def _on_expose(self, drawing, event):
# just mark lock as callbacks are already inside gtk thread
with self._gooey.lock_marker:
# get a new cairo context from the drawing area
context = self._drawing_area.window.cairo_create()
# set a clip region for the expose event
context.rectangle(event.area.x, event.area.y,
event.area.width, event.area.height)
context.clip()
# zoom and scroll
context.scale(self.zoom, self.zoom)
context.translate(*self.scroll)
# call draw handler to draw contents of canvas to cairo context
self.handle_draw(Brush(context, self.size))
return False
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
import time
import pyopencl as cl
from clutil import Buffer2D
import numpy as np
platforms = cl.get_platforms()
devices = platforms[0].get_devices()
devices = [devices[1]]
context = cl.Context(devices)
cm = cl.mem_flags
dim = (800, 608)
dStrokes = Buffer2D(context, cm.READ_WRITE, dim, dtype=np.uint8)
from Brush import Brush
brush = Brush(context, devices, dStrokes)
iterations = 10
t0 = time.clock()
for i in range(iterations):
brush.draw((0, 0), (100, 100))
dt = (time.clock() - t0) / iterations * 1000
print "%.2f milliseconds per iteration (mean)" % dt
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
hImg = padArray2D(np.array(img).view(np.uint32).squeeze(), shape, 'edge')
dImgGC = cl.Image(context,
cl.mem_flags.READ_ONLY,
cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.UNSIGNED_INT8),
dim
)
cl.enqueue_copy(queue, dImgGC, hImg, origin=(0,0), region=dim)
dImg = Buffer2D(context, cm.READ_WRITE | cm.COPY_HOST_PTR, hostbuf=hImg)
dStrokes = Buffer2D(context, cm.READ_WRITE, dim, dtype=np.uint8)
growCut = GrowCut(context, devices, dImgGC, GrowCut.NEIGHBOURHOOD.VON_NEUMANN, GrowCut.WEIGHT_POW2)
brush = Brush(context, devices, growCut.dLabelsIn)
brush.setLabel(TRI_BG)
iteration = 0
refresh = 100
def next():
global iteration
growCut.evolve(1)
sm.processTrimap(dStrokes, growCut.dLabelsOut, growCut.dStrengthIn, 0.95)
if growCut.isComplete:
window.updateCanvas()
timer.stop()
