299 lines
8.5 KiB
Lua
299 lines
8.5 KiB
Lua
local gm = {}
|
|
gm.Image = require 'graphicsmagick.Image'
|
|
require 'dok'
|
|
local image = require 'image'
|
|
local iproc = {}
|
|
local clip_eps8 = (1.0 / 255.0) * 0.5 - (1.0e-7 * (1.0 / 255.0) * 0.5)
|
|
|
|
function iproc.crop_mod4(src)
|
|
local w = src:size(3) % 4
|
|
local h = src:size(2) % 4
|
|
return iproc.crop(src, 0, 0, src:size(3) - w, src:size(2) - h)
|
|
end
|
|
function iproc.crop(src, w1, h1, w2, h2)
|
|
local dest
|
|
if src:dim() == 3 then
|
|
dest = src[{{}, { h1 + 1, h2 }, { w1 + 1, w2 }}]:clone()
|
|
else -- dim == 2
|
|
dest = src[{{ h1 + 1, h2 }, { w1 + 1, w2 }}]:clone()
|
|
end
|
|
return dest
|
|
end
|
|
function iproc.crop_nocopy(src, w1, h1, w2, h2)
|
|
local dest
|
|
if src:dim() == 3 then
|
|
dest = src[{{}, { h1 + 1, h2 }, { w1 + 1, w2 }}]
|
|
else -- dim == 2
|
|
dest = src[{{ h1 + 1, h2 }, { w1 + 1, w2 }}]
|
|
end
|
|
return dest
|
|
end
|
|
function iproc.byte2float(src)
|
|
local conversion = false
|
|
local dest = src
|
|
if src:type() == "torch.ByteTensor" then
|
|
conversion = true
|
|
dest = src:float():div(255.0)
|
|
end
|
|
return dest, conversion
|
|
end
|
|
function iproc.float2byte(src)
|
|
local conversion = false
|
|
local dest = src
|
|
if src:type() == "torch.FloatTensor" then
|
|
conversion = true
|
|
dest = (src + clip_eps8):mul(255.0)
|
|
dest:clamp(0, 255.0)
|
|
dest = dest:byte()
|
|
end
|
|
return dest, conversion
|
|
end
|
|
function iproc.scale(src, width, height, filter, blur)
|
|
local conversion, color
|
|
src, conversion = iproc.byte2float(src)
|
|
filter = filter or "Box"
|
|
if src:size(1) == 3 then
|
|
color = "RGB"
|
|
else
|
|
color = "I"
|
|
end
|
|
local im = gm.Image(src, color, "DHW")
|
|
im:size(math.ceil(width), math.ceil(height), filter, blur)
|
|
local dest = im:toTensor("float", color, "DHW")
|
|
if conversion then
|
|
dest = iproc.float2byte(dest)
|
|
end
|
|
return dest
|
|
end
|
|
function iproc.scale_with_gamma22(src, width, height, filter, blur)
|
|
local conversion
|
|
src, conversion = iproc.byte2float(src)
|
|
filter = filter or "Box"
|
|
local im = gm.Image(src, "RGB", "DHW")
|
|
im:gammaCorrection(1.0 / 2.2):
|
|
size(math.ceil(width), math.ceil(height), filter, blur):
|
|
gammaCorrection(2.2)
|
|
local dest = im:toTensor("float", "RGB", "DHW"):clamp(0.0, 1.0)
|
|
if conversion then
|
|
dest = iproc.float2byte(dest)
|
|
end
|
|
return dest
|
|
end
|
|
function iproc.padding(img, w1, w2, h1, h2)
|
|
local conversion
|
|
img, conversion = iproc.byte2float(img)
|
|
image = image or require 'image'
|
|
local dst_height = img:size(2) + h1 + h2
|
|
local dst_width = img:size(3) + w1 + w2
|
|
local flow = torch.Tensor(2, dst_height, dst_width)
|
|
flow[1] = torch.ger(torch.linspace(0, dst_height -1, dst_height), torch.ones(dst_width))
|
|
flow[2] = torch.ger(torch.ones(dst_height), torch.linspace(0, dst_width - 1, dst_width))
|
|
flow[1]:add(-h1)
|
|
flow[2]:add(-w1)
|
|
local dest = image.warp(img, flow, "simple", false, "clamp")
|
|
if conversion then
|
|
dest = iproc.float2byte(dest)
|
|
end
|
|
return dest
|
|
end
|
|
function iproc.zero_padding(img, w1, w2, h1, h2)
|
|
local conversion
|
|
img, conversion = iproc.byte2float(img)
|
|
image = image or require 'image'
|
|
local dst_height = img:size(2) + h1 + h2
|
|
local dst_width = img:size(3) + w1 + w2
|
|
local flow = torch.Tensor(2, dst_height, dst_width)
|
|
flow[1] = torch.ger(torch.linspace(0, dst_height -1, dst_height), torch.ones(dst_width))
|
|
flow[2] = torch.ger(torch.ones(dst_height), torch.linspace(0, dst_width - 1, dst_width))
|
|
flow[1]:add(-h1)
|
|
flow[2]:add(-w1)
|
|
local dest = image.warp(img, flow, "simple", false, "pad", 0)
|
|
if conversion then
|
|
dest = iproc.float2byte(dest)
|
|
end
|
|
return dest
|
|
end
|
|
function iproc.white_noise(src, std, rgb_weights, gamma)
|
|
gamma = gamma or 0.454545
|
|
local conversion
|
|
src, conversion = iproc.byte2float(src)
|
|
std = std or 0.01
|
|
|
|
local noise = torch.Tensor():resizeAs(src):normal(0, std)
|
|
if rgb_weights then
|
|
noise[1]:mul(rgb_weights[1])
|
|
noise[2]:mul(rgb_weights[2])
|
|
noise[3]:mul(rgb_weights[3])
|
|
end
|
|
|
|
local dest
|
|
if gamma ~= 0 then
|
|
dest = src:clone():pow(gamma):add(noise)
|
|
dest:clamp(0.0, 1.0)
|
|
dest:pow(1.0 / gamma)
|
|
else
|
|
dest = src + noise
|
|
end
|
|
if conversion then
|
|
dest = iproc.float2byte(dest)
|
|
end
|
|
return dest
|
|
end
|
|
function iproc.hflip(src)
|
|
local t
|
|
if src:type() == "torch.ByteTensor" then
|
|
t = "byte"
|
|
else
|
|
t = "float"
|
|
end
|
|
if src:size(1) == 3 then
|
|
color = "RGB"
|
|
else
|
|
color = "I"
|
|
end
|
|
local im = gm.Image(src, color, "DHW")
|
|
return im:flop():toTensor(t, color, "DHW")
|
|
end
|
|
function iproc.vflip(src)
|
|
local t
|
|
if src:type() == "torch.ByteTensor" then
|
|
t = "byte"
|
|
else
|
|
t = "float"
|
|
end
|
|
if src:size(1) == 3 then
|
|
color = "RGB"
|
|
else
|
|
color = "I"
|
|
end
|
|
local im = gm.Image(src, color, "DHW")
|
|
return im:flip():toTensor(t, color, "DHW")
|
|
end
|
|
local function rotate_with_warp(src, dst, theta, mode)
|
|
local height
|
|
local width
|
|
if src:dim() == 2 then
|
|
height = src:size(1)
|
|
width = src:size(2)
|
|
elseif src:dim() == 3 then
|
|
height = src:size(2)
|
|
width = src:size(3)
|
|
else
|
|
dok.error('src image must be 2D or 3D', 'image.rotate')
|
|
end
|
|
local flow = torch.Tensor(2, height, width)
|
|
local kernel = torch.Tensor({{math.cos(-theta), -math.sin(-theta)},
|
|
{math.sin(-theta), math.cos(-theta)}})
|
|
flow[1] = torch.ger(torch.linspace(0, 1, height), torch.ones(width))
|
|
flow[1]:mul(-(height -1)):add(math.floor(height / 2 + 0.5))
|
|
flow[2] = torch.ger(torch.ones(height), torch.linspace(0, 1, width))
|
|
flow[2]:mul(-(width -1)):add(math.floor(width / 2 + 0.5))
|
|
flow:add(-1, torch.mm(kernel, flow:view(2, height * width)))
|
|
dst:resizeAs(src)
|
|
return image.warp(dst, src, flow, mode, true, 'clamp')
|
|
end
|
|
function iproc.rotate(src, theta)
|
|
local conversion
|
|
src, conversion = iproc.byte2float(src)
|
|
local dest = torch.Tensor():typeAs(src):resizeAs(src)
|
|
rotate_with_warp(src, dest, theta, 'bilinear')
|
|
dest:clamp(0, 1)
|
|
if conversion then
|
|
dest = iproc.float2byte(dest)
|
|
end
|
|
return dest
|
|
end
|
|
function iproc.negate(src)
|
|
if src:type() == "torch.ByteTensor" then
|
|
return -src + 255
|
|
else
|
|
return -src + 1
|
|
end
|
|
end
|
|
|
|
function iproc.gaussian2d(kernel_size, sigma)
|
|
sigma = sigma or 1
|
|
local kernel = torch.Tensor(kernel_size, kernel_size)
|
|
local u = math.floor(kernel_size / 2) + 1
|
|
local amp = (1 / math.sqrt(2 * math.pi * sigma^2))
|
|
for x = 1, kernel_size do
|
|
for y = 1, kernel_size do
|
|
kernel[x][y] = amp * math.exp(-((x - u)^2 + (y - u)^2) / (2 * sigma^2))
|
|
end
|
|
end
|
|
kernel:div(kernel:sum())
|
|
return kernel
|
|
end
|
|
function iproc.rgb2y(src)
|
|
local conversion
|
|
src, conversion = iproc.byte2float(src)
|
|
local dest = torch.FloatTensor(1, src:size(2), src:size(3)):zero()
|
|
dest:add(0.299, src[1]):add(0.587, src[2]):add(0.114, src[3])
|
|
dest:clamp(0, 1)
|
|
if conversion then
|
|
dest = iproc.float2byte(dest)
|
|
end
|
|
return dest
|
|
end
|
|
|
|
local function test_conversion()
|
|
local a = torch.linspace(0, 255, 256):float():div(255.0)
|
|
local b = iproc.float2byte(a)
|
|
local c = iproc.byte2float(a)
|
|
local d = torch.linspace(0, 255, 256)
|
|
assert((a - c):abs():sum() == 0)
|
|
assert((d:float() - b:float()):abs():sum() == 0)
|
|
|
|
a = torch.FloatTensor({256.0, 255.0, 254.999}):div(255.0)
|
|
b = iproc.float2byte(a)
|
|
assert(b:float():sum() == 255.0 * 3)
|
|
|
|
a = torch.FloatTensor({254.0, 254.499, 253.50001}):div(255.0)
|
|
b = iproc.float2byte(a)
|
|
print(b)
|
|
assert(b:float():sum() == 254.0 * 3)
|
|
end
|
|
local function test_flip()
|
|
require 'sys'
|
|
require 'torch'
|
|
torch.setdefaulttensortype("torch.FloatTensor")
|
|
image = require 'image'
|
|
local src = image.lena()
|
|
local src_byte = src:clone():mul(255):byte()
|
|
|
|
print(src:size())
|
|
print((image.hflip(src) - iproc.hflip(src)):sum())
|
|
print((image.hflip(src_byte) - iproc.hflip(src_byte)):sum())
|
|
print((image.vflip(src) - iproc.vflip(src)):sum())
|
|
print((image.vflip(src_byte) - iproc.vflip(src_byte)):sum())
|
|
end
|
|
local function test_gaussian2d()
|
|
local t = {3, 5, 7}
|
|
for i = 1, #t do
|
|
local kp = iproc.gaussian2d(t[i], 0.5)
|
|
print(kp)
|
|
end
|
|
end
|
|
local function test_conv()
|
|
local image = require 'image'
|
|
local src = image.lena()
|
|
local kernel = torch.Tensor(3, 3):fill(1)
|
|
kernel:div(kernel:sum())
|
|
--local blur = image.convolve(iproc.padding(src, 1, 1, 1, 1), kernel, 'valid')
|
|
local blur = image.convolve(src, kernel, 'same')
|
|
print(src:size(), blur:size())
|
|
local diff = (blur - src):abs()
|
|
image.save("diff.png", diff)
|
|
image.display({image = blur, min=0, max=1})
|
|
image.display({image = diff, min=0, max=1})
|
|
end
|
|
|
|
--test_conversion()
|
|
--test_flip()
|
|
--test_gaussian2d()
|
|
--test_conv()
|
|
|
|
return iproc
|
|
|
|
|