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>
138 lines
3.8 KiB
Python
138 lines
3.8 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = ["numpy"]
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# ///
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"""
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@effect noise
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@version 1.0.0
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@author artdag
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@description
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Noise effect. Adds various types of noise to the image including
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static, gaussian, salt & pepper, and more.
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@param intensity float
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@range 0 1
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@default 0.2
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Noise intensity.
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@param mode string
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@enum gaussian uniform salt_pepper scanline
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@default gaussian
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Type of noise:
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- gaussian: smooth normal distribution
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- uniform: flat random noise
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- salt_pepper: random black/white pixels
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- scanline: horizontal line noise
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@param colored bool
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@default false
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Use colored noise instead of monochrome.
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@param animate bool
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@default true
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Different noise each frame.
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@param seed int
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@default 42
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Random seed for reproducible noise.
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@state rng DeterministicRNG
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Random number generator.
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@example
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(effect noise :intensity 0.3 :mode "gaussian")
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@example
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;; Static TV noise
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(effect noise :intensity 0.5 :mode "uniform" :animate true)
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"""
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import numpy as np
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from pathlib import Path
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import sys
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# Import DeterministicRNG from same directory
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_effects_dir = Path(__file__).parent
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if str(_effects_dir) not in sys.path:
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sys.path.insert(0, str(_effects_dir))
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from random import DeterministicRNG
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def process_frame(frame: np.ndarray, params: dict, state: dict) -> tuple:
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"""
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Apply noise 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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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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intensity = params.get("intensity", 0.2)
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mode = params.get("mode", "gaussian")
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colored = params.get("colored", False)
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animate = params.get("animate", True)
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seed = int(params.get("seed", 42))
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if state is None:
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state = {}
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if intensity <= 0:
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return frame, state
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# Initialize RNG
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if "rng" not in state:
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state["rng"] = DeterministicRNG(seed)
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rng = state["rng"]
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h, w = frame.shape[:2]
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result = frame.astype(np.float32)
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if mode == "gaussian":
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# Gaussian noise
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if colored:
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noise = np.array([[[rng.gaussian(0, intensity * 50) for _ in range(3)]
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for _ in range(w)]
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for _ in range(h)])
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else:
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noise_2d = np.array([[rng.gaussian(0, intensity * 50)
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for _ in range(w)]
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for _ in range(h)])
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noise = np.stack([noise_2d, noise_2d, noise_2d], axis=-1)
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result = result + noise
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elif mode == "uniform":
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# Uniform random noise
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if colored:
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noise = np.array([[[rng.uniform(-intensity * 100, intensity * 100) for _ in range(3)]
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for _ in range(w)]
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for _ in range(h)])
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else:
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noise_2d = np.array([[rng.uniform(-intensity * 100, intensity * 100)
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for _ in range(w)]
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for _ in range(h)])
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noise = np.stack([noise_2d, noise_2d, noise_2d], axis=-1)
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result = result + noise
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elif mode == "salt_pepper":
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# Salt and pepper noise
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for y in range(h):
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for x in range(w):
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if rng.uniform() < intensity * 0.1:
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if rng.uniform() < 0.5:
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result[y, x] = [0, 0, 0]
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else:
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result[y, x] = [255, 255, 255]
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elif mode == "scanline":
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# Horizontal scanline noise
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for y in range(h):
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if rng.uniform() < intensity * 0.2:
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noise_val = rng.uniform(-intensity * 100, intensity * 100)
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result[y] = result[y] + noise_val
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return np.clip(result, 0, 255).astype(np.uint8), state
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