Initial commit: video effects processing system

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>

effects/pixelate.py

@@ -0,0 +1,72 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = ["numpy", "opencv-python"]
+# ///
+"""
+@effect pixelate
+@version 1.0.0
+@author artdag
+
+@description
+Pixelate effect. Reduces resolution to create blocky, retro pixel art
+look. Great for 8-bit aesthetics.
+
+@param block_size int
+  @range 2 64
+  @default 8
+  Size of pixel blocks. Larger = more pixelated.
+
+@param maintain_edges bool
+  @default false
+  Try to preserve edges while pixelating.
+
+@example
+  (effect pixelate :block_size 16)
+
+@example
+  ;; Beat-reactive pixelation
+  (effect pixelate :block_size (bind bass :range [4 32]))
+"""
+
+import numpy as np
+import cv2
+
+
+def process_frame(frame: np.ndarray, params: dict, state: dict) -> tuple:
+    """
+    Apply pixelate effect to a video frame.
+
+    Args:
+        frame: Input frame as numpy array (H, W, 3) RGB uint8
+        params: Effect parameters
+            - block_size: pixel block size (default 8)
+            - maintain_edges: preserve edges (default False)
+        state: Persistent state dict
+
+    Returns:
+        Tuple of (processed_frame, new_state)
+    """
+    block_size = max(2, min(int(params.get("block_size", 8)), 64))
+    maintain_edges = params.get("maintain_edges", False)
+
+    if state is None:
+        state = {}
+
+    h, w = frame.shape[:2]
+
+    # Scale down then up to create pixelation
+    small_h = max(1, h // block_size)
+    small_w = max(1, w // block_size)
+
+    small = cv2.resize(frame, (small_w, small_h), interpolation=cv2.INTER_AREA)
+    result = cv2.resize(small, (w, h), interpolation=cv2.INTER_NEAREST)
+
+    if maintain_edges:
+        # Detect edges in original and overlay
+        gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+        edges = cv2.Canny(gray, 50, 150)
+        edges_dilated = cv2.dilate(edges, np.ones((2, 2), np.uint8))
+        edge_mask = edges_dilated > 0
+        result[edge_mask] = frame[edge_mask]
+
+    return result, state