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
self.rfb_model = rfb.rfb300(self.model_image_size, self.num_classes)
self.rfb_model.load_weights(self.model_path, by_name=True)
self.rfb_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))
NUM_CLASSES = 21
input_shape = (300, 300, 3)
priors = get_anchors()
bbox_util = BBoxUtility(NUM_CLASSES, priors)
# 0.1用于验证，0.9用于训练
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
num_val = int(len(lines)*val_split)
num_train = len(lines) - num_val
model = rfb300(input_shape, num_classes=NUM_CLASSES)
model.load_weights("model_data/rfb_weights.h5", by_name=True, skip_mismatch=True)
# 训练参数设置
logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
monitor='val_loss', save_weights_only=True, save_best_only=False, period=1)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=6, verbose=1)
BATCH_SIZE = 4
gen = Generator(bbox_util, BATCH_SIZE, lines[:num_train], lines[num_train:],
(input_shape[0], input_shape[1]),NUM_CLASSES)
for i in range(21):
model.layers[i].trainable = False
if True: