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>
73 lines
1.9 KiB
Python
73 lines
1.9 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 pixelate
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@version 1.0.0
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@author artdag
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@description
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Pixelate effect. Reduces resolution to create blocky, retro pixel art
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look. Great for 8-bit aesthetics.
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@param block_size int
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@range 2 64
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@default 8
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Size of pixel blocks. Larger = more pixelated.
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@param maintain_edges bool
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@default false
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Try to preserve edges while pixelating.
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@example
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(effect pixelate :block_size 16)
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@example
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;; Beat-reactive pixelation
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(effect pixelate :block_size (bind bass :range [4 32]))
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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 pixelate 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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- block_size: pixel block size (default 8)
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- maintain_edges: preserve edges (default False)
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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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block_size = max(2, min(int(params.get("block_size", 8)), 64))
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maintain_edges = params.get("maintain_edges", False)
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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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# Scale down then up to create pixelation
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small_h = max(1, h // block_size)
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small_w = max(1, w // block_size)
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small = cv2.resize(frame, (small_w, small_h), interpolation=cv2.INTER_AREA)
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result = cv2.resize(small, (w, h), interpolation=cv2.INTER_NEAREST)
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if maintain_edges:
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# Detect edges in original and overlay
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gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
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edges = cv2.Canny(gray, 50, 150)
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edges_dilated = cv2.dilate(edges, np.ones((2, 2), np.uint8))
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edge_mask = edges_dilated > 0
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result[edge_mask] = frame[edge_mask]
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return result, state
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