Python measure.compare_psnr方法代码示例

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def compare(inp_img, out_img, calc_ssim=True, calc_msssim=True, calc_psnr=True):
    inp_img = _read_if_not_array(inp_img)
    out_img = _read_if_not_array(out_img)

    assert inp_img.shape == out_img.shape

    def get_ssim():
        return compare_ssim(inp_img, out_img, multichannel=True, gaussian_weights=True, sigma=1.5)

    def get_msssim():
        return MultiScaleSSIM(make_batched(inp_img), make_batched(out_img))

    def get_psnr():
        return compare_psnr(inp_img, out_img)

    def _run_if(cond, fn):
        return fn() if cond else None

    return _run_if(calc_ssim, get_ssim), _run_if(calc_msssim, get_msssim), _run_if(calc_psnr, get_psnr) 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def slice_process(x1, x2, y):
    if len(x1.shape) == 3: x1 = x1[0,:,:]
    if len(x2.shape) == 3: x2 = x2[0,:,:]
    if len(y.shape) == 3: y = y[0,:,:]

    # a scaled and shifted version of pred and bilinear
    x1 = 2*x1 + 100
    x2 = 2*x2 + 100

    # normalize/scale images
    (y_norm1, x1_norm) = norm_minmse(y, x1)
    (y_norm2, x2_norm) = norm_minmse(y, x2)

    # calulate psnr and ssim of the normalized/scaled images
    psnr1 = compare_psnr(*(y_norm1, x1_norm), data_range = 1.)
    psnr2 = compare_psnr(*(y_norm2, x2_norm), data_range = 1.)
    ssim1 = compare_ssim(*(y_norm1, x1_norm), data_range = 1.)
    ssim2 = compare_ssim(*(y_norm2, x2_norm), data_range = 1.)
    return psnr1, ssim1, psnr2, ssim2, y_norm1, x1_norm, y_norm2, x2_norm 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def compute_psnr_and_ssim(image1, image2, border_size=0):
    """
    Computes PSNR and SSIM index from 2 images.
    We round it and clip to 0 - 255. Then shave 'scale' pixels from each border.
    """
    if len(image1.shape) == 2:
        image1 = image1.reshape(image1.shape[0], image1.shape[1], 1)
    if len(image2.shape) == 2:
        image2 = image2.reshape(image2.shape[0], image2.shape[1], 1)

    if image1.shape[0] != image2.shape[0] or image1.shape[1] != image2.shape[1] or image1.shape[2] != image2.shape[2]:
        return None

    image1 = trim_image_as_file(image1)
    image2 = trim_image_as_file(image2)

    if border_size > 0:
        image1 = image1[border_size:-border_size, border_size:-border_size, :]
        image2 = image2[border_size:-border_size, border_size:-border_size, :]

    psnr = compare_psnr(image1, image2, data_range=255)
    ssim = compare_ssim(image1, image2, win_size=11, gaussian_weights=True, multichannel=True, K1=0.01, K2=0.03,
                        sigma=1.5, data_range=255)
    return psnr, ssim 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def psnr(gt, pred):
    """ Compute Peak Signal to Noise Ratio metric (PSNR) """
    return compare_psnr(gt, pred, data_range=gt.max()) 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def calculate_psnr(output_img, target_img):
    target_tf = torch2numpy(target_img)
    output_tf = torch2numpy(output_img)
    psnr = 0.0
    n = 0.0
    for im_idx in range(output_tf.shape[0]):
        psnr += compare_psnr(target_tf[im_idx, ...],
                                             output_tf[im_idx, ...],
                                             data_range=255)
        n += 1.0
    return psnr / n 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def get_psnr(im1, im2):
    return compare_psnr(im1, im2, data_range=255) 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def __compare_psnr(planes):
  a, b = planes
  if (a == b).all():
    # Avoid "Warning: divide by zero encountered in double_scalars" generated
    # by skimage.measure.compare_psnr when a and b are exactly the same.
    return 100
  return skimage_psnr(a, b) 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def psnr(im1, im2):
    def im2double(im):
        min_val, max_val = 0, 255
        out = (im.astype(np.float64)-min_val) / (max_val-min_val)
        return out
        
    im1 = im2double(im1)
    im2 = im2double(im2)
    psnr = measure.compare_psnr(im1, im2, data_range=1)
    return psnr 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def psnr(y_true, y_pred, max_value=2):
  psnr_metric = measure.compare_psnr(y_true, y_pred, max_value)
  return psnr_metric 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def mpsnr(x_true, x_pred):
    """

    :param x_true: 高光谱图像：格式：(H, W, C)
    :param x_pred: 高光谱图像：格式：(H, W, C)
    :return: 计算原始高光谱数据与重构高光谱数据的均方误差
    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
    """
    n_bands = x_true.shape[2]
    p = [compare_psnr(x_true[:, :, k], x_pred[:, :, k], dynamic_range=np.max(x_true[:, :, k])) for k in range(n_bands)]
    return np.mean(p) 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def get_psnr(self, img_true, img_gen):
        return compare_psnr(img_true.astype(np.float32), img_gen.astype(np.float32)) 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def PSNR(self, gt, pred):
    #gt = gt.astype(np.float64)
    #pred = pred.astype(np.float64)
    #mse = np.mean((pred - gt)**2)
    #psnr = 10*np.log10(255*255/mse)
    #return psnr
    return compare_psnr(gt, pred, data_range=255) 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def test_bm3d(noise_data):
    """Tests BM3D grayscale image denoising."""
    img, noisy_img, noise_std_dev = noise_data

    out = pybm3d.bm3d.bm3d(noisy_img, noise_std_dev)

    noise_psnr = compare_psnr(img, noisy_img)
    out_psnr = compare_psnr(img, out)

    assert out_psnr > noise_psnr 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def test_bm3d_color(color_noise_data):
    """Tests BM3D color image denoising."""
    img, noisy_img, noise_std_dev = color_noise_data

    out = pybm3d.bm3d.bm3d(noisy_img, noise_std_dev)

    noise_psnr = compare_psnr(img, noisy_img)
    out_psnr = compare_psnr(img, out)

    assert out_psnr > noise_psnr 

# 需要导入模块: from skimage import measure [as 别名]
# 或者: from skimage.measure import compare_psnr [as 别名]
def calcPSNR(self, image1, image2):
        image1 *= 255
        image2 *= 255
        image1[image1>255] = 255
        image1[image1<0] = 0
        image2[image2>255] = 255
        image2[image2<0] = 0
        return compare_psnr(image1, image2, data_range=255)