def __pad(image, px, py):
if px == 0 and py == 0:
return image
d = image.dims()
pad_x = d[0]+2*px
pad_y = d[1]+2*py
return af.moddims(af.unwrap(image, pad_x, pad_y, pad_x, pad_y, px, py),
pad_x, pad_y, d[2])
def pool(image, w, s):
#TODO: Remove assumption of image.dims()[3] == 1
d0, d1, d2 = image.dims()
#TODO: remove reorder and moddims call
image = af.moddims(af.reorder(image, 2, 0, 1), 1, d2, d0, d1)
d0, d1, d2, d3 = image.dims()
h_o = (d2 - w)/s + 1
w_o = (d3 - w)/s + 1
tiles = af.unwrap(af.reorder(image, 2, 3, 1, 0), w, w, s, s)
return af.reorder(af.reorder(af.moddims(af.max(tiles, 0), d0, h_o, w_o,
d1), 0, 3, 1, 2), 2, 3, 1, 0)
def conv(weights, biases, image, wx, wy, sx = 1, sy = 1, px = 0, py = 0, groups = 1):
image = __pad(image, px, py)
batch = util.num_input(image)
n_filters = util.num_filters(weights)
n_channel = util.num_channels(weights)
w_i = image.dims()[0]
h_i = image.dims()[1]
w_o = (w_i - wx) / sx + 1
h_o = (h_i - wy) / sy + 1
tiles = af.unwrap(image, wx, wy, sx, sy)
weights = af.moddims(weights, wx*wy, n_channel, n_filters)
out = af.constant(0, batch, n_filters, w_o, h_o)
if groups > 1:
out = af.constant(0, w_o, h_o, n_filters, batch)
split_in = util.num_channels(image) / groups
s_i = split_in
split_out = n_filters / groups
s_o = split_out
for i in xrange(groups):
weights_slice = af.moddims(weights[:,:,i*s_o:(i+1)*s_o],
wx, wy, n_channel, split_out)
biases_slice = biases[i*s_o:(i+1)*s_o]
image_slice = image[:,:,i*s_i:(i+1)*s_i]
out[:,:,i*s_o:(i+1)*s_o] = conv(weights_slice,
biases_slice,
image_slice,
wx, wy, sx, sy, 0, 0, 1)
# out[:,i*s_o:(i+1)*s_o] = conv(weights_slice,
# biases_slice,
# image_slice,
# wx, wy, sx, sy, 0, 0, 1)
return out
#TODO: Speedup this section
for f in xrange(n_filters):
for d in xrange(n_channel):
tile_d = af.reorder(tiles[:,:,d],1,0)
weight_d = weights[:,d,f]
out[0,f] += af.moddims(af.matmul(tile_d, weight_d), 1,1, w_o, h_o)
out[0,f] += biases[f].to_list()[0]
return af.reorder(out, 2, 3, 1, 0)
def lrn(image, size, alpha, beta):
#TODO: Remove assumption of image.dims()[3] == 1
d0, d1, d2 = image.dims()
#TODO: remove reorder and moddims call
image = af.moddims(af.reorder(image, 2, 0, 1), 1, d2, d0, d1)
d0, d1, d2, d3 = image.dims()
pad = (size - 1) / 2
padded_size = d1+2*pad
out = af.constant(0,d1, d2, d3)
image = af.moddims(image, d1, d2, d3)
padded = af.moddims(af.unwrap(image, padded_size, d2, padded_size, d2, pad,0), padded_size, d2, d2)
padded = padded*padded
for i in xrange(size):
out += padded[i:i+d1]
out = 1 + (float(alpha)/size) * out
out = af.pow(out,beta)
#TODO: remove reorder call
return af.reorder(af.moddims(image/out, d0,d1,d2,d3), 2, 3, 1, 0)
def simple_image(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5, 5, 3)
dx, dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3, 2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx, dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
display_func(af.gaussian_kernel(3, 3))
display_func(af.gaussian_kernel(3, 3, 1, 1))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
a = af.randu(6, 6)
b = af.unwrap(a, 2, 2, 2, 2)
c = af.wrap(b, 6, 6, 2, 2, 2, 2)
display_func(a)
display_func(b)
display_func(c)
display_func(af.sat(a))
a = af.randu(10, 10, 3)
display_func(af.rgb2ycbcr(a))
display_func(af.ycbcr2rgb(a))
def simple_image(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5,5,3)
dx,dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3,2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx,dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
display_func(af.gaussian_kernel(3, 3))
display_func(af.gaussian_kernel(3, 3, 1, 1))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
a = af.randu(6,6)
b = af.unwrap(a, 2, 2, 2, 2)
c = af.wrap(b, 6, 6, 2, 2, 2, 2)
display_func(a)
display_func(b)
display_func(c)
display_func(af.sat(a))
a = af.randu(10,10,3)
display_func(af.rgb2ycbcr(a))
display_func(af.ycbcr2rgb(a))
a = af.randu(10, 10)
b = af.canny(a, low_threshold = 0.2, high_threshold = 0.8)
display_func(af.anisotropic_diffusion(a, 0.125, 1.0, 64, af.FLUX.QUADRATIC, af.DIFFUSION.GRAD))
def simple_image(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5, 5, 3)
dx, dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3, 2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx, dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
a = af.randu(6, 6)
b = af.unwrap(a, 2, 2, 2, 2)
c = af.wrap(b, 6, 6, 2, 2, 2, 2)
display_func(a)
display_func(b)
display_func(c)
display_func(af.sat(a))
a = af.randu(10, 10, 3)
display_func(af.rgb2ycbcr(a))
display_func(af.ycbcr2rgb(a))
def simple_image(verbose=False):
display_func = _util.display_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5, 5, 3)
dx, dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3, 2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
display_func(
af.confidenceCC(af.randu(10,
10), (af.randu(2) * 9).as_type(af.Dtype.u32),
(af.randu(2) * 9).as_type(af.Dtype.u32), 3, 3, 10,
0.1))
dx, dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
display_func(af.gaussian_kernel(3, 3))
display_func(af.gaussian_kernel(3, 3, 1, 1))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
a = af.randu(6, 6)
b = af.unwrap(a, 2, 2, 2, 2)
c = af.wrap(b, 6, 6, 2, 2, 2, 2)
display_func(a)
display_func(b)
display_func(c)
display_func(af.sat(a))
a = af.randu(10, 10, 3)
display_func(af.rgb2ycbcr(a))
display_func(af.ycbcr2rgb(a))
a = af.randu(10, 10)
b = af.canny(a, low_threshold=0.2, high_threshold=0.8)
display_func(
af.anisotropic_diffusion(a, 0.125, 1.0, 64, af.FLUX.QUADRATIC,
af.DIFFUSION.GRAD))
a = af.randu(10, 10)
psf = af.gaussian_kernel(3, 3)
cimg = af.convolve(a, psf)
display_func(
af.iterativeDeconv(cimg, psf, 100, 0.5, af.ITERATIVE_DECONV.LANDWEBER))
display_func(
af.iterativeDeconv(cimg, psf, 100, 0.5,
af.ITERATIVE_DECONV.RICHARDSONLUCY))
display_func(af.inverseDeconv(cimg, psf, 1.0, af.INVERSE_DECONV.TIKHONOV))