[Optimization] Reduce Rerun memory footprint via automatic image downsampling

[wuxiaoqiang12]

Description:

This PR optimizes the visualization pipeline by introducing an optional image downsampling mechanism for Rerun.

The Problem:
When recording or replaying high-resolution datasets (e.g., 1080p * n), Rerun's default behavior of caching full-resolution frames leads to excessive RAM consumption and system lag, especially during long recording sessions on edge devices.

The Solution:
Added a dynamic resizing logic in log_rerun_data. If the environment variable LEROBOT_RERUN_MAX_IMAGE_WIDTH is set, images sent to Rerun will be downsampled to that width while maintaining the aspect ratio.

Key Features:

• Non-intrusive: Uses an environment variable to avoid breaking existing API signatures.
• Efficiency: Uses cv2.INTER_AREA for high-quality downsampling.
• Preservation: This optimization only affects the Rerun preview; the original high-quality data saved to disk or used for training remains untouched.

Environment Variable:

• LEROBOT_RERUN_MAX_IMAGE_WIDTH: Set the maximum width (e.g., 480 or 640) for images displayed in Rerun.

Testing:

• Recorded a dataset at 1920x1080.
• With export LEROBOT_RERUN_MAX_IMAGE_WIDTH=480: Memory usage remained stable throughout the entire session.