o ߥi"@sddlZddlZddlZddlmZddlmZddlm Z ddl m Z ddl m Z mZe ddd Zd d Zd d Z  dddZ  dddZdS)N)convolve)gamma)model_file_download)imresize) reorder_image to_y_channelz3damo/cv_realbasicvsr_video-super-resolution_videolqzniqe_pris_params.npz)model_id file_pathcCs<|}tddd}t|}tt|dt|t|d}tt||dkd}tt||dkd}||}tt|dt|d}||dd|d}||ddd} t || d} || } |ttd| td| } |ttd| td| } | | | fS)a+Estimate AGGD (Asymmetric Generalized Gaussian Distribution) parameters. Args: block (ndarray): 2D Image block. Returns: tuple: alpha (float), beta_l (float) and beta_r (float) for the AGGD distribution (Estimating the parameters in Equation 7 in the paper). g?gn$@gMbP?r) flattennparange reciprocalsquarersqrtmeanabsargmin)blockgamgam_reciprocalr_gamleft_std right_stdgammahatrhatrhat1rhatnormarray_positionalphabeta_lbeta_rr$i/home/ubuntu/.local/lib/python3.10/site-packages/modelscope/metrics/video_super_resolution_metric/niqe.pyestimate_aggd_params" """ r&c Csg}t|\}}}||||dgddgddgddgddgg}tt|D].}tj|||dd}t||\}}}||td|td|}|||||gq(|S)zCompute features. Args: block (ndarray): 2D Image block. Returns: list: Features with length of 18. r rr )rr axis)r&extendrangelenrrollr) rfeatr!r"r#shiftsi shifted_blockrr$r$r%compute_feature3s r2`c Cs|jdks Jd|j\}}t||}t||} |d||d| |f}g} dD]t} t||dd} tttt||ddt| } || | d}g}t | D].}t |D]'}|||| |d|| ||| |d|| f}| t |q`qZ| t || dkrt |dd d d }|d}q.tj| dd } tj| dd }| t| jdd }tj|d d}tj||d}tt|||t||}t|}tt|}|S)aCalculate NIQE (Natural Image Quality Evaluator) metric. ``Paper: Making a "Completely Blind" Image Quality Analyzer`` This implementation could produce almost the same results as the official MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip Note that we do not include block overlap height and width, since they are always 0 in the official implementation. For good performance, it is advisable by the official implementation to divide the distorted image in to the same size patched as used for the construction of multivariate Gaussian model. Args: img (ndarray): Input image whose quality needs to be computed. The image must be a gray or Y (of YCbCr) image with shape (h, w). Range [0, 255] with float type. mu_pris_param (ndarray): Mean of a pre-defined multivariate Gaussian model calculated on the pristine dataset. cov_pris_param (ndarray): Covariance of a pre-defined multivariate Gaussian model calculated on the pristine dataset. gaussian_window (ndarray): A 7x7 Gaussian window used for smoothing the image. block_size_h (int): Height of the blocks in to which image is divided. Default: 96 (the official recommended value). block_size_w (int): Width of the blocks in to which image is divided. Default: 96 (the official recommended value). r zCInput image must be a gray or Y (of YCbCr) image with shape (h, w).r)r r nearest)moder o@g?T)scale antialiasingr(F)rowvar)ndimshapemathfloorrrrrrr+appendr2arrayr concatenatenanmeanisnananycovlinalgpinvmatmul transposefloatsqueeze)img mu_pris_paramcov_pris_paramgaussian_window block_size_h block_size_whw num_block_h num_block_w distparamr7musigma img_nomalizedr.idx_widx_hr mu_distparamdistparam_no_nan cov_distparam invcov_paramqualityr$r$r%niqeLsf      r`HWCyc Kstt}|d}|d}|d}|tj}|dkr>t||d}|dkr*t|}n|dkr9t|dtj d}t |}|d krN||| || f}| }t ||||} | S) aGCalculate NIQE (Natural Image Quality Evaluator) metric. ``Paper: Making a "Completely Blind" Image Quality Analyzer`` This implementation could produce almost the same results as the official MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip > MATLAB R2021a result for tests/data/baboon.png: 5.72957338 (5.7296) > Our re-implementation result for tests/data/baboon.png: 5.7295763 (5.7296) We use the official params estimated from the pristine dataset. We use the recommended block size (96, 96) without overlaps. Args: img (ndarray): Input image whose quality needs to be computed. The input image must be in range [0, 255] with float/int type. The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order) If the input order is 'HWC' or 'CHW', it will be converted to gray or Y (of YCbCr) image according to the ``convert_to`` argument. crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the metric calculation. input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'. Default: 'HWC'. convert_to (str): Whether converted to 'y' (of MATLAB YCbCr) or 'gray'. Default: 'y'. Returns: float: NIQE result. rLrMrNHW) input_orderrbgrayr6r) rloaddownloaded_file_pathastypefloat32rrcv2cvtColorCOLOR_BGR2GRAYrJroundr`) rK crop_borderrd convert_tokwargsniqe_pris_paramsrLrMrN niqe_resultr$r$r%calculate_niqes"     rs)r3r3)rarb)r<rjnumpyr scipy.ndimager scipy.specialrmodelscope.hub.file_downloadrAmodelscope.metrics.video_super_resolution_metric.matlab_functionsrs(     U