ó  ‰\c@ s|ddlmZddlmZmZmZddlZddlm Z ddl Z ddl m ZddlmZddlmZdd lmZd d gZd Ze jd dd gdddgd dd ggde jƒZe jddfde jƒZe jdddfde jƒZi dd6dd6dd6dd6dd6dd6dd6dd6d d!6d"d#6d$d%6d&d'6d(d)6d*d+6d,d-6d.d/6d0d16d2d36d4d56d6d76d8d96d:d;6d<d=6d>d?6d@dA6d dB6dCdD6dEdF6dGdH6dIdJ6dKdL6dMdN6ZeejƒƒZ dO„Z!dP„Z"dQe#fdR„ƒYZ$e%e&e%dS„Z'dTdU„Z(dV„Z)dW„Z*e*ƒdS(Xiÿÿÿÿ(tdivision(tsqrttatan2tpiN(twarn(tndimagei(tlabel(t_moments(twrapst regionpropst perimeters=regionprops and image moments (including moments, normalized moments, central moments, and inertia tensor) of 2D images will change from xy coordinates to rc coordinates in version 0.16. See http://scikit-image.org/docs/0.14.x/release_notes_and_installation.html#deprecations for details on how to avoid this message.itdtypeitareatAreatbboxt BoundingBoxt bbox_areatBoundingBoxAreatmoments_centraltCentralMomentstcentroidtCentroidt convex_areat ConvexAreat convex_imaget ConvexImagetcoordst Coordinatest eccentricityt Eccentricitytequivalent_diametert EquivDiametert euler_numbert EulerNumbertextenttExtentt filled_areat FilledAreat filled_imaget FilledImaget moments_hut HuMomentstimagetImageRtLabeltmajor_axis_lengthtMajorAxisLengtht max_intensityt MaxIntensitytmean_intensityt MeanIntensityt min_intensityt MinIntensitytminor_axis_lengthtMinorAxisLengthtmomentstMomentstmoments_normalizedtNormalizedMomentst orientationt Orientationt PerimetertsoliditytSoliditytweighted_moments_centraltWeightedCentralMomentstweighted_centroidtWeightedCentroidtweighted_moments_hutWeightedHuMomentstweighted_momentstWeightedMomentstweighted_moments_normalizedtWeightedNormalizedMomentsc stˆƒ‡fd†ƒ}|S(Nc sB|j}ˆj}||ko$|js:ˆ|ƒ||!scC s)|j}td„t||jƒDƒƒS(Ncs s"|]\}}||jVqdS(N(Rs(t.0tidxtslc((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pys &s(tweighted_local_centroidRqtzipRa(RUtctr((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyRB$s  cC s8|j}|ttj|jdtƒƒ|d|jS(NR i(i(RFRqRoR“RSR”(RUR•((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyR¤)s cC stj|jƒdƒS(Ni(RR7R’(RU((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyRF.scC s(|j}tj|jƒd|ddƒS(NtcenterR›i(R¤RRR’(RUR¦((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyR@2s cC stj|jƒS(N(RR(RH(RU((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyRD8scC stj|jdƒS(Ni(RR9R@(RU((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyRH<sc C s=t}|jdkr-d }|j|ƒ}ntt|ƒƒS( NRkR/R1R3RFR@RBR¤RDRH( RkR/R1R3RFR@RBR¤RDRH(t PROP_VALSRcR^t differencetitertsorted(RUtpropstunavailable_props((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyt__iter__@scC s7t||dƒ}|dk r"|St|t|ƒSdS(N(tgetattrR^tPROPS(RUtkeytvalue((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyt __getitem__Ss cC slt|tƒstSxRtD]J}y/tjjt||dƒt||dƒƒWqt k rctSXqWt S(N( t isinstanceR[ReR¨Rottestingt assert_equalR¯R^tAssertionErrorRh(RUtotherR±((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyt__eq__Zs   (.RKt __module__t__doc__RnRRR RRRRRRRZRRR R"R$R&R*RRRkR’R–R/R1R3R-R5R7RR(R9R;R R>RBR¤RFR@RDRHR®R³R¹(((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyR[asT !                      c C sÂ|jdkrtdƒ‚ntj|jtjƒsEtdƒ‚ng}tj|ƒ}xat|ƒD]S\}}|dkr…qgn|d}t |||||d|ƒ} |j | ƒqgW|S( sGMeasure properties of labeled image regions. Parameters ---------- label_image : (N, M) ndarray Labeled input image. Labels with value 0 are ignored. .. versionchanged:: 0.14.2 Previously, ``label_image`` was processed by ``numpy.squeeze`` and so any number of singleton dimensions was allowed. This resulted in inconsistent handling of images with singleton dimensions. To recover the old behaviour, use ``regionprops(np.squeeze(label_image), ...)``. intensity_image : (N, M) ndarray, optional Intensity (i.e., input) image with same size as labeled image. Default is None. cache : bool, optional Determine whether to cache calculated properties. The computation is much faster for cached properties, whereas the memory consumption increases. coordinates : 'rc' or 'xy', optional Coordinate conventions for 2D images. (Only 'rc' coordinates are supported for 3D images.) Returns ------- properties : list of RegionProperties Each item describes one labeled region, and can be accessed using the attributes listed below. Notes ----- The following properties can be accessed as attributes or keys: **area** : int Number of pixels of region. **bbox** : tuple Bounding box ``(min_row, min_col, max_row, max_col)``. Pixels belonging to the bounding box are in the half-open interval ``[min_row; max_row)`` and ``[min_col; max_col)``. **bbox_area** : int Number of pixels of bounding box. **centroid** : array Centroid coordinate tuple ``(row, col)``. **convex_area** : int Number of pixels of convex hull image. **convex_image** : (H, J) ndarray Binary convex hull image which has the same size as bounding box. **coords** : (N, 2) ndarray Coordinate list ``(row, col)`` of the region. **eccentricity** : float Eccentricity of the ellipse that has the same second-moments as the region. The eccentricity is the ratio of the focal distance (distance between focal points) over the major axis length. The value is in the interval [0, 1). When it is 0, the ellipse becomes a circle. **equivalent_diameter** : float The diameter of a circle with the same area as the region. **euler_number** : int Euler characteristic of region. Computed as number of objects (= 1) subtracted by number of holes (8-connectivity). **extent** : float Ratio of pixels in the region to pixels in the total bounding box. Computed as ``area / (rows * cols)`` **filled_area** : int Number of pixels of filled region. **filled_image** : (H, J) ndarray Binary region image with filled holes which has the same size as bounding box. **image** : (H, J) ndarray Sliced binary region image which has the same size as bounding box. **inertia_tensor** : (2, 2) ndarray Inertia tensor of the region for the rotation around its mass. **inertia_tensor_eigvals** : tuple The two eigen values of the inertia tensor in decreasing order. **intensity_image** : ndarray Image inside region bounding box. **label** : int The label in the labeled input image. **local_centroid** : array Centroid coordinate tuple ``(row, col)``, relative to region bounding box. **major_axis_length** : float The length of the major axis of the ellipse that has the same normalized second central moments as the region. **max_intensity** : float Value with the greatest intensity in the region. **mean_intensity** : float Value with the mean intensity in the region. **min_intensity** : float Value with the least intensity in the region. **minor_axis_length** : float The length of the minor axis of the ellipse that has the same normalized second central moments as the region. **moments** : (3, 3) ndarray Spatial moments up to 3rd order:: m_ji = sum{ array(x, y) * x^j * y^i } where the sum is over the `x`, `y` coordinates of the region. **moments_central** : (3, 3) ndarray Central moments (translation invariant) up to 3rd order:: mu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i } where the sum is over the `x`, `y` coordinates of the region, and `x_c` and `y_c` are the coordinates of the region's centroid. **moments_hu** : tuple Hu moments (translation, scale and rotation invariant). **moments_normalized** : (3, 3) ndarray Normalized moments (translation and scale invariant) up to 3rd order:: nu_ji = mu_ji / m_00^[(i+j)/2 + 1] where `m_00` is the zeroth spatial moment. **orientation** : float In 'rc' coordinates, angle between the 0th axis (rows) and the major axis of the ellipse that has the same second moments as the region, ranging from `-pi/2` to `pi/2` counter-clockwise. In `xy` coordinates, as above but the angle is now measured from the "x" or horizontal axis. **perimeter** : float Perimeter of object which approximates the contour as a line through the centers of border pixels using a 4-connectivity. **solidity** : float Ratio of pixels in the region to pixels of the convex hull image. **weighted_centroid** : array Centroid coordinate tuple ``(row, col)`` weighted with intensity image. **weighted_local_centroid** : array Centroid coordinate tuple ``(row, col)``, relative to region bounding box, weighted with intensity image. **weighted_moments** : (3, 3) ndarray Spatial moments of intensity image up to 3rd order:: wm_ji = sum{ array(x, y) * x^j * y^i } where the sum is over the `x`, `y` coordinates of the region. **weighted_moments_central** : (3, 3) ndarray Central moments (translation invariant) of intensity image up to 3rd order:: wmu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i } where the sum is over the `x`, `y` coordinates of the region, and `x_c` and `y_c` are the coordinates of the region's weighted centroid. **weighted_moments_hu** : tuple Hu moments (translation, scale and rotation invariant) of intensity image. **weighted_moments_normalized** : (3, 3) ndarray Normalized moments (translation and scale invariant) of intensity image up to 3rd order:: wnu_ji = wmu_ji / wm_00^[(i+j)/2 + 1] where ``wm_00`` is the zeroth spatial moment (intensity-weighted area). Each region also supports iteration, so that you can do:: for prop in region: print(prop, region[prop]) See Also -------- label References ---------- .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing: Core Algorithms. Springer-Verlag, London, 2009. .. [2] B. Jähne. Digital Image Processing. Springer-Verlag, Berlin-Heidelberg, 6. edition, 2005. .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image Features, from Lecture notes in computer science, p. 676. Springer, Berlin, 1993. .. [4] http://en.wikipedia.org/wiki/Image_moment Examples -------- >>> from skimage import data, util >>> from skimage.measure import label >>> img = util.img_as_ubyte(data.coins()) > 110 >>> label_img = label(img, connectivity=img.ndim) >>> props = regionprops(label_img) >>> # centroid of first labeled object >>> props[0].centroid (22.729879860483141, 81.912285234465827) >>> # centroid of first labeled object >>> props[0]['centroid'] (22.729879860483141, 81.912285234465827) iis"Only 2-D and 3-D images supported.s$Label image must be of integer type.iRm(iiN( Rdt TypeErrorRot issubdtypeR tintegerRŠt find_objectst enumerateR^R[tappend( RjRkRNRmtregionstobjectsRutslRR¬((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyR isÅ  ic C s3|dkrt}nt}|jtjƒ}tj||ddƒ}||}tjddtjƒ}d|ddd d d d g{s(treRßR R»tfinditertDOTALLtdict(Ràtdoctmatchestprop_doc((Rßs;lib/python2.7/site-packages/skimage/measure/_regionprops.pyt _parse_docsts    cC s¬tƒ}xœgttƒD]}|jdƒs|^qD]l}y||tt|ƒ_Wn*tk r„||tt|ƒj_nXtt|t tt|ƒƒƒq8WdS(NR‡( RçtdirR[t startswithR¯R»Rtim_functsetattrtproperty(Rætmembertp((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pyt_install_properties_docs€s  (+t __future__RtmathRRRR‚t itertoolstwarningsRtnumpyRotscipyRRŠt_labelRtRt functoolsRt__all__RšRÏR™RÊR‰RËt STREL_26_3DR°tsettvaluesR¨RRRZtobjectR[R^RhR R RçRï(((s;lib/python2.7/site-packages/skimage/measure/_regionprops.pytsv    !   ÿ  Ú 0