FloatImageView* med_filter(const T &src, size_t region_size)
{
if ((region_size < 1) || (region_size > std::min(src.nrows(), src.ncols())))
throw std::out_of_range("median_filter: region_size out of range");
size_t half_region_size = region_size / 2;
typename ImageFactory<T>::view_type* copy = ImageFactory<T>::new_view(src);
FloatImageData* data = new FloatImageData(src.size(), src.origin());
FloatImageView* view = new FloatImageView(*data);
for (coord_t y = 0; y < src.nrows(); ++y) {
for (coord_t x = 0; x < src.ncols(); ++x) {
// Define the region.
Point ul((coord_t)std::max(0, (int)x - (int)half_region_size),
(coord_t)std::max(0, (int)y - (int)half_region_size));
Point lr((coord_t)std::min(x + half_region_size, src.ncols() - 1),
(coord_t)std::min(y + half_region_size, src.nrows() - 1));
copy->rect_set(ul, lr);
view->set(Point(x, y), image_med(*copy));
}
}
delete copy;
return view;
}
T* wiener_filter(const T &src, size_t region_size, double noise_variance)
{
if ((region_size < 1) || (region_size > std::min(src.nrows(), src.ncols())))
throw std::out_of_range("niblack_threshold: region_size out of range");
// Compute regional statistics.
const FloatImageView* means = mean_filter(src, region_size);
const FloatImageView* variances = variance_filter(src, *means, region_size);
// Compute noise variance if needed.
if (noise_variance < 0) {
FloatImageData* orderedVariancesData
= new FloatImageData(variances->size(), variances->origin());
FloatImageView* orderedVariances
= new FloatImageView(*orderedVariancesData);
std::copy(variances->vec_begin(),
variances->vec_end(),
orderedVariances->vec_begin());
size_t area = orderedVariances->nrows() * orderedVariances->ncols();
std::nth_element(orderedVariances->vec_begin(),
orderedVariances->vec_begin() + (area - 1) / 2,
orderedVariances->vec_end());
noise_variance
= (double)*(orderedVariances->vec_begin() + (area - 1) / 2);
delete orderedVariancesData;
delete orderedVariances;
}
typedef typename T::value_type value_type;
typedef typename ImageFactory<T>::data_type data_type;
typedef typename ImageFactory<T>::view_type view_type;
data_type* data = new data_type(src.size(), src.origin());
view_type* view = new view_type(*data);
for (coord_t y = 0; y < src.nrows(); ++y) {
for (coord_t x = 0; x < src.ncols(); ++x) {
double mean = (double)means->get(Point(x, y));
double variance = (double)variances->get(Point(x, y));
// The estimate of noise variance will never be perfect, but in
// theory, it would be impossible for any region to have a local
// variance less than it. The following check eliminates that
// theoretical impossibility and has a side benefit of preventing
// division by zero.
if (variance < noise_variance) {
view->set(Point(x, y), (value_type)mean);
} else {
double multiplier = (variance - noise_variance) / variance;
double value = (double)src.get(Point(x, y));
view->set(Point(x, y),
(value_type)(mean + multiplier * (value - mean)));
}
}
}
delete means->data(); delete means;
delete variances->data(); delete variances;
return view;
}
FloatImageView* variance_filter(const T &src,
const FloatImageView &means,
size_t region_size)
{
if ((region_size < 1) || (region_size > std::min(src.nrows(), src.ncols())))
throw std::out_of_range("variance_filter: region_size out of range");
if (src.size() != means.size())
throw std::invalid_argument("variance_filter: sizes must match");
size_t half_region_size = region_size / 2;
// Compute squares of each element. This step avoid repeating the squaring
// operation for overlapping regions.
FloatImageData* squaredData = new FloatImageData(src.size(), src.origin());
FloatImageView* squares = new FloatImageView(*squaredData);
transform(src.vec_begin(),
src.vec_end(),
squares->vec_begin(),
double_squared<typename T::value_type>());
FloatImageData* data = new FloatImageData(src.size(), src.origin());
FloatImageView* view = new FloatImageView(*data);
for (coord_t y = 0; y < src.nrows(); ++y) {
for (coord_t x = 0; x < src.ncols(); ++x) {
// Define the region.
Point ul((coord_t)std::max(0, (int)x - (int)half_region_size),
(coord_t)std::max(0, (int)y - (int)half_region_size));
Point lr((coord_t)std::min(x + half_region_size, src.ncols() - 1),
(coord_t)std::min(y + half_region_size, src.nrows() - 1));
squares->rect_set(ul, lr);
// Compute the variance.
FloatPixel sum
= std::accumulate(squares->vec_begin(),
squares->vec_end(),
(FloatPixel)0);
size_t area = squares->nrows() * squares->ncols();
FloatPixel mean = means.get(Point(x,y));
view->set(Point(x, y), sum / area - mean * mean);
}
}
delete squaredData;
delete squares;
return view;
}
FloatPixel image_variance(const T &src)
{
FloatImageData* squaredData = new FloatImageData(src.size(), src.origin());
FloatImageView* squares = new FloatImageView(*squaredData);
transform(src.vec_begin(),
src.vec_end(),
squares->vec_begin(),
double_squared<typename T::value_type>());
FloatPixel sum
= std::accumulate(squares->vec_begin(),
squares->vec_end(),
(FloatPixel)0);
size_t area = src.nrows() * src.ncols();
FloatPixel mean = image_mean(src);
delete squaredData;
delete squares;
return sum / area - mean * mean;
}
// -----------------------------------------------------------------------------
PyObject* graph_create_minimum_spanning_tree_unique_distances(GraphObject* so,
PyObject* images, PyObject* uniq_dists) {
PyObject* images_seq = PySequence_Fast(images, "images must be iteratable");
if (images_seq == NULL)
return NULL;
static PyTypeObject* imagebase = 0;
if (imagebase == 0) {
PyObject* mod = PyImport_ImportModule(CHAR_PTR_CAST "gamera.gameracore");
if (mod == 0) {
PyErr_SetString(PyExc_RuntimeError, "Unable to load gameracore.\n");
Py_DECREF(images_seq);
return 0;
}
PyObject* dict = PyModule_GetDict(mod);
if (dict == 0) {
PyErr_SetString(PyExc_RuntimeError, "Unable to get module dictionary\n");
Py_DECREF(images_seq);
return 0;
}
imagebase = (PyTypeObject*)PyDict_GetItemString(dict, "Image");
}
// get the matrix
if (!PyObject_TypeCheck(uniq_dists, imagebase)
|| get_pixel_type(uniq_dists) != Gamera::FLOAT) {
PyErr_SetString(PyExc_TypeError, "uniq_dists must be a float image.");
Py_DECREF(images_seq);
return 0;
}
FloatImageView* dists = (FloatImageView*)((RectObject*)uniq_dists)->m_x;
if (dists->nrows() != dists->ncols()) {
PyErr_SetString(PyExc_TypeError, "image must be symmetric.");
Py_DECREF(images_seq);
return 0;
}
// get the graph ready
so->_graph->remove_all_edges();
GRAPH_UNSET_FLAG(so->_graph, FLAG_CYCLIC);
// make the list for sorting
typedef std::vector<std::pair<size_t, size_t> > index_vec_type;
index_vec_type indexes(((dists->nrows() * dists->nrows()) - dists->nrows()) / 2);
size_t row, col, index = 0;
for (row = 0; row < dists->nrows(); ++row) {
for (col = row + 1; col < dists->nrows(); ++col) {
indexes[index].first = row;
indexes[index++].second = col;
}
}
std::sort(indexes.begin(), indexes.end(), DistsSorter(dists));
// Add the nodes to the graph and build our map for later
int images_len = PySequence_Fast_GET_SIZE(images_seq);
std::vector<Node*> nodes(images_len);
int i;
for (i = 0; i < images_len; ++i) {
GraphDataPyObject* obj = new GraphDataPyObject(PySequence_Fast_GET_ITEM(images_seq, i));
nodes[i] = so->_graph->add_node_ptr(obj);
assert(nodes[i] != NULL);
}
Py_DECREF(images_seq);
// create the mst using kruskal
i = 0;
while (i < int(indexes.size()) && (int(so->_graph->get_nedges())
< (images_len - 1))) {
size_t row = indexes[i].first;
size_t col = indexes[i].second;
cost_t weight = dists->get(Point(col, row));
so->_graph->add_edge(nodes[row], nodes[col], weight);
++i;
}
RETURN_VOID();
}
