使用SRCNN进行测试

准备图片

# Read and prepare the test image for SRCNN.
def prepare_data(path):
    # Settings.
    data = []
    label = []
    padding = abs(size_input - size_label) / 2
    stride = 21
    # Read in image and convert to ycrcb color space.
    img_input = cv.imread(path)
    im = cv.cvtColor(img_input, cv.COLOR_BGR2YCR_CB)
    img = im2double(im) # Only use the luminance value.

传入一个图片，然后转化到YCrCB空间，并且归一化

    # Create groundtruth and baseline image.
    im_label = modcrop_color(img, scale=multiplier)
    color_base = modcrop_color(im, scale=multiplier)
    size = im_label.shape
    h = size[0]
    w = size[1]
    im_blur = scipy.misc.imresize(im_label, 1 / multiplier, interp='bicubic')
    im_input = scipy.misc.imresize(im_blur, multiplier * 1.0, interp='bicubic')

将测试的图片变模糊

    data = np.array(im_input[:,:,0]).reshape([1, h, w, 1])
    color = np.array(color_base[:,:,1:3])
    label = np.array(modcrop_color(img_input))

    return data, label, color

data表示灰度空间，color保留另外两个空间，不参与运算，label是合在一起的图片

准备图片2

def prepare_raw(path):
    # Settings.
    data = []
    color = []
    # Read in image and convert to ycrcb color space.
    img = cv.imread(path)
    im = cv.cvtColor(img, cv.COLOR_BGR2YCR_CB)
    img = im2double(im) # Only use the luminance value.

    size = img.shape
    img_temp = scipy.misc.imresize(img, [size[0] * multiplier, size[1] * multiplier], interp='bicubic')
    color_temp = scipy.misc.imresize(im, [size[0] * multiplier, size[1] * multiplier], interp='bicubic')
    im_label = img_temp[:, :, 0]
    im_color = color_temp[:, :, 1:3]

    data = np.array(im_label).reshape([1, img.shape[0] * multiplier, img.shape[1] * multiplier, 1])
    color = np.array(im_color)

    return data, color

im是转化到YCrCb空间下的图片，img是归一化的图片。
img_temp是放大之后的图片
color_temp是方法之后的归一化的图片

预测函数1

传入图片的路径和要保存的路径

# Initialization.
    images = tf.placeholder(tf.float32, [None, None, None, 1], name='images')
    model = SRCNN(images)

传给SRCNN的是一个一维的图片，我们只是训练一个灰度图片

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        print('Generating super-resolutioned image...')

        # Load the saved checkpoint.
        saver = tf.train.Saver()
        if load_ckpt(sess, ckpt_dir, saver):
            print('Successfully loaded checkpoint.')
        else:
            print('Failed to load checkpoint.')

加载训练的checkpoint

        if os.path.isfile(path):
            test_data, test_label, color = prepare_data(path)
            print('test_data has shape:', test_data.shape, 'label has shape:', test_label.shape)
            print('color has shape:', color.shape)

传入这个路径，然后得到这个图片的测试集(灰度)，以及何在一起的图片

            # Generate super-resolutioned image.
            conv_out = model.eval({images: test_data})    
            conv_out = conv_out.squeeze()
            result_bw = revert(conv_out)#从归一化恢复

            result = np.zeros([result_bw.shape[0], result_bw.shape[1], 3], dtype=np.uint8)
            result[:, :, 0] = result_bw
            result[:, :, 1:3] = color
            result = cv.cvtColor(result, cv.COLOR_YCrCb2RGB)# result经过经过卷积神经网络的图片

            bicubic = scipy.misc.imresize(test_label, 1 / multiplier, interp='bicubic')
            bicubic = scipy.misc.imresize(bicubic, multiplier * 1.0, interp='bicubic')
            bicubic = cv.cvtColor(bicubic, cv.COLOR_BGR2RGB)# 二三插值法

            # Save the image.
            save_path = os.path.join(save_dir, os.path.basename(path))
            scipy.misc.imsave(save_path, result)
            bicubic_path = os.path.join(save_dir, 'bicubic_' + os.path.basename(path))
            scipy.misc.imsave(bicubic_path, bicubic)
            print('Finished testing', path)# 分辨保存两种不同处理方式的图片