/usr/include/gamera/knnmodule.hpp is in python-gamera-dev 3.4.2+svn1437-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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*
* Copyright (C) 2001-2005 Ichiro Fujinaga, Michael Droettboom, and Karl MacMillan
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License
* as published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
*/
/*
This holds various functions for using k-NN and Python together.
*/
#ifndef KWM12172002_knnmodule
#define KWM12172002_knnmodule
#include <Python.h>
#include "knn.hpp"
using namespace Gamera;
using namespace Gamera::kNN;
/*
This enum is for selecting between the various methods of
computing the distance between two floating-point feature
vectors.
*/
enum DistanceType {
CITY_BLOCK,
EUCLIDEAN,
FAST_EUCLIDEAN
};
/*
get the feature vector from an image. image argument _must_ an image - no
type checking is performed.
*/
inline int image_get_fv(PyObject* image, double** buf, Py_ssize_t* len) {
ImageObject* x = (ImageObject*)image;
if (PyObject_CheckReadBuffer(x->m_features) < 0) {
return -1;
}
if (PyObject_AsReadBuffer(x->m_features, (const void**)buf, len) < 0) {
PyErr_SetString(PyExc_TypeError, "knn: Could not use image as read buffer.");
return -1;
}
if (*len == 0) {
return -1;
}
*len = *len / sizeof(double);
return 0;
}
/*
get the id_name from an image. The image argument _must_ be an image -
no type checking is performed.
*/
inline int image_get_id_name(PyObject* image, char** id_name, int* len) {
ImageObject* x = (ImageObject*)image;
// PyList_Size shoule type check the argument
if (PyList_Size(x->m_id_name) < 1) {
PyErr_SetString(PyExc_TypeError, "knn: id_name not a list or list is empty.");
return -1;
}
PyObject* id_tuple = PyList_GET_ITEM(x->m_id_name, 0);
if (PyTuple_Size(id_tuple) != 2) {
PyErr_SetString(PyExc_TypeError, "knn: id_name is not a tuple or is the wrong size.");
return -1;
}
PyObject* id = PyTuple_GET_ITEM(id_tuple, 1);
*id_name = PyString_AsString(id);
if (*id_name == 0) {
PyErr_SetString(PyExc_TypeError, "knn: could not get string from id_name tuple.");
return -1;
}
*len = PyString_GET_SIZE(id);
return 0;
}
/*
Compute the distance between two feature vectors.
*/
inline void compute_distance(DistanceType distance_type, const double* known_buf,
int known_len, const double* unknown_buf, double* distance,
const int* selections, const double* weights) {
if (distance_type == CITY_BLOCK) {
*distance = city_block_distance(known_buf, known_buf + known_len, unknown_buf,
selections, weights);
} else if (distance_type == FAST_EUCLIDEAN) {
*distance = fast_euclidean_distance(known_buf, known_buf + known_len, unknown_buf,
selections, weights);
} else {
*distance = euclidean_distance(known_buf, known_buf + known_len, unknown_buf,
selections, weights);
}
}
/*
Compute the distance between a known and an unknown image
with weights. This version takes an image and a buffer
for the unknown image.
*/
inline int compute_distance(DistanceType distance_type, PyObject* known,
double* unknown_buf, double* distance,
int* selections, double* weights, Py_ssize_t unknown_len) {
double* known_buf;
Py_ssize_t known_len;
if (image_get_fv(known, &known_buf, &known_len) < 0)
return -1;
if (unknown_len != known_len) {
PyErr_SetString(PyExc_IndexError, "Array lengths do not match");
return -1;
}
compute_distance(distance_type, known_buf, known_len, unknown_buf, distance,
selections, weights);
return 0;
}
/*
Compute the distance between a known and an unknown image with weights. This version takes
an image and a buffer for the unknown image. Arguments must be images - no type checking
is performed.
*/
inline int compute_distance(DistanceType distance_type, PyObject* known,
PyObject* unknown, double* distance,
int* selections, int selections_len,
double* weights, int weights_len) {
double *known_buf, *unknown_buf;
Py_ssize_t known_len, unknown_len;
if (image_get_fv(known, &known_buf, &known_len) < 0)
return -1;
if (image_get_fv(unknown, &unknown_buf, &unknown_len) < 0)
return -1;
if (unknown_len != known_len) {
PyErr_SetString(PyExc_IndexError, "Array lengths do not match");
return -1;
}
if (unknown_len != selections_len) {
PyErr_SetString(PyExc_IndexError, "Array lengths do not match");
return -1;
}
if (unknown_len != weights_len) {
PyErr_SetString(PyExc_IndexError, "Array lengths do not match");
return -1;
}
compute_distance(distance_type, known_buf, known_len, unknown_buf, distance,
selections, weights);
return 0;
}
#endif
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