图像计算常用的指标 PSNR， MAE， MSE， SSIM（python 代码）

import numpy  as np
import math

def psnr(img1, img2):
    mse = np.mean( (img1 - img2) ** 2 )
    if mse == 0:
        return 100
    PIXEL_MAX = 255.0
    return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))

def mse(img1, img2):
    mse = np.mean( (img1 - img2) ** 2 )
    return mse
    
def mae(img1, img2):
    mae = np.mean( abs(img1 - img2)  )
    return mae    
def ssim(y_true , y_pred):
    u_true = np.mean(y_true)
    u_pred = np.mean(y_pred)
    var_true = np.var(y_true)
    var_pred = np.var(y_pred)
    std_true = np.sqrt(var_true)
    std_pred = np.sqrt(var_pred)
    c1 = np.square(0.01*7)
    c2 = np.square(0.03*7)
    ssim = (2 * u_true * u_pred + c1) * (2 * std_pred * std_true + c2)
    denom = (u_true ** 2 + u_pred ** 2 + c1) * (var_pred + var_true + c2)
    return ssim/denom

## use the scikit package
from skimage.measure import compare_ssim as ssim
ssim(img1,img2) # for gray image
ssim(img1,img1,multichannel=True) ## for rgb

batch train in tensorflow

mse = np.square(x - gx).sum()

def batch_mae_frame_float(gen_frames, gt_frames):
    # [batch, width, height] or [batch, width, height, channel]
    if gen_frames.ndim == 3:
        axis = (1, 2)
    elif gen_frames.ndim == 4:
        axis = (1, 2, 3)
    x = np.float32(gen_frames)
    y = np.float32(gt_frames)
    mae = np.sum(np.absolute(x - y), axis=axis, dtype=np.float32)
    return np.mean(mae)


def batch_psnr(gen_frames, gt_frames):
    # [batch, width, height] or [batch, width, height, channel]
    if gen_frames.ndim == 3:
        axis = (1, 2)
    elif gen_frames.ndim == 4:
        axis = (1, 2, 3)
    x = np.int32(gen_frames)
    y = np.int32(gt_frames)
    num_pixels = float(np.size(gen_frames[0]))
    mse = np.sum((x - y) ** 2, axis=axis, dtype=np.float32) / num_pixels
    psnr = 20 * np.log10(255) - 10 * np.log10(mse)
    return np.mean(psnr)
for b in range(configs.batch_size):
   score, _ = compare_ssim(gx[b], x[b], full=True, multichannel=True)
   sim[i] += score
   # batch_id means the number of all the batch used in the test process
frame_mse = img_mse[i] / (batch_id * configs.batch_size * configs.n_gpu)) # i means the frame index
ssim = np.asarray(ssim, dtype=np.float32) / (configs.batch_size * batch_id)
psnr = np.asarray(psnr, dtype=np.float32) / batch_id
fmae = np.asarray(fmae, dtype=np.float32) / batch_id