用于TensorFlow的SSIMMS-SSIM

用于TensorFlow的SSIM / MS-SSIM

深入研究其他python实现之后，我终于可以在TensorFlow中实现一个正在运行的示例：

import tensorflow as tfimport numpy as npdef _tf_fspecial_gauss(size, sigma):    """Function to mimic the 'fspecial' gaussian MATLAB function    """    x_data, y_data = np.mgrid[-size//2 + 1:size//2 + 1, -size//2 + 1:size//2 + 1]    x_data = np.expand_dims(x_data, axis=-1)    x_data = np.expand_dims(x_data, axis=-1)    y_data = np.expand_dims(y_data, axis=-1)    y_data = np.expand_dims(y_data, axis=-1)    x = tf.constant(x_data, dtype=tf.float32)    y = tf.constant(y_data, dtype=tf.float32)    g = tf.exp(-((x**2 + y**2)/(2.0*sigma**2)))    return g / tf.reduce_sum(g)def tf_ssim(img1, img2, cs_map=False, mean_metric=True, size=11, sigma=1.5):    window = _tf_fspecial_gauss(size, sigma) # window shape [size, size]    K1 = 0.01    K2 = 0.03    L = 1  # depth of image (255 in case the image has a differnt scale)    C1 = (K1*L)**2    C2 = (K2*L)**2    mu1 = tf.nn.conv2d(img1, window, strides=[1,1,1,1], padding='VALID')    mu2 = tf.nn.conv2d(img2, window, strides=[1,1,1,1],padding='VALID')    mu1_sq = mu1*mu1    mu2_sq = mu2*mu2    mu1_mu2 = mu1*mu2    sigma1_sq = tf.nn.conv2d(img1*img1, window, strides=[1,1,1,1],padding='VALID') - mu1_sq    sigma2_sq = tf.nn.conv2d(img2*img2, window, strides=[1,1,1,1],padding='VALID') - mu2_sq    sigma12 = tf.nn.conv2d(img1*img2, window, strides=[1,1,1,1],padding='VALID') - mu1_mu2    if cs_map:        value = (((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*         (sigma1_sq + sigma2_sq + C2)),     (2.0*sigma12 + C2)/(sigma1_sq + sigma2_sq + C2))    else:        value = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*         (sigma1_sq + sigma2_sq + C2))    if mean_metric:        value = tf.reduce_mean(value)    return valuedef tf_ms_ssim(img1, img2, mean_metric=True, level=5):    weight = tf.constant([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], dtype=tf.float32)    mssim = []    mcs = []    for l in range(level):        ssim_map, cs_map = tf_ssim(img1, img2, cs_map=True, mean_metric=False)        mssim.append(tf.reduce_mean(ssim_map))        mcs.append(tf.reduce_mean(cs_map))        filtered_im1 = tf.nn.avg_pool(img1, [1,2,2,1], [1,2,2,1], padding='SAME')        filtered_im2 = tf.nn.avg_pool(img2, [1,2,2,1], [1,2,2,1], padding='SAME')        img1 = filtered_im1        img2 = filtered_im2    # list to tensor of dim D+1    mssim = tf.pack(mssim, axis=0)    mcs = tf.pack(mcs, axis=0)    value = (tf.reduce_prod(mcs[0:level-1]**weight[0:level-1])*      (mssim[level-1]**weight[level-1]))    if mean_metric:        value = tf.reduce_mean(value)    return value

这是如何运行它：

import numpy as npimport tensorflow as tffrom skimage import data, img_as_floatimage = data.camera()img = img_as_float(image)rows, cols = img.shapenoise = np.ones_like(img) * 0.2 * (img.max() - img.min())noise[np.random.random(size=noise.shape) > 0.5] *= -1img_noise = img + noise## TF CALC STARTBATCH_SIZE = 1CHANNELS = 1image1 = tf.placeholder(tf.float32, shape=[rows, cols])image2 = tf.placeholder(tf.float32, shape=[rows, cols])def image_to_4d(image):    image = tf.expand_dims(image, 0)    image = tf.expand_dims(image, -1)    return imageimage4d_1 = image_to_4d(image1)image4d_2 = image_to_4d(image2)ssim_index = tf_ssim(image4d_1, image4d_2)msssim_index = tf_ms_ssim(image4d_1, image4d_2)with tf.Session() as sess:    sess.run(tf.initialize_all_variables())    tf_ssim_none = sess.run(ssim_index,      feed_dict={image1: img, image2: img})    tf_ssim_noise = sess.run(ssim_index,       feed_dict={image1: img, image2: img_noise})    tf_msssim_none = sess.run(msssim_index,      feed_dict={image1: img, image2: img})    tf_msssim_noise = sess.run(msssim_index,       feed_dict={image1: img, image2: img_noise})###TF CALC ENDprint('tf_ssim_none', tf_ssim_none)print('tf_ssim_noise', tf_ssim_noise)print('tf_msssim_none', tf_msssim_none)print('tf_msssim_noise', tf_msssim_noise)

如果发现一些错误，请告诉我:)

编辑： 此实现仅支持灰度图像