# /// script
# requires-python = ">=3.10"
# dependencies = ["numpy", "opencv-python"]
# ///
"""
@effect zoom
@version 1.0.0
@author artdag

@description
Zooms into the center of the frame. Values > 1 zoom in (magnify),
values < 1 zoom out (shrink with black borders).

@param factor float
  @range 0.1 5
  @default 1.0
  Zoom factor. 1 = unchanged, 2 = 2x magnification, 0.5 = half size.

@param center_x float
  @range 0 1
  @default 0.5
  Horizontal center of zoom (0 = left, 1 = right).

@param center_y float
  @range 0 1
  @default 0.5
  Vertical center of zoom (0 = top, 1 = bottom).

@example
  (effect zoom :factor 1.5)

@example
  ;; Pulse zoom on bass
  (effect zoom :factor (bind bass :range [1.0 1.5] :transform sqrt))

@example
  ;; Zoom to corner
  (effect zoom :factor 2 :center_x 0 :center_y 0)
"""

import numpy as np
import cv2


def process_frame(frame: np.ndarray, params: dict, state: dict) -> tuple:
    """
    Zoom a video frame.

    Args:
        frame: Input frame as numpy array (H, W, 3) RGB uint8
        params: Effect parameters
            - factor: zoom multiplier (default 1.0)
            - center_x: horizontal center 0-1 (default 0.5)
            - center_y: vertical center 0-1 (default 0.5)
        state: Persistent state dict (unused)

    Returns:
        Tuple of (processed_frame, new_state)
    """
    factor = params.get("factor", 1.0)
    center_x = params.get("center_x", 0.5)
    center_y = params.get("center_y", 0.5)

    if factor is None or factor <= 0.01:
        factor = 1.0

    if factor == 1.0:
        return frame, state

    h, w = frame.shape[:2]

    # Calculate crop region for zoom in
    new_w = int(w / factor)
    new_h = int(h / factor)

    if new_w <= 0 or new_h <= 0:
        return frame, state

    # Calculate crop offset based on center
    x_start = int((w - new_w) * center_x)
    y_start = int((h - new_h) * center_y)

    # Clamp to valid range
    x_start = max(0, min(x_start, w - new_w))
    y_start = max(0, min(y_start, h - new_h))

    # Crop and resize back to original dimensions
    cropped = frame[y_start:y_start + new_h, x_start:x_start + new_w]
    result = cv2.resize(cropped, (w, h), interpolation=cv2.INTER_LINEAR)

    return result, state