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>
131 lines
3.5 KiB
Python
131 lines
3.5 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 neon_glow
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@version 1.0.0
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@author artdag
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@description
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Neon edge glow effect. Detects edges and applies a glowing colored outline.
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Great for cyberpunk/synthwave aesthetics synced to music.
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@param glow_radius float
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@range 0 50
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@default 15
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Blur radius for the glow. Bind to bass for pulsing glow.
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@param glow_intensity float
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@range 0.5 5
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@default 2.0
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Brightness multiplier for the glow.
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@param edge_low float
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@range 10 200
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@default 50
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Lower threshold for edge detection.
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@param edge_high float
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@range 50 300
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@default 150
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Upper threshold for edge detection.
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@param color_r int
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@range 0 255
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@default 0
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Red component of glow color.
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@param color_g int
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@range 0 255
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@default 255
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Green component of glow color.
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@param color_b int
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@range 0 255
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@default 255
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Blue component of glow color.
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@param background float
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@range 0 1
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@default 0.3
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How much of the original image shows through (0 = glow only).
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@example
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(effect neon_glow :glow_radius 20 :color_r 255 :color_g 0 :color_b 255)
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@example
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;; Pulsing cyan glow on bass
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(effect neon_glow :glow_radius (bind bass :range [5 30] :transform sqrt))
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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 neon glow 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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- glow_radius: blur radius (default 15)
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- glow_intensity: brightness (default 2.0)
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- edge_low: canny low threshold (default 50)
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- edge_high: canny high threshold (default 150)
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- color_r/g/b: glow color (default cyan 0,255,255)
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- background: original visibility (default 0.3)
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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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glow_radius = int(params.get("glow_radius", 15))
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glow_intensity = params.get("glow_intensity", 2.0)
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edge_low = int(params.get("edge_low", 50))
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edge_high = int(params.get("edge_high", 150))
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color_r = int(params.get("color_r", 0))
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color_g = int(params.get("color_g", 255))
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color_b = int(params.get("color_b", 255))
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background = params.get("background", 0.3)
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h, w = frame.shape[:2]
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color = np.array([color_r, color_g, color_b], dtype=np.float32)
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# Edge detection
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gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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edges = cv2.Canny(blurred, edge_low, edge_high)
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# Create colored edge image
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glow_base = np.zeros((h, w, 3), dtype=np.float32)
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for c in range(3):
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glow_base[:, :, c] = edges.astype(np.float32) * (color[c] / 255.0)
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# Apply blur for glow
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if glow_radius > 0:
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ksize = glow_radius * 2 + 1
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glow = cv2.GaussianBlur(glow_base, (ksize, ksize), 0)
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else:
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glow = glow_base
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# Intensify
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glow = glow * glow_intensity
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# Add sharp edges on top
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edge_layer = np.zeros((h, w, 3), dtype=np.float32)
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for c in range(3):
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edge_layer[:, :, c] = edges.astype(np.float32) * (color[c] / 255.0) * 255
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glow = np.maximum(glow, edge_layer)
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# Blend with original
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if background > 0:
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a = frame.astype(np.float32) / 255.0 * background
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b = glow / 255.0
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result = (1 - (1 - a) * (1 - b)) * 255 # Screen blend
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else:
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result = glow
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return np.clip(result, 0, 255).astype(np.uint8), state
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