dwm picom added

dwm/picom/src/kernel.c

@@ -0,0 +1,160 @@
+// SPDX-License-Identifier: MPL-2.0
+// Copyright (c) Yuxuan Shui <yshuiv7@gmail.com>
+
+#include <assert.h>
+#include <math.h>
+
+#include "compiler.h"
+#include "kernel.h"
+#include "log.h"
+#include "utils.h"
+
+/// Sum a region convolution kernel. Region is defined by a width x height rectangle whose
+/// top left corner is at (x, y)
+double sum_kernel(const conv *map, int x, int y, int width, int height) {
+	double ret = 0;
+
+	// Compute sum of values which are "in range"
+	int xstart = normalize_i_range(x, 0, map->w),
+	    xend = normalize_i_range(width + x, 0, map->w);
+	int ystart = normalize_i_range(y, 0, map->h),
+	    yend = normalize_i_range(height + y, 0, map->h);
+	assert(yend >= ystart && xend >= xstart);
+
+	int d = map->w;
+	if (map->rsum) {
+		// See sum_kernel_preprocess
+		double v1 = xstart ? map->rsum[(yend - 1) * d + xstart - 1] : 0;
+		double v2 = ystart ? map->rsum[(ystart - 1) * d + xend - 1] : 0;
+		double v3 = (xstart && ystart) ? map->rsum[(ystart - 1) * d + xstart - 1] : 0;
+		return map->rsum[(yend - 1) * d + xend - 1] - v1 - v2 + v3;
+	}
+
+	for (int yi = ystart; yi < yend; yi++) {
+		for (int xi = xstart; xi < xend; xi++) {
+			ret += map->data[yi * d + xi];
+		}
+	}
+
+	return ret;
+}
+
+double sum_kernel_normalized(const conv *map, int x, int y, int width, int height) {
+	double ret = sum_kernel(map, x, y, width, height);
+	if (ret < 0) {
+		ret = 0;
+	}
+	if (ret > 1) {
+		ret = 1;
+	}
+	return ret;
+}
+
+static inline double attr_const gaussian(double r, double x, double y) {
+	// Formula can be found here:
+	// https://en.wikipedia.org/wiki/Gaussian_blur#Mathematics
+	// Except a special case for r == 0 to produce sharp shadows
+	if (r == 0)
+		return 1;
+	return exp(-0.5 * (x * x + y * y) / (r * r)) / (2 * M_PI * r * r);
+}
+
+conv *gaussian_kernel(double r, int size) {
+	conv *c;
+	int center = size / 2;
+	double t;
+	assert(size % 2 == 1);
+
+	c = cvalloc(sizeof(conv) + (size_t)(size * size) * sizeof(double));
+	c->w = c->h = size;
+	c->rsum = NULL;
+	t = 0.0;
+
+	for (int y = 0; y < size; y++) {
+		for (int x = 0; x < size; x++) {
+			double g = gaussian(r, x - center, y - center);
+			t += g;
+			c->data[y * size + x] = g;
+		}
+	}
+
+	for (int y = 0; y < size; y++) {
+		for (int x = 0; x < size; x++) {
+			c->data[y * size + x] /= t;
+		}
+	}
+
+	return c;
+}
+
+/// Estimate the element of the sum of the first row in a gaussian kernel with standard
+/// deviation `r` and size `size`,
+static inline double estimate_first_row_sum(double size, double r) {
+	double factor = erf(size / r / sqrt(2));
+	double a = exp(-0.5 * size * size / (r * r)) / sqrt(2 * M_PI) / r;
+	return a / factor;
+}
+
+/// Pick a suitable gaussian kernel radius for a given kernel size. The returned radius
+/// is the maximum possible radius (<= size*2) that satisfies no sum of the rows in
+/// the kernel are less than `row_limit` (up to certain precision).
+static inline double gaussian_kernel_std_for_size(int size, double row_limit) {
+	assert(size > 0);
+	if (row_limit >= 1.0 / 2.0 / size) {
+		return size * 2;
+	}
+	double l = 0, r = size * 2;
+	while (r - l > 1e-2) {
+		double mid = (l + r) / 2.0;
+		double vmid = estimate_first_row_sum(size, mid);
+		if (vmid > row_limit) {
+			r = mid;
+		} else {
+			l = mid;
+		}
+	}
+	return (l + r) / 2.0;
+}
+
+/// Create a gaussian kernel with auto detected standard deviation. The choosen standard
+/// deviation tries to make sure the outer most pixels of the shadow are completely
+/// transparent, so the transition from shadow to the background is smooth.
+///
+/// @param[in] shadow_radius the radius of the shadow
+conv *gaussian_kernel_autodetect_deviation(int shadow_radius) {
+	assert(shadow_radius >= 0);
+	int size = shadow_radius * 2 + 1;
+
+	if (shadow_radius == 0) {
+		return gaussian_kernel(0, size);
+	}
+	double std = gaussian_kernel_std_for_size(shadow_radius, 1.0 / 256.0);
+	return gaussian_kernel(std, size);
+}
+
+/// preprocess kernels to make shadow generation faster
+/// shadow_sum[x*d+y] is the sum of the kernel from (0, 0) to (x, y), inclusive
+void sum_kernel_preprocess(conv *map) {
+	if (map->rsum) {
+		free(map->rsum);
+	}
+
+	auto sum = map->rsum = ccalloc(map->w * map->h, double);
+	sum[0] = map->data[0];
+
+	for (int x = 1; x < map->w; x++) {
+		sum[x] = sum[x - 1] + map->data[x];
+	}
+
+	const int d = map->w;
+	for (int y = 1; y < map->h; y++) {
+		sum[y * d] = sum[(y - 1) * d] + map->data[y * d];
+		for (int x = 1; x < map->w; x++) {
+			double tmp = sum[(y - 1) * d + x] + sum[y * d + x - 1] -
+			             sum[(y - 1) * d + x - 1];
+			sum[y * d + x] = tmp + map->data[y * d + x];
+		}
+	}
+}
+
+// vim: set noet sw=8 ts=8 :