超分辨率重构之SRCNN整理总结(六)

tensorflow版本SRCNN的相关基础笔记

tensorflow 使用flags定义命令行参数：
        tf定义了tf.app.flags，用于支持接受命令行传递参数，相当于接受argv。eg:  tf.app.flags.DEFINE_string('str_name', 'def_v_1',"descrip1")#第一个是参数名称，第二个参数是默认值，第三个是参数描述
        在命令行中可以python3 test.py –str_name liu –int_name 10传参数来启动main()函数，在pycharm中可以通过run->edit configurations然后在script parameters中输入–str_name liu –int_name 10来输入参数

比如在项目中可以通过这样的方式控制传入参数：

flags = tf.app.flags
flags.DEFINE_integer("epoch", 15000, "Number of epoch [15000]")
flags.DEFINE_integer("batch_size", 128, "The size of batch images [128]")
flags.DEFINE_integer("image_size", 33, "The size of image to use [33]")
flags.DEFINE_integer("label_size", 21, "The size of label to produce [21]")
flags.DEFINE_float("learning_rate", 1e-4, "The learning rate of gradient descent algorithm [1e-4]")
flags.DEFINE_integer("c_dim", 1, "Dimension of image color. [1]")
flags.DEFINE_integer("scale", 3, "The size of scale factor for preprocessing input image [3]")
flags.DEFINE_integer("stride", 21, "The size of stride to apply input image [21]") 

flags.DEFINE_string("checkpoint_dir", "checkpoint", "Name of checkpoint directory [checkpoint]")
flags.DEFINE_string("sample_dir", "sample", "Name of sample directory [sample]")
# flags.DEFINE_boolean("is_train", True, "True for training, False for testing [True]")   #训练
flags.DEFINE_boolean("is_train", False, "True for training, False for testing [True]")  #测试
FLAGS = flags.FLAGS

PSNR值的计算Python代码

def psnr(target, ref, scale):
    # target:目标图像  ref:参考图像  scale:尺寸大小
    target_data = np.array(target)
    target_data = target_data[scale:-scale, scale:-scale]

    ref_data = np.array(ref)
    ref_data = ref_data[scale:-scale, scale:-scale]

    diff = ref_data - target_data
    diff = diff.flatten('C')
    rmse = math.sqrt(np.mean(diff ** 2.))
    MSE = np.mean(diff ** 2.)
    return 20 * math.log10(1.0 / rmse)