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adas fix

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- lowered memory usage (now the train use ~3x less memory)
- improved speed (removed some bottlenecks and added fast_log)
- added support to grayscale images
- the LUV features are not computed if the image is grayscale (leads to
an improved detection rate)
- added various parameters (limit_ps, limit_bg, alpha, slidingStep,
is_grayscale)
- added detect() function for a better control of the resize factor
This commit is contained in:
manuele
2014-09-30 13:00:21 +02:00
parent 6a61e07cf4
commit 59099ecbc0
9 changed files with 460 additions and 115 deletions
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135
modules/xobjdetect/src/waldboost.cpp
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@@ -50,10 +50,93 @@ using std::cout;
using std::endl;
namespace cv
{
namespace xobjdetect
{
//sort in-place of columns of the input matrix
void sort_columns_without_copy(Mat& m, Mat indices)
{
if(indices.data == 0)
sortIdx(m, indices, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
Mat indices_of_indices;
sortIdx(indices, indices_of_indices, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
std::vector<bool> visited;
for(int c = 0; c<m.cols; c++)
visited.push_back(false);
int ind_v = 0;
Mat temp_column = Mat();
int next = 0;
Mat column;
while(ind_v < m.cols)
{
if(temp_column.data == 0)
{
(m.col(indices_of_indices.at<int>(0,ind_v))).copyTo(column);
}
else
{
temp_column.copyTo(column);
}
if(indices_of_indices.at<int>(0,next) != next) //value is in the right place
{
//store the next value to change
(m.col(indices_of_indices.at<int>(0,next))).copyTo(temp_column);
//insert the value to change at the right place
column.copyTo(m.col(indices_of_indices.at<int>(0,next)));
//find the index of the next value to change
next = indices_of_indices.at<int>(0,next);
//if the idenx is not visited yet
if(visited[next] == false)
{
//then mark it as visited, it will be computed in the next round
visited[next] = true;
}
else
{
//find first non visited index
int i = 0;
while(visited[i] == true && i<(int)visited.size())
{
i++;
}
ind_v = i;
next = i;
temp_column = Mat();
}
}
else // value is already at the right place
{
visited[next] = true;
int i = 0;
while(visited[i] == true && i<(int)visited.size())
{
i++;
}
next = i;
temp_column = Mat();
ind_v = i;
}
}
}
class WaldBoostImpl : public WaldBoost
{
@@ -63,8 +146,8 @@ public:
params_(params)
{}
virtual std::vector<int> train(const Mat& data,
const Mat& labels);
virtual std::vector<int> train(Mat& data,
const Mat& labels, bool use_fast_log=false);
virtual float predict(
const Ptr<FeatureEvaluator>& feature_evaluator) const;
@@ -138,13 +221,12 @@ void WaldBoostImpl::write(FileStorage& fs) const
}
vector<int> WaldBoostImpl::train(const Mat& data_, const Mat& labels_)
vector<int> WaldBoostImpl::train(Mat& data, const Mat& labels_, bool use_fast_log)
{
CV_Assert(labels_.rows == 1 && labels_.cols == data_.cols);
CV_Assert(data_.rows >= params_.weak_count);
CV_Assert(labels_.rows == 1 && labels_.cols == data.cols);
CV_Assert(data.rows >= params_.weak_count);
Mat labels, data;
data_.copyTo(data);
Mat labels;
labels_.copyTo(labels);
bool null_data = true;
@@ -175,18 +257,18 @@ vector<int> WaldBoostImpl::train(const Mat& data_, const Mat& labels_)
feature_indices_pool.push_back(ind);
vector<int> feature_indices;
vector<int> visited_features;
Mat_<float> trace = Mat_<float>::zeros(labels.rows, labels.cols);
stumps_.clear();
thresholds_.clear();
for( int i = 0; i < params_.weak_count; ++i)
{
cout << "stage " << i << endl;
{
Stump s;
int feature_ind = s.train(data, labels, weights);
cout << "feature_ind " << feature_ind << endl;
int feature_ind = s.train(data, labels, weights, visited_features, use_fast_log);
stumps_.push_back(s);
int ind = feature_indices_pool[feature_ind];
feature_indices_pool.erase(feature_indices_pool.begin() + feature_ind);
//we don't need to erase the feature index anymore, because we ignore them if already visited
//feature_indices_pool.erase(feature_indices_pool.begin() + feature_ind);
feature_indices.push_back(ind);
// Recompute weights
@@ -198,12 +280,13 @@ vector<int> WaldBoostImpl::train(const Mat& data_, const Mat& labels_)
weights.at<float>(0, col) *= exp(-label * h);
}
// Erase row for feature in data
Mat fixed_data;
fixed_data.push_back(data.rowRange(0, feature_ind));
fixed_data.push_back(data.rowRange(feature_ind + 1, data.rows));
// set to zero row for feature in data
for(int jc = 0; jc<data.cols; jc++)
{
data.at<int>(feature_ind, jc) = 0;
}
visited_features.push_back(feature_ind);
data = fixed_data;
// Normalize weights
@@ -218,7 +301,6 @@ vector<int> WaldBoostImpl::train(const Mat& data_, const Mat& labels_)
sortIdx(trace, indices, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
Mat new_weights = Mat_<float>::zeros(weights.rows, weights.cols);
Mat new_labels = Mat_<int>::zeros(labels.rows, labels.cols);
Mat new_data = Mat_<int>::zeros(data.rows, data.cols);
Mat new_trace;
for( int col = 0; col < new_weights.cols; ++col )
{
@@ -226,15 +308,12 @@ vector<int> WaldBoostImpl::train(const Mat& data_, const Mat& labels_)
weights.at<float>(0, indices.at<int>(0, col));
new_labels.at<int>(0, col) =
labels.at<int>(0, indices.at<int>(0, col));
for( int row = 0; row < new_data.rows; ++row )
{
new_data.at<int>(row, col) =
data.at<int>(row, indices.at<int>(0, col));
}
}
//sort in-place to save memory
sort_columns_without_copy(data, indices);
sort(trace, new_trace, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
// Compute threshold for trace
/*
int col = 0;
@@ -262,19 +341,16 @@ vector<int> WaldBoostImpl::train(const Mat& data_, const Mat& labels_)
}
thresholds_.push_back(new_trace.at<float>(0, max_col));
cout << "threshold " << *(thresholds_.end() - 1) << endl;
cout << "col " << max_col << " size " << data.cols << endl;
// Drop samples below threshold
new_data.colRange(max_col, new_data.cols).copyTo(data);
//uses Rois instead of copyTo to save memory
data = data(Rect(max_col, 0, data.cols - max_col, data.rows));
new_trace.colRange(max_col, new_trace.cols).copyTo(trace);
new_weights.colRange(max_col, new_weights.cols).copyTo(weights);
new_labels.colRange(max_col, new_labels.cols).copyTo(labels);
pos_count = count(labels, +1);
neg_count = count(labels, -1);
cout << "pos_count " << pos_count << "; neg_count " << neg_count << endl;
if( data.cols < 2 || neg_count == 0)
{
@@ -293,6 +369,7 @@ float WaldBoostImpl::predict(
{
int value = feature_evaluator->evaluate(i);
trace += stumps_[i].predict(value);
if( trace < thresholds_[i] )
return -1;
}