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)
# 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
inputs = Input(self.model_image_size)
self.retinanet_model = retinanet.resnet_retinanet(
self.num_classes, inputs)
self.retinanet_model.load_weights(self.model_path, by_name=True)
self.retinanet_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.'
#------------------#
# 计算种类数量
#------------------#
self.num_classes = len(self.class_names)
#------------------#
# 载入模型
#------------------#
self.retinanet_model = retinanet.resnet_retinanet(
self.num_classes, self.model_image_size)
self.retinanet_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))
import nets.retinanet as retinanet
import numpy as np
import keras
from keras.optimizers import Adam
from nets.retinanet_training import Generator
from nets.retinanet_training import focal, smooth_l1
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from utils.utils import BBoxUtility
from utils.anchors import get_anchors
if __name__ == "__main__":
NUM_CLASSES = 20
input_shape = (600, 600, 3)
annotation_path = '2007_train.txt'
inputs = keras.layers.Input(shape=input_shape)
model = retinanet.resnet_retinanet(NUM_CLASSES, inputs)
priors = get_anchors(model)
bbox_util = BBoxUtility(NUM_CLASSES, priors)
#-------------------------------------------#
# 权值文件的下载请看README
#-------------------------------------------#
model.load_weights("model_data/resnet50_coco_best_v2.1.0.h5",
by_name=True,
skip_mismatch=True)
# 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)
#--------------------------------------------#
# 该部分代码只用于看网络结构，并非测试代码
# map测试请看get_dr_txt.py、get_gt_txt.py
# 和get_map.py
#--------------------------------------------#
from nets.retinanet import resnet_retinanet
if __name__ == "__main__":
model = resnet_retinanet(80)
model.summary()
