product-image-processor

# /product-image-processor — Product Image Processor Download product images from a Google Sheet, normalize sizing, and remove backgrounds. Saves output at each processing stage. Works with the **master Google Sheet** — the 33-column schema defined in `../../schema/product-schema.md`. Image URLs are in column AC, product names in column E. Read `../../schema/sheet-conventions.md` for CRUD patterns with MCP tools. ## Step 1: Get Input If no arguments provided, ask the user: 1. **Spreadsheet ID** — the Google Sheets ID (from the URL: `docs.google.com/spreadsheets/d/{ID}/...`). 2. **Image URL column** — which column contains image URLs (default: `AC` in the master schema, or the user can specify) 3. **Name column** (optional) — which column has product names for file naming (default: `E` in the master schema). If not provided, derive names from the image URL/filename. 4. **Output location** — where to save the images. Suggest `./product-images-YYYY-MM-DD/` as default but let the user pick any path. 5. **Header row** — whether row 1 is a header (default: yes, row 2 in master schema) ## Step 2: Read URLs from Google Sheet Use `mcp__google-sheets__list_sheets` to inspect the sheet, then `mcp__google-sheets__get_sheet_data` to read the image URL column and optional name column. Build a list of `{ index, url, name }` entries. Skip empty rows. ## Step 3: Create Output Folders Create the output directory at the user's chosen path with 3 subfolders: ``` <output-path>/ ├── originals/ # Raw downloads ├── resized/ # Normalized sizing └── nobg/ # Background removed ``` If the folder already exists, append a suffix: `-2`, `-3`, etc. ## Step 4: Download Images Download each image using `curl` in Bash: ```bash curl -L -o "<output-path>" "<url>" ``` **IMPORTANT:** Use `curl`, NOT WebFetch. WebFetch processes content through an AI model which corrupts binary image data. Name files as: `001-product-name.png`, `002-product-name.png`, etc. - Slugify the product name: lowercase, replace spaces/special chars with hyphens, strip consecutive hyphens - If no name column, extract a name from the URL filename (strip extension and query params) - If the URL gives no usable name, use `001-image.png`, `002-image.png`, etc. If the downloaded file is not a PNG (check extension or content type), convert it to PNG during the resize step. ## Step 5: Resize Images Run a Python script to resize all images in `originals/` → `resized/`: ```python from PIL import Image import os, sys input_dir = sys.argv[1] # originals/ output_dir = sys.argv[2] # resized/ max_edge = int(sys.argv[3]) if len(sys.argv) > 3 else 2000 for fname in sorted(os.listdir(input_dir)): if not fname.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.gif', '.bmp', '.tiff')): continue try: img = Image.open(os.path.join(input_dir, fname)) img = img.convert("RGBA") w, h = img.size longest = max(w, h) if longest > max_edge: scale = max_edge / longest new_w, new_h = int(w * scale), int(h * scale) img = img.resize((new_w, new_h), Image.LANCZOS) out_name = os.path.splitext(fname)[0] + ".png" img.save(os.path.join(output_dir, out_name), "PNG") print(f"OK: {fname} → {out_name} ({img.size[0]}x{img.size[1]})") except Exception as e: print(f"FAIL: {fname} — {e}") ``` Rules: - Max **2000px** on the longest edge (configurable if user requests) - Preserve aspect ratio - Do NOT upscale — if already smaller than max, keep original dimensions - Convert everything to PNG (RGBA mode for transparency support) ## Step 6: Remove Backgrounds Check if `rembg` is installed. If not, install it: ```bash pip3 install rembg onnxruntime ``` Then run background removal on all resized images → `nobg/`: ```python from rembg import remove from PIL import Image import os, sys, io input_dir = sys.argv[1] # resized/ output_dir = sys.argv[2] # nobg/ for fname in sorted(os.listdir(input_dir)): if not fname.lower().endswith('.png'): continue try: input_path = os.path.join(input_dir, fname) with open(input_path, 'rb') as f: input_data = f.read() output_data = remove(input_data) img = Image.open(io.BytesIO(output_data)) img.save(os.path.join(output_dir, fname), "PNG") print(f"OK: {fname}") except Exception as e: print(f"FAIL: {fname} — {e}") ``` **Note:** The first run of rembg downloads the u2net model (~170MB). Warn the user this may take a minute. ## Step 7: Report Results After processing, print a summary: ``` ## Product Image Processing Complete 📁 Output: ./product-images-YYYY-MM-DD/ | Stage | Success | Failed | |-------------|---------|--------| | Downloaded | 12 | 1 | | Resized | 12 | 0 | | BG Removed | 12 | 0 | ### Failures - 003-chair-arm.png: Download failed (404 Not Found) ``` Include the full path to the output folder so the user can open it. ## Error Handling - **Download failures:** Log and continue. Don't block the pipeline for one bad URL. - **Resize failures:** Log and continue. Skip that image in the bg-removal step. - **rembg failures:** Log and continue. Some images (vectors, icons) may not process well. - **Sheet read errors:** Stop and report. Ask the user to verify the spreadsheet ID and column. ## Notes - Process images sequentially (not parallel) to avoid overwhelming the network or CPU - For large batches (50+ images), print progress every 10 images - The rembg model download only happens once — subsequent runs reuse the cached model