ó ‡ˆ\c@s?dZddlmZddlZddlZddlmZddlm Z ddl m Z m Z ddl mZd d d d d gZdd„Zd„Zdd„Zddejdd„Zdejdd„Zddejejd„Zdd„Zddd„Zddd„Zd„Zd efd„ƒYZdS(sj The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to extract features from images. iÿÿÿÿ(tproductN(tsparse(t as_stridedi(t check_arraytcheck_random_state(t BaseEstimatortPatchExtractortextract_patches_2dt grid_to_grapht img_to_graphtreconstruct_from_patches_2dicCstj|||ƒj|||fƒ}tj|dd…dd…dd…fjƒ|dd…dd…dd…fjƒfƒ}tj|dd…dd…fjƒ|dd…dd…fjƒfƒ}tj|d jƒ|djƒfƒ}tj|||fƒ}|S(s-Returns a list of edges for a 3D image. Parameters =========== n_x : integer The size of the grid in the x direction. n_y : integer The size of the grid in the y direction. n_z : integer, optional The size of the grid in the z direction, defaults to 1 Niÿÿÿÿi(tnptarangetreshapetvstacktravelthstack(tn_xtn_ytn_ztverticest edges_deept edges_rightt edges_downtedges((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyt_make_edges_3ds ).1%()cCs›|j\}}}tj||d|||d||||d|||f||d|||d||||d|||fƒ}|S(Nii(tshapeR tabs(RtimgRRRtgradient((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyt_compute_gradient_3d5scCstj|jƒ}||jƒ}tjtj|d|ƒtj|d|ƒƒ}|dd…|f}|dk rƒ||}nt|jƒƒr¤|jƒ}nd}tj tj |jƒƒtj|dƒƒ}||}|dkrò|S||fSdS(s'Apply a mask to edges (weighted or not)iiN( R R tsizeRt logical_andtin1dtNonetlentmaxt searchsortedtunique(tmaskRtweightstindstind_masktmaxvaltorder((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyt_mask_edges_weightsBs  .  cCs t|||ƒ}|dkrB|dkr6tj}qB|j}n|dk r»tj|ƒ}t||ƒ}|dk r£t|||ƒ\}}|jƒ|} n |j ƒ} | j } nŸ|dk r|j dtj dt ƒ}tj|dtj ƒ}t||ƒ}tj|ƒ} n|||} tj|jdd|ƒ}tj| d|ƒ} tj| ƒ} tj|d|dfƒ} tj|d|dfƒ} tjtj||| fƒtj| | fƒtj| | fƒff| | fd|ƒ}|tjkr|jƒS||ƒS(s:Auxiliary function for img_to_graph and grid_to_graph tdtypetcopyiiN(RR"R tintR.t atleast_3dRR-tsqueezeRRtastypetbooltFalsetasarraytsumtonesRR RRt coo_matrixtndarrayttoarray(RRRR'Rt return_asR.RR(tdiagtn_voxelstdiag_idxti_idxtj_idxtgraph((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyt _to_graphWs@            cCs=tj|ƒ}|j\}}}t|||||||ƒS(sÈGraph of the pixel-to-pixel gradient connections Edges are weighted with the gradient values. Read more in the :ref:`User Guide `. Parameters ---------- img : ndarray, 2D or 3D 2D or 3D image mask : ndarray of booleans, optional An optional mask of the image, to consider only part of the pixels. return_as : np.ndarray or a sparse matrix class, optional The class to use to build the returned adjacency matrix. dtype : None or dtype, optional The data of the returned sparse matrix. By default it is the dtype of img Notes ----- For scikit-learn versions 0.14.1 and prior, return_as=np.ndarray was handled by returning a dense np.matrix instance. Going forward, np.ndarray returns an np.ndarray, as expected. For compatibility, user code relying on this method should wrap its calls in ``np.asarray`` to avoid type issues. (R R1RRC(RR'R<R.RRR((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyR „sc Cs"t|||d|d|d|ƒS(sÅGraph of the pixel-to-pixel connections Edges exist if 2 voxels are connected. Parameters ---------- n_x : int Dimension in x axis n_y : int Dimension in y axis n_z : int, optional, default 1 Dimension in z axis mask : ndarray of booleans, optional An optional mask of the image, to consider only part of the pixels. return_as : np.ndarray or a sparse matrix class, optional The class to use to build the returned adjacency matrix. dtype : dtype, optional, default int The data of the returned sparse matrix. By default it is int Notes ----- For scikit-learn versions 0.14.1 and prior, return_as=np.ndarray was handled by returning a dense np.matrix instance. Going forward, np.ndarray returns an np.ndarray, as expected. For compatibility, user code relying on this method should wrap its calls in ``np.asarray`` to avoid type issues. R'R<R.(RC(RRRR'R<R.((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyR¦scCsÇ||d}||d}||}|r¿t|tjƒrN||krN|St|tjƒrp||krp|St|tjƒr¬d|ko™dknr¬t||ƒStd|ƒ‚n|SdS(s5Compute the number of patches that will be extracted in an image. Read more in the :ref:`User Guide `. Parameters ---------- i_h : int The image height i_w : int The image with p_h : int The height of a patch p_w : int The width of a patch max_patches : integer or float, optional default is None The maximum number of patches to extract. If max_patches is a float between 0 and 1, it is taken to be a proportion of the total number of patches. iis!Invalid value for max_patches: %rN(t isinstancetnumberstIntegraltRealR0t ValueError(ti_hti_wtp_htp_wt max_patchestn_htn_wt all_patches((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyt_compute_n_patchesÌs   ic Cs |j}t|tjƒr1t|g|ƒ}nt|tjƒrYt|g|ƒ}n|j}td„|Dƒƒ}||j}tj|jƒtj|ƒtj|ƒd}tt |ƒt |ƒƒ}tt |ƒt |ƒƒ} t |d|d| ƒ} | S(slExtracts patches of any n-dimensional array in place using strides. Given an n-dimensional array it will return a 2n-dimensional array with the first n dimensions indexing patch position and the last n indexing the patch content. This operation is immediate (O(1)). A reshape performed on the first n dimensions will cause numpy to copy data, leading to a list of extracted patches. Read more in the :ref:`User Guide `. Parameters ---------- arr : ndarray n-dimensional array of which patches are to be extracted patch_shape : integer or tuple of length arr.ndim Indicates the shape of the patches to be extracted. If an integer is given, the shape will be a hypercube of sidelength given by its value. extraction_step : integer or tuple of length arr.ndim Indicates step size at which extraction shall be performed. If integer is given, then the step is uniform in all dimensions. Returns ------- patches : strided ndarray 2n-dimensional array indexing patches on first n dimensions and containing patches on the last n dimensions. These dimensions are fake, but this way no data is copied. A simple reshape invokes a copying operation to obtain a list of patches: result.reshape([-1] + list(patch_shape)) css!|]}tdd|ƒVqdS(N(tsliceR"(t.0tst((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pys !siRtstrides( tndimRDREtNumberttupleRUR tarrayRtlistR( tarrt patch_shapetextraction_steptarr_ndimt patch_stridestslicestindexing_stridestpatch_indices_shapeRRUtpatches((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pytextract_patchesôs$   cCsv|jd \}}|\}}||kr:tdƒ‚n||krUtdƒ‚nt|dtƒ}|j||dfƒ}|jd}t|d|||fddƒ} t|||||ƒ} |r't|ƒ} | j||dd | ƒ} | j||dd | ƒ} | | | d f}n| }|jd|||ƒ}|jddkrn|j| ||fƒS|Sd S( s<Reshape a 2D image into a collection of patches The resulting patches are allocated in a dedicated array. Read more in the :ref:`User Guide `. Parameters ---------- image : array, shape = (image_height, image_width) or (image_height, image_width, n_channels) The original image data. For color images, the last dimension specifies the channel: a RGB image would have `n_channels=3`. patch_size : tuple of ints (patch_height, patch_width) the dimensions of one patch max_patches : integer or float, optional default is None The maximum number of patches to extract. If max_patches is a float between 0 and 1, it is taken to be a proportion of the total number of patches. random_state : int, RandomState instance or None, optional (default=None) Pseudo number generator state used for random sampling to use if `max_patches` is not None. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Returns ------- patches : array, shape = (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels) The collection of patches extracted from the image, where `n_patches` is either `max_patches` or the total number of patches that can be extracted. Examples -------- >>> from sklearn.feature_extraction import image >>> one_image = np.arange(16).reshape((4, 4)) >>> one_image array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> patches = image.extract_patches_2d(one_image, (2, 2)) >>> print(patches.shape) (9, 2, 2) >>> patches[0] array([[0, 1], [4, 5]]) >>> patches[1] array([[1, 2], [5, 6]]) >>> patches[8] array([[10, 11], [14, 15]]) is@Height of the patch should be less than the height of the image.s>Width of the patch should be less than the width of the image.tallow_ndiÿÿÿÿR\R]iRiN( RRHRtTrueR RdRQRtrandint(timaget patch_sizeRMt random_stateRIRJRKRLtn_colorstextracted_patchest n_patchestrngti_stj_sRc((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyR.s.<       c Cs0|d \}}|jdd!\}}tj|ƒ}||d}||d}xbt|tt|ƒt|ƒƒƒD]<\} \} } || | |…| | |…fc| 7`. Parameters ---------- patches : array, shape = (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels) The complete set of patches. If the patches contain colour information, channels are indexed along the last dimension: RGB patches would have `n_channels=3`. image_size : tuple of ints (image_height, image_width) or (image_height, image_width, n_channels) the size of the image that will be reconstructed Returns ------- image : array, shape = image_size the reconstructed image iii(RR tzerostzipRtrangetfloattmin( Rct image_sizeRIRJRKRLRRNROtptitj((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyR Žs7.*&cBs5eZdZdddd„Zdd„Zd„ZRS(s5Extracts patches from a collection of images Read more in the :ref:`User Guide `. Parameters ---------- patch_size : tuple of ints (patch_height, patch_width) the dimensions of one patch max_patches : integer or float, optional default is None The maximum number of patches per image to extract. If max_patches is a float in (0, 1), it is taken to mean a proportion of the total number of patches. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. cCs||_||_||_dS(N(RiRMRj(tselfRiRMRj((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyt__init__Ñs  cCs|S(sDo nothing and return the estimator unchanged This method is just there to implement the usual API and hence work in pipelines. Parameters ---------- X : array-like, shape [n_samples, n_features] Training data. ((RztXty((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pytfitÖs cCs4t|jƒ|_|jd \}}}tj||||dfƒ}|jd}|jdkry|d|df}n |j}|\}}t|||||jƒ} || f|} |dkrÖ| |f7} ntj | ƒ} xHt |ƒD]:\} } t | ||j|jƒ| | | | d| +qòW| S(sTransforms the image samples in X into a matrix of patch data. Parameters ---------- X : array, shape = (n_samples, image_height, image_width) or (n_samples, image_height, image_width, n_channels) Array of images from which to extract patches. For color images, the last dimension specifies the channel: a RGB image would have `n_channels=3`. Returns ------- patches : array, shape = (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels) The collection of patches extracted from the images, where `n_patches` is either `n_samples * max_patches` or the total number of patches that can be extracted. iiÿÿÿÿi iN( RRjRR R RiR"RQRMtemptyt enumerateR(RzR|tn_imagesRIRJt n_channelsRiRKRLRmt patches_shapeRctiiRh((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyt transformãs"    /N(t__name__t __module__t__doc__R"R{R~R…(((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyR»s (Rˆt itertoolsRREtnumpyR tscipyRtnumpy.lib.stride_tricksRtutilsRRtbaseRt__all__RRR"R-R9RCR R0RRQRdRR R(((s?lib/python2.7/site-packages/sklearn/feature_extraction/image.pyts2     ," % (:` -