feat : add simple self-benchmark using node js with the help of LLM and upgrade SixLabors version

.gitignore

@@ -1,2 +1,3 @@
 /bin/
-/obj/
+/obj/
+/test/benchmark_output/

StitcherApi.csproj

@@ -6,6 +6,6 @@
   </PropertyGroup>
   <ItemGroup>
     <PackageReference Include="Microsoft.AspNetCore.OpenApi" Version="8.0.5" />
-    <PackageReference Include="SixLabors.ImageSharp" Version="3.1.10" />
+    <PackageReference Include="SixLabors.ImageSharp" Version="3.1.11" />
   </ItemGroup>
 </Project>

test/benchmark.js

@@ -0,0 +1,259 @@
+import fs from "fs/promises";
+import path from "path";
+
+const API_ENDPOINT = "http://localhost:5229/api/image/generate";
+const NUM_REQUESTS_PER_SCENARIO = 10;
+const OUTPUT_DIR = "benchmark_output";
+
+const colors = {
+  reset: "\x1b[0m",
+  bright: "\x1b[1m",
+  dim: "\x1b[2m",
+  fg: {
+    cyan: "\x1b[36m",
+    green: "\x1b[32m",
+    yellow: "\x1b[33m",
+    red: "\x1b[31m",
+  },
+};
+
+const scenarios = [
+  {
+    name: "Small Canvas, Small Crop",
+    payload: {
+      canvas_rect: "A1:B2",
+      crop_offset: [0.25, 0.25],
+      crop_size: [0.5, 0.5],
+      output_scale: 1.0,
+    },
+  },
+  {
+    name: "Medium Canvas, Full Crop (Stitching)",
+    payload: {
+      canvas_rect: "C3:F6",
+      crop_offset: [0.0, 0.0],
+      crop_size: [1.0, 1.0],
+      output_scale: 0.5,
+    },
+  },
+  {
+    name: "Large Canvas, Small Corner Crop",
+    payload: {
+      canvas_rect: "A1:H12",
+      crop_offset: [0.0, 0.0],
+      crop_size: [0.1, 0.1],
+      output_scale: 1.0,
+    },
+  },
+  {
+    name: "Large Canvas, Center Crop",
+    payload: {
+      canvas_rect: "A1:AE55",
+      crop_offset: [0.45, 0.45],
+      crop_size: [0.1, 0.1],
+      output_scale: 1.0,
+    },
+  },
+  {
+    name: "Tall Canvas, Full Crop & Scale",
+    payload: {
+      canvas_rect: "A1:A20",
+      crop_offset: [0.0, 0.0],
+      crop_size: [1.0, 1.0],
+      output_scale: 0.1,
+    },
+  },
+  {
+    name: "Wide Canvas, Full Crop & Scale",
+    payload: {
+      canvas_rect: "A1:T1",
+      crop_offset: [0.0, 0.0],
+      crop_size: [1.0, 1.0],
+      output_scale: 0.1,
+    },
+  },
+  {
+    name: "Single Tile, Full Crop",
+    payload: {
+      canvas_rect: "K16:K16",
+      crop_offset: [0.0, 0.0],
+      crop_size: [1.0, 1.0],
+      output_scale: 1.0,
+    },
+  },
+  {
+    name: "Single Tile, Partial Crop",
+    payload: {
+      canvas_rect: "K16:K16",
+      crop_offset: [0.1, 0.1],
+      crop_size: [0.25, 0.25],
+      output_scale: 1.0,
+    },
+  },
+  {
+    name: "Large Canvas, Bottom-Right Crop",
+    payload: {
+      canvas_rect: "A1:AE55",
+      crop_offset: [0.95, 0.95],
+      crop_size: [0.05, 0.05],
+      output_scale: 1.0,
+    },
+  },
+  {
+    name: "Wide Canvas, Thin Horizontal Crop",
+    payload: {
+      canvas_rect: "A1:AE10",
+      crop_offset: [0.0, 0.5],
+      crop_size: [1.0, 0.01],
+      output_scale: 1.0,
+    },
+  },
+  {
+    name: "Tall Canvas, Thin Vertical Crop",
+    payload: {
+      canvas_rect: "A1:J55",
+      crop_offset: [0.5, 0.0],
+      crop_size: [0.01, 1.0],
+      output_scale: 1.0,
+    },
+  },
+  {
+    name: "Medium Canvas, Heavy Scaling",
+    payload: {
+      canvas_rect: "D4:G8",
+      crop_offset: [0.0, 0.0],
+      crop_size: [1.0, 1.0],
+      output_scale: 0.05,
+    },
+  },
+  {
+    name: "MAXIMUM CANVAS (Streaming Test)",
+    payload: {
+      canvas_rect: "A1:AE55",
+      crop_offset: [0.0, 0.0],
+      crop_size: [1.0, 1.0],
+      output_scale: 0.01,
+    },
+  },
+];
+
+const calculateStats = (times) => {
+  if (times.length === 0) return null;
+  const sum = times.reduce((a, b) => a + b, 0);
+  const avg = sum / times.length;
+  const stdDev = Math.sqrt(
+    times.map((x) => Math.pow(x - avg, 2)).reduce((a, b) => a + b, 0) /
+      times.length
+  );
+  const sorted = [...times].sort((a, b) => a - b);
+  return {
+    avg: avg,
+    stdDev: stdDev,
+    median: sorted[Math.floor(sorted.length / 2)],
+    min: sorted[0],
+    max: sorted[sorted.length - 1],
+    throughput: 1000 / avg,
+  };
+};
+
+async function runBenchmark() {
+  console.log(
+    `${colors.bright}${colors.fg.cyan}--- Starting Image Stitcher API Benchmark ---${colors.reset}`
+  );
+
+  try {
+    await fs.mkdir(OUTPUT_DIR, { recursive: true });
+  } catch (e) {
+    console.error(
+      `${colors.fg.red}Error creating output directory: ${e.message}${colors.reset}`
+    );
+    return;
+  }
+
+  const allResults = [];
+
+  for (const scenario of scenarios) {
+    console.log(
+      `\n${colors.fg.yellow}Running Scenario: '${scenario.name}' (${NUM_REQUESTS_PER_SCENARIO} requests)...${colors.reset}`
+    );
+    const responseTimes = [];
+
+    for (let i = 0; i < NUM_REQUESTS_PER_SCENARIO; i++) {
+      try {
+        const startTime = performance.now();
+        const response = await fetch(API_ENDPOINT, {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify(scenario.payload),
+        });
+        const endTime = performance.now();
+
+        if (response.ok) {
+          responseTimes.push(endTime - startTime);
+          if (i === 0) {
+            const arrayBuffer = await response.arrayBuffer();
+            const buffer = Buffer.from(arrayBuffer);
+            const fileName = `${scenario.name
+              .replace(/[\s(),]/g, "_")
+              .toLowerCase()}.png`;
+            const filePath = path.join(OUTPUT_DIR, fileName);
+            await fs.writeFile(filePath, buffer);
+            console.log(
+              `  ${colors.dim}Saved output image to '${filePath}'${colors.reset}`
+            );
+          } else {
+            await response.arrayBuffer();
+          }
+        } else {
+          console.log(
+            `  ${colors.fg.red}Request ${i + 1} failed with status ${
+              response.status
+            }${colors.reset}`
+          );
+        }
+      } catch (e) {
+        console.error(
+          `  ${colors.fg.red}Request ${i + 1} failed with an exception: ${
+            e.message
+          }${colors.reset}`
+        );
+
+        break;
+      }
+    }
+
+    const stats = calculateStats(responseTimes);
+    if (stats) {
+      allResults.push({ name: scenario.name, stats });
+      console.log(
+        `  ${colors.fg.green}Average Latency: ${stats.avg.toFixed(2)} ms${
+          colors.reset
+        }`
+      );
+    }
+  }
+
+  console.log(
+    `\n${colors.bright}${colors.fg.cyan}--- Benchmark Summary ---${colors.reset}`
+  );
+  for (const result of allResults) {
+    console.log(`\n${colors.bright}Scenario: ${result.name}${colors.reset}`);
+    const { stats } = result;
+    console.log(
+      `  Avg Latency:    ${stats.avg.toFixed(2)} ms (+/- ${stats.stdDev.toFixed(
+        2
+      )} ms)`
+    );
+    console.log(
+      `  Details (ms):   Median: ${stats.median.toFixed(
+        2
+      )} | Min: ${stats.min.toFixed(2)} | Max: ${stats.max.toFixed(2)}`
+    );
+    console.log(`  Avg Throughput: ${stats.throughput.toFixed(2)} req/s`);
+  }
+  console.log(
+    `\n${colors.bright}${colors.fg.cyan}--- Benchmark Complete ---${colors.reset}`
+  );
+}
+
+runBenchmark();

test/package.json

@@ -0,0 +1,12 @@
+{
+  "name": "api-benchmark",
+  "version": "1.0.0",
+  "description": "A benchmark script for the Image Stitcher API.",
+  "main": "benchmark.js",
+  "type": "module",
+  "scripts": {
+    "start": "node benchmark.js"
+  },
+  "author": "",
+  "license": "ISC"
+}