0-Ph(ddlZddlZddlmZddlmZddlmZddl m Z m Z m Z ddl mZddlmZd gZdd ddd d d d ZdS) N)uniform_filter)utils)gaussian)_supported_float_typecheck_shape_equalitywarn)crop) dtype_rangestructural_similarityFwin_sizegradient data_range channel_axisgaussian_weightsfullc t||t|j} |st|||d||} | ||j|} t j| | } |rt j|j| } |rt j|j| }||jz}tj tj |}t| D]}t||||||fi| }|r$|r"|\| |<| ||<|||<T|r|\| |<| ||<k|r|\| |<|||<|| |<| } |r|r| | |fS|r| | fS|r| |fS| S|dd}|dd}|d d }|d krt#d |d krt#d |d krt#d|dd}|rd}|"|rt%||zdz}d|zdz}nd}t jt j|j|z d krt#d|dzdkst#d|t j|jt js$t j|jt jrt#d|j|jkrt/ddt0|jj\}}||z }t j|jt jr1|jt jkrt/dd|ddzdzd|j}|rt8}||d d!}n t:}d"|i}|| d#$}|| d#$}||z}|r ||dz z }nd%}||fi|}||fi|} |||zfi|}!|||zfi|}"|||zfi|}#||!||zz z}$||"| | zz z}%||#|| zz z}&|}'||'zdz}(||'zdz})d|z| z|(zd|&z|)z|dz| dzz|(z|$|%z|)zf\}*}+},}-|,|-z}.|*|+z|.z }|dz dz}/t?||/t j } |r[||*|.z fi||z}0|0|| |-z fi||zz }0|0|||+|*z z| |-|,z z|zz |.z fi|z }0|0d|j!z z}0|r| |0|fS| |0fS|r| |fS| S)&a Compute the mean structural similarity index between two images. Please pay attention to the `data_range` parameter with floating-point images. Parameters ---------- im1, im2 : ndarray Images. Any dimensionality with same shape. win_size : int or None, optional The side-length of the sliding window used in comparison. Must be an odd value. If `gaussian_weights` is True, this is ignored and the window size will depend on `sigma`. gradient : bool, optional If True, also return the gradient with respect to im2. data_range : float, optional The data range of the input image (difference between maximum and minimum possible values). By default, this is estimated from the image data type. This estimate may be wrong for floating-point image data. Therefore it is recommended to always pass this scalar value explicitly (see note below). channel_axis : int or None, optional If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels. .. versionadded:: 0.19 ``channel_axis`` was added in 0.19. gaussian_weights : bool, optional If True, each patch has its mean and variance spatially weighted by a normalized Gaussian kernel of width sigma=1.5. full : bool, optional If True, also return the full structural similarity image. Other Parameters ---------------- use_sample_covariance : bool If True, normalize covariances by N-1 rather than, N where N is the number of pixels within the sliding window. K1 : float Algorithm parameter, K1 (small constant, see [1]_). K2 : float Algorithm parameter, K2 (small constant, see [1]_). sigma : float Standard deviation for the Gaussian when `gaussian_weights` is True. Returns ------- mssim : float The mean structural similarity index over the image. grad : ndarray The gradient of the structural similarity between im1 and im2 [2]_. This is only returned if `gradient` is set to True. S : ndarray The full SSIM image. This is only returned if `full` is set to True. Notes ----- If `data_range` is not specified, the range is automatically guessed based on the image data type. However for floating-point image data, this estimate yields a result double the value of the desired range, as the `dtype_range` in `skimage.util.dtype.py` has defined intervals from -1 to +1. This yields an estimate of 2, instead of 1, which is most often required when working with image data (as negative light intensities are nonsensical). In case of working with YCbCr-like color data, note that these ranges are different per channel (Cb and Cr have double the range of Y), so one cannot calculate a channel-averaged SSIM with a single call to this function, as identical ranges are assumed for each channel. To match the implementation of Wang et al. [1]_, set `gaussian_weights` to True, `sigma` to 1.5, `use_sample_covariance` to False, and specify the `data_range` argument. .. versionchanged:: 0.16 This function was renamed from ``skimage.measure.compare_ssim`` to ``skimage.metrics.structural_similarity``. References ---------- .. [1] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600-612. https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf, :DOI:`10.1109/TIP.2003.819861` .. [2] Avanaki, A. N. (2009). Exact global histogram specification optimized for structural similarity. Optical Review, 16, 613-621. :arxiv:`0901.0065` :DOI:`10.1007/s10043-009-0119-z` Nr )dtype)axisK1g{Gz?K2gQ?sigmag?rzK1 must be positivezK2 must be positivezsigma must be positiveuse_sample_covarianceTg @g?raDwin_size exceeds image extent. 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