cli.py

"""Command Line Interface (CLI) for CV Lab Camera operations."""

from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

from developer_api import load_image, process_image, save_image
from batch.dataset_processor import process_dataset


def main():
    parser = argparse.ArgumentParser(description="CV Lab Camera Command Line Interface for developers.")
    subparsers = parser.add_subparsers(dest="command", help="Sub-commands")

    # Filter subcommand
    filter_parser = subparsers.add_parser("filter", help="Apply built-in or saved image filter")
    filter_parser.add_argument("-i", "--input", required=True, help="Input image file path")
    filter_parser.add_argument("-f", "--filter", default="Sepia", help="Filter name (e.g. Sepia, Grayscale, Blur)")
    filter_parser.add_argument("-p", "--params", help="JSON params string")
    filter_parser.add_argument("-o", "--output", required=True, help="Output image file path")

    # Transform subcommand
    trans_parser = subparsers.add_parser("transform", help="Apply geometric transformation")
    trans_parser.add_argument("-i", "--input", required=True, help="Input image file path")
    trans_parser.add_argument("--op", default="Rotation", choices=["Translation", "Rotation", "Scaling", "Reflection"], help="Transformation type")
    trans_parser.add_argument("--angle", type=float, default=30.0, help="Rotation angle in degrees")
    trans_parser.add_argument("--tx", type=int, default=30, help="X translation offset")
    trans_parser.add_argument("--ty", type=int, default=30, help="Y translation offset")
    trans_parser.add_argument("-o", "--output", required=True, help="Output image file path")

    # Morphology subcommand
    morph_parser = subparsers.add_parser("morph", help="Apply morphological operations")
    morph_parser.add_argument("-i", "--input", required=True, help="Input image file path")
    morph_parser.add_argument("--op", default="Opening", help="Morphology operation (e.g. Opening, Erosion, Dilation)")
    morph_parser.add_argument("--size", type=int, default=5, help="Kernel size")
    morph_parser.add_argument("-o", "--output", required=True, help="Output image file path")

    # AR subcommand
    ar_parser = subparsers.add_parser("ar", help="Apply face AR filter")
    ar_parser.add_argument("-i", "--input", required=True, help="Input face image file path")
    ar_parser.add_argument("-f", "--filter", default="Glasses", help="AR filter name (e.g. Glasses, Cyberpunk Visor, Crown & Star Sparkles)")
    ar_parser.add_argument("-o", "--output", required=True, help="Output image file path")

    # Style subcommand
    style_parser = subparsers.add_parser("style", help="Apply neural style transfer")
    style_parser.add_argument("-i", "--input", required=True, help="Content image file path")
    style_parser.add_argument("-s", "--style-image", required=True, help="Style image file path")
    style_parser.add_argument("--max-size", type=int, default=512, help="Output max image resolution")
    style_parser.add_argument("-o", "--output", required=True, help="Output image file path")

    # Batch subcommand
    batch_parser = subparsers.add_parser("batch", help="Process image directory or list")
    batch_parser.add_argument("--files", nargs="*", help="List of input image or zip files")
    batch_parser.add_argument("--dir", help="Input directory")
    batch_parser.add_argument("-f", "--filter", default="Grayscale", help="Filter or pipeline name")
    batch_parser.add_argument("-o", "--output-zip", default="processed.zip", help="Output zip file path")

    args = parser.parse_args()

    if not args.command:
        parser.print_help()
        sys.exit(1)

    if args.command == "filter":
        params = json.loads(args.params) if args.params else {}
        _, meta = process_image(args.input, "filter", operation_name=args.filter, params=params, output_path=args.output)
        print(f"Applied filter '{args.filter}' -> Saved to {args.output}")

    elif args.command == "transform":
        params = {"angle": args.angle, "tx": args.tx, "ty": args.ty}
        _, meta = process_image(args.input, "transform", operation_name=args.op, params=params, output_path=args.output)
        print(f"Applied transform '{args.op}' -> Saved to {args.output}")

    elif args.command == "morph":
        params = {"size": args.size}
        _, meta = process_image(args.input, "morphology", operation_name=args.op, params=params, output_path=args.output)
        print(f"Applied morphology '{args.op}' -> Saved to {args.output}")

    elif args.command == "ar":
        _, meta = process_image(args.input, "ar", operation_name=args.filter, output_path=args.output)
        print(f"Applied AR filter '{args.filter}' ({meta.get('status')}) -> Saved to {args.output}")

    elif args.command == "style":
        params = {"style_image": args.style_image, "max_size": args.max_size}
        _, meta = process_image(args.input, "style", params=params, output_path=args.output)
        print(f"Applied neural style transfer -> Saved to {args.output}")

    elif args.command == "batch":
        zip_path = process_dataset(args.files, args.dir, args.filter)
        Path(zip_path).rename(args.output_zip)
        print(f"Batch dataset processed with '{args.filter}' -> Saved archive to {args.output_zip}")


if __name__ == "__main__":
    main()