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Commit 2ebafae7 authored by Davis King's avatar Davis King
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added missing asserts

parent 1037b661
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Showing with 134 additions and 10 deletions
dlib/image_processing/render_face_detections.h
View file @ 2ebafae7
......@@ -18,6 +18,12 @@ namespace dlib
std::vector<image_window::overlay_line> lines;
for (unsigned long i = 0; i < dets.size(); ++i)
{
DLIB_CASSERT(dets[i].num_parts() == 68,
"\t std::vector<image_window::overlay_line> render_face_detections()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t dets["<<i<<"].num_parts(): " << dets[i].num_parts()
);
const full_object_detection& d = dets[i];
for (unsigned long i = 1; i <= 16; ++i)
lines.push_back(image_window::overlay_line(d.part(i), d.part(i-1), color));
......
dlib/image_processing/shape_predictor.h
View file @ 2ebafae7
......@@ -171,7 +171,7 @@ namespace dlib
const matrix<float,0,1>& to_shape
)
{
DLIB_CASSERT(from_shape.size() == to_shape.size() && (from_shape.size()%2) == 0 && from_shape.size() > 0,"");
DLIB_ASSERT(from_shape.size() == to_shape.size() && (from_shape.size()%2) == 0 && from_shape.size() > 0,"");
std::vector<vector<float,2> > from_points, to_points;
const unsigned long num = from_shape.size()/2;
from_points.reserve(num);
......@@ -415,7 +415,12 @@ namespace dlib
unsigned long depth
)
{
DLIB_CASSERT(depth > 0, "");
DLIB_CASSERT(depth > 0,
"\t void shape_predictor_trainer::set_cascade_depth()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t depth: " << depth
);
_cascade_depth = depth;
}
......@@ -426,7 +431,12 @@ namespace dlib
unsigned long depth
)
{
DLIB_CASSERT(depth > 0, "");
DLIB_CASSERT(depth > 0,
"\t void shape_predictor_trainer::set_tree_depth()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t depth: " << depth
);
_tree_depth = depth;
}
......@@ -437,7 +447,11 @@ namespace dlib
unsigned long num
)
{
DLIB_CASSERT( num > 0, "");
DLIB_CASSERT( num > 0,
"\t void shape_predictor_trainer::set_num_trees_per_cascade_level()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t num: " << num
);
_num_trees_per_cascade_level = num;
}
......@@ -447,7 +461,12 @@ namespace dlib
double nu
)
{
DLIB_CASSERT(nu > 0,"");
DLIB_CASSERT(nu > 0,
"\t void shape_predictor_trainer::set_nu()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t nu: " << nu
);
_nu = nu;
}
......@@ -463,7 +482,12 @@ namespace dlib
unsigned long amount
)
{
DLIB_CASSERT(amount > 0, "");
DLIB_CASSERT(amount > 0,
"\t void shape_predictor_trainer::set_oversampling_amount()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t amount: " << amount
);
_oversampling_amount = amount;
}
......@@ -473,7 +497,12 @@ namespace dlib
unsigned long size
)
{
DLIB_CASSERT(size > 1, "");
DLIB_CASSERT(size > 1,
"\t void shape_predictor_trainer::set_feature_pool_size()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t size: " << size
);
_feature_pool_size = size;
}
......@@ -483,7 +512,12 @@ namespace dlib
double lambda
)
{
DLIB_CASSERT(lambda > 0, "");
DLIB_CASSERT(lambda > 0,
"\t void shape_predictor_trainer::set_lambda()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t lambda: " << lambda
);
_lambda = lambda;
}
......@@ -493,7 +527,12 @@ namespace dlib
unsigned long num
)
{
DLIB_CASSERT(num > 0, "");
DLIB_CASSERT(num > 0,
"\t void shape_predictor_trainer::set_num_test_splits()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t num: " << num
);
_num_test_splits = num;
}
......@@ -526,7 +565,42 @@ namespace dlib
) const
{
using namespace impl;
DLIB_CASSERT(images.size() == objects.size() && images.size() > 0, "");
DLIB_CASSERT(images.size() == objects.size() && images.size() > 0,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t images.size(): " << images.size()
<< "\n\t objects.size(): " << objects.size()
);
// make sure the objects agree on the number of parts and that there is at
// least one full_object_detection.
unsigned long num_parts = 0;
for (unsigned long i = 0; i < objects.size(); ++i)
{
for (unsigned long j = 0; j < objects[i].size(); ++j)
{
if (num_parts == 0)
{
num_parts = objects[i][j].num_parts();
}
else
{
DLIB_CASSERT(objects[i][j].num_parts() == num_parts,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t All the objects must agree on the number of parts. "
<< "\n\t objects["<<i<<"]["<<j<<"].num_parts(): " << objects[i][j].num_parts()
<< "\n\t num_parts: " << num_parts
);
}
}
}
DLIB_CASSERT(num_parts != 0,
"\t shape_predictor shape_predictor_trainer::train()"
<< "\n\t You must give at least one full_object_detection if you want to train a shape model and it must have parts."
);
rnd.set_seed(get_random_seed());
......@@ -917,6 +991,38 @@ namespace dlib
const std::vector<std::vector<double> >& scales
)
{
// make sure requires clause is not broken
#ifdef ENABLE_ASSERTS
DLIB_CASSERT( images.size() == objects.size() ,
"\t double test_shape_predictor()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t images.size(): " << images.size()
<< "\n\t objects.size(): " << objects.size()
);
for (unsigned long i = 0; i < objects.size(); ++i)
{
for (unsigned long j = 0; j < objects[i].size(); ++j)
{
DLIB_CASSERT(objects[i][j].num_parts() == sp.num_parts(),
"\t double test_shape_predictor()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t objects["<<i<<"]["<<j<<"].num_parts(): " << objects[i][j].num_parts()
<< "\n\t sp.num_parts(): " << sp.num_parts()
);
}
if (scales.size() != 0)
{
DLIB_CASSERT(objects[i].size() == scales[i].size(),
"\t double test_shape_predictor()"
<< "\n\t Invalid inputs were given to this function. "
<< "\n\t objects["<<i<<"].size(): " << objects[i].size()
<< "\n\t scales["<<i<<"].size(): " << scales[i].size()
);
}
}
#endif
running_stats<double> rs;
for (unsigned long i = 0; i < objects.size(); ++i)
{
......
dlib/image_processing/shape_predictor_abstract.h
View file @ 2ebafae7
......@@ -348,8 +348,16 @@ namespace dlib
) const;
/*!
requires
- image_array is a dlib::array of image objects where each image object
implements the interface defined in dlib/image_processing/generic_image.h
- images.size() == objects.size()
- images.size() > 0
- for some i: objects[i].size() != 0
(i.e. there has to be at least one full_object_detection in the training set)
- for all valid i,j,k,l:
- objects[i][j].num_parts() == objects[k][l].num_parts()
(i.e. all objects must agree on the number of parts)
- objects[i][j].num_parts() > 0
ensures
- This object will try to learn to predict the locations of an object's parts
based on the object bounding box (i.e. full_object_detection::get_rect())
......@@ -376,6 +384,8 @@ namespace dlib
);
/*!
requires
- image_array is a dlib::array of image objects where each image object
implements the interface defined in dlib/image_processing/generic_image.h
- images.size() == objects.size()
- for all valid i and j:
- objects[i][j].num_parts() == sp.num_parts()
......@@ -413,6 +423,8 @@ namespace dlib
);
/*!
requires
- image_array is a dlib::array of image objects where each image object
implements the interface defined in dlib/image_processing/generic_image.h
- images.size() == objects.size()
- for all valid i and j:
- objects[i][j].num_parts() == sp.num_parts()
......
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