a  d!@sddlZddlZddlZddlmmZddlm Z m Z ddl m Z ddl mZedddZedd d Zedd d Zedd dZddZddZdS)N) reorder_image to_y_channel) rgb2ycbcr_pt)METRIC_REGISTRYHWCFcKs|j|jks&Jd|jd|jd|dvr>td|dt||d}t||d}|dkr||| || d f}||| || d f}|rt|}t|}|tj}|tj}t||d }|dkrtd Sd t d |S)a'Calculate PSNR (Peak Signal-to-Noise Ratio). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img (ndarray): Images with range [0, 255]. img2 (ndarray): Images with range [0, 255]. crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: PSNR result. Image shapes are different: , .rZCHWWrong input_order ,. Supported input_orders are "HWC" and "CHW" input_orderr.inf$@g @) shape ValueErrorrrastypenpfloat64meanfloatlog10)imgimg2 crop_borderrtest_y_channelkwargsmser b/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/basicsr/metrics/psnr_ssim.pycalculate_psnr s"&    r"cKs|j|jks&Jd|jd|jd|dkrv|dddd|| || f}|dddd|| || f}|rt|dd}t|dd}|tj}|tj}tj||dgd d }d td |d S)a Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: PSNR result. rrr rNTZy_onlyrr)Zdimrg?g:0yE>)rrtotorchrrr)rrrrrrr r r!calculate_psnr_pt3s&$$    r)cKs|j|jks&Jd|jd|jd|dvr>td|dt||d}t||d}|dkr||| || d f}||| || d f}|rt|}t|}|tj}|tj}g}t|jd D]$}|t |d |f|d |fqt | S) a Calculate SSIM (structural similarity). Ref: Image quality assessment: From error visibility to structural similarity The results are the same as that of the official released MATLAB code in https://ece.uwaterloo.ca/~z70wang/research/ssim/. For three-channel images, SSIM is calculated for each channel and then averaged. Args: img (ndarray): Images with range [0, 255]. img2 (ndarray): Images with range [0, 255]. crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: SSIM result. rrr r r r r r.r) rrrrrrrrangeappend_ssimarrayr)rrrrrrZssimsir r r!calculate_ssimTs"&    "r/cKs|j|jks&Jd|jd|jd|dkrv|dddd|| || f}|dddd|| || f}|rt|dd}t|dd}|tj}|tj}t|d|d}|S) aCalculate SSIM (structural similarity) (PyTorch version). Ref: Image quality assessment: From error visibility to structural similarity The results are the same as that of the official released MATLAB code in https://ece.uwaterloo.ca/~z70wang/research/ssim/. For three-channel images, SSIM is calculated for each channel and then averaged. Args: img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: SSIM result. rrr rNTr#go@)rrr'r(r _ssim_pth)rrrrrZssimr r r!calculate_ssim_pts&$$    r1cCs"d}d}tdd}t||}t|d|ddddf}t|d|ddddf}|d}|d} ||} t|dd|ddddf|} t|dd|ddddf| } t||d|ddddf| } d| |d| ||| || | |}|S) a-Calculate SSIM (structural similarity) for one channel images. It is called by func:`calculate_ssim`. Args: img (ndarray): Images with range [0, 255] with order 'HWC'. img2 (ndarray): Images with range [0, 255] with order 'HWC'. Returns: float: SSIM result. (\@zGBM@ ?r)cv2getGaussianKernelrouter transposeZfilter2Dr)rrc1c2kernelwindowmu1mu2mu1_sqmu2_sqmu1_mu2 sigma1_sq sigma2_sqsigma12ssim_mapr r r!r,s  &&&0r,cCsTd}d}tdd}t||}t|dddd| dddd |j  |j }t j||dd|jdd}t j||dd|jdd}|d}|d} ||} t j|||dd|jdd|} t j|||dd|jdd| } t j|||dd|jdd| } d| || | |}d| ||| ||}|gd S) a/Calculate SSIM (structural similarity) (PyTorch version). It is called by func:`calculate_ssim_pt`. Args: img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). Returns: float: SSIM result. r2r3r4r5r%r)Zstridepaddinggroupsrr$)r9r:rr;r<r(Z from_numpyviewexpandsizer'ZdtypeZdeviceFZconv2drpowr)rrr=r>r?r@rArBrCrDrErFrGrHZcs_maprIr r r!r0s  8  """r0)rF)F)rF)F)r9numpyrr(Ztorch.nn.functionalnnZ functionalrOZbasicsr.metrics.metric_utilrrZbasicsr.utils.color_utilrZbasicsr.utils.registryrregisterr"r)r/r1r,r0r r r r!s    '  / '