void joint_histogram(double* H, unsigned int clampI, unsigned int clampJ, PyArrayIterObject* iterI, const PyArrayObject* imJ_padded, const double* Tvox, int affine, int interp) { const signed short* J=(signed short*)imJ_padded->data; size_t dimJX=imJ_padded->dimensions[0]-2; size_t dimJY=imJ_padded->dimensions[1]-2; size_t dimJZ=imJ_padded->dimensions[2]-2; signed short Jnn[8]; double W[8]; signed short *bufI, *bufJnn; double *bufW; signed short i, j; size_t off; size_t u2 = imJ_padded->dimensions[2]; size_t u3 = u2+1; size_t u4 = imJ_padded->dimensions[1]*u2; size_t u5 = u4+1; size_t u6 = u4+u2; size_t u7 = u6+1; double wx, wy, wz, wxwy, wxwz, wywz; double W0, W2, W3, W4; size_t x, y, z; int nn, nx, ny, nz; double Tx, Ty, Tz; double *bufTvox = (double*)Tvox; void (*interpolate)(unsigned int, double*, unsigned int, const signed short*, const double*, int, void*); void* interp_params = NULL; rk_state rng; /* Reset the source image iterator */ PyArray_ITER_RESET(iterI); /* Make sure the iterator the iterator will update coordinate values */ UPDATE_ITERATOR_COORDS(iterI); /* Set interpolation method */ if (interp==0) interpolate = &_pv_interpolation; else if (interp>0) interpolate = &_tri_interpolation; else { /* interp < 0 */ interpolate = &_rand_interpolation; rk_seed(-interp, &rng); interp_params = (void*)(&rng); } /* Re-initialize joint histogram */ memset((void*)H, 0, clampI*clampJ*sizeof(double)); /* Looop over source voxels */ while(iterI->index < iterI->size) { /* Source voxel intensity */ bufI = (signed short*)PyArray_ITER_DATA(iterI); i = bufI[0]; /* Compute the transformed grid coordinates of current voxel */ if (affine) { /* Get voxel coordinates and apply transformation on-the-fly*/ x = iterI->coordinates[0]; y = iterI->coordinates[1]; z = iterI->coordinates[2]; _affine_transform(&Tx, &Ty, &Tz, Tvox, x, y, z); } else /* Use precomputed transformed coordinates */ bufTvox = _precomputed_transform(&Tx, &Ty, &Tz, (const double*)bufTvox); /* Test whether the current voxel is below the intensity threshold, or the transformed point is completly outside the reference grid */ if ((i>=0) && (Tx>-1) && (Tx<dimJX) && (Ty>-1) && (Ty<dimJY) && (Tz>-1) && (Tz<dimJZ)) { /* Nearest neighbor (floor coordinates in the padded image, hence +1). Notice that using the floor function doubles excetution time. FIXME: see if we can replace this with assembler instructions. */ nx = FLOOR(Tx) + 1; ny = FLOOR(Ty) + 1; nz = FLOOR(Tz) + 1; /* The convention for neighbor indexing is as follows: * * Floor slice Ceil slice * * 2----6 3----7 y * | | | | ^ * | | | | | * 0----4 1----5 ---> x */ /*** Trilinear interpolation weights. Note: wx = nnx + 1 - Tx, where nnx is the location in the NON-PADDED grid */ wx = nx - Tx; wy = ny - Ty; wz = nz - Tz; wxwy = wx*wy; wxwz = wx*wz; wywz = wy*wz; /*** Prepare buffers */ bufJnn = Jnn; bufW = W; /*** Initialize neighbor list */ off = nx*u4 + ny*u2 + nz; nn = 0; /*** Neighbor 0: (0,0,0) */ W0 = wxwy*wz; APPEND_NEIGHBOR(off, W0); /*** Neighbor 1: (0,0,1) */ APPEND_NEIGHBOR(off+1, wxwy-W0); /*** Neighbor 2: (0,1,0) */ W2 = wxwz-W0; APPEND_NEIGHBOR(off+u2, W2); /*** Neightbor 3: (0,1,1) */ W3 = wx-wxwy-W2; APPEND_NEIGHBOR(off+u3, W3); /*** Neighbor 4: (1,0,0) */ W4 = wywz-W0; APPEND_NEIGHBOR(off+u4, W4); /*** Neighbor 5: (1,0,1) */ APPEND_NEIGHBOR(off+u5, wy-wxwy-W4); /*** Neighbor 6: (1,1,0) */ APPEND_NEIGHBOR(off+u6, wz-wxwz-W4); /*** Neighbor 7: (1,1,1) */ APPEND_NEIGHBOR(off+u7, 1-W3-wy-wz+wywz); /* Update the joint histogram using the desired interpolation technique */ interpolate(i, H, clampJ, Jnn, W, nn, interp_params); } /* End of IF TRANSFORMS INSIDE */ /* Update source index */ PyArray_ITER_NEXT(iterI); } /* End of loop over voxels */ return; }
int joint_histogram(PyArrayObject* JH, unsigned int clampI, unsigned int clampJ, PyArrayIterObject* iterI, const PyArrayObject* imJ_padded, const PyArrayObject* Tvox, long interp) { const signed short* J=(signed short*)imJ_padded->data; size_t dimJX=imJ_padded->dimensions[0]-2; size_t dimJY=imJ_padded->dimensions[1]-2; size_t dimJZ=imJ_padded->dimensions[2]-2; signed short Jnn[8]; double W[8]; signed short *bufI, *bufJnn; double *bufW; signed short i, j; size_t off; size_t u2 = imJ_padded->dimensions[2]; size_t u3 = u2+1; size_t u4 = imJ_padded->dimensions[1]*u2; size_t u5 = u4+1; size_t u6 = u4+u2; size_t u7 = u6+1; double wx, wy, wz, wxwy, wxwz, wywz; double W0, W2, W3, W4; int nn, nx, ny, nz; double *H = (double*)PyArray_DATA(JH); double Tx, Ty, Tz; double *tvox = (double*)PyArray_DATA(Tvox); void (*interpolate)(unsigned int, double*, unsigned int, const signed short*, const double*, int, void*); void* interp_params = NULL; prng_state rng; /* Check assumptions regarding input arrays. If it fails, the function will return -1 without doing anything else. iterI : assumed to iterate over a signed short encoded, possibly non-contiguous array. imJ_padded : assumed C-contiguous (last index varies faster) & signed short encoded. H : assumed C-contiguous. Tvox : assumed C-contiguous: either a 3x4=12-sized array (or bigger) for an affine transformation or a 3xN array for a pre-computed transformation, with N equal to the size of the array corresponding to iterI (no checking done) */ if (PyArray_TYPE(iterI->ao) != NPY_SHORT) { fprintf(stderr, "Invalid type for the array iterator\n"); return -1; } if ( (!PyArray_ISCONTIGUOUS(imJ_padded)) || (!PyArray_ISCONTIGUOUS(JH)) || (!PyArray_ISCONTIGUOUS(Tvox)) ) { fprintf(stderr, "Some non-contiguous arrays\n"); return -1; } /* Reset the source image iterator */ PyArray_ITER_RESET(iterI); /* Set interpolation method */ if (interp==0) interpolate = &_pv_interpolation; else if (interp>0) interpolate = &_tri_interpolation; else { /* interp < 0 */ interpolate = &_rand_interpolation; prng_seed(-interp, &rng); interp_params = (void*)(&rng); } /* Re-initialize joint histogram */ memset((void*)H, 0, clampI*clampJ*sizeof(double)); /* Looop over source voxels */ while(iterI->index < iterI->size) { /* Source voxel intensity */ bufI = (signed short*)PyArray_ITER_DATA(iterI); i = bufI[0]; /* Compute the transformed grid coordinates of current voxel */ Tx = *tvox; tvox++; Ty = *tvox; tvox++; Tz = *tvox; tvox++; /* Test whether the current voxel is below the intensity threshold, or the transformed point is completly outside the reference grid */ if ((i>=0) && (Tx>-1) && (Tx<dimJX) && (Ty>-1) && (Ty<dimJY) && (Tz>-1) && (Tz<dimJZ)) { /* Nearest neighbor (floor coordinates in the padded image, hence +1). Notice that using the floor function doubles excetution time. FIXME: see if we can replace this with assembler instructions. */ nx = FLOOR(Tx) + 1; ny = FLOOR(Ty) + 1; nz = FLOOR(Tz) + 1; /* The convention for neighbor indexing is as follows: * * Floor slice Ceil slice * * 2----6 3----7 y * | | | | ^ * | | | | | * 0----4 1----5 ---> x */ /*** Trilinear interpolation weights. Note: wx = nnx + 1 - Tx, where nnx is the location in the NON-PADDED grid */ wx = nx - Tx; wy = ny - Ty; wz = nz - Tz; wxwy = wx*wy; wxwz = wx*wz; wywz = wy*wz; /*** Prepare buffers */ bufJnn = Jnn; bufW = W; /*** Initialize neighbor list */ off = nx*u4 + ny*u2 + nz; nn = 0; /*** Neighbor 0: (0,0,0) */ W0 = wxwy*wz; APPEND_NEIGHBOR(off, W0); /*** Neighbor 1: (0,0,1) */ APPEND_NEIGHBOR(off+1, wxwy-W0); /*** Neighbor 2: (0,1,0) */ W2 = wxwz-W0; APPEND_NEIGHBOR(off+u2, W2); /*** Neightbor 3: (0,1,1) */ W3 = wx-wxwy-W2; APPEND_NEIGHBOR(off+u3, W3); /*** Neighbor 4: (1,0,0) */ W4 = wywz-W0; APPEND_NEIGHBOR(off+u4, W4); /*** Neighbor 5: (1,0,1) */ APPEND_NEIGHBOR(off+u5, wy-wxwy-W4); /*** Neighbor 6: (1,1,0) */ APPEND_NEIGHBOR(off+u6, wz-wxwz-W4); /*** Neighbor 7: (1,1,1) */ APPEND_NEIGHBOR(off+u7, 1-W3-wy-wz+wywz); /* Update the joint histogram using the desired interpolation technique */ interpolate(i, H, clampJ, Jnn, W, nn, interp_params); } /* End of IF TRANSFORMS INSIDE */ /* Update source index */ PyArray_ITER_NEXT(iterI); } /* End of loop over voxels */ return 0; }