feat: add "Crop to Content" mode to smart crop tool

Adds a new mode that trims uniform-color borders around the subject,
like GIMP's "Crop to Content." Includes configurable tolerance threshold
and optional pad-to-square with target size for e-commerce workflows.

The original attention-based crop is preserved as "Focus Crop" mode.

Closes #7

apps/api/src/routes/tools/smart-crop.ts

@@ -4,27 +4,67 @@ import { z } from "zod";
 import { createToolRoute } from "../tool-factory.js";
 
 const settingsSchema = z.object({
-  width: z.number().int().positive(),
-  height: z.number().int().positive(),
+  mode: z.enum(["attention", "content"]).default("attention"),
+  // Attention mode: resize to target dimensions using subject detection
+  width: z.number().int().positive().optional(),
+  height: z.number().int().positive().optional(),
+  // Content mode: trim uniform borders, optionally pad to square
+  threshold: z.number().int().min(0).max(255).default(30),
+  padToSquare: z.boolean().default(false),
+  padColor: z.string().default("#ffffff"),
+  targetSize: z.number().int().positive().optional(),
 });
 
 /**
- * Smart crop using Sharp's attention-based strategy.
- * Uses entropy/saliency detection to find the most interesting region.
- * No Python needed.
+ * Smart crop with two modes:
+ * - "attention": Sharp's entropy/saliency detection to crop to the most interesting region
+ * - "content": Trims uniform-color borders (like GIMP's "Crop to Content"),
+ *    optionally pads to a square at a target size
  */
 export function registerSmartCrop(app: FastifyInstance) {
   createToolRoute(app, {
     toolId: "smart-crop",
     settingsSchema,
     process: async (inputBuffer, settings, filename) => {
-      const result = await sharp(inputBuffer)
-        .resize(settings.width, settings.height, {
-          fit: "cover",
-          position: sharp.strategy.attention,
-        })
-        .png()
-        .toBuffer();
+      let result: Buffer;
+
+      if (settings.mode === "content") {
+        // Crop to content: trim uniform borders
+        const pipeline = sharp(inputBuffer).trim({ threshold: settings.threshold });
+        let trimmed = await pipeline.toBuffer({ resolveWithObject: true });
+
+        if (settings.padToSquare || settings.targetSize) {
+          const meta = await sharp(trimmed.data).metadata();
+          const w = meta.width ?? 1;
+          const h = meta.height ?? 1;
+          const target = settings.targetSize || Math.max(w, h);
+          const padR = Math.round(parseInt(settings.padColor.slice(1, 3), 16));
+          const padG = Math.round(parseInt(settings.padColor.slice(3, 5), 16));
+          const padB = Math.round(parseInt(settings.padColor.slice(5, 7), 16));
+
+          trimmed = await sharp(trimmed.data)
+            .resize({
+              width: target,
+              height: target,
+              fit: "contain",
+              background: { r: padR, g: padG, b: padB, alpha: 1 },
+            })
+            .toBuffer({ resolveWithObject: true });
+        }
+
+        result = trimmed.data;
+      } else {
+        // Attention mode: resize to target using subject detection
+        const w = settings.width ?? 1080;
+        const h = settings.height ?? 1080;
+        result = await sharp(inputBuffer)
+          .resize(w, h, {
+            fit: "cover",
+            position: sharp.strategy.attention,
+          })
+          .png()
+          .toBuffer();
+      }
 
       const outputFilename = `${filename.replace(/\.[^.]+$/, "")}_smartcrop.png`;
       return { buffer: result, filename: outputFilename, contentType: "image/png" };