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329 lines
13 KiB
ReStructuredText
329 lines
13 KiB
ReStructuredText
Camera Calibration and 3D Reconstruction
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========================================
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.. highlight:: cpp
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ocl::StereoBM_OCL
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---------------------
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.. ocv:class:: ocl::StereoBM_OCL
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Class computing stereo correspondence (disparity map) using the block matching algorithm. ::
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class CV_EXPORTS StereoBM_OCL
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{
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public:
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enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
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enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
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//! the default constructor
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StereoBM_OCL();
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//! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
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StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
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//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
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//! Output disparity has CV_8U type.
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void operator() ( const oclMat &left, const oclMat &right, oclMat &disparity);
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//! Some heuristics that tries to estmate
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// if current GPU will be faster then CPU in this algorithm.
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// It queries current active device.
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static bool checkIfGpuCallReasonable();
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int preset;
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int ndisp;
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int winSize;
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// If avergeTexThreshold == 0 => post procesing is disabled
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// If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
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// SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
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// i.e. input left image is low textured.
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float avergeTexThreshold;
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private:
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/* hidden */
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};
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The class also performs pre- and post-filtering steps: Sobel pre-filtering (if ``PREFILTER_XSOBEL`` flag is set) and low textureness filtering (if ``averageTexThreshols > 0`` ). If ``avergeTexThreshold = 0`` , low textureness filtering is disabled. Otherwise, the disparity is set to 0 in each point ``(x, y)`` , where for the left image
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.. math::
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\sum HorizontalGradiensInWindow(x, y, winSize) < (winSize \cdot winSize) \cdot avergeTexThreshold
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This means that the input left image is low textured.
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ocl::StereoBM_OCL::StereoBM_OCL
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-----------------------------------
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Enables :ocv:class:`ocl::StereoBM_OCL` constructors.
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.. ocv:function:: ocl::StereoBM_OCL::StereoBM_OCL()
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.. ocv:function:: ocl::StereoBM_OCL::StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ)
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:param preset: Parameter presetting:
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* **BASIC_PRESET** Basic mode without pre-processing.
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* **PREFILTER_XSOBEL** Sobel pre-filtering mode.
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:param ndisparities: Number of disparities. It must be a multiple of 8 and less or equal to 256.
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:param winSize: Block size.
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ocl::StereoBM_OCL::operator ()
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----------------------------------
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Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair.
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.. ocv:function:: void ocl::StereoBM_OCL::operator ()(const oclMat& left, const oclMat& right, oclMat& disparity)
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:param left: Left image. Only ``CV_8UC1`` type is supported.
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:param right: Right image with the same size and the same type as the left one.
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:param disparity: Output disparity map. It is a ``CV_8UC1`` image with the same size as the input images.
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ocl::StereoBM_OCL::checkIfGpuCallReasonable
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-----------------------------------------------
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Uses a heuristic method to estimate whether the current GPU is faster than the CPU in this algorithm. It queries the currently active device.
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.. ocv:function:: bool ocl::StereoBM_OCL::checkIfGpuCallReasonable()
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ocl::StereoBeliefPropagation
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--------------------------------
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.. ocv:class:: ocl::StereoBeliefPropagation
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Class computing stereo correspondence using the belief propagation algorithm. ::
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class CV_EXPORTS StereoBeliefPropagation
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{
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public:
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enum { DEFAULT_NDISP = 64 };
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enum { DEFAULT_ITERS = 5 };
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enum { DEFAULT_LEVELS = 5 };
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static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels);
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explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
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int iters = DEFAULT_ITERS,
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int levels = DEFAULT_LEVELS,
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int msg_type = CV_16S);
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StereoBeliefPropagation(int ndisp, int iters, int levels,
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float max_data_term, float data_weight,
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float max_disc_term, float disc_single_jump,
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int msg_type = CV_32F);
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void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
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void operator()(const oclMat &data, oclMat &disparity);
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int ndisp;
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int iters;
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int levels;
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float max_data_term;
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float data_weight;
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float max_disc_term;
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float disc_single_jump;
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int msg_type;
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private:
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/* hidden */
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};
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The class implements algorithm described in [Felzenszwalb2006]_ . It can compute own data cost (using a truncated linear model) or use a user-provided data cost.
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.. note::
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``StereoBeliefPropagation`` requires a lot of memory for message storage:
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.. math::
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width \_ step \cdot height \cdot ndisp \cdot 4 \cdot (1 + 0.25)
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and for data cost storage:
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.. math::
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width\_step \cdot height \cdot ndisp \cdot (1 + 0.25 + 0.0625 + \dotsm + \frac{1}{4^{levels}})
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``width_step`` is the number of bytes in a line including padding.
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ocl::StereoBeliefPropagation::StereoBeliefPropagation
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---------------------------------------------------------
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Enables the :ocv:class:`ocl::StereoBeliefPropagation` constructors.
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.. ocv:function:: ocl::StereoBeliefPropagation::StereoBeliefPropagation(int ndisp = DEFAULT_NDISP, int iters = DEFAULT_ITERS, int levels = DEFAULT_LEVELS, int msg_type = CV_16S)
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.. ocv:function:: ocl::StereoBeliefPropagation::StereoBeliefPropagation(int ndisp, int iters, int levels, float max_data_term, float data_weight, float max_disc_term, float disc_single_jump, int msg_type = CV_32F)
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:param ndisp: Number of disparities.
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:param iters: Number of BP iterations on each level.
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:param levels: Number of levels.
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:param max_data_term: Threshold for data cost truncation.
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:param data_weight: Data weight.
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:param max_disc_term: Threshold for discontinuity truncation.
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:param disc_single_jump: Discontinuity single jump.
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:param msg_type: Type for messages. ``CV_16SC1`` and ``CV_32FC1`` types are supported.
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``StereoBeliefPropagation`` uses a truncated linear model for the data cost and discontinuity terms:
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.. math::
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DataCost = data \_ weight \cdot \min ( \lvert Img_Left(x,y)-Img_Right(x-d,y) \rvert , max \_ data \_ term)
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.. math::
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DiscTerm = \min (disc \_ single \_ jump \cdot \lvert f_1-f_2 \rvert , max \_ disc \_ term)
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For more details, see [Felzenszwalb2006]_.
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By default, :ocv:class:`ocl::StereoBeliefPropagation` uses floating-point arithmetics and the ``CV_32FC1`` type for messages. But it can also use fixed-point arithmetics and the ``CV_16SC1`` message type for better performance. To avoid an overflow in this case, the parameters must satisfy the following requirement:
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.. math::
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10 \cdot 2^{levels-1} \cdot max \_ data \_ term < SHRT \_ MAX
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ocl::StereoBeliefPropagation::estimateRecommendedParams
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-----------------------------------------------------------
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Uses a heuristic method to compute the recommended parameters ( ``ndisp``, ``iters`` and ``levels`` ) for the specified image size ( ``width`` and ``height`` ).
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.. ocv:function:: void ocl::StereoBeliefPropagation::estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels)
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ocl::StereoBeliefPropagation::operator ()
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---------------------------------------------
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Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair or data cost.
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.. ocv:function:: void ocl::StereoBeliefPropagation::operator ()(const oclMat& left, const oclMat& right, oclMat& disparity)
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.. ocv:function:: void ocl::StereoBeliefPropagation::operator ()(const oclMat& data, oclMat& disparity)
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:param left: Left image. ``CV_8UC1`` , ``CV_8UC3`` and ``CV_8UC4`` types are supported.
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:param right: Right image with the same size and the same type as the left one.
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:param data: User-specified data cost, a matrix of ``msg_type`` type and ``Size(<image columns>*ndisp, <image rows>)`` size.
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:param disparity: Output disparity map. If ``disparity`` is empty, the output type is ``CV_16SC1`` . Otherwise, the type is retained.
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ocl::StereoConstantSpaceBP
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------------------------------
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.. ocv:class:: ocl::StereoConstantSpaceBP
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Class computing stereo correspondence using the constant space belief propagation algorithm. ::
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class CV_EXPORTS StereoConstantSpaceBP
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{
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public:
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enum { DEFAULT_NDISP = 128 };
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enum { DEFAULT_ITERS = 8 };
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enum { DEFAULT_LEVELS = 4 };
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enum { DEFAULT_NR_PLANE = 4 };
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static void estimateRecommendedParams(int width, int height, int &ndisp, int &iters, int &levels, int &nr_plane);
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explicit StereoConstantSpaceBP(
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int ndisp = DEFAULT_NDISP,
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int iters = DEFAULT_ITERS,
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int levels = DEFAULT_LEVELS,
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int nr_plane = DEFAULT_NR_PLANE,
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int msg_type = CV_32F);
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StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
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float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
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int min_disp_th = 0,
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int msg_type = CV_32F);
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void operator()(const oclMat &left, const oclMat &right, oclMat &disparity);
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int ndisp;
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int iters;
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int levels;
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int nr_plane;
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float max_data_term;
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float data_weight;
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float max_disc_term;
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float disc_single_jump;
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int min_disp_th;
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int msg_type;
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bool use_local_init_data_cost;
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private:
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/* hidden */
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};
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The class implements algorithm described in [Yang2010]_. ``StereoConstantSpaceBP`` supports both local minimum and global minimum data cost initialization algorithms. For more details, see the paper mentioned above. By default, a local algorithm is used. To enable a global algorithm, set ``use_local_init_data_cost`` to ``false`` .
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ocl::StereoConstantSpaceBP::StereoConstantSpaceBP
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-----------------------------------------------------
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Enables the :ocv:class:`ocl::StereoConstantSpaceBP` constructors.
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.. ocv:function:: ocl::StereoConstantSpaceBP::StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP, int iters = DEFAULT_ITERS, int levels = DEFAULT_LEVELS, int nr_plane = DEFAULT_NR_PLANE, int msg_type = CV_32F)
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.. ocv:function:: ocl::StereoConstantSpaceBP::StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane, float max_data_term, float data_weight, float max_disc_term, float disc_single_jump, int min_disp_th = 0, int msg_type = CV_32F)
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:param ndisp: Number of disparities.
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:param iters: Number of BP iterations on each level.
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:param levels: Number of levels.
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:param nr_plane: Number of disparity levels on the first level.
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:param max_data_term: Truncation of data cost.
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:param data_weight: Data weight.
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:param max_disc_term: Truncation of discontinuity.
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:param disc_single_jump: Discontinuity single jump.
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:param min_disp_th: Minimal disparity threshold.
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:param msg_type: Type for messages. ``CV_16SC1`` and ``CV_32FC1`` types are supported.
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``StereoConstantSpaceBP`` uses a truncated linear model for the data cost and discontinuity terms:
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.. math::
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DataCost = data \_ weight \cdot \min ( \lvert I_2-I_1 \rvert , max \_ data \_ term)
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.. math::
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DiscTerm = \min (disc \_ single \_ jump \cdot \lvert f_1-f_2 \rvert , max \_ disc \_ term)
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For more details, see [Yang2010]_.
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By default, ``StereoConstantSpaceBP`` uses floating-point arithmetics and the ``CV_32FC1`` type for messages. But it can also use fixed-point arithmetics and the ``CV_16SC1`` message type for better performance. To avoid an overflow in this case, the parameters must satisfy the following requirement:
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.. math::
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10 \cdot 2^{levels-1} \cdot max \_ data \_ term < SHRT \_ MAX
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ocl::StereoConstantSpaceBP::estimateRecommendedParams
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---------------------------------------------------------
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Uses a heuristic method to compute parameters (ndisp, iters, levelsand nrplane) for the specified image size (widthand height).
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.. ocv:function:: void ocl::StereoConstantSpaceBP::estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane)
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ocl::StereoConstantSpaceBP::operator ()
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-------------------------------------------
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Enables the stereo correspondence operator that finds the disparity for the specified rectified stereo pair.
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.. ocv:function:: void ocl::StereoConstantSpaceBP::operator ()(const oclMat& left, const oclMat& right, oclMat& disparity)
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:param left: Left image. ``CV_8UC1`` , ``CV_8UC3`` and ``CV_8UC4`` types are supported.
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:param right: Right image with the same size and the same type as the left one.
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:param disparity: Output disparity map. If ``disparity`` is empty, the output type is ``CV_16SC1`` . Otherwise, the output type is ``disparity.type()`` .
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