406cc7c0c7
Add S-expression based video effects pipeline with modular effect definitions, constructs, and recipe files. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
92 lines
2.2 KiB
Python
92 lines
2.2 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = ["numpy", "opencv-python"]
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# ///
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"""
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@effect zoom
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@version 1.0.0
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@author artdag
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@description
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Zooms into the center of the frame. Values > 1 zoom in (magnify),
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values < 1 zoom out (shrink with black borders).
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@param factor float
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@range 0.1 5
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@default 1.0
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Zoom factor. 1 = unchanged, 2 = 2x magnification, 0.5 = half size.
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@param center_x float
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@range 0 1
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@default 0.5
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Horizontal center of zoom (0 = left, 1 = right).
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@param center_y float
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@range 0 1
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@default 0.5
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Vertical center of zoom (0 = top, 1 = bottom).
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@example
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(effect zoom :factor 1.5)
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@example
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;; Pulse zoom on bass
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(effect zoom :factor (bind bass :range [1.0 1.5] :transform sqrt))
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@example
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;; Zoom to corner
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(effect zoom :factor 2 :center_x 0 :center_y 0)
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"""
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import numpy as np
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import cv2
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def process_frame(frame: np.ndarray, params: dict, state: dict) -> tuple:
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"""
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Zoom a video frame.
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Args:
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frame: Input frame as numpy array (H, W, 3) RGB uint8
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params: Effect parameters
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- factor: zoom multiplier (default 1.0)
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- center_x: horizontal center 0-1 (default 0.5)
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- center_y: vertical center 0-1 (default 0.5)
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state: Persistent state dict (unused)
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Returns:
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Tuple of (processed_frame, new_state)
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"""
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factor = params.get("factor", 1.0)
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center_x = params.get("center_x", 0.5)
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center_y = params.get("center_y", 0.5)
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if factor is None or factor <= 0.01:
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factor = 1.0
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if factor == 1.0:
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return frame, state
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h, w = frame.shape[:2]
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# Calculate crop region for zoom in
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new_w = int(w / factor)
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new_h = int(h / factor)
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if new_w <= 0 or new_h <= 0:
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return frame, state
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# Calculate crop offset based on center
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x_start = int((w - new_w) * center_x)
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y_start = int((h - new_h) * center_y)
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# Clamp to valid range
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x_start = max(0, min(x_start, w - new_w))
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y_start = max(0, min(y_start, h - new_h))
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# Crop and resize back to original dimensions
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cropped = frame[y_start:y_start + new_h, x_start:x_start + new_w]
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result = cv2.resize(cropped, (w, h), interpolation=cv2.INTER_LINEAR)
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return result, state
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