self.class_names) + 1
# 载入模型，如果原来的模型里已经包括了模型结构则直接载入。
# 否则先构建模型再载入
try:
self.ssd_model = tf.keras.models.load_model(model_path,
compile=False)
except:
self.ssd_model = ssd.SSD300(self.model_image_size,
self.num_classes)
self.ssd_model.load_weights(self.model_path, by_name=True)
else:
num_anchors = 8753
assert self.ssd_model.layers[-1].output_shape[-1] == \
num_anchors/len(self.ssd_model.output) * (self.num_classes + 5), \
'Mismatch between model and given anchor and class sizes'
#self.ssd_model.summary()
print('{} model, anchors, and classes loaded.'.format(model_path))
# 画框设置不同的颜色
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
#-------------------------------#
# 计算总的类的数量
#-------------------------------#
self.num_classes = len(self.class_names) + 1
#-------------------------------#
# 载入模型与权值
#-------------------------------#
self.ssd_model = ssd.SSD300(self.input_shape,
self.num_classes,
anchors_size=self.anchors_size)
self.ssd_model.load_weights(self.model_path, by_name=True)
print('{} model, anchors, and classes loaded.'.format(model_path))
# 画框设置不同的颜色
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
self.num_classes = len(self.class_names) + 1
self.ssd_model = ssd.SSD300(self.model_image_size, self.num_classes)
self.ssd_model.load_weights(self.model_path, by_name=True)
self.ssd_model.summary()
print('{} model, anchors, and classes loaded.'.format(model_path))
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# all the classes in training set plus background
self.num_classes = len(self.class_names) + 1
# load trained model
# and their trained parameters of this model
self.ssd_model = ssd.SSD300(self.model_image_size, self.num_classes)
self.ssd_model.load_weights(self.model_path, by_name=True)
self.ssd_model.summary()
print('{} model, anchors, and classes loaded.'.format(model_path))
# set different color of different classes of object
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
