There is a thinning pip package that is currently getting compiled only with Python2.
When I install it with sudo pip install thinning and then attempt to import thinning, I get an error:
ImportError: /usr/lib/python3.5/site-packages/thinning.cpython-35m-x86_64-linux-gnu.so: undefined symbol: Py_InitModule3
I assume this is because of Py_InitModule3 is not used by Python3 anymore.
Here is complete c source file:
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include "Python.h"
#include "arrayobject.h"
#include <stdlib.h>
#include <assert.h>
#include <stdbool.h>
#include <limits.h>
static PyObject *guo_hall_thinning(PyObject *self, PyObject *args);
int _guo_hall_thinning(unsigned char* binary_image, int width, int height);
void initthinning(void);
/* ==== Set up the methods table ====================== */
static PyMethodDef thinningMethods[] = {
{"guo_hall_thinning",guo_hall_thinning, METH_VARARGS,
"Takes a 2D numpy UBYTE array in C-order and thins it in place using the algorithm by Guo and Hall."
"Images that come out of cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) have the right format."
"\n\n"
"We assume that the dimensions of the image fit into an int on your platform. If your computer for some"
"reason has a 2 byte int and lots of memory so that the image can become too large, bad things can happen."
"\n\n"
"interface:\n"
"\tguo_hall_thinning(segmented_image)"
"\tsegmented_image is a NumPy matrix,"
"\treturns the same NumPy matrix (thinned)"},
{NULL, NULL, 0, NULL} /* Sentinel - marks the end of this structure */
};
/* ==== Initialize the C_test functions ====================== */
void initthinning() {
PyObject* module = Py_InitModule3("thinning",thinningMethods, "Thinning of segmented images. See https://bitbucket.org/adrian_n/thinning.");
PyModule_AddStringConstant(module, "__author__", "Adrian Neumann <[email protected]>");
PyModule_AddStringConstant(module, "__version__", "1.2.3");
import_array(); // Must be present for NumPy. Called first after above line.
}
/* ==== Guo Hall Thinning =========
Takes a 2D numpy UBYTE array in C-order and thins it in place using the algorithm by Guo and Hall.
Images that come out of cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) have the right format.
We assume that the dimensions of the image fit into an int on your platform. If your computer for some
reason has a 2 byte int and lots of memory so that the image can become too large, bad things can happen.
interface: guo_hall_thinning(segmented_image)
segmented_image is a NumPy matrix,
returns the same NumPy matrix (thinned)
*/
static PyObject *guo_hall_thinning(PyObject *self, PyObject *args)
{
PyArrayObject *segmented_image;
/* Parse tuples separately since args will differ between C fcns */
if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &segmented_image)) {
return NULL;
}
if (NULL == segmented_image) {
PyErr_SetString(PyExc_TypeError, "Parameter is not a valid image");
return NULL;
}
if (PyArray_TYPE(segmented_image) != NPY_UBYTE || !PyArray_CHKFLAGS(segmented_image, NPY_ARRAY_CARRAY)) {
PyErr_SetString(PyExc_TypeError, "Parameter is not a grayscale image");
return NULL;
}
npy_intp* shape = PyArray_DIMS(segmented_image);
int height = (int)shape[0];
int width = (int)shape[1];
unsigned char *in_data = PyArray_DATA(segmented_image);
if (height>=3 && width>=3) {
int ok = _guo_hall_thinning(in_data, width, height);
if (ok<0) {
return PyErr_NoMemory();
}
}
Py_INCREF(segmented_image);
return (PyObject*)segmented_image;
}
int nonzero_clever(const unsigned char* arr, unsigned int start, unsigned int len) {
/* find the first nonzero element from arr[start] to arr[start+len-1] (inclusive)
look at a long long at a time to be faster on 64 bit cpus */
const unsigned int step=sizeof(unsigned long long)/sizeof(unsigned char);
unsigned int i=start;
//unsigned types should throw exceptions on under/overflow...
while(len>step && i<len-step) {
if (*((unsigned long long*)(arr +i))==0) {
i+=step;
} else {
int j=0;
while(arr[i+j]==0) j++;
return i+j;
}
}
while(i<len) {
if (arr[i]!=0) { return i;}
i++;
}
return len;
}
int guo_hall_iteration(const unsigned char* binary_image, unsigned char* mask, const unsigned int width, const unsigned int height, const int iteration) {
/* one iteration of the algorithm by guo and hall. see their paper for an explanation.
We only consider nonzero elemets of the image. We never reinitialize the mask, once a pixel is
black, it will never become white again anyway. */
unsigned int changed = 0;
for (unsigned int j = 1; j < height-1; j++) {
const unsigned char* line = binary_image+j*width;
unsigned int start=0;
const int len = width-1;
while(start+1<len) {
start = nonzero_clever(line, start+1, len);
if (start==len) break;
const unsigned int i = start;
assert(line[i]!=0);
assert(binary_image[i + j*width]!=0);
const bool p2 = binary_image[i-1 + width*j];
const bool p6 = binary_image[i+1 + width*j];
const bool p9 = binary_image[i-1 + width*(j-1)];
const bool p8 = binary_image[i + width*(j-1)];
const bool p7 = binary_image[i+1 + width*(j-1)];
const bool p3 = binary_image[i-1 + width*(j+1)];
const bool p4 = binary_image[i + width*(j+1)];
const bool p5 = binary_image[i+1 + width*(j+1)];
const unsigned int C = ((!p2 && (p3 || p4)) +
(!p4 && (p5 || p6)) +
(!p6 && (p7 || p8)) +
(!p8 && (p9 || p2)));
// printf("%d %d %d %d %d %d %d %d\n",p2,p3,p4,p5,p6,p7,p8,p9);
if (C==1) {
const unsigned int N1 = (p9 || p2) + (p3 || p4) + (p5 || p6) + (p7 || p8);
const unsigned int N2 = (p2 || p3) + (p4 || p5) + (p6 || p7) + (p8 || p9);
const unsigned int N = N1 < N2 ? N1 : N2;
unsigned int m;
if (iteration == 0)
{m = (p8 && (p6 || p7 || !p9));}
else
{m = (p4 && (p2 || p3 || !p5));}
if (2 <= N && N <= 3 && m == 0) {
mask[i + width*j] = 0;
changed += 1;
}
}
}
}
return changed;
}
void andImage(unsigned char* image, const unsigned char* mask, const int size) {
/* calculate image &=mask.
to be faster on 64 bit cpus, we do this one long long at a time */
const int step = sizeof(unsigned long long)/sizeof(unsigned char);
unsigned long long* image_l = (unsigned long long*)image;
const unsigned long long* mask_l = (unsigned long long*) mask;
unsigned int i=0;
for(; size/step>2 && i<size/step-2; i+=2) {
image_l[i] = image_l[i] & mask_l[i];
image_l[i+1] = image_l[i+1] & mask_l[i+1];
}
for(i=i*step; i<size; ++i) {
image[i] = image[i] & mask[i];
}
}
int _guo_hall_thinning(unsigned char* binary_image, int width, int height) {
/* return -1 if we can't allocate the memory for the mask, else 0 */
int changed;
unsigned char* mask = (unsigned char*) malloc(width*height*sizeof(unsigned char));
if (mask==NULL) {
return -1;
}
memset(mask, UCHAR_MAX, width*height);
do {
changed = guo_hall_iteration(binary_image, mask, width, height, 0);
andImage(binary_image, mask, width*height);
changed += guo_hall_iteration(binary_image, mask, width, height, 1);
andImage(binary_image, mask, width*height);
} while (changed != 0);
free(mask);
return 0;
}
I've started reading Porting Extension Modules to Python 3 but I must admit there is little I can understand.
I tried to change Py_InitModule to Python 3 analogue PyModule_Create with some other code adjustments but it didn't work. Unfortunately this thinning module is a hard dependency for our application. So, I am pretty stuck right now without time and knowledge how to port this module to Python3.