mirror of https://github.com/silx-kit/pyFAI.git
still a bug in texture (image2d)
This commit is contained in:
Jerome Kieffer
parent
83f558befd
commit
6df7575bd2
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@ -131,6 +131,29 @@ lut_integrate( const __global float *weights,
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};//if bins
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};//end kernel
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/**
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* \brief Performs 1d azimuthal integration with full pixel splitting based on a LUT
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*
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* An image instensity value is spread across the bins according to the positions stored in the LUT.
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* The lut_index contains the positions of the pixel in the input array
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* Values of 0 in the mask are processed and values of 1 ignored as per PyFAI
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*
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* @param weights Float pointer to global memory storing the input image.
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* @param bins Unsigned int: number of output bins wanted (and pre-calculated)
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* @param lut_size Unsigned int: dimension of the look-up table
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* @param lut Pointer to a struct of [("idx",uint32),("coef": float)] where idx is the 1d-index of input pixels and coef is the weight of that pixel
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* @param do_dummy Bool/int: shall the dummy pixel be checked. Dummy pixel are pixels marked as bad and ignored
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* @param dummy Float: value for bad pixels
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* @param delta_dummy Float: precision for bad pixel value
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* @param do_dark Bool/int: shall dark-current correction be applied ?
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* @param dark Float pointer to global memory storing the dark image.
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* @param do_flat Bool/int: shall flat-field correction be applied ? (could contain polarization corrections)
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* @param flat Float pointer to global memory storing the flat image.
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* @param outData Float pointer to the output 1D array with the weighted histogram
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* @param outCount Float pointer to the output 1D array with the unweighted histogram
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* @param outMerged Float pointer to the output 1D array with the diffractogram
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*/
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__kernel void
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lut_integrate_single( const __global float *weights,
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const uint bins,
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@ -181,3 +204,82 @@ lut_integrate_single( const __global float *weights,
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outMerge[i] = (float) sum_data / sum_count;
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};//if bins
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};//end kernel
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/**
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* \brief Performs 1d azimuthal integration with full pixel splitting based on a LUT
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*
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* An image instensity value is spread across the bins according to the positions stored in the LUT.
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* The lut_index contains the positions of the pixel in the input array
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* Values of 0 in the mask are processed and values of 1 ignored as per PyFAI
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*
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* @param weights Float pointer to global memory storing the input image.
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* @param bins Unsigned int: number of output bins wanted (and pre-calculated)
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* @param lut_size Unsigned int: dimension of the look-up table
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* @param lut Pointer to a struct of [("idx",uint32),("coef": float)] where idx is the 1d-index of input pixels and coef is the weight of that pixel
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* @param do_dummy Bool/int: shall the dummy pixel be checked. Dummy pixel are pixels marked as bad and ignored
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* @param dummy Float: value for bad pixels
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* @param delta_dummy Float: precision for bad pixel value
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* @param do_dark Bool/int: shall dark-current correction be applied ?
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* @param dark Float pointer to global memory storing the dark image.
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* @param do_flat Bool/int: shall flat-field correction be applied ? (could contain polarization corrections)
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* @param flat Float pointer to global memory storing the flat image.
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* @param outData Float pointer to the output 1D array with the weighted histogram
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* @param outCount Float pointer to the output 1D array with the unweighted histogram
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* @param outMerged Float pointer to the output 1D array with the diffractogram
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*/
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__kernel void
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lut_integrate_image( __read_only image2d_t weights,
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const uint dimX,
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const uint dimY,
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const uint bins,
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const uint lut_size,
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const __global struct lut_point_t *lut,
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const int do_dummy,
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const float dummy,
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const float delta_dummy,
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const int do_dark,
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const __global float *dark,
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const int do_flat,
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const __global float *flat,
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__global float *outData,
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__global float *outCount,
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__global float *outMerge
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)
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{
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uint idx, k, j, i= get_global_id(0);
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bigfloat_t sum_data = 0.0;
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bigfloat_t sum_count = 0.0;
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const bigfloat_t epsilon = 1e-10;
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float coef, data;
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const sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
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if(i < bins)
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{
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for (j=0;j<lut_size;j++)
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{
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k = i*lut_size+j;
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idx = lut[k].idx;
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coef = lut[k].coef;
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if((idx == 0) && (coef <= 0.0))
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break;
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data = read_imagef(weights, sampler, (int2)(idx/dimX , idx%dimX)).s0;
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//data = weights[idx];
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if( (!do_dummy) || (delta_dummy && (fabs(data-dummy) > delta_dummy))|| (data!=dummy) )
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{
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if(do_dark)
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data -= dark[idx];
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if(do_flat)
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data /= flat[idx];
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sum_data += coef * data;
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sum_count += coef;
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};//test dummy
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};//for j
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outData[i] = (float) sum_data;
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outCount[i] = (float) sum_count;
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if (sum_count > epsilon)
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outMerge[i] = (float) sum_data / sum_count;
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};//if bins
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};//end kernel
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2616
src/splitBBoxLUT.c
2616
src/splitBBoxLUT.c
File diff suppressed because it is too large
Load Diff
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@ -20,9 +20,9 @@ edf = os.path.join(root, "LaB6_0020.edf")
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img = fabio.open(edf)
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ai = pyFAI.load(poni)
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ai.xrpd(img.data, bins)
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tth = ai._ttha.ravel().astype("float32")
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dtth = ai._dttha.ravel().astype("float32")
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data = img.data.ravel().astype("float32")
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tth = ai._ttha.ravel().astype(numpy.float32)
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dtth = ai._dttha.ravel().astype(numpy.float32)
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data = img.data.ravel().astype(numpy.float32)
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import splitBBox
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t0 = time.time()
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@ -80,19 +80,31 @@ pylab.plot(ra, rb, label="Original")
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import pyopencl
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mf = pyopencl.mem_flags
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ct = pyopencl.channel_type
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co = pyopencl.channel_order
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ctx = pyopencl.create_some_context()
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q = pyopencl.CommandQueue(ctx)
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program = pyopencl.Program(ctx, open("../openCL/ocl_azim_LUT.cl").read()).build()
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t3 = time.time()
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weights_buf = pyopencl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=data)
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#weights_img = pyopencl.image_from_array(ctx, ary=img.data.astype(numpy.float32), mode="r", norm_int=False, num_channels=1)
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#print co.INTENSITY, ct.FLOAT,
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#imf = pyopencl.ImageFormat(numpy.uint32(co.INTENSITY), numpy.uint32(ct.FLOAT))
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#weights_img = pyopencl.Image(ctx, flags=mf.READ_ONLY | mf.COPY_HOST_PTR,
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# format=imf,
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# hostbuf=data,
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# pitches=(img.data.shape[-1],))
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#image_from_array(ctx, ary=img.data.astype(numpy.float32), mode="r", norm_int=False, num_channels=1)
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#lut_idx_buf = pyopencl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=integ.lut_idx.astype(numpy.uint32))
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#lut_coef_buf = pyopencl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=integ.lut_coef)
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lut_buf = pyopencl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=integ.lut)
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None_buf = pyopencl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=numpy.zeros(1, dtype=numpy.float32))
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outData_buf = pyopencl.Buffer(ctx, mf.WRITE_ONLY, numpy.dtype("float32").itemsize * bins)
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outCount_buf = pyopencl.Buffer(ctx, mf.WRITE_ONLY, numpy.dtype("float32").itemsize * bins)
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outMerge_buf = pyopencl.Buffer(ctx, mf.WRITE_ONLY, numpy.dtype("float32").itemsize * bins)
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args = (weights_buf,
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outData_buf = pyopencl.Buffer(ctx, mf.WRITE_ONLY, numpy.dtype(numpy.float32).itemsize * bins)
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outCount_buf = pyopencl.Buffer(ctx, mf.WRITE_ONLY, numpy.dtype(numpy.float32).itemsize * bins)
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outMerge_buf = pyopencl.Buffer(ctx, mf.WRITE_ONLY, numpy.dtype(numpy.float32).itemsize * bins)
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args = (#weights_img, numpy.uint32(img.dim1), numpy.uint32(img.dim0),
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weights_buf,
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numpy.uint32(2048),
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numpy.uint32(integ.lut_size),
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# lut_idx_buf,
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