sharp/src/operations.cc

254 lines
8.5 KiB
C++

#include <algorithm>
#include <tuple>
#include <vips/vips8>
#include "common.h"
#include "operations.h"
using vips::VImage;
using vips::VError;
namespace sharp {
/*
Alpha composite src over dst with given gravity.
Assumes alpha channels are already premultiplied and will be unpremultiplied after.
*/
VImage Composite(VImage src, VImage dst, const int gravity) {
using sharp::CalculateCrop;
using sharp::HasAlpha;
if (!HasAlpha(src)) {
throw VError("Overlay image must have an alpha channel");
}
if (!HasAlpha(dst)) {
throw VError("Image to be overlaid must have an alpha channel");
}
if (src.width() > dst.width() || src.height() > dst.height()) {
throw VError("Overlay image must have same dimensions or smaller");
}
// Enlarge overlay src, if required
if (src.width() < dst.width() || src.height() < dst.height()) {
// Calculate the (left, top) coordinates of the output image within the input image, applying the given gravity.
int left;
int top;
std::tie(left, top) = CalculateCrop(dst.width(), dst.height(), src.width(), src.height(), gravity);
// Embed onto transparent background
std::vector<double> background { 0.0, 0.0, 0.0, 0.0 };
src = src.embed(left, top, dst.width(), dst.height(), VImage::option()
->set("extend", VIPS_EXTEND_BACKGROUND)
->set("background", background)
);
}
// Split src into non-alpha and alpha channels
VImage srcWithoutAlpha = src.extract_band(0, VImage::option()->set("n", src.bands() - 1));
VImage srcAlpha = src[src.bands() - 1] * (1.0 / 255.0);
// Split dst into non-alpha and alpha channels
VImage dstWithoutAlpha = dst.extract_band(0, VImage::option()->set("n", dst.bands() - 1));
VImage dstAlpha = dst[dst.bands() - 1] * (1.0 / 255.0);
//
// Compute normalized output alpha channel:
//
// References:
// - http://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending
// - https://github.com/jcupitt/ruby-vips/issues/28#issuecomment-9014826
//
// out_a = src_a + dst_a * (1 - src_a)
// ^^^^^^^^^^^
// t0
VImage t0 = srcAlpha.linear(-1.0, 1.0);
VImage outAlphaNormalized = srcAlpha + dstAlpha * t0;
//
// Compute output RGB channels:
//
// Wikipedia:
// out_rgb = (src_rgb * src_a + dst_rgb * dst_a * (1 - src_a)) / out_a
// ^^^^^^^^^^^
// t0
//
// Omit division by `out_a` since `Compose` is supposed to output a
// premultiplied RGBA image as reversal of premultiplication is handled
// externally.
//
VImage outRGBPremultiplied = srcWithoutAlpha + dstWithoutAlpha * t0;
// Combine RGB and alpha channel into output image:
return outRGBPremultiplied.bandjoin(outAlphaNormalized * 255.0);
}
/*
* Stretch luminance to cover full dynamic range.
*/
VImage Normalize(VImage image) {
// Get original colourspace
VipsInterpretation typeBeforeNormalize = image.interpretation();
if (typeBeforeNormalize == VIPS_INTERPRETATION_RGB) {
typeBeforeNormalize = VIPS_INTERPRETATION_sRGB;
}
// Convert to LAB colourspace
VImage lab = image.colourspace(VIPS_INTERPRETATION_LAB);
// Extract luminance
VImage luminance = lab[0];
// Find luminance range
VImage stats = luminance.stats();
double min = stats(0, 0)[0];
double max = stats(1, 0)[0];
if (min != max) {
// Extract chroma
VImage chroma = lab.extract_band(1, VImage::option()->set("n", 2));
// Calculate multiplication factor and addition
double f = 100.0 / (max - min);
double a = -(min * f);
// Scale luminance, join to chroma, convert back to original colourspace
VImage normalized = luminance.linear(f, a).bandjoin(chroma).colourspace(typeBeforeNormalize);
// Attach original alpha channel, if any
if (HasAlpha(image)) {
// Extract original alpha channel
VImage alpha = image[image.bands() - 1];
// Join alpha channel to normalised image
return normalized.bandjoin(alpha);
} else {
return normalized;
}
}
return image;
}
/*
* Gamma encoding/decoding
*/
VImage Gamma(VImage image, double const exponent) {
if (HasAlpha(image)) {
// Separate alpha channel
VImage imageWithoutAlpha = image.extract_band(0,
VImage::option()->set("n", image.bands() - 1));
VImage alpha = image[image.bands() - 1];
return imageWithoutAlpha.gamma(VImage::option()->set("exponent", exponent)).bandjoin(alpha);
} else {
return image.gamma(VImage::option()->set("exponent", exponent));
}
}
/*
* Gaussian blur. Use sigma of -1.0 for fast blur.
*/
VImage Blur(VImage image, double const sigma) {
if (sigma == -1.0) {
// Fast, mild blur - averages neighbouring pixels
VImage blur = VImage::new_matrixv(3, 3,
1.0, 1.0, 1.0,
1.0, 1.0, 1.0,
1.0, 1.0, 1.0);
blur.set("scale", 9.0);
return image.conv(blur);
} else {
// Slower, accurate Gaussian blur
return image.gaussblur(sigma);
}
}
/*
* Sharpen flat and jagged areas. Use sigma of -1.0 for fast sharpen.
*/
VImage Sharpen(VImage image, double const sigma, double const flat, double const jagged) {
if (sigma == -1.0) {
// Fast, mild sharpen
VImage sharpen = VImage::new_matrixv(3, 3,
-1.0, -1.0, -1.0,
-1.0, 32.0, -1.0,
-1.0, -1.0, -1.0);
sharpen.set("scale", 24.0);
return image.conv(sharpen);
} else {
// Slow, accurate sharpen in LAB colour space, with control over flat vs jagged areas
return image.sharpen(
VImage::option()->set("sigma", sigma)->set("m1", flat)->set("m2", jagged)
);
}
}
/*
Calculate crop area based on image entropy
*/
std::tuple<int, int> EntropyCrop(VImage image, int const outWidth, int const outHeight) {
int left = 0;
int top = 0;
int const inWidth = image.width();
int const inHeight = image.height();
if (inWidth > outWidth) {
// Reduce width by repeated removing slices from edge with lowest entropy
int width = inWidth;
double leftEntropy = 0.0;
double rightEntropy = 0.0;
// Max width of each slice
int const maxSliceWidth = static_cast<int>(ceil((inWidth - outWidth) / 8.0));
while (width > outWidth) {
// Width of current slice
int const slice = std::min(width - outWidth, maxSliceWidth);
if (leftEntropy == 0.0) {
// Update entropy of left slice
leftEntropy = Entropy(image.extract_area(left, 0, slice, inHeight));
}
if (rightEntropy == 0.0) {
// Update entropy of right slice
rightEntropy = Entropy(image.extract_area(width - slice - 1, 0, slice, inHeight));
}
// Keep slice with highest entropy
if (leftEntropy >= rightEntropy) {
// Discard right slice
rightEntropy = 0.0;
} else {
// Discard left slice
leftEntropy = 0.0;
left = left + slice;
}
width = width - slice;
}
}
if (inHeight > outHeight) {
// Reduce height by repeated removing slices from edge with lowest entropy
int height = inHeight;
double topEntropy = 0.0;
double bottomEntropy = 0.0;
// Max height of each slice
int const maxSliceHeight = static_cast<int>(ceil((inHeight - outHeight) / 8.0));
while (height > outHeight) {
// Height of current slice
int const slice = std::min(height - outHeight, maxSliceHeight);
if (topEntropy == 0.0) {
// Update entropy of top slice
topEntropy = Entropy(image.extract_area(0, top, inWidth, slice));
}
if (bottomEntropy == 0.0) {
// Update entropy of bottom slice
bottomEntropy = Entropy(image.extract_area(0, height - slice - 1, inWidth, slice));
}
// Keep slice with highest entropy
if (topEntropy >= bottomEntropy) {
// Discard bottom slice
bottomEntropy = 0.0;
} else {
// Discard top slice
topEntropy = 0.0;
top = top + slice;
}
height = height - slice;
}
}
return std::make_tuple(left, top);
}
/*
Calculate the Shannon entropy for an image
*/
double Entropy(VImage image) {
return image.hist_find().hist_entropy();
}
} // namespace sharp