Programming language: [your favorite programming language]

Create a complete program that analyzes the bounding box dimensions in a YOLO/Darknet training dataset and visualizes their size distribution.

The program will be placed in the main project folder. Beneath the project folder will be one or more clip folders. Each clip folder contains images and labels subfolders. The program must recursively search all clip folders, read every valid label file and combine the bounding boxes from the entire dataset into a single set of results.

The annotation files use the standard YOLO/Darknet format, with five whitespace-separated values per object:

class_id  center_x  center_y  width  height

The coordinates and dimensions are normalized between 0 and 1. Convert the normalized width and height values to pixel dimensions using the image resolution. Assume all images in the dataset have the same resolution and determine that resolution from the first valid .png, .jpg or .jpeg image found in the dataset.

Ignore empty label files and skip annotation lines that do not contain exactly five values.

Generate a single figure containing two side-by-side plots:
- A scatterplot showing bounding box width versus bounding box height
- A histogram showing the frequency distribution of bounding box areas

By default, display the results using absolute pixel dimensions. Add an optional --scaled command-line flag that instead displays bounding box width and height normalized between 0 and 1 relative to the image dimensions. The histogram should also display normalized box areas.

The program should be executable from the terminal. Provide build or setup instructions, required dependencies, and commands appropriate to the chosen language for running the program against the current project folder, represented by a single dot, both with and without the --scaled flag. The program should automatically process the entire dataset and produce a single figure representing all clip folders. Use suitable libraries for image loading, numerical processing, plotting, and command-line argument parsing.