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mean average error of the detector. In fact, The \n\
return value of this function is identical to that of dlib's \n\
shape_predictor_trainer() routine. Therefore, see the documentation \n\
for shape_predictor_trainer() for a detailed definition of the mean average error.");
m.def("test_shape_predictor", test_shape_predictor_with_images_no_scales_py,
py::arg("images"), py::arg("detections"), py::arg("shape_predictor"),
"requires \n\
- len(images) == len(object_detections) \n\
- images should be a list of numpy matrices that represent images, either RGB or grayscale. \n\
- object_detections should be a list of lists of dlib.full_object_detection objects. \
Each dlib.full_object_detection contains the bounding box and the lists of points that make up the object parts.\n\
ensures \n\
- shape_predictor should be a file produced by the train_shape_predictor() \n\
routine. \n\
- This function tests the predictor against the dataset and returns the \n\
mean average error of the detector. In fact, The \n\
return value of this function is identical to that of dlib's \n\
shape_predictor_trainer() routine. Therefore, see the documentation \n\
for shape_predictor_trainer() for a detailed definition of the mean average error.");
m.def("test_shape_predictor", test_shape_predictor_with_images_py,
py::arg("images"), py::arg("detections"), py::arg("scales"), py::arg("shape_predictor"),
"requires \n\
- len(images) == len(object_detections) \n\
- len(object_detections) == len(scales) \n\
- for every sublist in object_detections: len(object_detections[i]) == len(scales[i]) \n\
- scales is a list of floating point scales that each predicted part location \
should be divided by. Useful for normalization. \n\
- images should be a list of numpy matrices that represent images, either RGB or grayscale. \n\
- object_detections should be a list of lists of dlib.full_object_detection objects. \
Each dlib.full_object_detection contains the bounding box and the lists of points that make up the object parts.\n\
ensures \n\
- shape_predictor should be a file produced by the train_shape_predictor() \n\
routine. \n\
- This function tests the predictor against the dataset and returns the \n\
mean average error of the detector. In fact, The \n\
return value of this function is identical to that of dlib's \n\
shape_predictor_trainer() routine. Therefore, see the documentation \n\
for shape_predictor_trainer() for a detailed definition of the mean average error.");
}
}
//
// Copyright (c) 2009-2010 Mikko Mononen [email protected]
//
// This software is provided 'as-is', without any express or implied
// warranty. In no event will the authors be held liable for any damages
// arising from the use of this software.
// Permission is granted to anyone to use this software for any purpose,
// including commercial applications, and to alter it and redistribute it
// freely, subject to the following restrictions:
// 1. The origin of this software must not be misrepresented; you must not
// claim that you wrote the original software. If you use this software
// in a product, an acknowledgment in the product documentation would be
// appreciated but is not required.
// 2. Altered source versions must be plainly marked as such, and must not be
// misrepresented as being the original software.
// 3. This notice may not be removed or altered from any source distribution.
//
#include "DetourCommon.h"
#include "DetourMath.h"
#include "DetourStatus.h"
#include "DetourAssert.h"
#include "DetourTileCacheBuilder.h"
#include <string.h>
template<class T> class dtFixedArray
{
dtTileCacheAlloc* m_alloc;
T* m_ptr;
const int m_size;
inline void operator=(dtFixedArray<T>& p);
public:
inline dtFixedArray(dtTileCacheAlloc* a, const int s) : m_alloc(a), m_ptr((T*)a->alloc(sizeof(T)*s)), m_size(s) {}
inline ~dtFixedArray() { if (m_alloc) m_alloc->free(m_ptr); }
inline operator T*() { return m_ptr; }
inline int size() const { return m_size; }
};
inline int getDirOffsetX(int dir)
{
const int offset[4] = { -1, 0, 1, 0, };
return offset[dir&0x03];
}
inline int getDirOffsetY(int dir)
{
const int offset[4] = { 0, 1, 0, -1 };
return offset[dir&0x03];
}
static const int MAX_VERTS_PER_POLY = 6; // TODO: use the DT_VERTS_PER_POLYGON
static const int MAX_REM_EDGES = 48; // TODO: make this an expression.