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refactor(yape06): de-duplicate yape06 module (#47) #68

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Merged
kalwalt merged 1 commit into from
Jul 8, 2026
Merged

refactor(yape06): de-duplicate yape06 module (#47) #68

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86 changes: 1 addition & 85 deletions src/jsfeatNext.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -529,91 +529,7 @@ jsfeatNext.orb = orb;

jsfeatNext.yape = yape;

jsfeatNext.yape06 = class yape06 extends jsfeatNext {
public laplacian_threshold: number;
public min_eigen_value_threshold: number;

constructor() {
super();
this.laplacian_threshold = 30;
this.min_eigen_value_threshold = 25;
}

detect(src: matrix_t, points: keypoint_t[], border: number): number {
if (typeof border === "undefined") {
border = 5;
}
let x = 0,
y = 0;
const w = src.cols,
h = src.rows,
srd_d = src.data;
const Dxx = 5,
Dyy = (5 * w) | 0;
const Dxy = (3 + 3 * w) | 0,
Dyx = (3 - 3 * w) | 0;
const lap_buf = this.cache.get_buffer((w * h) << 2);
const laplacian = lap_buf.i32;
let lv = 0,
row = 0,
rowx = 0,
min_eigen_value = 0,
pt;
let number_of_points = 0;
const lap_thresh = this.laplacian_threshold;
const eigen_thresh = this.min_eigen_value_threshold;

const sx = Math.max(5, border) | 0;
const sy = Math.max(3, border) | 0;
const ex = Math.min(w - 5, w - border) | 0;
const ey = Math.min(h - 3, h - border) | 0;

x = w * h;
while (--x >= 0) {
laplacian[x] = 0;
}
compute_laplacian(srd_d, laplacian, w, Dxx, Dyy, sx, sy, ex, ey);

row = (sy * w + sx) | 0;
for (y = sy; y < ey; ++y, row += w) {
for (x = sx, rowx = row; x < ex; ++x, ++rowx) {
lv = laplacian[rowx];
if (
(lv < -lap_thresh &&
lv < laplacian[rowx - 1] &&
lv < laplacian[rowx + 1] &&
lv < laplacian[rowx - w] &&
lv < laplacian[rowx + w] &&
lv < laplacian[rowx - w - 1] &&
lv < laplacian[rowx + w - 1] &&
lv < laplacian[rowx - w + 1] &&
lv < laplacian[rowx + w + 1]) ||
(lv > lap_thresh &&
lv > laplacian[rowx - 1] &&
lv > laplacian[rowx + 1] &&
lv > laplacian[rowx - w] &&
lv > laplacian[rowx + w] &&
lv > laplacian[rowx - w - 1] &&
lv > laplacian[rowx + w - 1] &&
lv > laplacian[rowx - w + 1] &&
lv > laplacian[rowx + w + 1])
) {
min_eigen_value = hessian_min_eigen_value(srd_d, rowx, lv, Dxx, Dyy, Dxy, Dyx);
if (min_eigen_value > eigen_thresh) {
pt = points[number_of_points];
(pt.x = x), (pt.y = y), (pt.score = min_eigen_value);
++number_of_points;
++x, ++rowx; // skip next pixel since this is maxima in 3x3
}
}
}
}

this.cache.put_buffer(lap_buf);

return number_of_points;
}
};
jsfeatNext.yape06 = yape06;

jsfeatNext.motion_estimator = class motion_estimator extends jsfeatNext {
constructor() {
Expand Down
92 changes: 90 additions & 2 deletions src/yape06/yape06.ts
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Original file line number Diff line number Diff line change
@@ -1,7 +1,95 @@
import jsfeatNext from "../core/core";
import { matrix_t } from "../matrix_t/matrix_t";
import { keypoint_t } from "../keypoint_t/keypoint_t";
export class yape06 {
import { compute_laplacian, hessian_min_eigen_value } from "./yape06_utils";

/**
* Real implementation, moved out of the src/jsfeatNext.ts monolith (issue #47).
* This file previously held a type-only stub — the implementation below is the
* inline code from the monolith, verbatim.
*/
export class yape06 extends jsfeatNext {
public laplacian_threshold: number;
public min_eigen_value_threshold: number;

constructor() {
super();
this.laplacian_threshold = 30;
this.min_eigen_value_threshold = 25;
}

detect(src: matrix_t, points: keypoint_t[], border: number): number {
throw new Error("Method not implemented.");
if (typeof border === "undefined") {
border = 5;
}
let x = 0,
y = 0;
const w = src.cols,
h = src.rows,
srd_d = src.data;
const Dxx = 5,
Dyy = (5 * w) | 0;
const Dxy = (3 + 3 * w) | 0,
Dyx = (3 - 3 * w) | 0;
const lap_buf = this.cache.get_buffer((w * h) << 2);
const laplacian = lap_buf.i32;
let lv = 0,
row = 0,
rowx = 0,
min_eigen_value = 0,
pt;
let number_of_points = 0;
const lap_thresh = this.laplacian_threshold;
const eigen_thresh = this.min_eigen_value_threshold;

const sx = Math.max(5, border) | 0;
const sy = Math.max(3, border) | 0;
const ex = Math.min(w - 5, w - border) | 0;
const ey = Math.min(h - 3, h - border) | 0;

x = w * h;
while (--x >= 0) {
laplacian[x] = 0;
}
compute_laplacian(srd_d, laplacian, w, Dxx, Dyy, sx, sy, ex, ey);

row = (sy * w + sx) | 0;
for (y = sy; y < ey; ++y, row += w) {
for (x = sx, rowx = row; x < ex; ++x, ++rowx) {
lv = laplacian[rowx];
if (
(lv < -lap_thresh &&
lv < laplacian[rowx - 1] &&
lv < laplacian[rowx + 1] &&
lv < laplacian[rowx - w] &&
lv < laplacian[rowx + w] &&
lv < laplacian[rowx - w - 1] &&
lv < laplacian[rowx + w - 1] &&
lv < laplacian[rowx - w + 1] &&
lv < laplacian[rowx + w + 1]) ||
(lv > lap_thresh &&
lv > laplacian[rowx - 1] &&
lv > laplacian[rowx + 1] &&
lv > laplacian[rowx - w] &&
lv > laplacian[rowx + w] &&
lv > laplacian[rowx - w - 1] &&
lv > laplacian[rowx + w - 1] &&
lv > laplacian[rowx - w + 1] &&
lv > laplacian[rowx + w + 1])
) {
min_eigen_value = hessian_min_eigen_value(srd_d, rowx, lv, Dxx, Dyy, Dxy, Dyx);
if (min_eigen_value > eigen_thresh) {
pt = points[number_of_points];
(pt.x = x), (pt.y = y), (pt.score = min_eigen_value);
++number_of_points;
++x, ++rowx; // skip next pixel since this is maxima in 3x3
}
}
}
}

this.cache.put_buffer(lap_buf);

return number_of_points;
}
}