9i'SSKJr SSKrSSKrSSKrSSKJr SSKJ r SSK J r SSK J r Jr \ R"\ \R S9"S S \ 55rSS jrSS jrSS jrSrSrSrSrg))DictN)Metrics) default_group)Metric)METRICS MetricKeys) group_key module_namecb^\rSrSrSrSrSrU4SjrS\S\4Sjr S r SS jr S r S r S rU=r$)ImageDenoiseMetricz[[U] 5 /Ul/UlgN)superr __init__predslabels)self __class__s g/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/metrics/image_denoise_metric.pyrImageDenoiseMetric.__init__s  $02  outputsinputscU[RnU[RnURR U5 UR R U5 gr)r label_name pred_namerappendr)rrr ground_truths eval_resultss raddImageDenoiseMetric.addsH 2 = => 1;;<  ,' =)rc n//p![URUR5HEup4UR[ USUSSS95 UR[ USUSSS95 MG [ R[R"U5[ R[R"U50$)Nr) crop_border) ziprrr!calculate_psnrcalculate_ssimr PSNRnpmeanSSIM)r psnr_list ssim_listrlabels revaluateImageDenoiseMetric.evaluate%s!29 T[[9MT   ^E!Hd1g1M N   ^E!Hd1g1M N: OORWWY/ OORWWY/  rcURRUR5 URRUR5 gr)rextendr)rothers rmergeImageDenoiseMetric.merge/s. %++& 5<<(rc2URUR4$r)rr)rs r __getstate__ImageDenoiseMetric.__getstate__3szz4;;&&rcBUR5 UuUlUlgr)rrr)rstates r __setstate__ImageDenoiseMetric.__setstate__6s "' DKr)rr)r6r )__name__ __module__ __qualname____firstlineno____doc__r rrrr$r2r7r:r>__static_attributes__ __classcell__)rs@rr r sCIJ *4**  )'((rr cUS;a[SUS35e[UR5S:XaUSnUS:XaURSSS5nU$) aReorder images to 'HWC' order. If the input_order is (h, w), return (h, w, 1); If the input_order is (c, h, w), return (h, w, c); If the input_order is (h, w, c), return as it is. Args: img (ndarray): Input image. input_order (str): Whether the input order is 'HWC' or 'CHW'. If the input image shape is (h, w), input_order will not have effects. Default: 'HWC'. Returns: ndarray: reordered image. HWCCHWWrong input_order z,. Supported input_orders are 'HWC' and 'CHW').NrJrr) ValueErrorlenshape transpose)img input_orders r reorder_imagerS;s`.(  -Y Z   399~)nemmAq!$ JrcURUR:Xd"SURSURS35eUS;a[SUS35e[U5[R:Xag[ UR5S:XaUR S5nUR5R5R5RS S S5n[U5[R:Xag[ UR5S:XaUR S5nUR5R5R5RS S S5n[XS 9n[XS 9nUR[R5nUR[R5nUS:waXU*2X"*2S 4nXU*2X"*2S 4nS nU"X5$)a.Calculate PSNR (Peak Signal-to-Noise Ratio). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img1 (ndarray/tensor): Images with range [0, 255]/[0, 1]. img2 (ndarray/tensor): Images with range [0, 255]/[0, 1]. crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the PSNR calculation. input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: psnr result. Image shapes are different: , .rHrK,. Supported input_orders are "HWC" and "CHW"rrrLrR.c[R"X- S-5nUS:Xa [S5$UR5S::aSOSnS[R"U[R "U5- 5-$)NrLrinfrg?go@g4@)r,r-floatmaxlog10sqrt)img1img2mse max_values r_psnrcalculate_psnr.._psnr{s\ggt{Q&' !8< ((*/Bt RXXi"''#,6777rrOrMtypetorchTensorrNsqueezedetachcpunumpyrPrSastyper,float64)rarbr'rRres rr)r)Ts :: #D &tzzl"TZZLBD #.(  .   DzU\\! tzz?a <<?D{{}  "((*44Q1= DzU\\! tzz?a <<?D{{}  "((*44Q1=  7D  7D ;;rzz "D ;;rzz "Da ,k,.FKL ,k,.FKL8  rc^URUR:Xd"SURSURS35eUS;a[SUS35e[U5[R:Xag[ UR5S:XaUR S5nUR5R5R5RS S S5n[U5[R:Xag[ UR5S:XaUR S5nUR5R5R5RS S S5n[XS 9n[XS 9nUR[R5nUR[R5nUS:waXU*2X"*2S 4nXU*2X"*2S 4nU4S jnU"X5$)aCalculate 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: img1 (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 SSIM 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. 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