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>
143 lines
3.9 KiB
Python
143 lines
3.9 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = ["numpy", "scipy"]
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# ///
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"""
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@effect scatter
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@version 1.0.0
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@author artdag
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@description
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Scatter effect. Randomly redistributes pixels in local area creating
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a dissolving, dispersed look.
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@param amount float
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@range 0 100
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@default 10
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Scatter radius in pixels. Bind to energy for reactive dissolve.
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@param randomize_per_frame bool
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@default false
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Different scatter pattern each frame (vs. static pattern).
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@param grain float
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@range 0 1
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@default 0
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Add film grain to scattered result.
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@param seed int
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@default 42
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Random seed for reproducible patterns.
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@state rng DeterministicRNG
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Random number generator for displacement.
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@state displacement_map ndarray
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Cached displacement map for static mode.
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@example
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(effect scatter :amount 20)
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@example
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;; Reactive dissolve
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(effect scatter :amount (bind energy :range [5 50]) :randomize_per_frame true)
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"""
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import numpy as np
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from scipy import ndimage
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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 scatter 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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- amount: scatter radius (default 10)
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- randomize_per_frame: animate pattern (default False)
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- grain: add film grain (default 0)
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- seed: random seed (default 42)
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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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amount = int(np.clip(params.get("amount", 10), 0, 100))
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randomize = params.get("randomize_per_frame", False)
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grain = params.get("grain", 0)
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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 amount < 1:
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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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# Generate or reuse displacement map
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last_size = state.get("last_size")
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if randomize or "displacement_map" not in state or last_size != (h, w):
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# Generate new displacement map
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displacement = np.zeros((h, w, 2), dtype=np.float32)
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for y in range(h):
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for x in range(w):
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displacement[y, x, 0] = rng.uniform(-amount, amount)
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displacement[y, x, 1] = rng.uniform(-amount, amount)
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state["displacement_map"] = displacement
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state["last_size"] = (h, w)
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displacement_map = state["displacement_map"]
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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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# Apply displacement
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new_y = y_coords + displacement_map[:, :, 0]
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new_x = x_coords + displacement_map[:, :, 1]
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# Clip to valid range
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new_y = np.clip(new_y, 0, h - 1)
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new_x = np.clip(new_x, 0, w - 1)
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# Sample from displaced positions
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result = np.zeros_like(frame)
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for c in range(frame.shape[2] if len(frame.shape) > 2 else 1):
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if len(frame.shape) > 2:
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result[:, :, c] = ndimage.map_coordinates(
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frame[:, :, c],
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[new_y, new_x],
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order=1,
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mode='reflect'
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)
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else:
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result = ndimage.map_coordinates(
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frame,
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[new_y, new_x],
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order=1,
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mode='reflect'
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)
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# Add grain
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if grain > 0:
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noise = np.array([[[rng.uniform(-grain * 20, grain * 20) for _ in range(3)]
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for _ in range(w)]
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for _ in range(h)])
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result = np.clip(result.astype(np.float32) + noise, 0, 255).astype(np.uint8)
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return result, state
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