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>
122 lines
3.1 KiB
Python
122 lines
3.1 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 swirl
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@version 1.0.0
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@author artdag
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@description
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Spiral/vortex distortion that twists the image around a center point.
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Creates whirlpool-like effects. Great for psychedelic/hypnotic visuals.
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@param strength float
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@range -10 10
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@default 1.0
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Swirl strength in radians. Positive = counter-clockwise, negative = clockwise.
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@param radius float
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@range 0.1 2
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@default 0.5
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Effect radius as fraction of image size. Larger = wider swirl.
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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 swirl (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 swirl (0 = top, 1 = bottom).
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@param falloff string
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@enum linear quadratic gaussian
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@default quadratic
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How swirl strength decreases from center:
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- linear: constant decrease
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- quadratic: sharper center, softer edges
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- gaussian: smooth bell curve
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@example
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(effect swirl :strength 2)
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@example
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;; Reactive swirl
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(effect swirl :strength (bind energy :range [0 5]))
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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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Apply swirl distortion to 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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- strength: swirl amount in radians (default 1.0)
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- radius: effect radius as fraction (default 0.5)
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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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- falloff: linear/quadratic/gaussian (default quadratic)
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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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strength = params.get("strength", 1.0)
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radius_frac = params.get("radius", 0.5)
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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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falloff = params.get("falloff", "quadratic")
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if strength == 0:
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return frame, state
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h, w = frame.shape[:2]
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# Calculate center and radius in pixels
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cx = w * center_x
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cy = h * center_y
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radius = max(w, h) * radius_frac
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# Create coordinate grids
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y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float64)
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# Calculate distance and angle from center
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dx = x_coords - cx
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dy = y_coords - cy
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dist = np.sqrt(dx**2 + dy**2)
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angle = np.arctan2(dy, dx)
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# Normalized distance for falloff
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norm_dist = dist / radius
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# Calculate falloff factor
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if falloff == "linear":
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factor = np.maximum(0, 1 - norm_dist)
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elif falloff == "gaussian":
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factor = np.exp(-norm_dist**2 * 2)
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else: # quadratic
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factor = np.maximum(0, 1 - norm_dist**2)
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# Apply swirl rotation
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new_angle = angle + strength * factor
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# Calculate new coordinates
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new_x = (cx + dist * np.cos(new_angle)).astype(np.float32)
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new_y = (cy + dist * np.sin(new_angle)).astype(np.float32)
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# Remap
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result = cv2.remap(
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frame, new_x, new_y,
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cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_REFLECT
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)
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return result, state
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