참고소스 수정본

참고/playwright-main/packages/utils/image_tools/stats.ts

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+/**
+ * Copyright (c) Microsoft Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import type { ImageChannel } from './imageChannel';
+
+export interface Stats {
+  c1: ImageChannel;
+  c2: ImageChannel;
+
+  // Compute mean value. See https://en.wikipedia.org/wiki/Mean
+  meanC1(x1: number, y1: number, x2: number, y2: number): number;
+  meanC2(x1: number, y1: number, x2: number, y2: number): number;
+  // Compute **population** (not sample) variance. See https://en.wikipedia.org/wiki/Variance
+  varianceC1(x1: number, y1: number, x2: number, y2: number): number;
+  varianceC2(x1: number, y1: number, x2: number, y2: number): number;
+  // Compute covariance. See https://en.wikipedia.org/wiki/Covariance
+  covariance(x1: number, y1: number, x2: number, y2: number): number;
+}
+
+// Image channel has a 8-bit depth.
+const DYNAMIC_RANGE = 2 ** 8 - 1;
+
+export function ssim(stats: Stats, x1: number, y1: number, x2: number, y2: number): number {
+  const mean1 = stats.meanC1(x1, y1, x2, y2);
+  const mean2 = stats.meanC2(x1, y1, x2, y2);
+  const var1 = stats.varianceC1(x1, y1, x2, y2);
+  const var2 = stats.varianceC2(x1, y1, x2, y2);
+  const cov = stats.covariance(x1, y1, x2, y2);
+  const c1 = (0.01 * DYNAMIC_RANGE) ** 2;
+  const c2 = (0.03 * DYNAMIC_RANGE) ** 2;
+  return (2 * mean1 * mean2 + c1) * (2 * cov + c2) / (mean1 ** 2 + mean2 ** 2 + c1) / (var1 + var2 + c2);
+}
+
+export class FastStats implements Stats {
+  c1: ImageChannel;
+  c2: ImageChannel;
+
+  private _partialSumC1: number[];
+  private _partialSumC2: number[];
+  private _partialSumMult: number[];
+  private _partialSumSq1: number[];
+  private _partialSumSq2: number[];
+
+  constructor(c1: ImageChannel, c2: ImageChannel) {
+    this.c1 = c1;
+    this.c2 = c2;
+    const { width, height } = c1;
+
+    this._partialSumC1 = new Array(width * height);
+    this._partialSumC2 = new Array(width * height);
+    this._partialSumSq1 = new Array(width * height);
+    this._partialSumSq2 = new Array(width * height);
+    this._partialSumMult = new Array(width * height);
+
+    const recalc = (mx: number[], idx: number, initial: number, x: number, y: number) => {
+      mx[idx] = initial;
+      if (y > 0)
+        mx[idx] += mx[(y - 1) * width + x];
+      if (x > 0)
+        mx[idx] += mx[y * width + x - 1];
+      if (x > 0 && y > 0)
+        mx[idx] -= mx[(y - 1) * width + x - 1];
+    };
+
+    for (let y = 0; y < height; ++y) {
+      for (let x = 0; x < width; ++x) {
+        const idx = y * width + x;
+        recalc(this._partialSumC1, idx, this.c1.data[idx], x, y);
+        recalc(this._partialSumC2, idx, this.c2.data[idx], x, y);
+        recalc(this._partialSumSq1, idx, this.c1.data[idx] * this.c1.data[idx], x, y);
+        recalc(this._partialSumSq2, idx, this.c2.data[idx] * this.c2.data[idx], x, y);
+        recalc(this._partialSumMult, idx, this.c1.data[idx] * this.c2.data[idx], x, y);
+      }
+    }
+  }
+
+  _sum(partialSum: number[], x1: number, y1: number, x2: number, y2: number): number {
+    const width = this.c1.width;
+    let result = partialSum[y2 * width + x2];
+    if (y1 > 0)
+      result -= partialSum[(y1 - 1) * width + x2];
+    if (x1 > 0)
+      result -= partialSum[y2 * width + x1 - 1];
+    if (x1 > 0 && y1 > 0)
+      result += partialSum[(y1 - 1) * width + x1 - 1];
+    return result;
+  }
+
+  meanC1(x1: number, y1: number, x2: number, y2: number): number {
+    const N = (y2 - y1 + 1) * (x2 - x1 + 1);
+    return this._sum(this._partialSumC1, x1, y1, x2, y2) / N;
+  }
+
+  meanC2(x1: number, y1: number, x2: number, y2: number): number {
+    const N = (y2 - y1 + 1) * (x2 - x1 + 1);
+    return this._sum(this._partialSumC2, x1, y1, x2, y2) / N;
+  }
+
+  varianceC1(x1: number, y1: number, x2: number, y2: number): number {
+    const N = (y2 - y1 + 1) * (x2 - x1 + 1);
+    return (this._sum(this._partialSumSq1, x1, y1, x2, y2) - (this._sum(this._partialSumC1, x1, y1, x2, y2) ** 2) / N) / N;
+  }
+
+  varianceC2(x1: number, y1: number, x2: number, y2: number): number {
+    const N = (y2 - y1 + 1) * (x2 - x1 + 1);
+    return (this._sum(this._partialSumSq2, x1, y1, x2, y2) - (this._sum(this._partialSumC2, x1, y1, x2, y2) ** 2) / N) / N;
+  }
+
+  covariance(x1: number, y1: number, x2: number, y2: number): number {
+    const N = (y2 - y1 + 1) * (x2 - x1 + 1);
+    return (this._sum(this._partialSumMult, x1, y1, x2, y2) - this._sum(this._partialSumC1, x1, y1, x2, y2) * this._sum(this._partialSumC2, x1, y1, x2, y2) / N) / N;
+  }
+}