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- Committer: tranx
- Date: 2010-10-01 16:56:14 UTC
- Revision ID: tranx@havane-20101001165614-u938mdd1y1fgd0o5
initial commit
- files added:
- demos
- lib
- lib/gb
- nodes
- wrappers
added
removed
lib/siftgpu.py
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# -*- coding: utf-8 -*-
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import os,numpy
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import pycvfext.keypoints
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from pycvf.core.errors import pycvf_warning
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import scipy
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import scipy.spatial
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from scipy.weave import converters
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from scipy.weave import inline
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class SiftGPU:
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def __init__(self,params=["-cuda","0", "-v","1"],nodescr=False):
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self.params=params
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self.nodescr=nodescr
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def process(self,img):
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if (img.ndim==2):
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img=img[:,:,numpy.newaxis]
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assert(img.ndim==3)
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img=numpy.require(img,numpy.uint8,'C')
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SIFTGPU_DIR=os.path.join(pycvfext.keypoints.__path__[0],'wrappers','SiftGPU')
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ny,nx,c=img.shape
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code="""
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if (!sg_initialized) {
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siftgpu=new SiftGPU();
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char * params[%d]=%s;
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siftgpu->ParseParam(%d,params);
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siftgpu->CreateContextGL();
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int support = siftgpu->VerifyContextGL();
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if(support != SiftGPU::SIFTGPU_FULL_SUPPORTED) {
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fprintf(stderr,"Your video card is not fully supported\\n");
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exit(-1);
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}
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fprintf(stderr,"Sift GPU is initialized...\\n");
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sg_initialized=1;
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}
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//gl_format (e.g. GL_LUMINANCE, GL_RGB) is the format of the pixel data
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//gl_type (e.g. GL_UNSIGNED_BYTE, GL_FLOAT) is the data type of the pixel data;
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siftgpu->RunSIFT(nx, ny, img_array->data, %s, GL_UNSIGNED_BYTE);
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int numfeat=siftgpu->GetFeatureNum();
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PyArray_Dims keys_d;
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//npy_intp * keys_dims=(npy_intp *)malloc(sizeof(npy_intp)*2);
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//npy_intp * keys_dims=PyArray_DIMS(keys_ar_array);
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npy_intp keys_dims[2];
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keys_dims[0]=numfeat;
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keys_dims[1]=4;
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PyArray_Dims desc_d;
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//npy_intp * desc_dims=(npy_intp *)malloc(sizeof(npy_intp)*2);//PyArray_DIMS(desc_ar_array);
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npy_intp desc_dims[2];
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desc_dims[0]=numfeat;
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desc_dims[1]=128;
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keys_d.len=2;
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desc_d.len=2;
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keys_d.ptr=keys_dims;
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desc_d.ptr=desc_dims;
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PyArray_Resize(keys_ar_array,&keys_d,0,NPY_CORDER);
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if (!nodescr){
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PyArray_Resize(desc_ar_array,&desc_d,0,NPY_CORDER);
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}
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siftgpu->GetFeatureVector((SiftGPU::SiftKeypoint *)keys_ar_array->data,(float*)((nodescr)?NULL:desc_ar_array->data));
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fprintf(stderr,"On C side we are done...\\n");
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"""%(len(self.params), "{%s}"%(",".join(map(lambda x:("\"%s\""%(x,) if type(x) ==str else "\"%r\""%(x,) ) ,self.params))),len(self.params), ("GL_LUMINANCE" if c==1 else "GL_RGB"))
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#assert(c==1)
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keys_ar=numpy.zeros((0,4),dtype=numpy.float32)
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desc_ar=numpy.zeros((0,128),dtype=numpy.float32)
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os.environ["LD_LIBRARY_PATH"]=":".join(os.environ.get( "LD_LIBRARY_PATH","").split(":")+[os.path.join(SIFTGPU_DIR,'linux','bin'),os.path.join('/usr','local','cuda','lib')])
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#print os.environ
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nodescr=int(self.nodescr)
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inline(code, ['img','nx','ny','desc_ar','keys_ar','nodescr'],
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headers=[],
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support_code="""
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#include "GL/glew.h"
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#include "GlobalUtil.h"
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#include "GLTexImage.h"
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#include <SiftGPU.h>
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#include <cstdio>
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SiftGPU* siftgpu=NULL;
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int sg_initialized=0;
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""",
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sources=[],
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libraries=['siftgpu','GLEW','glut','IL','GL'],
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include_dirs=[os.path.join(SIFTGPU_DIR,'include'),os.path.join(SIFTGPU_DIR,'src','SiftGPU')],
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library_dirs=[os.path.join(SIFTGPU_DIR,'linux','bin'),os.path.join('/usr','local','cuda','lib')],
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compiler='gcc', extra_compile_args=['-fPIC'], extra_link_args=['-fPIC'] )
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#print "INLINE DONE",img.mean()
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return keys_ar, desc_ar
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def SiftMatchGPU(descr1,descr2,maxsift=2048,distmax = 100.0, ratiomax = 100.0, mutual_best_match = 0):
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SIFTGPU_DIR=os.path.join(pycvfext.keypoints.__path__[0],'wrappers','SiftGPU')
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match_buf=numpy.zeros((maxsift,2),dtype=numpy.int32)
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nmatch=0
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code="""
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if (!smg_initialized) {
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siftmatchgpu=new SiftMatchGPU(maxsift);
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if (siftmatchgpu->VerifyContextGL()==0) {
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fprintf(stderr,"Sift Matching Initialization Failed !!\\n");
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}
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smg_initialized=1;
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}
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siftmatchgpu->SetDescriptors(0,descr1_array->dimensions[0],descr1);
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siftmatchgpu->SetDescriptors(1,descr2_array->dimensions[0],descr2);
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/*
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int *_match_buf[2];
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_match_buf[0]=match_buf;
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_match_buf[1]=match_buf+maxsift;
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*/
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nmatch[0] = siftmatchgpu->GetSiftMatch(maxsift,(int [%s][2])(int *) match_buf,distmax,ratiomax,mutual_best_match);
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fprintf(stderr,"On C side we are done... %%d\\n",nmatch[0]);
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"""%(maxsift,)
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nmatch=numpy.ndarray([0,],dtype=numpy.int32)
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os.environ["LD_LIBRARY_PATH"]=":".join(os.environ.get( "LD_LIBRARY_PATH","").split(":")+[os.path.join(SIFTGPU_DIR,'linux','bin'),os.path.join('/usr','local','cuda','lib')])
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ir=inline(code,
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['descr1','descr2','match_buf','nmatch','maxsift','distmax', 'ratiomax', 'mutual_best_match'],
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headers=[],
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support_code="""
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#include "GL/glew.h"
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#include "GlobalUtil.h"
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#include "GLTexImage.h"
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#include <SiftGPU.h>
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#include <cstdio>
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SiftMatchGPU* siftmatchgpu=NULL;
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int smg_initialized=0;
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""",
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sources=[],
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libraries=['siftgpu','GLEW','glut','IL','GL'],
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include_dirs=[os.path.join(SIFTGPU_DIR,'include'),os.path.join(SIFTGPU_DIR,'src','SiftGPU')],
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library_dirs=[os.path.join(SIFTGPU_DIR,'linux','bin'),os.path.join('/usr','local','cuda','lib')],
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compiler='gcc', extra_compile_args=['-fPIC','-fpermissive'], extra_link_args=['-fPIC','-fpermissive'] ) # TODO: find the correct C++ writting
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#print ir,match_buf,nmatch
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return match_buf[:nmatch[0]]
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def match_dist1(descr1, descr2,subdist=scipy.spatial.distance.cosine):
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##
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## The distance is simply the sum of the minimal distances
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import scipy.spatial
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if (len(descr1)==0):
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return numpy.inf
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if (len(descr2)==0):
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return numpy.inf
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#print type(descr1),type(descr2),descr1.shape, descr2.shape
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md=scipy.spatial.distance.cdist(descr1,descr2,subdist).min(axis=0).mean()
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return md
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def match_dist1b(descr1, descr2,subdist=scipy.spatial.distance.cosine):
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##
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## The distance is simply the sum of the minimal distances
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import scipy.spatial
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if (len(descr1)==0):
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return numpy.inf
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if (len(descr2)==0):
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return numpy.inf
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#print type(descr1),type(descr2),descr1.shape, descr2.shape
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md=scipy.spatial.distance.cdist(descr1,descr2,subdist).min(axis=1).cumprod()[-1]
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return md
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def match_dist1x(descr1, descr2):
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##
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## The distance is simply the number of unmatched descriptors...
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##
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#print "sh1",descr1.shape
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#print "sh2",descr2.shape
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#print "[",descr1.shape[0],descr2.shape[0]
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nmatch=SiftMatchGPU(descr1,descr2).shape[0]
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#print nmatch,"]"
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print "NM=",nmatch
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maxmatch=min(descr1.shape[0],descr2.shape[0])
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return 1.-(nmatch/maxmatch)
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def match_dist2(descr1, descr2):
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##
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## Other possibility we care only on the number of match
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##
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nmatch=SiftMatchGPU(descr1,descr2).shape[0]
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return (nmatch+1)**-1
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def match_dist3(kpdescr1, kpdescr2):
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##
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## Other possibility we care only on the number of match
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##
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##
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## We also care about the diversity of the directions....
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##
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kp1,descr1=kpdescr1
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kp2,descr2=kpdescr2
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match=SiftMatchGPU(descr1,descr2)
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nmatch=match.shape[0]
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if nmatch==0:
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return numpy.inf
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dirvec=kp2[match[numpy.arange(nmatch),1]]-kp1[match[numpy.arange(nmatch),0]]
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#xm=dirvec.mean(axis=0)
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xs=dirvec.std(axis=0).mean()
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##
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##
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## the smaler xs is the better the approximation is
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return xs**(nmatch) #**-1
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if __name__=="__main__":
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import time
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for i in range(60):
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a,b=SiftGPU().process(scipy.lena())
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print a,a.min(axis=0),a.max(axis=0)#,b,i
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#print a.shape,b.shape,time.clock(),i
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b'\\ No newline at end of file'
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