Miscellaneous Image Transformations =================================== .. highlight:: cpp .. index:: adaptiveThreshold cv::adaptiveThreshold --------------------- .. cfunction:: void adaptiveThreshold( const Mat\& src, Mat\& dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C ) Applies an adaptive threshold to an array. :param src: Source 8-bit single-channel image :param dst: Destination image; will have the same size and the same type as ``src`` :param maxValue: The non-zero value assigned to the pixels for which the condition is satisfied. See the discussion :param adaptiveMethod: Adaptive thresholding algorithm to use, ``ADAPTIVE_THRESH_MEAN_C`` or ``ADAPTIVE_THRESH_GAUSSIAN_C`` (see the discussion) :param thresholdType: Thresholding type; must be one of ``THRESH_BINARY`` or ``THRESH_BINARY_INV`` :param blockSize: The size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on :param C: The constant subtracted from the mean or weighted mean (see the discussion); normally, it's positive, but may be zero or negative as well The function transforms a grayscale image to a binary image according to the formulas: * **THRESH_BINARY** .. math:: dst(x,y) = \fork{\texttt{maxValue}}{if $src(x,y) > T(x,y)$}{0}{otherwise} * **THRESH_BINARY_INV** .. math:: dst(x,y) = \fork{0}{if $src(x,y) > T(x,y)$}{\texttt{maxValue}}{otherwise} where :math:`T(x,y)` is a threshold calculated individually for each pixel. #. For the method ``ADAPTIVE_THRESH_MEAN_C`` the threshold value :math:`T(x,y)` is the mean of a :math:`\texttt{blockSize} \times \texttt{blockSize}` neighborhood of :math:`(x, y)` , minus ``C`` . #. For the method ``ADAPTIVE_THRESH_GAUSSIAN_C`` the threshold value :math:`T(x, y)` is the weighted sum (i.e. cross-correlation with a Gaussian window) of a :math:`\texttt{blockSize} \times \texttt{blockSize}` neighborhood of :math:`(x, y)` , minus ``C`` . The default sigma (standard deviation) is used for the specified ``blockSize`` , see :func:`getGaussianKernel` . The function can process the image in-place. See also: :func:`threshold` , :func:`blur` , :func:`GaussianBlur` .. index:: cvtColor cv::cvtColor ------------ .. cfunction:: void cvtColor( const Mat\& src, Mat\& dst, int code, int dstCn=0 ) Converts image from one color space to another :param src: The source image, 8-bit unsigned, 16-bit unsigned ( ``CV_16UC...`` ) or single-precision floating-point :param dst: The destination image; will have the same size and the same depth as ``src`` :param code: The color space conversion code; see the discussion :param dstCn: The number of channels in the destination image; if the parameter is 0, the number of the channels will be derived automatically from ``src`` and the ``code`` The function converts the input image from one color space to another. In the case of transformation to-from RGB color space the ordering of the channels should be specified explicitly (RGB or BGR). The conventional ranges for R, G and B channel values are: * 0 to 255 for ``CV_8U`` images * 0 to 65535 for ``CV_16U`` images and * 0 to 1 for ``CV_32F`` images. Of course, in the case of linear transformations the range does not matter, but in the non-linear cases the input RGB image should be normalized to the proper value range in order to get the correct results, e.g. for RGB :math:`\rightarrow` L*u*v* transformation. For example, if you have a 32-bit floating-point image directly converted from 8-bit image without any scaling, then it will have 0..255 value range, instead of the assumed by the function 0..1. So, before calling ``cvtColor`` , you need first to scale the image down: :: img *= 1./255; cvtColor(img, img, CV_BGR2Luv); .. The function can do the following transformations: * Transformations within RGB space like adding/removing the alpha channel, reversing the channel order, conversion to/from 16-bit RGB color (R5:G6:B5 or R5:G5:B5), as well as conversion to/from grayscale using: .. math:: \text{RGB[A] to Gray:} \quad Y \leftarrow 0.299 \cdot R + 0.587 \cdot G + 0.114 \cdot B and .. math:: \text{Gray to RGB[A]:} \quad R \leftarrow Y, G \leftarrow Y, B \leftarrow Y, A \leftarrow 0 The conversion from a RGB image to gray is done with: :: cvtColor(src, bwsrc, CV_RGB2GRAY); .. Some more advanced channel reordering can also be done with :func:`mixChannels` . * RGB :math:`\leftrightarrow` CIE XYZ.Rec 709 with D65 white point ( ``CV_BGR2XYZ, CV_RGB2XYZ, CV_XYZ2BGR, CV_XYZ2RGB`` ): .. math:: \begin{bmatrix} X \\ Y \\ Z \end{bmatrix} \leftarrow \begin{bmatrix} 0.412453 & 0.357580 & 0.180423 \\ 0.212671 & 0.715160 & 0.072169 \\ 0.019334 & 0.119193 & 0.950227 \end{bmatrix} \cdot \begin{bmatrix} R \\ G \\ B \end{bmatrix} .. math:: \begin{bmatrix} R \\ G \\ B \end{bmatrix} \leftarrow \begin{bmatrix} 3.240479 & -1.53715 & -0.498535 \\ -0.969256 & 1.875991 & 0.041556 \\ 0.055648 & -0.204043 & 1.057311 \end{bmatrix} \cdot \begin{bmatrix} X \\ Y \\ Z \end{bmatrix} :math:`X` , :math:`Y` and :math:`Z` cover the whole value range (in the case of floating-point images :math:`Z` may exceed 1). * RGB :math:`\leftrightarrow` YCrCb JPEG (a.k.a. YCC) ( ``CV_BGR2YCrCb, CV_RGB2YCrCb, CV_YCrCb2BGR, CV_YCrCb2RGB`` ) .. math:: Y \leftarrow 0.299 \cdot R + 0.587 \cdot G + 0.114 \cdot B .. math:: Cr \leftarrow (R-Y) \cdot 0.713 + delta .. math:: Cb \leftarrow (B-Y) \cdot 0.564 + delta .. math:: R \leftarrow Y + 1.403 \cdot (Cr - delta) .. math:: G \leftarrow Y - 0.344 \cdot (Cr - delta) - 0.714 \cdot (Cb - delta) .. math:: B \leftarrow Y + 1.773 \cdot (Cb - delta) where .. math:: delta = \left \{ \begin{array}{l l} 128 & \mbox{for 8-bit images} \\ 32768 & \mbox{for 16-bit images} \\ 0.5 & \mbox{for floating-point images} \end{array} \right . Y, Cr and Cb cover the whole value range. * RGB :math:`\leftrightarrow` HSV ( ``CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB`` ) in the case of 8-bit and 16-bit images R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range .. math:: V \leftarrow max(R,G,B) .. math:: S \leftarrow \fork{\frac{V-min(R,G,B)}{V}}{if $V \neq 0$}{0}{otherwise} .. math:: H \leftarrow \forkthree{{60(G - B)}/{S}}{if $V=R$}{{120+60(B - R)}/{S}}{if $V=G$}{{240+60(R - G)}/{S}}{if $V=B$} if :math:`H<0` then :math:`H \leftarrow H+360` On output :math:`0 \leq V \leq 1` , :math:`0 \leq S \leq 1` , :math:`0 \leq H \leq 360` . The values are then converted to the destination data type: * 8-bit images .. math:: V \leftarrow 255 V, S \leftarrow 255 S, H \leftarrow H/2 \text{(to fit to 0 to 255)} * 16-bit images (currently not supported) .. math:: V <- 65535 V, S <- 65535 S, H <- H * 32-bit images H, S, V are left as is * RGB :math:`\leftrightarrow` HLS ( ``CV_BGR2HLS, CV_RGB2HLS, CV_HLS2BGR, CV_HLS2RGB`` ). in the case of 8-bit and 16-bit images R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range. .. math:: V_{max} \leftarrow {max}(R,G,B) .. math:: V_{min} \leftarrow {min}(R,G,B) .. math:: L \leftarrow \frac{V_{max} + V_{min}}{2} .. math:: S \leftarrow \fork { \frac{V_{max} - V_{min}}{V_{max} + V_{min}} }{if $L < 0.5$ } { \frac{V_{max} - V_{min}}{2 - (V_{max} + V_{min})} }{if $L \ge 0.5$ } .. math:: H \leftarrow \forkthree {{60(G - B)}/{S}}{if $V_{max}=R$ } {{120+60(B - R)}/{S}}{if $V_{max}=G$ } {{240+60(R - G)}/{S}}{if $V_{max}=B$ } if :math:`H<0` then :math:`H \leftarrow H+360` On output :math:`0 \leq L \leq 1` , :math:`0 \leq S \leq 1` , :math:`0 \leq H \leq 360` . The values are then converted to the destination data type: * 8-bit images .. math:: V \leftarrow 255 \cdot V, S \leftarrow 255 \cdot S, H \leftarrow H/2 \; \text{(to fit to 0 to 255)} * 16-bit images (currently not supported) .. math:: V <- 65535 \cdot V, S <- 65535 \cdot S, H <- H * 32-bit images H, S, V are left as is * RGB :math:`\leftrightarrow` CIE L*a*b* ( ``CV_BGR2Lab, CV_RGB2Lab, CV_Lab2BGR, CV_Lab2RGB`` ) in the case of 8-bit and 16-bit images R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range .. math:: \vecthree{X}{Y}{Z} \leftarrow \vecthreethree{0.412453}{0.357580}{0.180423}{0.212671}{0.715160}{0.072169}{0.019334}{0.119193}{0.950227} \cdot \vecthree{R}{G}{B} .. math:: X \leftarrow X/X_n, \text{where} X_n = 0.950456 .. math:: Z \leftarrow Z/Z_n, \text{where} Z_n = 1.088754 .. math:: L \leftarrow \fork{116*Y^{1/3}-16}{for $Y>0.008856$}{903.3*Y}{for $Y \le 0.008856$} .. math:: a \leftarrow 500 (f(X)-f(Y)) + delta .. math:: b \leftarrow 200 (f(Y)-f(Z)) + delta where .. math:: f(t)= \fork{t^{1/3}}{for $t>0.008856$}{7.787 t+16/116}{for $t\leq 0.008856$} and .. math:: delta = \fork{128}{for 8-bit images}{0}{for floating-point images} On output :math:`0 \leq L \leq 100` , :math:`-127 \leq a \leq 127` , :math:`-127 \leq b \leq 127` The values are then converted to the destination data type: * 8-bit images .. math:: L \leftarrow L*255/100, \; a \leftarrow a + 128, \; b \leftarrow b + 128 * 16-bit images currently not supported * 32-bit images L, a, b are left as is * RGB :math:`\leftrightarrow` CIE L*u*v* ( ``CV_BGR2Luv, CV_RGB2Luv, CV_Luv2BGR, CV_Luv2RGB`` ) in the case of 8-bit and 16-bit images R, G and B are converted to floating-point format and scaled to fit 0 to 1 range .. math:: \vecthree{X}{Y}{Z} \leftarrow \vecthreethree{0.412453}{0.357580}{0.180423}{0.212671}{0.715160}{0.072169}{0.019334}{0.119193}{0.950227} \cdot \vecthree{R}{G}{B} .. math:: L \leftarrow \fork{116 Y^{1/3}}{for $Y>0.008856$}{903.3 Y}{for $Y\leq 0.008856$} .. math:: u' \leftarrow 4*X/(X + 15*Y + 3 Z) .. math:: v' \leftarrow 9*Y/(X + 15*Y + 3 Z) .. math:: u \leftarrow 13*L*(u' - u_n) \quad \text{where} \quad u_n=0.19793943 .. math:: v \leftarrow 13*L*(v' - v_n) \quad \text{where} \quad v_n=0.46831096 On output :math:`0 \leq L \leq 100` , :math:`-134 \leq u \leq 220` , :math:`-140 \leq v \leq 122` . The values are then converted to the destination data type: * 8-bit images .. math:: L \leftarrow 255/100 L, \; u \leftarrow 255/354 (u + 134), \; v \leftarrow 255/256 (v + 140) * 16-bit images currently not supported * 32-bit images L, u, v are left as is The above formulas for converting RGB to/from various color spaces have been taken from multiple sources on Web, primarily from the Charles Poynton site http://www.poynton.com/ColorFAQ.html * Bayer :math:`\rightarrow` RGB ( ``CV_BayerBG2BGR, CV_BayerGB2BGR, CV_BayerRG2BGR, CV_BayerGR2BGR, CV_BayerBG2RGB, CV_BayerGB2RGB, CV_BayerRG2RGB, CV_BayerGR2RGB`` ) The Bayer pattern is widely used in CCD and CMOS cameras. It allows one to get color pictures from a single plane where R,G and B pixels (sensors of a particular component) are interleaved like this: .. math:: \newcommand{\Rcell}{\color{red}R} \newcommand{\Gcell}{\color{green}G} \newcommand{\Bcell}{\color{blue}B} \definecolor{BackGray}{rgb}{0.8,0.8,0.8} \begin{array}{ c c c c c } \Rcell & \Gcell & \Rcell & \Gcell & \Rcell \\ \Gcell & \colorbox{BackGray}{\Bcell} & \colorbox{BackGray}{\Gcell} & \Bcell & \Gcell \\ \Rcell & \Gcell & \Rcell & \Gcell & \Rcell \\ \Gcell & \Bcell & \Gcell & \Bcell & \Gcell \\ \Rcell & \Gcell & \Rcell & \Gcell & \Rcell \end{array} The output RGB components of a pixel are interpolated from 1, 2 or 4 neighbors of the pixel having the same color. There are several modifications of the above pattern that can be achieved by shifting the pattern one pixel left and/or one pixel up. The two letters :math:`C_1` and :math:`C_2` in the conversion constants ``CV_Bayer`` :math:`C_1 C_2` ``2BGR`` and ``CV_Bayer`` :math:`C_1 C_2` ``2RGB`` indicate the particular pattern type - these are components from the second row, second and third columns, respectively. For example, the above pattern has very popular "BG" type. .. index:: distanceTransform cv::distanceTransform --------------------- .. cfunction:: void distanceTransform( const Mat\& src, Mat\& dst, int distanceType, int maskSize ) .. cfunction:: void distanceTransform( const Mat\& src, Mat\& dst, Mat\& labels, int distanceType, int maskSize ) Calculates the distance to the closest zero pixel for each pixel of the source image. :param src: 8-bit, single-channel (binary) source image :param dst: Output image with calculated distances; will be 32-bit floating-point, single-channel image of the same size as ``src`` :param distanceType: Type of distance; can be ``CV_DIST_L1, CV_DIST_L2`` or ``CV_DIST_C`` :param maskSize: Size of the distance transform mask; can be 3, 5 or ``CV_DIST_MASK_PRECISE`` (the latter option is only supported by the first of the functions). In the case of ``CV_DIST_L1`` or ``CV_DIST_C`` distance type the parameter is forced to 3, because a :math:`3\times 3` mask gives the same result as a :math:`5\times 5` or any larger aperture. :param labels: The optional output 2d array of labels - the discrete Voronoi diagram; will have type ``CV_32SC1`` and the same size as ``src`` . See the discussion The functions ``distanceTransform`` calculate the approximate or precise distance from every binary image pixel to the nearest zero pixel. (for zero image pixels the distance will obviously be zero). When ``maskSize == CV_DIST_MASK_PRECISE`` and ``distanceType == CV_DIST_L2`` , the function runs the algorithm described in [Felzenszwalb04]_ . In other cases the algorithm [Borgefors86]_ is used, that is, for pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal or knight's move (the latest is available for a :math:`5\times 5` mask). The overall distance is calculated as a sum of these basic distances. Because the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (that is denoted as ``a`` ), all the diagonal shifts must have the same cost (denoted ``b`` ), and all knight's moves must have the same cost (denoted ``c`` ). For ``CV_DIST_C`` and ``CV_DIST_L1`` types the distance is calculated precisely, whereas for ``CV_DIST_L2`` (Euclidian distance) the distance can be calculated only with some relative error (a :math:`5\times 5` mask gives more accurate results). For ``a`` , ``b`` and ``c`` OpenCV uses the values suggested in the original paper: .. table:: ============== =================== ====================== ``CV_DIST_C`` :math:`(3\times 3)` a = 1, b = 1 \ ============== =================== ====================== ``CV_DIST_L1`` :math:`(3\times 3)` a = 1, b = 2 \ ``CV_DIST_L2`` :math:`(3\times 3)` a=0.955, b=1.3693 \ ``CV_DIST_L2`` :math:`(5\times 5)` a=1, b=1.4, c=2.1969 \ ============== =================== ====================== Typically, for a fast, coarse distance estimation ``CV_DIST_L2`` , a :math:`3\times 3` mask is used, and for a more accurate distance estimation ``CV_DIST_L2`` , a :math:`5\times 5` mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels. The second variant of the function does not only compute the minimum distance for each pixel :math:`(x, y)` , but it also identifies the nearest the nearest connected component consisting of zero pixels. Index of the component is stored in :math:`\texttt{labels}(x, y)` . The connected components of zero pixels are also found and marked by the function. In this mode the complexity is still linear. That is, the function provides a very fast way to compute Voronoi diagram for the binary image. Currently, this second variant can only use the approximate distance transform algorithm. .. index:: floodFill cv::floodFill ------------- .. cfunction:: int floodFill( Mat\& image, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 ) .. cfunction:: int floodFill( Mat\& image, Mat\& mask, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 ) Fills a connected component with the given color. :param image: Input/output 1- or 3-channel, 8-bit or floating-point image. It is modified by the function unless the ``FLOODFILL_MASK_ONLY`` flag is set (in the second variant of the function; see below) :param mask: (For the second function only) Operation mask, should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller. The function uses and updates the mask, so the user takes responsibility of initializing the ``mask`` content. Flood-filling can't go across non-zero pixels in the mask, for example, an edge detector output can be used as a mask to stop filling at edges. It is possible to use the same mask in multiple calls to the function to make sure the filled area do not overlap. **Note** : because the mask is larger than the filled image, a pixel :math:`(x, y)` in ``image`` will correspond to the pixel :math:`(x+1, y+1)` in the ``mask`` :param seed: The starting point :param newVal: New value of the repainted domain pixels :param loDiff: Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component :param upDiff: Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component :param rect: The optional output parameter that the function sets to the minimum bounding rectangle of the repainted domain :param flags: The operation flags. Lower bits contain connectivity value, 4 (by default) or 8, used within the function. Connectivity determines which neighbors of a pixel are considered. Upper bits can be 0 or a combination of the following flags: * **FLOODFILL_FIXED_RANGE** if set, the difference between the current pixel and seed pixel is considered, otherwise the difference between neighbor pixels is considered (i.e. the range is floating) * **FLOODFILL_MASK_ONLY** (for the second variant only) if set, the function does not change the image ( ``newVal`` is ignored), but fills the mask The functions ``floodFill`` fill a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at :math:`(x,y)` is considered to belong to the repainted domain if: * grayscale image, floating range .. math:: \texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff} * grayscale image, fixed range .. math:: \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)+ \texttt{upDiff} * color image, floating range .. math:: \texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r .. math:: \texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g .. math:: \texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b * color image, fixed range .. math:: \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_r+ \texttt{upDiff} _r .. math:: \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_g+ \texttt{upDiff} _g .. math:: \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_b+ \texttt{upDiff} _b where :math:`src(x',y')` is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a pixel's color/brightness should be close enough to the: * color/brightness of one of its neighbors that are already referred to the connected component in the case of floating range * color/brightness of the seed point in the case of fixed range. By using these functions you can either mark a connected component with the specified color in-place, or build a mask and then extract the contour or copy the region to another image etc. Various modes of the function are demonstrated in ``floodfill.c`` sample. See also: :func:`findContours` .. index:: inpaint cv::inpaint ----------- .. cfunction:: void inpaint( const Mat\& src, const Mat\& inpaintMask, Mat\& dst, double inpaintRadius, int flags ) Inpaints the selected region in the image. :param src: The input 8-bit 1-channel or 3-channel image. :param inpaintMask: The inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that needs to be inpainted. :param dst: The output image; will have the same size and the same type as ``src`` :param inpaintRadius: The radius of a circlular neighborhood of each point inpainted that is considered by the algorithm. :param flags: The inpainting method, one of the following: * **INPAINT_NS** Navier-Stokes based method. * **INPAINT_TELEA** The method by Alexandru Telea [Telea04]_ The function reconstructs the selected image area from the pixel near the area boundary. The function may be used to remove dust and scratches from a scanned photo, or to remove undesirable objects from still images or video. See http://en.wikipedia.org/wiki/Inpainting for more details. .. index:: integral cv::integral ------------ .. cfunction:: void integral( const Mat\& image, Mat\& sum, int sdepth=-1 ) .. cfunction:: void integral( const Mat\& image, Mat\& sum, Mat\& sqsum, int sdepth=-1 ) .. cfunction:: void integral( const Mat\& image, Mat\& sum, Mat\& sqsum, Mat\& tilted, int sdepth=-1 ) Calculates the integral of an image. :param image: The source image, :math:`W \times H` , 8-bit or floating-point (32f or 64f) :param sum: The integral image, :math:`(W+1)\times (H+1)` , 32-bit integer or floating-point (32f or 64f) :param sqsum: The integral image for squared pixel values, :math:`(W+1)\times (H+1)` , double precision floating-point (64f) :param tilted: The integral for the image rotated by 45 degrees, :math:`(W+1)\times (H+1)` , the same data type as ``sum`` :param sdepth: The desired depth of the integral and the tilted integral images, ``CV_32S`` , ``CV_32F`` or ``CV_64F`` The functions ``integral`` calculate one or more integral images for the source image as following: .. math:: \texttt{sum} (X,Y) = \sum _{x \texttt{thresh}$}{0}{otherwise} * **THRESH_BINARY_INV** .. math:: \texttt{dst} (x,y) = \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{maxVal}}{otherwise} * **THRESH_TRUNC** .. math:: \texttt{dst} (x,y) = \fork{\texttt{threshold}}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{src}(x,y)}{otherwise} * **THRESH_TOZERO** .. math:: \texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if $\texttt{src}(x,y) > \texttt{thresh}$}{0}{otherwise} * **THRESH_TOZERO_INV** .. math:: \texttt{dst} (x,y) = \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{src}(x,y)}{otherwise} Also, the special value ``THRESH_OTSU`` may be combined with one of the above values. In this case the function determines the optimal threshold value using Otsu's algorithm and uses it instead of the specified ``thresh`` . The function returns the computed threshold value. Currently, Otsu's method is implemented only for 8-bit images. .. image:: ../../pics/threshold.png See also: :func:`adaptiveThreshold` , :func:`findContours` , :func:`compare` , :func:`min` , :func:`max` .. index:: watershed cv::watershed ------------- .. cfunction:: void watershed( const Mat\& image, Mat\& markers ) Does marker-based image segmentation using watershed algrorithm :param image: The input 8-bit 3-channel image. :param markers: The input/output 32-bit single-channel image (map) of markers. It should have the same size as ``image`` The function implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in [Meyer92]_ . Before passing the image to the function, user has to outline roughly the desired regions in the image ``markers`` with positive ( :math:`>0` ) indices, i.e. every region is represented as one or more connected components with the pixel values 1, 2, 3 etc (such markers can be retrieved from a binary mask using :func:`findContours` and :func:`drawContours` , see ``watershed.cpp`` demo). The markers will be "seeds" of the future image regions. All the other pixels in ``markers`` , which relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. On the output of the function, each pixel in markers is set to one of values of the "seed" components, or to -1 at boundaries between the regions. Note, that it is not necessary that every two neighbor connected components are separated by a watershed boundary (-1's pixels), for example, in case when such tangent components exist in the initial marker image. Visual demonstration and usage example of the function can be found in OpenCV samples directory; see ``watershed.cpp`` demo. See also: :func:`findContours`