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>
117 lines
3.0 KiB
Python
117 lines
3.0 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 fisheye
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@version 1.0.0
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@author artdag
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@description
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Barrel/fisheye lens distortion. Positive values bulge outward (fisheye),
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negative values pinch inward (pincushion). Great for emphasis effects.
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@param strength float
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@range -1 1
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@default 0.3
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Distortion strength. Positive = fisheye bulge, negative = pincushion pinch.
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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 distortion (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 distortion (0 = top, 1 = bottom).
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@param zoom_correction bool
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@default true
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Automatically zoom to hide black edges on fisheye.
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@example
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(effect fisheye :strength 0.5)
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@example
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;; Pulse fisheye on bass
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(effect fisheye :strength (bind bass :range [0 0.8] :transform sqrt))
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@example
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;; Pincushion effect
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(effect fisheye :strength -0.3)
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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 fisheye/barrel 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: distortion strength -1 to 1 (default 0.3)
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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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- zoom_correction: auto-zoom for fisheye (default True)
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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", 0.3)
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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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zoom_correction = params.get("zoom_correction", True)
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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 in pixels
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cx = w * center_x
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cy = h * center_y
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# Create coordinate grids
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y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float32)
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# Normalize coordinates relative to center
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x_norm = (x_coords - cx) / (w / 2)
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y_norm = (y_coords - cy) / (h / 2)
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# Calculate radius from center
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r = np.sqrt(x_norm**2 + y_norm**2)
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# Apply barrel/pincushion distortion
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if strength > 0:
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# Barrel distortion (fisheye)
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r_distorted = r * (1 + strength * r**2)
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else:
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# Pincushion distortion
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r_distorted = r / (1 - strength * r**2 + 0.001)
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# Calculate scale factor
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scale = np.where(r > 0, r_distorted / r, 1)
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# Apply zoom correction to hide black edges
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if zoom_correction and strength > 0:
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zoom = 1 + strength * 0.5
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scale = scale / zoom
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# Calculate new coordinates
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new_x = (x_norm * scale * (w / 2) + cx).astype(np.float32)
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new_y = (y_norm * scale * (h / 2) + cy).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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