Media Processor

😫 The Pain Point

E-commerce managers often have folders full of product images with different sizes and aspect ratios. Uploading these to a website (which usually requires uniform 1000x1000 squares) looks messy. Manually resizing and adding a logo to 500+ images in Photoshop takes hours.

🚀 Agentic Solution

A “Smart Image Processor” that standardizes your entire catalog in seconds with full progress tracking.

Key Features:

• Smart Padding: Adds white borders instead of cropping, preserving the whole product.
• Auto-Scaling: Resizes your watermark so it never covers the product, regardless of image size.
• Batch Processing: Handles thousands of images with progress bar and ETA.
• Error Handling: Skips corrupt images with warnings instead of crashing.

⚔️ Phase 1: Commander (Quick Fix)

For a quick one-off batch.

Prompt:

“I have a folder named input containing varied product images (JPG, PNG). I also have a watermark.png file.

Write a robust Python script using Pillow to process these images:

1. Smart Resize: Resize every image to fit within a 1000x1000 pixel canvas without cropping.
   • Maintain original aspect ratio.
   • Fill any empty space with a white background.
   • Ensure the final output is exactly 1000x1000.
2. Watermark:
   • Overlay watermark.png in the center of the image.
   • Auto-scale: If the watermark is larger than 30% of the canvas width, resize it down to 30% width (maintain aspect ratio).
   • Apply 50% opacity to the watermark.
3. Output: Save processed images to an output folder as JPG (quality=90). Handle RGBA to RGB conversion correctly.

Print a progress log (e.g., ‘Processed 1/500…’). Skip corrupt images with warning.”

Result: A script that processes 500 images in < 1 minute with full error handling.

🏗️ Phase 2: Architect (Permanent Tool)

For regular use by content teams.

Engineering Prompt:

*Role:** Python GUI Developer (PyQt6 Specialist)
**Task:** Create a Cross-Platform Desktop App named "E-commerce Watermark Pro".

**Objective:** A standalone desktop tool to visually position a watermark on a base image and batch process a folder of images.

**Tech Stack:**
* Language: Python 3.10+
* GUI Library: PyQt6 (Must ensure cross-platform compatibility for Windows & macOS).
* Image Processing: Pillow (PIL).
* Packaging: PyInstaller.

**Functional Requirements:**
1.  **UI Layout (PyQt6):**
    * Left Panel: Controls (Load Images, Opacity/Scale Sliders, Start Button).
    * Right Panel: Interactive Canvas (QGraphicsScene) for Drag & Drop positioning of the watermark.
    * *Constraint:* Use standard PyQt6 widgets so they render natively on both OS.

2.  **Batch Processing Logic:**
    * Smart Resize to 1000x1000 (White padding).
    * Apply watermark based on visual coordinates.
    * Multithreading: Use `QThread` to prevent the UI from freezing during batch processing (Critical for macOS specifically to avoid the "Beachball of Death").

3.  **Deliverables:**
    * `main.py`: Complete source code.
    * `requirements.txt`: Dependencies.
    * **Build Instructions:**
        * Create a `build_app.py` or provide specific `pyinstaller` terminal commands to generate:
            * `.exe` (Single file) for Windows.
            * `.app` bundle for macOS.

🧠 Prompt Decoding

• Smart Resize: The request “fit within 1000x1000 without cropping” tells the AI to calculate aspect ratios and apply padding (ImageOps.pad in Pillow), avoiding distorted images.
• Auto-scale: Essential for batch processing. Determining watermark size relative to the canvas (e.g., “30% width”) prevents it from being tiny on 4K images or huge on thumbnails.

🛠️ Instructions

1. Put images in input/ folder.
2. Run the script or use the visual setup tool.
3. Check output/ for standardized, watermarked images.