PSNR値とSSIM値の計算-pythonに基づく

super resolutionタスクでの評価指標についてpytorchフレームワークと最近よく使われているEDSRコードについて簡単にまとめます.EDSRコードでは、テストの正確性のためにSRピクチャ生成を保存してからmatlabコードで計算し、yチャネル上でpsnrとssimを計算しますが、時間がかかる可能性がありますので、ここではpythonコードに直接統合し、最終的に計算した結果はmatlabコードで計算した結果と同じです.

1、      SR  ：
sr = self.model(lr, idx_scale)
2、   tensor   numpy:
visuals = utility.get_current_visual(lr, hr, sr, self.args.rgb_range)
  ：get_current_visual(...)
def get_current_visual(lr, hr ,sr, rgb_range):
    """
    return LR SR (HR) images
    """
    out_dict = OrderedDict()
    out_dict['LR'] = lr.data[0].float().cpu()
    out_dict['SR'] = sr.data[0].float().cpu()
    out_dict['HR'] = hr.data[0].float().cpu()

    out_dict['LR'], out_dict['HR'], out_dict['SR'] = \
    Tensor2np([out_dict['LR'], out_dict['HR'], out_dict['SR']], rgb_range)
    return out_dict
        
def Tensor2np(tensor_list, rgb_range):
    def _Tensor2numpy(tensor, rgb_range):
        array = np.transpose(quantize(tensor, rgb_range).numpy(), (1, 2, 0)).astype(np.uint8)
        return array
    return [_Tensor2numpy(tensor, rgb_range) for tensor in tensor_list]

def quantize(img, rgb_range):
    pixel_range = 255 / rgb_range
    return img.mul(pixel_range).clamp(0, 255).round
3、  psnr ssim 
psnr, ssim = utility.calc_metrics(visuals['SR'], visuals['HR'], crop_border=scale)

PSNR値とSSIM値の計算に重点を置きます.

 RGB       yCbCr
def rgb2ycbcr(img, only_y=True):
    '''same as matlab rgb2ycbcr
    only_y: only return Y channel
    Input:
        uint8, [0, 255]
        float, [0, 1]
    '''
    in_img_type = img.dtype
    img.astype(np.float32)
    if in_img_type != np.uint8:
        img *= 255.
    # convert
    if only_y:
        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
    else:
        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
                                [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
    if in_img_type == np.uint8:
        rlt = rlt.round()
    else:
        rlt /= 255.
    return rlt.astype(in_img_type)
#########################calc_metrics#############################
def calc_metrics(img1, img2, crop_border, test_Y=True):
    #
    img1 = img1 / 255.
    img2 = img2 / 255.

    if test_Y and img1.shape[2] == 3:  # evaluate on Y channel in YCbCr color space
        im1_in = rgb2ycbcr(img1)
        im2_in = rgb2ycbcr(img2)
    else:
        im1_in = img1
        im2_in = img2

    if im1_in.ndim == 3:
        cropped_im1 = im1_in[crop_border:-crop_border, crop_border:-crop_border, :]
        cropped_im2 = im2_in[crop_border:-crop_border, crop_border:-crop_border, :]
    elif im1_in.ndim == 2:
        cropped_im1 = im1_in[crop_border:-crop_border, crop_border:-crop_border]
        cropped_im2 = im2_in[crop_border:-crop_border, crop_border:-crop_border]
    else:
        raise ValueError('Wrong image dimension: {}. Should be 2 or 3.'.format(im1_in.ndim))

    psnr = calc_psnr(cropped_im1 * 255, cropped_im2 * 255)
    ssim = calc_ssim(cropped_im1 * 255, cropped_im2 * 255)
    return psnr, ssim

def calc_psnr(img1, img2):
    # img1 and img2 have range [0, 255]
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2)**2)
    if mse == 0:
        return float('inf')
    return 20 * math.log10(255.0 / math.sqrt(mse))

def ssim(img1, img2):
    C1 = (0.01 * 255)**2
    C2 = (0.03 * 255)**2

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                                            (sigma1_sq + sigma2_sq + C2))
    return ssim_map.mean()


def calc_ssim(img1, img2):
    '''calculate SSIM
    the same outputs as MATLAB's
    img1, img2: [0, 255]
    '''
    if not img1.shape == img2.shape:
        raise ValueError('Input images must have the same dimensions.')
    if img1.ndim == 2:
        return ssim(img1, img2)
    elif img1.ndim == 3:
        if img1.shape[2] == 3:
            ssims = []
            for i in range(3):
                ssims.append(ssim(img1, img2))
            return np.array(ssims).mean()
        elif img1.shape[2] == 1:
            return ssim(np.squeeze(img1), np.squeeze(img2))
    else:
        raise ValueError('Wrong input image dimensions.')