label.py

---

Wrapper module to classify & label channel confluences using OpenCL.

Requires PyOpenCL.

Imports streamlines module pocl.py. Imports functions from streamlines module useful.py.

---

streamlines.label.label_confluences(cl_state, info, data, verbose)

Label channel confluences.

Parameters:

• cl_state (obj) –
• info (obj) –
• data (obj) –
• verbose (bool) –

Code

"""
---------------------------------------------------------------------

Wrapper module to classify & label channel confluences using `OpenCL`_.

Requires `PyOpenCL`_.

Imports streamlines module :doc:`pocl`.
Imports functions from streamlines module :doc:`useful`.


---------------------------------------------------------------------

.. _OpenCL: https://www.khronos.org/opencl
.. _PyOpenCL: https://documen.tician.de/pyopencl/index.html

"""

import pyopencl as cl
import pyopencl.array
import numpy as np
import os
os.environ['PYTHONUNBUFFERED']='True'
import warnings

from streamlines import pocl
from streamlines.useful import vprint, pick_seeds, check_sizes

__all__ = ['label_confluences']

pdebug = print

def label_confluences( cl_state, info, data, verbose ):
    """
    Label channel confluences.
    
    Args:
        cl_state (obj):
        info (obj):
        data (obj):
        verbose (bool):
        
    """
    vprint(verbose,'Labeling confluences...')
    
    # Prepare CL essentials
    cl_state.kernel_source \
        = pocl.read_kernel_source(cl_state.src_path,['essentials.cl','updatetraj.cl',
                                                     'label.cl'])
            
    # Check all thin channel pixels
    pad              = info.pad_width
    is_thinchannel   = info.is_thinchannel
    seed_point_array = pick_seeds(mask=data.mask_array, map=data.mapping_array, 
                                  flag=is_thinchannel, pad=pad)
        
    # Prepare memory, buffers 
    array_dict = { 'seed_point': {'array': seed_point_array,      'rwf': 'RO'},
                   'mask':       {'array': data.mask_array,       'rwf': 'RO'}, 
                   'uv':         {'array': data.uv_array,         'rwf': 'RO'}, 
                   'dn_slt':     {'array': data.slt_array[:,:,0], 'rwf': 'RO'}, 
                   'mapping':    {'array': data.mapping_array,    'rwf': 'RW'}, 
                   'count':      {'array': data.count_array,      'rwf': 'RW'}, 
                   'link':       {'array': data.link_array,       'rwf': 'RW'} }
    info.n_seed_points = seed_point_array.shape[0]
    if ( info.n_seed_points==0 ):
        # Flag an error - empty seeds list
        return False
    check_sizes(info.nx_padded,info.ny_padded, array_dict)
    
    # Do integrations on the GPU
    cl_state.kernel_fn = 'label_confluences'
    pocl.gpu_compute(cl_state, info, array_dict, info.verbose)
    
    # Done
    vprint(verbose,'...done')  
    # Flag all went well
    return True