keras-faster-rcnn性能评价指标代码

以下是基于Keras实现的Faster R-CNN的性能评价指标代码：

from keras.models import Model
from keras_frcnn import config, data_generators
from keras_frcnn import losses as losses_fn
from keras_frcnn import metrics as metrics_fn

# 加载模型配置
config_output_filename = "config.pickle"
with open(config_output_filename, "rb") as f_in:
    C = pickle.load(f_in)

# 创建数据生成器
test_imgs, _, _ = data_generators.get_data(
    C,
    C.test_path,
    mode='test'
)
test_gen = data_generators.get_anchor_gt(
    test_imgs,
    C,
    mode='test'
)

# 加载模型
model_path = "model_frcnn.hdf5"
model = load_model(model_path, custom_objects={
    "rpn_loss_cls": losses_fn.rpn_loss_cls,
    "rpn_loss_regr": losses_fn.rpn_loss_regr,
    "class_loss_cls": losses_fn.class_loss_cls,
    "class_loss_regr": losses_fn.class_loss_regr,
    "class_acc": metrics_fn.class_acc,
    "mean_overlapping_bboxes": metrics_fn.mean_overlapping_bboxes
})

# 评估模型性能
X, Y, image_data, debug_img, debug_num_pos = next(test_gen)
result = model.evaluate(X, Y)

# 打印性能指标
print("Losses: ", result)
print("Classification accuracy: ", result[1])
print("Classification loss: ", result[2])
print("Regression loss: ", result[3])

其中，`config.pickle`文件保存了模型配置；`model_frcnn.hdf5`是训练好的模型文件；`get_data`和`get_anchor_gt`函数用于创建数据生成器；`evaluate`函数评估模型性能；`class_acc`和`mean_overlapping_bboxes`是自定义的性能指标函数。