def test_cuda_array(pic):
pic_h = cp.push_host(pic)
# downsample pic_h
tmp = (np.array([pic_h.h,pic_h.w])/2).astype('uint32')
down = cp.dev_matrix_rmf(tmp[0],tmp[1])
ca = cp.dev_cuda_array_f(pic_h.h,pic_h.w)
ca.assign(pic_h)
cp.gaussian_pyramid_downsample(down,ca)
# upsample downsampled
ca = cp.dev_cuda_array_f(down.h,down.w)
ca.assign(down)
up = cp.dev_matrix_rmf(pic_h.h, pic_h.w)
cp.gaussian_pyramid_upsample(up,ca)
print cp.pull(up)
plt.matshow(cp.pull(down))
plt.title("Downsampled")
plt.matshow(pic)
plt.title("Original")
plt.matshow(cp.pull(up))
plt.title("Upsampled")
plt.show()
ca.dealloc()
def test_cuda_array(pic):
pic_h = cp.push_host(pic)
# downsample pic_h
tmp = (np.array([pic_h.h, pic_h.w]) / 2).astype('uint32')
down = cp.dev_matrix_rmf(tmp[0], tmp[1])
ca = cp.dev_cuda_array_f(pic_h.h, pic_h.w)
ca.assign(pic_h)
cp.gaussian_pyramid_downsample(down, ca)
# upsample downsampled
ca = cp.dev_cuda_array_f(down.h, down.w)
ca.assign(down)
up = cp.dev_matrix_rmf(pic_h.h, pic_h.w)
cp.gaussian_pyramid_upsample(up, ca)
print cp.pull(up)
plt.matshow(cp.pull(down))
plt.title("Downsampled")
plt.matshow(pic)
plt.title("Original")
plt.matshow(cp.pull(up))
plt.title("Upsampled")
plt.show()
ca.dealloc()
def test_pixel_classes():
w, h = 512, 512
input_channels, pyramid_channels = 4, 3
pic = Image.open("tests/data/lena.bmp").resize((w, h)).convert("RGBA")
pic = np.asarray(pic).reshape(h, w * 4)
pic_d = cp.push(pic)
pyr = cp.dev_image_pyramid_f(pic_d.h / 2, pic_d.w / input_channels / 2, 4,
pyramid_channels)
pyr.build(pic_d, 4)
plt.matshow(pic[0:h:2, 0:4 * w:8])
#plt.matshow(cp.pull(pyr.get(1,0)))
#plt.title("Channel0")
#plt.matshow(cp.pull(pyr.get(1,1)))
#plt.title("Channel1")
#plt.matshow(cp.pull(pyr.get(1,2)))
#plt.title("Channel2")
#plt.matshow(cp.pull(pyr.get_all_channels(1)))
#plt.title("allchannels level 1")
#plt.show()
# create source image from higher level of pyramid
pic1 = pyr.get_all_channels(0)
for i in xrange(10):
smooth(pic1)
plt.matshow(cp.pull(pic1)[:h / 2, :w])
ca = cp.dev_cuda_array_f(pic1.h, pic1.w, 1)
ca.assign(pic1)
# create destination matrix
pic0 = pyr.get(0)
dst = cp.dev_matrix_rmuc(pic0.h, pic0.w * 4) # uchar4
# generate pixel classes and visualize
cp.get_pixel_classes(dst, ca, 1)
tmp = cp.pull(dst)
tmp = Image.frombuffer("CMYK", (pic0.w, pic0.h),
cp.pull(dst).flatten(), "raw", "CMYK", 0, 1).resize(
(2 * 512, 2 * 512), Image.NEAREST)
tmp.show()
print cp.pull(dst)
plt.show()
def test_pixel_classes():
w, h = 512,512
input_channels, pyramid_channels = 4,3
pic = Image.open("tests/data/lena.bmp").resize((w,h)).convert("RGBA")
pic = np.asarray(pic).reshape(h,w*4)
pic_d = cp.push(pic)
pyr = cp.dev_image_pyramid_f(pic_d.h/2,pic_d.w/input_channels/2,4,pyramid_channels)
pyr.build(pic_d,4)
plt.matshow(pic[0:h:2,0:4*w:8])
#plt.matshow(cp.pull(pyr.get(1,0)))
#plt.title("Channel0")
#plt.matshow(cp.pull(pyr.get(1,1)))
#plt.title("Channel1")
#plt.matshow(cp.pull(pyr.get(1,2)))
#plt.title("Channel2")
#plt.matshow(cp.pull(pyr.get_all_channels(1)))
#plt.title("allchannels level 1")
#plt.show()
# create source image from higher level of pyramid
pic1 = pyr.get_all_channels(0)
for i in xrange(10): smooth(pic1)
plt.matshow(cp.pull(pic1)[:h/2,:w])
ca = cp.dev_cuda_array_f(pic1.h,pic1.w,1)
ca.assign(pic1)
# create destination matrix
pic0 = pyr.get(0)
dst = cp.dev_matrix_rmuc(pic0.h,pic0.w*4) # uchar4
# generate pixel classes and visualize
cp.get_pixel_classes(dst,ca,1)
tmp = cp.pull(dst)
tmp = Image.frombuffer("CMYK", (pic0.w,pic0.h), cp.pull(dst).flatten(), "raw", "CMYK", 0, 1 ).resize((2*512,2*512), Image.NEAREST)
tmp.show()
print cp.pull(dst)
plt.show()
def smooth(pic):
ca = cp.dev_cuda_array_f(pic.h,pic.w,1)
ca.assign(pic)
cp.gaussian(pic,ca)
ca.dealloc()
def smooth(pic):
ca = cp.dev_cuda_array_f(pic.h, pic.w, 1)
ca.assign(pic)
cp.gaussian(pic, ca)
ca.dealloc()
