OpenCV的softcascade代码分析

发布时间：2021-11-20 14:39:28 所属栏目：PHP教程来源：互联网

导读：//头文件为softcascade.hpp、core.hpp //实现文件为octave.cpp //但是还涉及到了ml.hpp和Dtree.cpp等文件 //softcascade检测器训练函数的代码如下： bool BoostedSoftCascadeOctave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int

//头文件为softcascade.hpp、core.hpp
//实现文件为octave.cpp
//但是还涉及到了ml.hpp和Dtree.cpp等文件
//softcascade检测器训练函数的代码如下：

bool BoostedSoftCascadeOctave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)
{//第1个参数dataset是一个可以与训练集通信的实例，第2个参数表示特征集，第3个参数是需要训练的弱分类器树数量，第4个参数是分类器树的深度
    CV_Assert(treeDepth == 2); //？要求弱分类树的深度只能为2吗？
    CV_Assert(weaks > 0);

    params.max_depth = treeDepth;
    params.weak_count = weaks;

    // 1. fill integrals and classes，？这里计算了每个样本的所有积分图和标定了标签吗？是的，对于正样本，response向量中响应的元素值设置为了1，负样本设置为了0
    processPositives(dataset);
    generateNegatives(dataset);

    // 2. only simple case (all features used) //使用所有的特征
    int nfeatures = pool->size();
    cv::Mat varIdx(1, nfeatures, CV_32SC1);
    int* ptr = varIdx.ptr<int>(0);

    for (int x = 0; x < nfeatures; ++x)
        ptr[x] = x;

    // 3. only simple case (all samples used) //使用所有训练样本
    int nsamples = npositives + nnegatives;
    cv::Mat sampleIdx(1, nsamples, CV_32SC1);
    ptr = sampleIdx.ptr<int>(0);

    for (int x = 0; x < nsamples; ++x)
        ptr[x] = x;

    // 4. ICF has an ordered response.
    cv::Mat varType(1, nfeatures + 1, CV_8UC1); //指定按特征响应排序？最后多的一个元素表示什么意思？是不是表示将要输入训练函数train()（在倒数第3行）中的response向量保存的是类标签
    uchar* uptr = varType.ptr<uchar>(0);
    for (int x = 0; x < nfeatures; ++x)
        uptr[x] = CV_VAR_ORDERED; //这个排序无法理解！
    uptr[nfeatures] = CV_VAR_CATEGORICAL;

    trainData.create(nfeatures, nsamples, CV_32FC1); //生成用来训练用的数据矩阵，看下面的双重循环就知道，其中每行对应到一个特征，每列对应到一个样本，该矩阵每个元素保存的是某样本某特征的积分图。

    for (int fi = 0; fi < nfeatures; ++fi)
    {
        float* dptr = trainData.ptr<float>(fi);
        for (int si = 0; si < nsamples; ++si)
        {
            dptr[si] = pool->apply(fi, si, integrals);
        }
    }

    cv::Mat missingMask;

    bool ok = train(trainData, responses, varIdx, sampleIdx, varType, missingMask); //？主要的训练函数，trainData是用特征表示的训练集矩阵，responses是样本对应的类标签，varIdx是所学习的特征的所有索引，sampleIdx是训练样本的索引，？varType表示是否对特征响应排序？，missingMask用来保存错误的分类。
    if (!ok)
        CV_Error(CV_StsInternal, "ERROR: tree can not be trained");
    return ok;

}

//其中用到的训练函数如下，可见它调用了boost类的train()函数，并且推测得到的检测器保存在params中。
//它调用了CvBoostTree::train()函数来,并加入了params参数，看来在BoostedSoftCascadeOctave类中没有params成员，但是最终的检测器时保存在该成员中的，到现在为止还是训练整个的检测器:
bool BoostedSoftCascadeOctave::train( const cv::Mat& _trainData, const cv::Mat& _responses, const cv::Mat& varIdx,
      const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
{
    bool update = false;
    return cv::Boost::train(_trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, params,
    update);
}

//boost类的train()函数定义如下（在boost.cpp中）：
bool
CvBoost::train( const CvMat* _train_data, int _tflag,
              const CvMat* _responses, const CvMat* _var_idx,
              const CvMat* _sample_idx, const CvMat* _var_type,
              const CvMat* _missing_mask,
              CvBoostParams _params, bool _update )
{//顺着来，到了这里，_train_data是训练数据，每行对应到一个特征每列对应到一个样本；_tflag表明了训练数据矩阵的每列对应到一个样本；
//_responsens是各样本对应的类标签；_var_idx是特征索引（序号），_sample_idx是样本索引，_var_type在这里表示按特征响应排序；_missing_mask不知何意；
//_params保存训练得到的检测器；_update为false。这里使用CvBoost.tree->train()函数训练每一个弱分类器
//
    bool ok = false;
    CvMemStorage* storage = 0;

    CV_FUNCNAME( "CvBoost::train" );

    __BEGIN__;

    int i;

    set_params( _params );//初始化检测器参数

    cvReleaseMat( &active_vars );
    cvReleaseMat( &active_vars_abs );

    if( !_update || !data ) //准备好训练用的数据，并确定只包含正负样本两类，分配保存弱分类器的存储空间
    {
        clear();
        data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx, //准备训练数据，但是这里怎么还要用到boost的参数_params呢?
            _sample_idx, _var_type, _missing_mask, _params, true, true );

        if( data->get_num_classes() != 2 )
            CV_ERROR( CV_StsNotImplemented,
            "Boosted trees can only be used for 2-class classification." );
        CV_CALL( storage = cvCreateMemStorage() );
        weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage ); //这是CvBoost类中保存弱分类器的向量？
        storage = 0;
    }
    else
    {
        data->set_data( _train_data, _tflag, _responses, _var_idx,
            _sample_idx, _var_type, _missing_mask, _params, true, true, true );
    }

    if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) )
        data->do_responses_copy();

    update_weights( 0 ); //将各样本权重平均分配

    for( i = 0; i < params.weak_count; i++ ) //训练weak_count个弱分类器
    {
        CvBoostTree* tree = new CvBoostTree;
        if( !tree->train( data, subsample_mask, this ) ) //主要的训练函数,subsample_mask似乎是一个输出参数，查了其初始值是值为0的指针，记录弱分类器正确分类的样本，也许初始值是全0的向量？
               //第三个参数是训练出的弱分类器要连接的‘宿主’分类器
        {
            delete tree;
            break;
        }
        //cvCheckArr( get_weak_response());
        cvSeqPush( weak, &tree );
        update_weights( tree ); //这里是不是根据训练出的弱分类器的分类情况调整各样本的权重？
        trim_weights();
        if( cvCountNonZero(subsample_mask) == 0 )
            break;
    }

    if(weak->total > 0)//释放存储空间
    {
        get_active_vars(); // recompute active_vars* maps and condensed_idx's in the splits.
        data->is_classifier = true;
        data->free_train_data();
        ok = true;
    }
    else
        clear();

    __END__;

    return ok;
}

//CvBoostTree::train()函数定义如下，它用来训练单个弱分类器，它进一步调用了CvDTree::do_train()函数：
CvBoostTree::train( CvDTreeTrainData* _train_data,
                    const CvMat* _subsample_idx, CvBoost* _ensemble )
{
    clear();
    ensemble = _ensemble;
    data = _train_data;
    data->shared = true;
    return do_train( _subsample_idx );
}
//CvDTree::do_train()函数定义如下(在文件tree.cpp中，头文件为ml.hpp）：

bool CvDTree::do_train( const CvMat* _subsample_idx )
{
    bool result = false;

    CV_FUNCNAME( "CvDTree::do_train" );

    __BEGIN__;

    root = data->subsample_data( _subsample_idx ); //明显是选择参与训练的样本

    CV_CALL( try_split_node(root));

    if( root->split )
    {
        CV_Assert( root->left );
        CV_Assert( root->right );

        if( data->params.cv_folds > 0 )
            CV_CALL( prune_cv() );

        if( !data->shared )
            data->free_train_data();

        result = true;
    }

    __END__;

    return result;
}

//do_train()的核心函数如下：
CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
{
    CvDTreeNode* root = 0;
    CvMat* isubsample_idx = 0;
    CvMat* subsample_co = 0;

    bool isMakeRootCopy = true;

    CV_FUNCNAME( "CvDTreeTrainData::subsample_data" );

    __BEGIN__;

    if( !data_root )
        CV_ERROR( CV_StsError, "No training data has been set" );

    if( _subsample_idx )
    {
        CV_CALL( isubsample_idx = cvPreprocessIndexArray( _subsample_idx, sample_count )); //如果已训练出了一些弱分类器，则在这里进行一定的处理。_subsample_idx只能是一个行向量或者是列向量
                       //_subsample_idx中保存的可能是选中的样本的索引，也可能长度为sample_count的表明选择的'0''1'掩膜，但
                       //输出只包含了选择的样本的编号，并且进行了排序。
        if( isubsample_idx->cols + isubsample_idx->rows - 1 == sample_count ) //isubsample_idx是一个指向行向量或者列向量的指针，这里验证元素个数与样本数是否相等。
        {
            const int* sidx = isubsample_idx->data.i;
            for( int i = 0; i < sample_count; i++ )
            {
                if( sidx[i] != i )
                {
                    isMakeRootCopy = false; //若尚无任何弱分类器，则'isMakeRootCopy = true'，
                    break;
                }
            }
        }
        else
            isMakeRootCopy = false;
    }

    if( isMakeRootCopy )
    {
        // make a copy of the root node
        CvDTreeNode temp;
        int i;
        root = new_node( 0, 1, 0, 0 );
        temp = *root;
        *root = *data_root;
        root->num_valid = temp.num_valid;
        if( root->num_valid )
        {
            for( i = 0; i < var_count; i++ )
                root->num_valid[i] = data_root->num_valid[i];
        }
        root->cv_Tn = temp.cv_Tn;
        root->cv_node_risk = temp.cv_node_risk;
        root->cv_node_error = temp.cv_node_error;
    }
    else
    {
        int* sidx = isubsample_idx->data.i;
        // co - array of count/offset pairs (to handle duplicated values in _subsample_idx)
        int* co, cur_ofs = 0;
        int vi, i;
        int workVarCount = get_work_var_count(); //得到已使用的特征个数？
        int count = isubsample_idx->rows + isubsample_idx->cols - 1; //该弱分类器判断为正的样本个数

        root = new_node( 0, count, 1, 0 );

        CV_CALL( subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 ));
        cvZero( subsample_co );
        co = subsample_co->data.i;
        for( i = 0; i < count; i++ )
            co[sidx[i]*2]++;
        for( i = 0; i < sample_count; i++ )
        {
            if( co[i*2] )
            {
                co[i*2+1] = cur_ofs;
                cur_ofs += co[i*2];
            }
            else
                co[i*2+1] = -1;
        }

        cv::AutoBuffer<uchar> inn_buf(sample_count*(2*sizeof(int) + sizeof(float)));
        for( vi = 0; vi < workVarCount; vi++ )
        {
            int ci = get_var_type(vi);

            if( ci >= 0 || vi >= var_count )
            {
                int num_valid = 0;
                const int* src = CvDTreeTrainData::get_cat_var_data( data_root, vi, (int*)(uchar*)inn_buf );

                if (is_buf_16u)
                {
                    unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                        vi*sample_count + root->offset);
                    for( i = 0; i < count; i++ )
                    {
                        int val = src[sidx[i]];
                        udst[i] = (unsigned short)val;
                        num_valid += val >= 0;
                    }
                }
                else
                {
                    int* idst = buf->data.i + root->buf_idx*get_length_subbuf() +
                        vi*sample_count + root->offset;
                    for( i = 0; i < count; i++ )
                    {
                        int val = src[sidx[i]];
                        idst[i] = val;
                        num_valid += val >= 0;
                    }
                }

                if( vi < var_count )
                    root->set_num_valid(vi, num_valid);
            }
            else
            {
                int *src_idx_buf = (int*)(uchar*)inn_buf;
                float *src_val_buf = (float*)(src_idx_buf + sample_count);
                int* sample_indices_buf = (int*)(src_val_buf + sample_count);
                const int* src_idx = 0;
                const float* src_val = 0;
                get_ord_var_data( data_root, vi, src_val_buf, src_idx_buf, &src_val, &src_idx, sample_indices_buf );
                int j = 0, idx, count_i;
                int num_valid = data_root->get_num_valid(vi);

                if (is_buf_16u)
                {
                    unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                        vi*sample_count + data_root->offset);
                    for( i = 0; i < num_valid; i++ )
                    {
                        idx = src_idx[i];
                        count_i = co[idx*2];
                        if( count_i )
                            for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
                                udst_idx[j] = (unsigned short)cur_ofs;
                    }

                    root->set_num_valid(vi, j);

                    for( ; i < sample_count; i++ )
                    {
                        idx = src_idx[i];
                        count_i = co[idx*2];
                        if( count_i )
                            for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
                                udst_idx[j] = (unsigned short)cur_ofs;
                    }
                }
                else
                {
                    int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
                        vi*sample_count + root->offset;
                    for( i = 0; i < num_valid; i++ )
                    {
                        idx = src_idx[i];
                        count_i = co[idx*2];
                        if( count_i )
                            for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
                                idst_idx[j] = cur_ofs;
                    }

                    root->set_num_valid(vi, j);

                    for( ; i < sample_count; i++ )
                    {
                        idx = src_idx[i];
                        count_i = co[idx*2];
                        if( count_i )
                            for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
                                idst_idx[j] = cur_ofs;
                    }
                }
            }
        }
        // sample indices subsampling
        const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
        if (is_buf_16u)
        {
            unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                workVarCount*sample_count + root->offset);
            for (i = 0; i < count; i++)
                sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
        }
        else
        {
            int* sample_idx_dst = buf->data.i + root->buf_idx*get_length_subbuf() +
                workVarCount*sample_count + root->offset;
            for (i = 0; i < count; i++)
                sample_idx_dst[i] = sample_idx_src[sidx[i]];
        }
    }

    __END__;

    cvReleaseMat( &isubsample_idx );
    cvReleaseMat( &subsample_co );

    return root;
}

//do_train()另一个核心函数如下：

void CvDTree::try_split_node( CvDTreeNode* node )
{
    CvDTreeSplit* best_split = 0;
    int i, n = node->sample_count, vi;
    bool can_split = true;
    double quality_scale;

    calc_node_value( node );

    if( node->sample_count <= data->params.min_sample_count ||
        node->depth >= data->params.max_depth )
        can_split = false;

    if( can_split && data->is_classifier )
    {
        // check if we have a "pure" node,
        // we assume that cls_count is filled by calc_node_value()
        int* cls_count = data->counts->data.i;
        int nz = 0, m = data->get_num_classes();
        for( i = 0; i < m; i++ )
            nz += cls_count[i] != 0;
        if( nz == 1 ) // there is only one class
            can_split = false;
    }
    else if( can_split )
    {
        if( sqrt(node->node_risk)/n < data->params.regression_accuracy )
            can_split = false;
    }

    if( can_split )
    {
        best_split = find_best_split(node);
        // TODO: check the split quality ...
        node->split = best_split;
    }
    if( !can_split || !best_split )
    {
        data->free_node_data(node);
        return;
    }

    quality_scale = calc_node_dir( node );
    if( data->params.use_surrogates )
    {
        // find all the surrogate splits
        // and sort them by their similarity to the primary one
        for( vi = 0; vi < data->var_count; vi++ )
        {
            CvDTreeSplit* split;
            int ci = data->get_var_type(vi);

            if( vi == best_split->var_idx )
                continue;

            if( ci >= 0 )
                split = find_surrogate_split_cat( node, vi );
            else
                split = find_surrogate_split_ord( node, vi );

            if( split )
            {
                // insert the split
                CvDTreeSplit* prev_split = node->split;
                split->quality = (float)(split->quality*quality_scale);

                while( prev_split->next &&
                      prev_split->next->quality > split->quality )
                    prev_split = prev_split->next;
                split->next = prev_split->next;
                prev_split->next = split;
            }
        }
    }
    split_node_data( node );
    try_split_node( node->left );
    try_split_node( node->right );
}

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