def get_variance(clctx, features, reductions, buf_in):
"""Using the *features* and *reductions* programs, find Var[*buf_in*]."""
gs, wgs = clctx.gs, clctx.wgs
buf = cl.Image(clctx.ctx, cl.mem_flags.READ_WRITE, clctx.ifmt, (gs, gs))
mean = reduction.run_reduction(clctx, reductions.reduction_sum, buf_in)
mean /= gs * gs
features.variance(clctx.queue, (gs, gs), (wgs, wgs), buf_in, buf)
variance = reduction.run_reduction(clctx, reductions.reduction_sum, buf)
variance /= gs * gs
variance -= mean ** 2
buf.release()
return variance
def get_edges(clctx, features, reductions, blurs, buf_in, summarise=True):
"""
Using the *features* and *reductions* programs, and *blurs* program with
sigma=2.0, find all edge pixels in *buf_in* and return the count.
"""
gs, wgs = clctx.gs, clctx.wgs
bufa = cl.Image(clctx.ctx, cl.mem_flags.READ_WRITE, clctx.ifmt, (gs, gs))
bufb = cl.Image(clctx.ctx, cl.mem_flags.READ_WRITE, clctx.ifmt, (gs, gs))
bufc = cl.Image(clctx.ctx, cl.mem_flags.READ_WRITE, clctx.ifmt, (gs, gs))
blurs.convolve_x(clctx.queue, (gs, gs), (wgs, wgs), buf_in, bufb)
blurs.convolve_y(clctx.queue, (gs, gs), (wgs, wgs), bufb, bufa)
blurs.convolve_x(clctx.queue, (gs, gs), (wgs, wgs), bufa, bufc)
blurs.convolve_y(clctx.queue, (gs, gs), (wgs, wgs), bufc, bufb)
features.subtract(clctx.queue, (gs, gs), (wgs, wgs), bufb, bufa, bufc)
features.edges(clctx.queue, (gs, gs), (wgs, wgs), bufc, bufa)
counts = reduction.run_reduction(clctx, reductions.reduction_sum, bufa)
if not summarise:
edges = np.empty((gs, gs, 4), np.float32)
cl.enqueue_copy(clctx.queue, edges, bufa,
origin=(0, 0), region=(gs, gs))
bufa.release()
bufb.release()
bufc.release()
if summarise:
return counts
else:
return edges
def get_entropy(clctx, features, reductions, buf_in):
"""Using the *features* and *reductions* programs, find H[*buf_in*]."""
gs, wgs = clctx.gs, clctx.wgs
buf = cl.Image(clctx.ctx, cl.mem_flags.READ_WRITE, clctx.ifmt, (gs, gs))
features.entropy(clctx.queue, (gs, gs), (wgs, wgs), buf_in, buf)
entropy = reduction.run_reduction(clctx, reductions.reduction_sum, buf)
entropy /= ((gs * gs) / (wgs * wgs))
buf.release()
return entropy
def get_blobs(clctx, features, reductions, blurs, buf_in, summarise=True):
"""
Using the *features* and *reductions* programs, and *blurs* program with
sigma=sqrt(2.0), find all the blobs in *buf_in* at five scales and return
the count at each scale.
"""
counts = np.empty((5, 4), np.float32)
gs, wgs = clctx.gs, clctx.wgs
mf = cl.mem_flags
bufa = cl.Image(clctx.ctx, cl.mem_flags.READ_WRITE, clctx.ifmt, (gs, gs))
cl.enqueue_copy(clctx.queue, bufa, buf_in, src_origin=(0, 0),
dest_origin=(0, 0), region=(gs, gs))
l_prev, l_curr, g_prev = None, None, bufa
if not summarise:
blobs = []
for i in range(7):
# Prepare next layer
d = gs // (2**i)
swg = wgs if wgs <= d else d
g_blurr = cl.Image(clctx.ctx, mf.READ_WRITE, clctx.ifmt, (d, d))
g_temp = cl.Image(clctx.ctx, mf.READ_WRITE, clctx.ifmt, (d, d))
l_next = cl.Image(clctx.ctx, mf.READ_WRITE, clctx.ifmt, (d, d))
blurs.convolve_x(clctx.queue, (d, d), (swg, swg), g_prev, g_temp)
blurs.convolve_y(clctx.queue, (d, d), (swg, swg), g_temp, g_blurr)
features.subtract(clctx.queue, (d, d), (swg, swg),
g_blurr, g_prev, l_next)
# Find blobs in current layer
if i >= 2:
d = gs // (2**(i-1))
swg = wgs if wgs <= d else d
out = cl.Image(clctx.ctx, mf.READ_WRITE, clctx.ifmt, (d, d))
features.blobs(clctx.queue, (d, d), (swg, swg),
l_prev, l_curr, l_next, out)
rs = reductions.reduction_sum
counts[i-2] = reduction.run_reduction(clctx, rs, out)
if not summarise:
blobs.append(np.empty((d, d, 4), np.float32))
cl.enqueue_copy(clctx.queue, blobs[-1], out,
origin=(0, 0), region=(d, d))
out.release()
# Resize current layer to start the next layer
d = gs // (2**(i+1))
swg = wgs if wgs <= d else d
g_resize = cl.Image(clctx.ctx, mf.READ_WRITE, clctx.ifmt, (d, d))
features.subsample(clctx.queue, (d, d), (swg, swg), g_blurr, g_resize)
# Cycle through buffers
g_blurr.release()
g_temp.release()
g_prev.release()
if l_prev:
l_prev.release()
g_prev = g_resize
l_prev = l_curr
l_curr = l_next
if summarise:
return counts
else:
return blobs
