Add ai_denoise and ai_raw_denoise example scripts for the darktable.ai Lua API
This commit is contained in:
Andrii Ryzhkov
323
examples/ai_denoise.lua
Normal file
323
examples/ai_denoise.lua
Normal file
@@ -0,0 +1,323 @@
|
||||
--[[
|
||||
This file is part of darktable,
|
||||
copyright (c) 2026 darktable developers.
|
||||
|
||||
darktable is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
darktable is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with darktable. If not, see <http://www.gnu.org/licenses/>.
|
||||
]]
|
||||
--[[
|
||||
ai_denoise - example script demonstrating the darktable.ai Lua
|
||||
API by denoising the selected images with tile-based inference.
|
||||
|
||||
Adds an "AI denoise" panel to the lighttable right column with a
|
||||
"denoise selected" button. Each selected image is loaded through
|
||||
the develop pipeline (so denoise sees the output of all enabled
|
||||
IOPs, not the original raw), run tile-by-tile through the active
|
||||
denoise model, and saved as a 16-bit TIFF grouped with the source.
|
||||
|
||||
Demonstrates:
|
||||
* model lookup via dt.ai.model_for_task
|
||||
* tensor crop/paste for tile-based inference
|
||||
* linear <-> sRGB conversion (NR models train on gamma input)
|
||||
* edge-replicated padding to suppress border artifacts
|
||||
* background job with progress bar and cancellation
|
||||
|
||||
LIMITATIONS
|
||||
* Image must be at least TILE_SIZE x TILE_SIZE (768 x 768 for
|
||||
denoise-nind / denoise-nafnet); smaller inputs return an error.
|
||||
* If a save fails mid-batch the Lua error propagates and aborts
|
||||
the whole job. Wrap denoise_one in pcall if you need
|
||||
batch-robust behaviour.
|
||||
|
||||
ADDITIONAL SOFTWARE NEEDED FOR THIS SCRIPT
|
||||
* a denoise model enabled in preferences -> AI (denoise-nind,
|
||||
denoise-nafnet, ...)
|
||||
|
||||
USAGE
|
||||
* require this script from your luarc
|
||||
* select one or more images in lighttable
|
||||
* click the "denoise selected" button in the AI denoise panel
|
||||
|
||||
BUGS, COMMENTS, SUGGESTIONS
|
||||
* file an issue on https://github.com/darktable-org/darktable
|
||||
|
||||
CHANGES
|
||||
]]
|
||||
|
||||
local dt = require "darktable"
|
||||
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
-- C O N S T A N T S
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
local MODULE_NAME = "ai_denoise"
|
||||
|
||||
-- darktable's NR models use static-shape ONNX: each model has a fixed
|
||||
-- input dimension baked in at training time. The Lua API doesn't
|
||||
-- expose this, so keep TILE_SIZE in sync with the model's
|
||||
-- `attributes.input_sizes` in its config.json.
|
||||
local TILE_SIZE = 768 -- denoise-nind, denoise-nafnet
|
||||
|
||||
-- overlap between adjacent tiles. larger values produce smoother
|
||||
-- seams at the cost of more compute per pixel
|
||||
local OVERLAP = 64
|
||||
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
-- F U N C T I O N S
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
-- Surround the input with `pad` pixels on every side, replicating the
|
||||
-- outermost rows/columns ("edge clamp" padding). Boundary tiles then
|
||||
-- see plausible context instead of a literal image edge, so the
|
||||
-- network's internal zero-padding no longer leaks into the visible
|
||||
-- output as colour artifacts along the image rim.
|
||||
local function pad_replicate(input, pad)
|
||||
local C = input:shape()[2]
|
||||
local H = input:shape()[3]
|
||||
local W = input:shape()[4]
|
||||
local padded = dt.ai.create_tensor({1, C, H + 2 * pad, W + 2 * pad})
|
||||
|
||||
-- center: original image
|
||||
padded:paste(input, pad, pad)
|
||||
|
||||
-- left and right strips: replicate first / last column of input
|
||||
local left_col = input:crop(0, 0, H, 1)
|
||||
local right_col = input:crop(0, W - 1, H, 1)
|
||||
for i = 0, pad - 1 do
|
||||
padded:paste(left_col, pad, i)
|
||||
padded:paste(right_col, pad, pad + W + i)
|
||||
end
|
||||
|
||||
-- top and bottom strips: replicate first / last row of the already
|
||||
-- horizontally-padded buffer so the four corners get filled correctly
|
||||
local top_row = padded:crop(pad, 0, 1, W + 2 * pad)
|
||||
local bottom_row = padded:crop(pad + H - 1, 0, 1, W + 2 * pad)
|
||||
for i = 0, pad - 1 do
|
||||
padded:paste(top_row, i, 0)
|
||||
padded:paste(bottom_row, pad + H + i, 0)
|
||||
end
|
||||
|
||||
return padded
|
||||
end
|
||||
|
||||
-- Tile a [1,C,H,W] tensor through the model with overlap-blended seams.
|
||||
-- Each tile is tile_size x tile_size, successive tiles step by
|
||||
-- (tile_size - 2*overlap), and only the central (non-overlap) region
|
||||
-- of each tile is pasted into the output. Image-boundary edges keep
|
||||
-- their full extent; only image-interior seams drop the overlap margin.
|
||||
--
|
||||
-- The trailing tile in each axis is pinned to the image boundary so
|
||||
-- the residual right/bottom strip is never smaller than 2*overlap --
|
||||
-- otherwise it would have to be skipped (the model has a fixed input
|
||||
-- size) and those pixels would never be written.
|
||||
local function run_tiled(ctx, input, tile_size, overlap,
|
||||
job, base_pct, span_pct)
|
||||
local C = input:shape()[2]
|
||||
local H = input:shape()[3]
|
||||
local W = input:shape()[4]
|
||||
if H < tile_size or W < tile_size then
|
||||
return nil, string.format("image too small (%dx%d) for tile %d",
|
||||
W, H, tile_size)
|
||||
end
|
||||
|
||||
local step = tile_size - 2 * overlap
|
||||
|
||||
-- enumerate tile origins along one axis, ending exactly at the boundary
|
||||
local function origins(extent)
|
||||
local out, p = {}, 0
|
||||
while p + tile_size < extent do
|
||||
out[#out + 1] = p
|
||||
p = p + step
|
||||
end
|
||||
out[#out + 1] = math.max(0, extent - tile_size)
|
||||
return out
|
||||
end
|
||||
local ys, xs = origins(H), origins(W)
|
||||
local total = #xs * #ys
|
||||
|
||||
local output = dt.ai.create_tensor({1, C, H, W})
|
||||
local done = 0
|
||||
|
||||
for _, ty in ipairs(ys) do
|
||||
for _, tx in ipairs(xs) do
|
||||
if job and not job.valid then return nil, "cancelled" end
|
||||
|
||||
local tile_in = input:crop(ty, tx, tile_size, tile_size)
|
||||
local tile_out = ctx:run(tile_in)
|
||||
|
||||
-- central region to keep: drop overlap on every inner edge;
|
||||
-- full extent at image-boundary edges
|
||||
local at_top = (ty == 0)
|
||||
local at_left = (tx == 0)
|
||||
local at_bottom = (ty + tile_size >= H)
|
||||
local at_right = (tx + tile_size >= W)
|
||||
|
||||
local cy = at_top and 0 or overlap
|
||||
local cx = at_left and 0 or overlap
|
||||
local ch = tile_size - cy - (at_bottom and 0 or overlap)
|
||||
local cw = tile_size - cx - (at_right and 0 or overlap)
|
||||
|
||||
output:paste(tile_out:crop(cy, cx, ch, cw), ty + cy, tx + cx)
|
||||
|
||||
done = done + 1
|
||||
if job then
|
||||
job.percent = base_pct + span_pct * (done / total)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
return output
|
||||
end
|
||||
|
||||
-- Denoise one image: load through the develop pipeline, run tiled
|
||||
-- inference, save as 16-bit TIFF, group with the source.
|
||||
local function denoise_one(ctx, img, tile_size, job, base_pct, span_pct)
|
||||
-- darktable returns the develop-pipeline output as scene-linear RGB;
|
||||
-- the NR models were trained on sRGB-gamma input, so we re-encode
|
||||
-- before inference and decode back to linear before saving
|
||||
local input = dt.ai.load_image(img)
|
||||
if not input then return false, "load_image failed" end
|
||||
input:linear_to_srgb()
|
||||
|
||||
-- pad so the image's outer rim is no longer at a tile boundary;
|
||||
-- the padded strip absorbs the network's edge artifacts and gets
|
||||
-- cropped away after inference
|
||||
local H = input:shape()[3]
|
||||
local W = input:shape()[4]
|
||||
local padded_in = pad_replicate(input, OVERLAP)
|
||||
|
||||
local padded_out, err = run_tiled(ctx, padded_in, tile_size, OVERLAP,
|
||||
job, base_pct, span_pct)
|
||||
if not padded_out then return false, err end
|
||||
|
||||
local output = padded_out:crop(OVERLAP, OVERLAP, H, W)
|
||||
output:srgb_to_linear()
|
||||
|
||||
local base = img.filename:match("(.+)%..+$") or img.filename
|
||||
local out_path = img.path .. "/" .. base .. "_denoised.tif"
|
||||
output:save_tiff(out_path, 16, img)
|
||||
|
||||
-- import the result and group with the source so they live together
|
||||
-- in the lighttable
|
||||
local imported = dt.database.import(out_path)
|
||||
if imported then imported:group_with(img) end
|
||||
return true
|
||||
end
|
||||
|
||||
-- Button handler: looks up the active denoise model, fans out the
|
||||
-- selected images, and reports progress via a cancellable job.
|
||||
local function process_denoise()
|
||||
local model_id = dt.ai.model_for_task("denoise")
|
||||
if not model_id then
|
||||
dt.print("no denoise model enabled -- pick one in preferences -> AI")
|
||||
return
|
||||
end
|
||||
|
||||
local images = dt.gui.selection()
|
||||
if #images == 0 then
|
||||
dt.print("select at least one image")
|
||||
return
|
||||
end
|
||||
|
||||
-- progress + cancel job. the cancel callback flips job.valid; the
|
||||
-- main loop polls it between tiles to stop cleanly
|
||||
local job = dt.gui.create_job(
|
||||
string.format("AI denoise (%d image%s)",
|
||||
#images, #images == 1 and "" or "s"),
|
||||
true,
|
||||
function(j) j.valid = false end)
|
||||
|
||||
dt.print_log(string.format(
|
||||
"[ai_denoise] starting: %d image%s with model %s",
|
||||
#images, #images == 1 and "" or "s", model_id))
|
||||
local t_start = os.time()
|
||||
|
||||
local ctx = dt.ai.load_model(model_id)
|
||||
if not ctx then
|
||||
dt.print("failed to load model: " .. model_id)
|
||||
job.valid = false
|
||||
return
|
||||
end
|
||||
|
||||
local span = 1.0 / #images
|
||||
local done = 0
|
||||
for i, img in ipairs(images) do
|
||||
if not job.valid then break end
|
||||
dt.print_log(string.format("[ai_denoise] [%d/%d] %s",
|
||||
i, #images, img.filename))
|
||||
local ok, err = denoise_one(ctx, img, TILE_SIZE, job,
|
||||
(i - 1) * span, span)
|
||||
if not ok then
|
||||
dt.print(string.format("[%d/%d] %s: %s",
|
||||
i, #images, img.filename, tostring(err)))
|
||||
dt.print_log(string.format("[ai_denoise] [%d/%d] %s FAILED: %s",
|
||||
i, #images, img.filename, tostring(err)))
|
||||
else
|
||||
done = done + 1
|
||||
end
|
||||
end
|
||||
|
||||
ctx:close()
|
||||
job.valid = false
|
||||
local elapsed = os.time() - t_start
|
||||
dt.print(string.format("denoise complete: %d image%s",
|
||||
#images, #images == 1 and "" or "s"))
|
||||
dt.print_log(string.format(
|
||||
"[ai_denoise] finished: %d/%d in %ds",
|
||||
done, #images, elapsed))
|
||||
end
|
||||
|
||||
-- script_manager integration: called when the user disables the script.
|
||||
-- darktable can't remove a registered lib from the UI at runtime, so we
|
||||
-- only stop any in-flight work; the panel itself remains until restart.
|
||||
local function destroy()
|
||||
-- nothing persistent to clean up: each click runs its own job and
|
||||
-- closes its own model context
|
||||
end
|
||||
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
-- M A I N
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
dt.register_lib(
|
||||
MODULE_NAME, -- plugin id
|
||||
"AI denoise", -- displayed name
|
||||
true, -- expandable
|
||||
false, -- no reset button
|
||||
{[dt.gui.views.lighttable] = {"DT_UI_CONTAINER_PANEL_RIGHT_CENTER", 100}},
|
||||
dt.new_widget("box") {
|
||||
orientation = "vertical",
|
||||
dt.new_widget("button") {
|
||||
label = "denoise selected",
|
||||
clicked_callback = process_denoise,
|
||||
}
|
||||
},
|
||||
nil, nil
|
||||
)
|
||||
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
-- S C R I P T M A N A G E R I N T E G R A T I O N
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
local script_data = {}
|
||||
|
||||
script_data.metadata = {
|
||||
name = "ai_denoise",
|
||||
purpose = "tile-based AI denoise using the darktable.ai Lua API",
|
||||
author = "Andrii Ryzhkov <andrii.ryzhkov@pm.me>",
|
||||
help = "https://docs.darktable.org/lua/stable/lua.scripts.manual/scripts/examples/ai_denoise"
|
||||
}
|
||||
|
||||
script_data.destroy = destroy
|
||||
|
||||
return script_data
|
||||
412
examples/ai_raw_denoise.lua
Normal file
412
examples/ai_raw_denoise.lua
Normal file
@@ -0,0 +1,412 @@
|
||||
--[[
|
||||
This file is part of darktable,
|
||||
copyright (c) 2026 darktable developers.
|
||||
|
||||
darktable is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
darktable is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with darktable. If not, see <http://www.gnu.org/licenses/>.
|
||||
]]
|
||||
--[[
|
||||
ai_raw_denoise - example script demonstrating the darktable.ai Lua
|
||||
API by denoising the selected raw images with tile-based inference
|
||||
on the raw CFA.
|
||||
|
||||
Adds an "AI raw denoise" panel to the lighttable right column with
|
||||
a "denoise selected" button. Each selected image's raw CFA is
|
||||
loaded, Bayer-packed to 4 channels, run tile-by-tile through the
|
||||
currently enabled rawdenoise model (which denoises AND demosaicks
|
||||
in one pass), and saved as a LinearRaw DNG grouped with the
|
||||
source image.
|
||||
|
||||
Demonstrates:
|
||||
* model lookup via dt.ai.model_for_task
|
||||
* loading model variant files via dt.ai.load_model file arg
|
||||
* raw CFA loading via dt.ai.load_raw
|
||||
* tensor bayer_pack for 4-channel CFA tiling
|
||||
* tile loop with non-trivial input->output scale (here 1 -> 2)
|
||||
* edge-replicated padding to suppress border artifacts
|
||||
* LinearRaw DNG output via dt.ai.save_dng_linear
|
||||
* background job with progress bar and cancellation
|
||||
|
||||
LIMITATIONS
|
||||
* Bayer sensors only. The script loads model_bayer.onnx and uses
|
||||
bayer_pack, which assumes a 2x2 Bayer layout. X-Trans and
|
||||
Foveon sensors need model_linear.onnx with a different
|
||||
preprocessing pipeline (no bayer_pack, demosaicked input).
|
||||
* Image must be at least TILE_SIZE x TILE_SIZE in packed pixels
|
||||
(1024 x 1024 of CFA for the default 512 packed tile size).
|
||||
* If a save fails mid-batch the Lua error propagates and aborts
|
||||
the whole job. Wrap denoise_one in pcall if you need
|
||||
batch-robust behaviour.
|
||||
|
||||
ADDITIONAL SOFTWARE NEEDED FOR THIS SCRIPT
|
||||
* a rawdenoise model enabled in preferences -> AI
|
||||
(e.g. rawdenoise-nind)
|
||||
|
||||
USAGE
|
||||
* require this script from your luarc
|
||||
* select one or more raw images in lighttable
|
||||
* click the "denoise selected" button in the AI raw denoise panel
|
||||
|
||||
BUGS, COMMENTS, SUGGESTIONS
|
||||
* file an issue on https://github.com/darktable-org/darktable
|
||||
|
||||
CHANGES
|
||||
]]
|
||||
|
||||
local dt = require "darktable"
|
||||
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
-- C O N S T A N T S
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
local MODULE_NAME = "ai_raw_denoise"
|
||||
|
||||
-- The rawdenoise-nind bayer model is a denoise+demosaic combo:
|
||||
-- input : [1, 4, 512, 512] packed CFA (4 phase planes)
|
||||
-- output : [1, 3, 1024, 1024] linear RGB at 2x the input resolution
|
||||
-- So each tile-size pixel of the input covers a 2x2 CFA block, and the
|
||||
-- model emits two output pixels per input pixel in each axis.
|
||||
-- TILE_SIZE is in packed pixels (model input); SCALE turns those into
|
||||
-- output pixels. Keep in sync with `attributes.input_sizes` in the
|
||||
-- model's config.json.
|
||||
local TILE_SIZE = 512 -- rawdenoise-nind
|
||||
local SCALE = 2 -- model output dim / input dim
|
||||
local OUT_CH = 3 -- model emits linear RGB
|
||||
|
||||
-- overlap between adjacent tiles, in packed pixels (input space).
|
||||
-- larger values produce smoother seams at the cost of more compute
|
||||
local OVERLAP = 32
|
||||
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
-- F U N C T I O N S
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
-- Surround the input with `pad` pixels on every side, replicating the
|
||||
-- outermost rows/columns ("edge clamp" padding). Boundary tiles then
|
||||
-- see plausible context instead of a literal image edge, so the
|
||||
-- network's internal zero-padding no longer leaks into the visible
|
||||
-- output as colour artifacts along the image rim.
|
||||
local function pad_replicate(input, pad)
|
||||
local C = input:shape()[2]
|
||||
local H = input:shape()[3]
|
||||
local W = input:shape()[4]
|
||||
local padded = dt.ai.create_tensor({1, C, H + 2 * pad, W + 2 * pad})
|
||||
|
||||
-- center: original image
|
||||
padded:paste(input, pad, pad)
|
||||
|
||||
-- left and right strips: replicate first / last column of input
|
||||
local left_col = input:crop(0, 0, H, 1)
|
||||
local right_col = input:crop(0, W - 1, H, 1)
|
||||
for i = 0, pad - 1 do
|
||||
padded:paste(left_col, pad, i)
|
||||
padded:paste(right_col, pad, pad + W + i)
|
||||
end
|
||||
|
||||
-- top and bottom strips: replicate first / last row of the already
|
||||
-- horizontally-padded buffer so the four corners get filled correctly
|
||||
local top_row = padded:crop(pad, 0, 1, W + 2 * pad)
|
||||
local bottom_row = padded:crop(pad + H - 1, 0, 1, W + 2 * pad)
|
||||
for i = 0, pad - 1 do
|
||||
padded:paste(top_row, i, 0)
|
||||
padded:paste(bottom_row, pad + H + i, 0)
|
||||
end
|
||||
|
||||
return padded
|
||||
end
|
||||
|
||||
-- Tile a [1,C_in,H,W] tensor through a model that emits [1,C_out,H*scale,
|
||||
-- W*scale]. Each tile is tile_size x tile_size in input space; successive
|
||||
-- tiles step by (tile_size - 2*overlap); only the central (non-overlap)
|
||||
-- region of each tile's output is pasted into the result, with all
|
||||
-- offsets multiplied by `scale`. Image-boundary edges keep their full
|
||||
-- extent; only image-interior seams drop the overlap margin.
|
||||
--
|
||||
-- The trailing tile in each axis is pinned to the image boundary so
|
||||
-- the residual right/bottom strip is never smaller than 2*overlap --
|
||||
-- otherwise it would have to be skipped (the model has a fixed input
|
||||
-- size) and those pixels would never be written.
|
||||
local function run_tiled(ctx, input, tile_size, overlap, scale, c_out,
|
||||
job, base_pct, span_pct)
|
||||
local H = input:shape()[3]
|
||||
local W = input:shape()[4]
|
||||
if H < tile_size or W < tile_size then
|
||||
return nil, string.format("image too small (%dx%d) for tile %d",
|
||||
W, H, tile_size)
|
||||
end
|
||||
|
||||
local step = tile_size - 2 * overlap
|
||||
|
||||
-- enumerate tile origins along one axis, ending exactly at the boundary
|
||||
local function origins(extent)
|
||||
local out, p = {}, 0
|
||||
while p + tile_size < extent do
|
||||
out[#out + 1] = p
|
||||
p = p + step
|
||||
end
|
||||
out[#out + 1] = math.max(0, extent - tile_size)
|
||||
return out
|
||||
end
|
||||
local ys, xs = origins(H), origins(W)
|
||||
local total = #xs * #ys
|
||||
|
||||
-- output is scale-times larger spatially and may have a different
|
||||
-- channel count than the input
|
||||
local output = dt.ai.create_tensor({1, c_out, H * scale, W * scale})
|
||||
local done = 0
|
||||
|
||||
for _, ty in ipairs(ys) do
|
||||
for _, tx in ipairs(xs) do
|
||||
if job and not job.valid then return nil, "cancelled" end
|
||||
|
||||
local tile_in = input:crop(ty, tx, tile_size, tile_size)
|
||||
local tile_out = ctx:run(tile_in)
|
||||
|
||||
-- central region to keep: drop overlap on every inner edge;
|
||||
-- full extent at image-boundary edges. all dimensions in OUTPUT
|
||||
-- (post-scale) space, since that's where we paste
|
||||
local at_top = (ty == 0)
|
||||
local at_left = (tx == 0)
|
||||
local at_bottom = (ty + tile_size >= H)
|
||||
local at_right = (tx + tile_size >= W)
|
||||
|
||||
local ov = overlap * scale
|
||||
local tos = tile_size * scale
|
||||
local cy = at_top and 0 or ov
|
||||
local cx = at_left and 0 or ov
|
||||
local ch = tos - cy - (at_bottom and 0 or ov)
|
||||
local cw = tos - cx - (at_right and 0 or ov)
|
||||
|
||||
output:paste(tile_out:crop(cy, cx, ch, cw),
|
||||
ty * scale + cy, tx * scale + cx)
|
||||
|
||||
done = done + 1
|
||||
if job then
|
||||
job.percent = base_pct + span_pct * (done / total)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
return output
|
||||
end
|
||||
|
||||
-- Denoise one raw image. The bayer rawdenoise model denoises AND
|
||||
-- demosaicks in one pass, so its output is linear RGB at the
|
||||
-- original CFA resolution (not a CFA mosaic). Saved as a LinearRaw
|
||||
-- DNG, which darktable can re-import as an already-debayered raw.
|
||||
--
|
||||
-- Pipeline:
|
||||
-- load CFA [1,1,H,W] + image metadata
|
||||
-- -> bayer_pack [1,4,H/2,W/2]
|
||||
-- -> normalize to [0, 1] using black/white levels (the model is
|
||||
-- trained on (val - black) / (white - black), not raw ADC)
|
||||
-- -> pad to [1,4,H/2+2P,W/2+2P]
|
||||
-- -> tile through model (scale=2, c_out=3) -> [1,3,(H+4P),(W+4P)]
|
||||
-- -> crop padding back off -> [1,3,H,W]
|
||||
-- -> save linear DNG -> import + group with source
|
||||
local function denoise_one(ctx, img, tile_size, job, base_pct, span_pct)
|
||||
-- load_raw returns (tensor, meta). tensor is [1,1,H,W] in raw ADC
|
||||
-- units; meta carries black/white/wb/CFA info we need to normalise
|
||||
-- the input into the [0, 1] range the model was trained on
|
||||
local cfa, meta = dt.ai.load_raw(img)
|
||||
if not cfa then return false, "load_raw failed" end
|
||||
|
||||
-- crop off optical-black / masked pixels (Canon and others ship
|
||||
-- the full sensor buffer including non-light-sensing strips on
|
||||
-- the top/left). visible_* and crop_* come from the image
|
||||
-- metadata; we snap crop offsets down to an even pixel so the
|
||||
-- 2x2 CFA phase at the visible-region origin matches the buffer
|
||||
-- (otherwise bayer_pack would group the wrong colours)
|
||||
if meta.crop_x and meta.crop_y
|
||||
and (meta.crop_x > 0 or meta.crop_y > 0)
|
||||
then
|
||||
local cx = meta.crop_x - (meta.crop_x % 2)
|
||||
local cy = meta.crop_y - (meta.crop_y % 2)
|
||||
local cw = meta.visible_width + (meta.crop_x - cx)
|
||||
local ch = meta.visible_height + (meta.crop_y - cy)
|
||||
-- ensure even dims for bayer_pack
|
||||
cw = cw - (cw % 2)
|
||||
ch = ch - (ch % 2)
|
||||
cfa = cfa:crop(cy, cx, ch, cw)
|
||||
end
|
||||
|
||||
-- uniform-black approximation: most sensors report identical black
|
||||
-- levels across the 4 CFA sites, in which case a single scalar
|
||||
-- (val - black) / (white - black) normalisation is exact. cameras
|
||||
-- with non-uniform per-site black levels (e.g. some PDAF sensors)
|
||||
-- would need per-plane scaling -- left as an exercise
|
||||
local black = meta.black_level
|
||||
local range = meta.white_level - black
|
||||
if range <= 0 then range = 65535 end
|
||||
|
||||
-- pack the 1-channel CFA into 4 phase planes so each channel holds
|
||||
-- one Bayer site type; the model expects [1,4,H/2,W/2] input
|
||||
local packed = cfa:bayer_pack()
|
||||
packed:scale_add(1.0 / range, -black / range)
|
||||
local pH = packed:shape()[3]
|
||||
local pW = packed:shape()[4]
|
||||
|
||||
-- save the post-normalisation input mean for gain-matching the
|
||||
-- model output: the bayer rawdenoise model emits values on its own
|
||||
-- internal scale, not [0, 1], so we rescale so output mean matches
|
||||
-- input mean (the per-tile equivalent of dt's C reference path)
|
||||
local in_mean = packed:mean()
|
||||
|
||||
-- pad so the image rim is no longer at a tile boundary; the padded
|
||||
-- strip absorbs the network's edge artifacts and is cropped away
|
||||
-- after inference (in output space, where the pad covers 2*OVERLAP
|
||||
-- pixels per side)
|
||||
local padded_in = pad_replicate(packed, OVERLAP)
|
||||
|
||||
local padded_out, err = run_tiled(ctx, padded_in, tile_size, OVERLAP,
|
||||
SCALE, OUT_CH,
|
||||
job, base_pct, span_pct)
|
||||
if not padded_out then return false, err end
|
||||
|
||||
-- crop the padded region (in output coords: pad * scale per side)
|
||||
-- back to the original-CFA resolution
|
||||
local out_rgb = padded_out:crop(OVERLAP * SCALE, OVERLAP * SCALE,
|
||||
pH * SCALE, pW * SCALE)
|
||||
|
||||
-- gain-match: rescale the output so its mean matches the input
|
||||
-- mean, bringing the model's internal-scale output back into the
|
||||
-- [0, 1] range save_dng_linear expects
|
||||
local out_mean = out_rgb:mean()
|
||||
if out_mean > 1e-8 then
|
||||
out_rgb:scale_add(in_mean / out_mean)
|
||||
end
|
||||
|
||||
local base = img.filename:match("(.+)%..+$") or img.filename
|
||||
local out_path = img.path .. "/" .. base .. "_rawdenoised.dng"
|
||||
-- LinearRaw DNG: tensor values must be normalised to [0, 1] camRGB;
|
||||
-- the model handles black-subtract + scale internally, so we just
|
||||
-- pass the output through
|
||||
dt.ai.save_dng_linear(out_rgb, img, out_path)
|
||||
|
||||
-- import the result and group with the source so they live together
|
||||
-- in the lighttable
|
||||
local imported = dt.database.import(out_path)
|
||||
if imported then imported:group_with(img) end
|
||||
return true
|
||||
end
|
||||
|
||||
-- Button handler: looks up the active rawdenoise model, fans out the
|
||||
-- selected images, and reports progress via a cancellable job.
|
||||
local function process_raw_denoise()
|
||||
local model_id = dt.ai.model_for_task("rawdenoise")
|
||||
if not model_id then
|
||||
dt.print("no rawdenoise model enabled -- pick one in preferences -> AI")
|
||||
return
|
||||
end
|
||||
|
||||
local images = dt.gui.selection()
|
||||
if #images == 0 then
|
||||
dt.print("select at least one image")
|
||||
return
|
||||
end
|
||||
|
||||
-- progress + cancel job. the cancel callback flips job.valid; the
|
||||
-- main loop polls it between tiles to stop cleanly
|
||||
local job = dt.gui.create_job(
|
||||
string.format("AI raw denoise (%d image%s)",
|
||||
#images, #images == 1 and "" or "s"),
|
||||
true,
|
||||
function(j) j.valid = false end)
|
||||
|
||||
dt.print_log(string.format(
|
||||
"[ai_raw_denoise] starting: %d image%s with model %s",
|
||||
#images, #images == 1 and "" or "s", model_id))
|
||||
local t_start = os.time()
|
||||
|
||||
-- rawdenoise packages ship multiple files (one per CFA type); pick
|
||||
-- the Bayer variant. X-Trans / Foveon would use model_linear.onnx
|
||||
-- with a different preprocessing pipeline (no bayer_pack)
|
||||
local ctx = dt.ai.load_model(model_id, nil, "model_bayer.onnx")
|
||||
if not ctx then
|
||||
dt.print("failed to load model: " .. model_id)
|
||||
job.valid = false
|
||||
return
|
||||
end
|
||||
|
||||
local span = 1.0 / #images
|
||||
local done = 0
|
||||
for i, img in ipairs(images) do
|
||||
if not job.valid then break end
|
||||
dt.print_log(string.format("[ai_raw_denoise] [%d/%d] %s",
|
||||
i, #images, img.filename))
|
||||
local ok, err = denoise_one(ctx, img, TILE_SIZE, job,
|
||||
(i - 1) * span, span)
|
||||
if not ok then
|
||||
dt.print(string.format("[%d/%d] %s: %s",
|
||||
i, #images, img.filename, tostring(err)))
|
||||
dt.print_log(string.format("[ai_raw_denoise] [%d/%d] %s FAILED: %s",
|
||||
i, #images, img.filename, tostring(err)))
|
||||
else
|
||||
done = done + 1
|
||||
end
|
||||
end
|
||||
|
||||
ctx:close()
|
||||
job.valid = false
|
||||
local elapsed = os.time() - t_start
|
||||
dt.print(string.format("raw denoise complete: %d image%s",
|
||||
#images, #images == 1 and "" or "s"))
|
||||
dt.print_log(string.format(
|
||||
"[ai_raw_denoise] finished: %d/%d in %ds",
|
||||
done, #images, elapsed))
|
||||
end
|
||||
|
||||
-- script_manager integration: called when the user disables the script.
|
||||
-- darktable can't remove a registered lib from the UI at runtime, so we
|
||||
-- only stop any in-flight work; the panel itself remains until restart.
|
||||
local function destroy()
|
||||
-- nothing persistent to clean up: each click runs its own job and
|
||||
-- closes its own model context
|
||||
end
|
||||
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
-- M A I N
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
dt.register_lib(
|
||||
MODULE_NAME, -- plugin id
|
||||
"AI raw denoise", -- displayed name
|
||||
true, -- expandable
|
||||
false, -- no reset button
|
||||
{[dt.gui.views.lighttable] = {"DT_UI_CONTAINER_PANEL_RIGHT_CENTER", 100}},
|
||||
dt.new_widget("box") {
|
||||
orientation = "vertical",
|
||||
dt.new_widget("button") {
|
||||
label = "denoise selected",
|
||||
clicked_callback = process_raw_denoise,
|
||||
}
|
||||
},
|
||||
nil, nil
|
||||
)
|
||||
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
-- S C R I P T M A N A G E R I N T E G R A T I O N
|
||||
-- - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
local script_data = {}
|
||||
|
||||
script_data.metadata = {
|
||||
name = "ai_raw_denoise",
|
||||
purpose = "tile-based AI raw denoise using the darktable.ai Lua API",
|
||||
author = "Andrii Ryzhkov <andrii.ryzhkov@pm.me>",
|
||||
help = "https://docs.darktable.org/lua/stable/lua.scripts.manual/scripts/examples/ai_raw_denoise"
|
||||
}
|
||||
|
||||
script_data.destroy = destroy
|
||||
|
||||
return script_data
|
||||
Reference in New Issue
Block a user