文件列表:

Labeling\Labeling (0, 2016-01-11)
Labeling\Labeling\block.h (7466, 2006-11-07)
Labeling\Labeling\energy.h (10348, 2010-08-31)
Labeling\Labeling\example.cpp (13232, 2010-04-28)
Labeling\Labeling\GCoptimization.cpp (61540, 2011-04-12)
Labeling\Labeling\GCoptimization.h (27069, 2010-07-22)
Labeling\Labeling\graph.cpp (3005, 2010-04-26)
Labeling\Labeling\graph.h (17790, 2010-04-25)
Labeling\Labeling\LinkedBlockList.cpp (1453, 2005-08-25)
Labeling\Labeling\LinkedBlockList.h (1765, 2005-07-21)
Labeling\Labeling\matlab (0, 2016-01-11)
Labeling\Labeling\matlab\bin (0, 2016-01-11)
Labeling\Labeling\matlab\GCO_BuildLib.m (2553, 2010-11-25)
Labeling\Labeling\matlab\GCO_ComputeEnergy.m (387, 2009-10-27)
Labeling\Labeling\matlab\GCO_Create.m (526, 2010-04-26)
Labeling\Labeling\matlab\GCO_Delete.m (223, 2010-04-13)
Labeling\Labeling\matlab\GCO_ExpandOnAlpha.m (303, 2009-10-27)
Labeling\Labeling\matlab\GCO_Expansion.m (1091, 2010-04-28)
Labeling\Labeling\matlab\GCO_GetLabeling.m (670, 2009-10-27)
Labeling\Labeling\matlab\GCO_ListHandles.m (370, 2010-04-13)
Labeling\Labeling\matlab\GCO_LoadLib.m (507, 2010-07-05)
Labeling\Labeling\matlab\gco_matlab.cpp (18497, 2010-09-11)
Labeling\Labeling\matlab\GCO_SetDataCost.m (2068, 2010-07-05)
Labeling\Labeling\matlab\GCO_SetLabelCost.m (1695, 2010-04-23)
Labeling\Labeling\matlab\GCO_SetLabeling.m (787, 2010-04-23)
Labeling\Labeling\matlab\GCO_SetLabelOrder.m (607, 2010-04-22)
Labeling\Labeling\matlab\GCO_SetNeighbors.m (1198, 2010-04-20)
Labeling\Labeling\matlab\GCO_SetSmoothCost.m (1380, 2010-04-23)
Labeling\Labeling\matlab\GCO_SetVerbosity.m (1155, 2010-04-26)
Labeling\Labeling\matlab\GCO_Swap.m (702, 2010-04-19)
Labeling\Labeling\matlab\GCO_UnitTest.m (16858, 2010-07-08)
Labeling\Labeling\maxflow.cpp (17631, 2010-04-26)

############################################################################## # # # GCoptimization - software for energy minimization with graph cuts # # Version 3.0 # # http://www.csd.uwo.ca/faculty/olga/software.html # # # # Copyright 2007-2010 Olga Veksler # # Andrew Delong # # # ############################################################################## C++ requires at least Visual C++ 2005 (VC8) or GCC 4.03. Supports 32 or ***-bit. See matlab/README.TXT for bundled MATLAB wrapper and its documentation. IMPORTANT: To use this software, YOU MUST CITE the following in any resulting publication: [1] Efficient Approximate Energy Minimization via Graph Cuts. Y. Boykov, O. Veksler, R.Zabih. IEEE TPAMI, 20(12):1222-1239, Nov 2001. [2] What Energy Functions can be Minimized via Graph Cuts? V. Kolmogorov, R.Zabih. IEEE TPAMI, 26(2):147-159, Feb 2004. [3] An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. Y. Boykov, V. Kolmogorov. IEEE TPAMI, 26(9):1124-1137, Sep 2004. Furthermore, if you use the label cost feature (setLabelCost), you should cite [4] Fast Approximate Energy Minimization with Label Costs. A. Delong, A. Osokin, H. N. Isack, Y. Boykov. In CVPR, June 2010. This software can be used only for research purposes. For commercial purposes, be aware that there is a US patent on the main algorithm itself: R. Zabih, Y. Boykov, O. Veksler, "System and method for fast approximate energy minimization via graph cuts", United Stated Patent 6,744,923, June 1, 2004 Together with this library implemented by O. Veksler, we provide, with the permission of the V. Kolmogorov and Y. Boykov, the following two libraries: 1) energy.h Developed by V. Kolmogorov, this implements the binary energy minimization technique described in [2] above. We use this to implement the binary energy minimization step for the alpha-expansion and swap algorithms. The graph construction provided by "energy.h" is more efficient than the original graph construction for alpha-expansion described in [1]. Again, this software can be used only for research purposes. IF YOU USE THIS SOFTWARE (energy.h), YOU SHOULD CITE THE AFOREMENTIONED PAPER [2] IN ANY RESULTING PUBLICATION. 2) maxflow.cpp, graph.cpp, graph.h, block.h Developed by Y. Boykov and V. Kolmogorov while at Siemens Corporate Research, algorithm [3] was later reimplemented by V. Kolmogorov based on open publications and we use his implementation here with permission. If you use either of these libraries for research purposes, you should cite the aforementioned papers in any resulting publication. ################################################################## 2. License & disclaimer. Copyright 2007-2010 Olga Veksler Andrew Delong This software and its modifications can be used and distributed for research purposes only. Publications resulting from use of this code must cite publications according to the rules given above. Only Olga Veksler has the right to redistribute this code, unless expressed permission is given otherwise. Commercial use of this code, any of its parts, or its modifications is not permited. The copyright notices must not be removed in case of any modifications. This Licence commences on the date it is electronically or physically delivered to you and continues in effect unless you fail to comply with any of the terms of the License and fail to cure such breach within 30 days of becoming aware of the breach, in which case the Licence automatically terminates. This Licence is governed by the laws of Canada and all disputes arising from or relating to this Licence must be brought in Toronto, Ontario. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ################################################################## 3. Energy Minimization This software is for minimizing sums of three types of terms: E(labeling) = DataCosts(labeling) + SmoothCosts(labeling) + LabelCosts(labeling). More specifically, DataCosts(l) = sum_p D_p(l_p) where l_p is a potential label for site p, and D_p is the cost of assigning l_p, SmoothCosts(l) = sum_pq V_pq(l_p,l_q) where p and q are two distinct site, and V_pq is the cost for assigning l_p, l_q, and LabelCosts(l) = sum_L' h_L'(l) where L' is some subset of labels L, and h_L' adds a cost iff at least one label from L' appears in the labeling l. Here we have a finite set of sites (or pixels) P and a finite set of labels L. A labeling l is assignments of labels in L to pixels in P. The individual pixels are referred to with small letters p and q, label of pixel p is denoted by l_p, and the set of all label-pixel assignments is denoted by l, that is l = {l_p | p in P}. The first term in the energy function E(l) is typically called the data term, and it consists of the sum over all pixels p of the penalty(or cost) D(p,l_p), what should be the cost of assigning label l_p to pixel p. D(p,l_p) can be arbitrary. The second term is a sum over all pairs of neighboring pixels {p,q}. That is there is a neighborhood relation on the set of pixels (this relationship is symmetric, that is if p is a neighbor of q then q is a neighbor of p). Here we assume that the neighbor pairs are unordered. This means that if pixels p and q are neighbors, then there is either Vpq(l_p,l_q) in the second sum of the energy, or Vqp(l_q,l_p), but not both. This is not a restriction, since in practice, one can always go from the ordered energy to the unordered one. This second term is typically called the smoothness term. The third term is a sum over all labels (or, more generally, all subsets of labels) such that each label can have non-negative penalties associated with its use. This "label cost" feature is used to encourage labelings that use as fewer unique labels or, more generally, labels from as few unique subsets as possible. The expansion algorithm for energy minimization can be used whenever for any 3 labels a,b,c V(a,a) + V(b,c) <= V(a,c)+V(b,a). In other words, expansion algorithm can be used if the binary energy for the expansion algorithm step is regular, using V. Kolmogorov's terminology. The swap algorithm for energy minimization can be used whenever for any 2 labels a,b V(a,a) + V(b,b) <= V(a,b)+V(b,a). In other words, swap algorithm can be used if the binary energy for the swap algorithm step is regular, using V. Kolmogorov's terminology. ################################################################## 4. Data Types typedef SiteID; // index of a site (pixel); default is int32 typedef LabelID; // index of a label; default is int32 typedef EnergyType; // total energy value; default is int*** typedef EnergyTermType; // individual energy term; default is int32 For efficiency it is best to use integral types when possible; be sure that they are large enough to avoid integer overflow. By default, the library will warn if energy terms are dangerously large (larger than GCO_MAX_ENERGYTERM=10000000). Keep in mind that float/double may cause expansion/swap to report small increase in energy, due to arithmetic error during max-flow computation. ########################################################################### 5. Specifying the energy Before optimizing the energy, one has to specify it, that is specify the number of labels, number of pixels, neighborhood system, the data terms, and the smoothness terms. There are 2 constructors to use, one in case of the grid graph, and another in case of a general graph. In all cases, it is assumed that the sites go between 0...num_sites-1, and labels go between 0....num_labels-1. For a grid (4-connected) graph, site at coordinates (x,y) is numbered with x+y*width, where width is the width of the grid, that is the row-major ordering of arrays is assumed. ________________________________________________________________________________________________ Constructor A. GCoptimizationGridGraph(int width, int height,int num_labels); Use this constructor only for grid of size width by height. If you use this constructor, 4 connected grid neigbhorhood structure is assumed, so you don't need to specify neighborhood structure separately (and indeed cannot do so). _______________________________________________________________________________________________ Constructor B. GCoptimizationGeneralGraph(int num_sites,int num_labels); Use this constructor for general graphs. If you use this constructor, you must setup up neighborhood system using function. You can either specify neighbors individually or all at once. i) setNeighbors(SiteID s1, SiteID s2, EnergyTermType weight=1); Specifies neighbors individually. You must call this function exactly once for any pair of neighboring sites s1 and s2. That is if you call setNeighbors(s1,s2) then you should not call setNeighbors(s2,s1). If Vpq(l_p,l_q) = V(l_p,l_q)*w_pq, where V(l_p,l_q) is some function that depends only on the labels l_p,l_q, then specify w_pq by using: setNeighbors(p,q,w_pq). ii) To pass in all neighbor information at once, use function: void setAllNeighbors(SiteID *numNeighbors,SiteID **neighborsIndexes,EnergyTermType **neighborsWeights); Here: (a) numNeighbors is an array of size num_sites, and numNeighbors[i] is the number of neighbors for site i (b) neighborIndexes is an array of size num_pixels which stores of pointers. Namely, neighborsIndexes[i] is a pointer to the array storing the sites which are neighbors to site i (c) neighborWeights is an array of size num_sites, and neighborWeighs[i] is a pointer to array storing the weights between site i and its neighbors in the same order as neighborIndexes[i] stores the indexes of neighbors. Example: if sites i and j are neighbors, then for some k and m, neighborsIndexes[i][k] == j and neighborsIndexes[j][m] = i. Then neighborWeights[i][k] = w_ij and neighborWeights[j][m] = w_ij, where w_ij is the weight betwen neighbors i and j, that is V_ij = w_ij *V(l_i,l_j) _______________________________________________________________________________________________ 6. Setting the data costs, smooth costs, and label costs. The following functions can be called any time before or after expansion. ------------------------Data Costs (unary terms)----------------------- (a) void setDataCost(EnergyTermType *dataArray); dataArray is an array s.t. the data cost for pixel p and label l is stored at dataArray[pixel*num_labels+l]. If the current neighborhood system is a grid, then the data term for label l and pixel with coordinates (x,y) is stored at dataArray[(x+y*width)*num_labels + l]. Thus the size of array dataArray is num_pixels*num_labels. Can call this function only one time. (b) void setDataCost(DataCostFn fn); DataCostFn is a pointer to a function f(Pixel p, Label l), s.t. the data cost for pixel p to have label l is given by f(p,l). Can call this function only one time. (c) void setDataCost(DataCostFnExtra fn,void *extraData); DataCostFnExtra is a pointer to a function f(SiteID p, LabelID l,void *extraData), s.t. the data cost for pixel p to have label l is given by f(p,l,extraData). Can call this function only one time. (d) void setDataCost(SiteID s, LabelID l, EnergyTermType e); sets up D(s,l) = 3; You must call this function for each pixel and each label. (e) void setDataCostFunctor(DataCostFunctor* f); Experienced C++ users can subclass our DataCostFunctor base class to achieve a similar functionality as (b) or (c) above. By overriding the compute() method of DataCostFunctor, your compute() method will be called by the GCoptimization class each time a data penalty must be computed. (f) struct SparseDataCost { SiteID site; EnergyTermType cost; }; void setDataCost(LabelID l, SparseDataCost *costs, SiteID count); For some applications, each label is feasible for only a small fraction of the overall sites. One way to do this is to simply assign high cost to any infeasible site. A much more efficient way is to specify exactly which sites are feasible for each label. Do this by calling setDataCost(label, costs_for_label, num_costs_for_label) once for each label. The cost array will be copied internally, so your cost array can be freed. Note that giveDataEnergy() will add a huge constant for each site that is assigned an infeasible label in the current labeling. ------------------------Smooth Costs (pairwise terms)----------------------- (a) void setSmoothCost(EnergyTermType *V) V is an array of smoothness costs, such that V_pq(label1,label2) is stored at V[label1+num_labels*label2] If graph is a grid, then using this function only if the smooth costs are not spacially varying that is the smoothness penalty V depends only on labels, but not on sites. If the graph is not a grid, then you can specify spacially varying coefficients w_pq when you set up the neighborhood system using setNeighbor(p,q,w_pq) function. In this case, V_pq(label1,label2) = V[label1+num_labels*label2]*w_pq. This function can be called only one time. (b) void setSmoothCost(SmoothCostFn fn); fn is pointer to a function f(s1,s2,l1,l2) such that smoothness penalty for neigboring sites s1 and s2 to have labels, respectively, l1 and l2 is f(s1,s2,l1,l2). This function can be called only one time. (c) void setSmoothCost(SmoothCostFnExtra fn,void *extraData); Same as above, but can pass an extra pointer to the data needed for computation (d) void setSmoothCost(LabelID l1, LabelID l2, EnergyTermType e) sets up V(l1,l2) = e. Must call this function for each pair of labels (l1,l2). Notice that for any l1 and l2, you must call this function on (l1,l2) AND (l2,l1). V(l1,l2) has to be equal to V(l2,l1) in this case. (e) void setSmoothCostVH(EnergyTermType *V, EnergyTermType *vCosts, EnergyTermType *hCosts); This function should be used only if the graph is a grid (GCoptimizationGridGraph class). Array V is the same as above, under (a). Arrays hCosts and vCosts have the same size as the image (that is width*height), and are used to set the spatially varying coefficients w_pq. If p = (x,y) and q = (x+1,y), then w_pq = hCosts[x+y*width], and so the smoothness penalty for pixels (x,y) and (x+1,y) to have labels label1 and label2, that is V_pq(label1,label2) = V[label1+num_labels*label2]*hCosts[x+y*width] If p = (x,y) and q = (x,y+q), then w_pq = vCosts[x+y*width], and so the smoothness penalty for pixels (x,y) and (x,y+1) to have labels label1 and label2, that is V_pq(label1,label2) = V[label1+num_labels*label2]*vCosts[x+y*width] This function can be only called one time. (f) void setSmoothCostFunctor(SmoothCostFunctor* f); Experienced C++ users can subclass our SmoothCostFunctor base class to achieve a similar functionality as (b) or (c) above. By overriding the compute() method of SmoothCostFunctor, your compute() method will be called by the GCoptimization class each time a smoothness penalty must be computed. ------------------------Label Costs (global indicator potentials)-------------------- (a) void setLabelCost(EnergyTermType cost); Penalize the appearance of all labels equally. Replaces all current label costs, if any. (b) void setLabelCost(EnergyTermType* costArray); Set each individual label cost separately. The costArray must have one entry for each possible label. Replaces all current label costs, if any. The cost array will be copied intenally, so your array can be freed. (c) void setLabelSubsetCost(LabelID* labels, LabelID numLabels, EnergyTermType cost); Set cost for a specific subset of labels. The cost will be imposed iff the current labeling contains at least one label from the 'labels' array. The labels array will be copied internally, so your array can be freed. ################################################################## 6. Optimizing the energy You can optimize the energy and get the resulting labeling using the following functions. Notice that they can be called as many times as one wishes after the constructor has been called and the data/smoothness terms (and the neighborhood system, if general graph) has beeen set. The initial labeling is set to consists of all 0's. Use function setLabel(SiteID pixelP, LabelID labelL), described under heading (x) in this section to initialize the labeling to anything else (but in the valid range, of course, labels must be between 0 and num_labels-1) a) EnergyType expansion(int max_num_iterations=-1); Runs the expansion algorithm until convergence (convergence is guaranteed) or, if max_num_iterations > 0, until a certain number of cycles (iterations). Returns the energy of the resulting labeling. b) bool alpha_expansion(LabelID alpha_label); Performs expansion on the label specified by alpha_label. Returns true if the energy was decreased, false otherwise. c) EnergyType swap(int max_num_iterations=-1); Runs the alpha-beta swap algorithm until convergence (convergence is guaranteed) or, if max_num_iterations > 0, until a certain number of cycles (iterations). Returns the energy of ... ...

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