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>
112 lines
3.1 KiB
Python
112 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 outline
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@version 1.0.0
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@author artdag
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@description
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Outline / Toon Edges effect. Extracts and displays edges as outlines,
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optionally with fill. Great for cartoon/comic book aesthetics.
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@param edge_thickness int
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@range 1 10
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@default 2
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Thickness of outlines in pixels.
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@param threshold float
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@range 20 300
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@default 100
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Edge detection sensitivity.
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@param outline_color list
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@default [0, 0, 0]
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RGB color for outlines (default black).
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@param fill_mode string
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@enum original solid transparent
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@default original
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What to show in non-edge areas:
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- original: keep source image
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- solid: fill with solid color
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- transparent: black background
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@param fill_color list
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@default [255, 255, 255]
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RGB color for solid fill mode.
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@example
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(effect outline :edge_thickness 3 :threshold 80)
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@example
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;; White outlines on black
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(effect outline :outline_color [255 255 255] :fill_mode "transparent")
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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 outline effect 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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- edge_thickness: outline width (default 2)
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- threshold: edge sensitivity (default 100)
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- outline_color: RGB tuple (default [0,0,0])
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- fill_mode: original/solid/transparent (default original)
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- fill_color: RGB tuple for solid fill (default [255,255,255])
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state: Persistent state dict
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Returns:
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Tuple of (processed_frame, new_state)
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"""
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thickness = max(1, min(int(params.get("edge_thickness", 2)), 10))
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threshold = params.get("threshold", 100)
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outline_color = params.get("outline_color", [0, 0, 0])
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fill_mode = params.get("fill_mode", "original")
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fill_color = params.get("fill_color", [255, 255, 255])
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if state is None:
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state = {}
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h, w = frame.shape[:2]
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# Convert to grayscale
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gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
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# Apply edge detection
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edges = cv2.Canny(gray, int(threshold * 0.5), int(threshold))
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# Dilate edges for thickness
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if thickness > 1:
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kernel = np.ones((thickness, thickness), np.uint8)
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edges = cv2.dilate(edges, kernel, iterations=1)
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# Create result based on fill mode
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if fill_mode == "original":
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result = frame.copy()
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elif fill_mode == "solid":
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if isinstance(fill_color, (list, tuple)) and len(fill_color) >= 3:
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result = np.full((h, w, 3), fill_color[:3], dtype=np.uint8)
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else:
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result = np.full((h, w, 3), 255, dtype=np.uint8)
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else: # transparent/none
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result = np.zeros((h, w, 3), dtype=np.uint8)
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# Apply outline color where edges exist
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if isinstance(outline_color, (list, tuple)) and len(outline_color) >= 3:
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color = np.array(outline_color[:3], dtype=np.uint8)
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else:
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color = np.array([0, 0, 0], dtype=np.uint8)
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edge_mask = edges > 0
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result[edge_mask] = color
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return result, state
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