..ocv:function:: void adaptiveThreshold( InputArray src, OutputArray dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C )
:param dstCn:number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from ``src`` and ``code`` .
space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR).
Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
In case of linear transformations, the range does not matter.
But in case of a non-linear transformation, an input RGB image should be normalized to the proper value range to get the correct results, for example, for RGB
:math:`\rightarrow` L*u*v* transformation. For example, if you have a 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will have the 0..255 value range instead of 0..1 assumed by the function. So, before calling ``cvtColor`` , you need first to scale the image down:::
If you use ``cvtColor`` with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.
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:
The above formulae for converting RGB to/from various color spaces have been taken from multiple sources on the web, primarily from the Charles Poynton site
Bayer :math:`\rightarrow` RGB ( ``COLOR_BayerBG2BGR, COLOR_BayerGB2BGR, COLOR_BayerRG2BGR, COLOR_BayerGR2BGR, COLOR_BayerBG2RGB, COLOR_BayerGB2RGB, COLOR_BayerRG2RGB, COLOR_BayerGR2RGB`` ). The Bayer pattern is widely used in CCD and CMOS cameras. It enables you to get color pictures from a single plane where R,G, and B pixels (sensors of a particular component) are interleaved as follows:
:param maskSize:Size of the distance transform mask. It can be 3, 5, or ``CV_DIST_MASK_PRECISE`` (the latter option is only supported by the first function). In case of the ``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 :math:`5\times 5` or any larger aperture.
:param labels:Optional output 2D array of labels (the discrete Voronoi diagram). It has the type ``CV_32SC1`` and the same size as ``src`` . See the details below.
:param labelType:Type of the label array to build. If ``labelType==DIST_LABEL_CCOMP`` then each connected component of zeros in ``src`` (as well as all the non-zero pixels closest to the connected component) will be assigned the same label. If ``labelType==DIST_LABEL_PIXEL`` then each zero pixel (and all the non-zero pixels closest to it) gets its own label.
When ``maskSize == CV_DIST_MASK_PRECISE`` and ``distanceType == CV_DIST_L2`` , the function runs the algorithm described in [Felzenszwalb04]_. This algorithm is parallelized with the TBB library.
component consisting of zero pixels (``labelType==DIST_LABEL_CCOMP``) or the nearest zero pixel (``labelType==DIST_LABEL_PIXEL``). Index of the component/pixel is stored in
When ``labelType==DIST_LABEL_CCOMP``, the function automatically finds connected components of zero pixels in the input image and marks them with distinct labels. When ``labelType==DIST_LABEL_CCOMP``, the function scans through the input image and marks all the zero pixels with distinct labels.
: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 the details below.
:param mask:(For the second function only) Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller. The function uses and updates the mask, so you take responsibility of initializing the ``mask`` content. Flood-filling cannot 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 does not overlap.
..note:: Since the mask is larger than the filled image, a pixel :math:`(x, y)` in ``image`` corresponds to the pixel :math:`(x+1, y+1)` in the ``mask`` .
: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 flags:Operation flags. Lower bits contain a connectivity value, 4 (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 (that is, the range is floating).
***FLOODFILL_MASK_ONLY** If set, the function does not change the image ( ``newVal`` is ignored), but fills the mask with the value in bits 8-16 of ``flags`` (that is, the fill value is set to newValue by adding (newValue << 8) to the ``flags``). The flag can be used for the second variant only.
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:`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 color/brightness of the pixel should be close enough to:
Use these functions to 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, and so on. Various modes of the function are demonstrated in the ``floodfill.cpp`` sample.
Using these integral images, you can calculate sa um, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:
It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently.
As a practical example, the next figure shows the calculation of the integral of a straight rectangle ``Rect(3,3,3,2)`` and of a tilted rectangle ``Rect(5,1,2,3)`` . The selected pixels in the original ``image`` are shown, as well as the relative pixels in the integral images ``sum`` and ``tilted`` .
Before passing the image to the function, you have to roughly outline the desired regions in the image ``markers`` with positive (``>0``) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using :ocv:func:`findContours` and :ocv:func:`drawContours` (see the ``watershed.cpp`` demo). The markers are "seeds" of the future image regions. All the other pixels in ``markers`` , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the regions.
Visual demonstration and usage example of the function can be found in the OpenCV samples directory (see the ``watershed.cpp`` demo).
..note:: Any two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, they can touch each other in the initial marker image passed to the function.
:param mask:Input/output 8-bit single-channel mask. The mask is initialized by the function when ``mode`` is set to ``GC_INIT_WITH_RECT``. Its elements may have one of following values:
:param rect:ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when ``mode==GC_INIT_WITH_RECT`` .
:param iterCount:Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with ``mode==GC_INIT_WITH_MASK`` or ``mode==GC_EVAL`` .
***GC_INIT_WITH_RECT** The function initializes the state and the mask using the provided rectangle. After that it runs ``iterCount`` iterations of the algorithm.
***GC_INIT_WITH_MASK** The function initializes the state using the provided mask. Note that ``GC_INIT_WITH_RECT`` and ``GC_INIT_WITH_MASK`` can be combined. Then, all the pixels outside of the ROI are automatically initialized with ``GC_BGD`` .
..[Telea04] Alexandru Telea, *An Image Inpainting Technique Based on the Fast Marching Method*. Journal of Graphics, GPU, and Game Tools 9 1, pp 23-34 (2004)
