class QuickBrush(Brush):
lWorksize = (16, 16)
def __init__(self, context, devices, d_img, d_labels):
Brush.__init__(self, context, devices, d_labels)
self.context = context
self.queue = cl.CommandQueue(context,
properties=cl.command_queue_properties.PROFILING_ENABLE)
nComponentsFg = 4
nComponentsBg = 4
self.nDim = 3
self.dim = d_img.dim
filename = os.path.join(os.path.dirname(__file__), 'quick.cl')
program = createProgram(context, context.devices, [], filename)
# self.kernSampleBg = cl.Kernel(program, 'sampleBg')
self.kern_get_samples = cl.Kernel(program, 'get_samples')
self.lWorksize = (16, 16)
self.gWorksize = roundUp(self.dim, self.lWorksize)
nSamples = 4 * (self.gWorksize[0] / self.lWorksize[0]) * (
self.gWorksize[1] / self.lWorksize[1])
# self.gmmFg_cpu = mixture.GMM(4)
self.gmmFg = GMM(context, 65, nComponentsFg, 10240)
self.gmmBg = GMM(context, 65, nComponentsBg, nSamples)
self.hScore = np.empty(self.dim, np.float32)
self.hSampleFg = np.empty((10240, ), np.uint32)
self.hSampleBg = np.empty((12000, ), np.uint32)
self.hA = np.empty((max(nComponentsFg, nComponentsBg), 8), np.float32)
self.d_img = d_img
cm = cl.mem_flags
self.dSampleFg = cl.Buffer(context, cm.READ_WRITE, size=4 * 10240)
self.dSampleBg = cl.Buffer(context, cm.READ_WRITE, size=4 * 12000)
self.dA = cl.Buffer(context, cm.READ_ONLY | cm.COPY_HOST_PTR, hostbuf=self.hA)
self.dScoreFg = Buffer2D(context, cm.READ_WRITE, self.dim, np.float32)
self.dScoreBg = Buffer2D(context, cm.READ_WRITE, self.dim, np.float32)
#self.points = Set()
self.capPoints = 200 * 200 * 300 #brush radius 200, stroke length 300
self.points = np.empty((self.capPoints), np.uint32)
# self.colorize = Colorize.Colorize(clContext, clContext.devices)
# self.hTriFlat = self.hTri.reshape(-1)
# self.probBg(1200)
self.h_img = np.empty(self.dim, np.uint32)
self.h_img = self.h_img.ravel()
cl.enqueue_copy(self.queue, self.h_img, self.d_img, origin=(0, 0), region=self.dim).wait()
self.samples_bg_idx = np.random.randint(0, self.dim[0] * self.dim[1], 12000)
self.hSampleBg = self.h_img[self.samples_bg_idx]
cl.enqueue_copy(self.queue, self.dSampleBg, self.hSampleBg).wait()
w,m,c = self.gmmBg.fit(self.dSampleBg, 300, retParams=True)
print w
print m
print c
self.gmmBg.score(self.d_img, self.dScoreBg)
pass
def draw(self, p0, p1):
Brush.draw(self, p0, p1)
#self.probFg(x1-20, x1+20, y1-20, y1+20)
#return
"""color = self.colorTri[self.type]
#self.argsScore[5] = np.int32(self.nComponentsFg)
#seed = []
hasSeeds = False
redoBg = False
minX = sys.maxint
maxX = -sys.maxint
minY = sys.maxint
maxY = -sys.maxint
for point in self.points[0:nPoints]:
#if self.hTriFlat[point] != color:
self.hTriFlat[point] = color
#seed += point
hasSeeds = True
minX = min(minX, point%self.width)
maxX = max(maxX, point%self.width)
minY = min(minY, point/self.width)
maxY = max(maxY, point/self.width)
#if (point[1]*self.width + point[0]) in self.randIdx:
# redoBg = True
#if redoBg:
# self.probBg(0)
#if len(seed) == 0:
if not hasSeeds:
return
minX = max(0, minX-DILATE)
maxX = min(self.width-1, maxX + DILATE)
minY = max(0, minY-DILATE)
maxY = min(self.height-1, maxY + DILATE)
"""
args = [
np.int32(self.n_points),
self.d_points,
cl.Sampler(self.context, False, cl.addressing_mode.NONE,
cl.filter_mode.NEAREST),
self.d_img,
self.dSampleFg
]
gWorksize = roundUp((self.n_points, ), (256, ))
self.kern_get_samples(self.queue, gWorksize, (256,), *args).wait()
cl.enqueue_copy(self.queue, self.hSampleFg, self.dSampleFg)
# print self.hSampleFg.view(np.uint8).reshape(10240, 4)[0:self.n_points, :]
# print self.n_points
self.gmmFg.fit(self.dSampleFg, self.n_points)
# print w
# print m
# print c
self.gmmFg.score(self.d_img, self.dScoreFg)
# self.argsSampleBg = [
# self.d_labels,
# np.int32(self.label),
# cl.Sampler(self.context, False, cl.addressing_mode.NONE,
# cl.filter_mode.NEAREST),
# self.d_img,
# self.dSampleFg
# ]
#
# gWorksize = roundUp(self.dim, (16, 16))
#
# self.kernSampleBg(self.queue, gWorksize, (16, 16),
# *(self.argsSampleBg)).wait()
# cl.enqueue_copy(self.queue, self.hSampleBg, self.dSampleBg).wait()
pass
def probFg(self, d_samples, n_points):
# if True:
# tri = self.hTri[minY:maxY, minX:maxX]
# b = (tri == self.colorTri[self.type])
#
# samplesFg = self.hSrc[minY:maxY, minX:maxX]
# samplesFg = samplesFg[b]
# else:
# DILATE = 5
# samplesFg = self.hSrc[minY:maxY, minX:maxX].ravel()
#gpu = False
#self.prob(self.gmmFG, samplesFg, self.dScoreFg, gpu)
#self.gmmFg_cpu.fit(samplesFg)
#print 'cpu', self.gmmFg_cpu.weights_
#a = calcA_cpu(self.gmmFg_cpu.weights_.astype(np.float32), self.gmmFg_cpu.means_.astype(np.float32), self.gmmFg_cpu.covars_.astype(np.float32))
#cl.enqueue_copy(self.queue, self.gmmFg.dA, a).wait()
#weights, means, covars = self.gmmFg.fit(samplesFg, retParams=True)
#a = calcA_cpu(weights, means[:, 0:3], covars[:, 0:3])
#cl.enqueue_copy(self.queue, self.gmmFg.dA, a).wait()
w,m,c = self.gmmFg.fit(d_samples, n_points, retParams=True)
print w
print m
print c
#print 'gpu', weights
self.gmmFg.score(self.d_img, self.dScoreFg)
#score returns float64, not float32 -> convert with astype
#self.hScore = -self.gmmFG.score(self.rgb.reshape(-1, 3)).astype(np.float32)
"""
def drawCircle(self, xc, yc, points=None):
r = self.radius
for y in xrange(-r, r):
for x in xrange(-r, r):
if points != None:
points.add((xc+x, yc+y))
"""
def probBg(self, nSamples):
#self.kernSampleBg(self.queue, self.gWorksize, self.lWorksize, *(self.argsSampleBg)).wait()
#cl.enqueue_copy(self.queue, self.hSampleBg, self.dSampleBg).wait()
self.bgIdx = np.where(self.hTri.ravel() != self.colorTri[self.type])[0]
self.randIdx = self.bgIdx[np.random.randint(0, len(self.bgIdx), 2000)]
self.bgIdx = np.setdiff1d(self.bgIdx, self.randIdx)
self.hSampleBg[0:len(self.randIdx)] = self.hSrc.view(np.uint32).ravel()[
self.randIdx]
cl.enqueue_copy(self.queue, self.dSampleBg, self.hSampleBg).wait()
#print self.gmmBg.fit(self.hSrc.view(np.uint32).ravel()[self.randIdx], retParams=True)
self.gmmBg.fit(self.hSrc.view(np.uint32).ravel()[self.randIdx])
#self.gmmBg.fit(self.dSampleBg, nSamples=len(self.randIdx))
self.gmmBg.score(self.dSrc, self.dScoreBg)
