forked from ScanTailor-Advanced/scantailor-advanced
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
c575887
commit 15fe51d
Showing
9 changed files
with
325 additions
and
5 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,157 @@ | ||
| /* | ||
| Scan Tailor - Interactive post-processing tool for scanned pages. | ||
| Copyright (C) 2015 Joseph Artsimovich <joseph.artsimovich@gmail.com> | ||
| This program 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. | ||
| This program 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 this program. If not, see <http://www.gnu.org/licenses/>. | ||
| */ | ||
|
|
||
| #include <algorithm> | ||
| #include <cstdint> | ||
| #include <cassert> | ||
| #include <cmath> | ||
| #include <stdexcept> | ||
| #include <QSize> | ||
| #include <QtGlobal> | ||
| #include "GrayImage.h" | ||
| #include "IntegralImage.h" | ||
| #include "WienerFilter.h" | ||
|
|
||
| namespace imageproc{ | ||
|
|
||
| GrayImage wienerFilter(GrayImage const& image, QSize const& window_size, double const noise_sigma) { | ||
| GrayImage dst(image); | ||
| wienerFilterInPlace(dst, window_size, noise_sigma); | ||
| return dst; | ||
| } | ||
|
|
||
| void wienerFilterInPlace(GrayImage& image, QSize const& window_size, double const noise_sigma) { | ||
| if (window_size.isEmpty()) { | ||
| throw std::invalid_argument("wienerFilter: empty window_size"); | ||
| } | ||
| if (noise_sigma < 0) { | ||
| throw std::invalid_argument("wienerFilter: negative noise_sigma"); | ||
| } | ||
| if (image.isNull()) { | ||
| return; | ||
| } | ||
|
|
||
| int const w = image.width(); | ||
| int const h = image.height(); | ||
| double const noise_variance = noise_sigma * noise_sigma; | ||
|
|
||
| IntegralImage<uint32_t> integral_image(w, h); | ||
| IntegralImage<uint64_t> integral_sqimage(w, h); | ||
|
|
||
| uint8_t* image_line = image.data(); | ||
| int const image_stride = image.stride(); | ||
|
|
||
| for (int y = 0; y < h; ++y) { | ||
| integral_image.beginRow(); | ||
| integral_sqimage.beginRow(); | ||
| for (int x = 0; x < w; ++x) { | ||
| uint32_t const pixel = image_line[x]; | ||
| integral_image.push(pixel); | ||
| integral_sqimage.push(pixel * pixel); | ||
| } | ||
| image_line += image_stride; | ||
| } | ||
|
|
||
| int const window_lower_half = window_size.height() >> 1; | ||
| int const window_upper_half = window_size.height() - window_lower_half; | ||
| int const window_left_half = window_size.width() >> 1; | ||
| int const window_right_half = window_size.width() - window_left_half; | ||
|
|
||
| image_line = image.data(); | ||
| for (int y = 0; y < h; ++y) { | ||
| int const top = ((y - window_lower_half) < 0) ? 0 : (y - window_lower_half); | ||
| int const bottom = ((y + window_upper_half) < h) ? (y + window_upper_half) : h; // exclusive | ||
|
|
||
| for (int x = 0; x < w; ++x) { | ||
| int const left = ((x - window_left_half) < 0) ? 0 : (x - window_left_half); | ||
| int const right = ((x + window_right_half) < w) ? (x + window_right_half) : w; // exclusive | ||
| int const area = (bottom - top) * (right - left); | ||
| assert(area > 0); // because window_size > 0 and w > 0 and h > 0 | ||
|
|
||
| QRect const rect(left, top, right - left, bottom - top); | ||
| double const window_sum = integral_image.sum(rect); | ||
| double const window_sqsum = integral_sqimage.sum(rect); | ||
|
|
||
| double const r_area = 1.0 / area; | ||
| double const mean = window_sum * r_area; | ||
| double const sqmean = window_sqsum * r_area; | ||
| double const variance = sqmean - mean * mean; | ||
|
|
||
| if (variance > 1e-6) { | ||
| double const src_pixel = (double) image_line[x]; | ||
| double const dst_pixel = mean + (src_pixel - mean) * | ||
| (((variance - noise_variance) < 0.0) ? 0.0 : (variance - noise_variance)) / variance; | ||
| image_line[x] = (uint8_t) ((dst_pixel < 0.0) ? 0.0 : ((dst_pixel < 255.0) ? dst_pixel : 255.0)); | ||
| } | ||
| } | ||
| image_line += image_stride; | ||
| } | ||
| } | ||
|
|
||
| QImage wienerColorFilter(QImage const& image, QSize const& window_size, double const coef) { | ||
| QImage dst(image); | ||
| wienerColorFilterInPlace(dst, window_size, coef); | ||
| return dst; | ||
| } | ||
|
|
||
| void wienerColorFilterInPlace(QImage& image, QSize const& window_size, double const coef) { | ||
| if (image.isNull()) { | ||
| return; | ||
| } | ||
| if (window_size.isEmpty()) { | ||
| throw std::invalid_argument("wienerFilter: empty window_size"); | ||
| } | ||
|
|
||
| if (coef > 0.0) { | ||
| int const w = image.width(); | ||
| int const h = image.height(); | ||
| uint8_t* image_line = (uint8_t*) image.bits(); | ||
| int const image_bpl = image.bytesPerLine(); | ||
| unsigned int const cnum = image_bpl / w; | ||
|
|
||
| GrayImage gray = GrayImage(image); | ||
| uint8_t* gray_line = gray.data(); | ||
| int const gray_bpl = gray.stride(); | ||
| GrayImage wiener(wienerFilter(gray, window_size, 255.0 * coef)); | ||
| uint8_t* wiener_line = wiener.data(); | ||
| int const wiener_bpl = wiener.stride(); | ||
|
|
||
| for (int y = 0; y < h; ++y) { | ||
| for (int x = 0; x < w; ++x) { | ||
| float const origin = gray_line[x]; | ||
| float color = wiener_line[x]; | ||
|
|
||
| float const colscale = (color + 1.0f) / (origin + 1.0f); | ||
| float const coldelta = color - origin * colscale; | ||
| for (unsigned int c = 0; c < cnum; ++c) | ||
| { | ||
| int const indx = x * cnum + c; | ||
| float origcol = image_line[indx]; | ||
| float val = origcol * colscale + coldelta; | ||
| val = (val < 0.0f) ? 0.0f : (val < 255.0f) ? val : 255.0f; | ||
| image_line[indx] = (uint8_t) (val + 0.5f); | ||
| } | ||
| } | ||
| image_line += image_bpl; | ||
| gray_line += gray_bpl; | ||
| wiener_line += wiener_bpl; | ||
| } | ||
| } | ||
| } | ||
|
|
||
| } // namespace imageproc |
Oops, something went wrong.