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>
140 lines
3.7 KiB
Python
140 lines
3.7 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 color_grade
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@version 1.0.0
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@author artdag
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@description
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Color grading effect. Applies cinematic color adjustments including
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shadows/midtones/highlights tinting, lift/gamma/gain, and temperature.
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@param shadows list
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@default [0, 0, 0]
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RGB tint for dark areas.
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@param midtones list
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@default [0, 0, 0]
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RGB tint for middle tones.
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@param highlights list
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@default [0, 0, 0]
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RGB tint for bright areas.
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@param lift float
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@range -0.5 0.5
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@default 0
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Raise/lower shadow levels.
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@param gamma float
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@range 0.5 2
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@default 1
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Midtone brightness curve.
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@param gain float
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@range 0.5 2
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@default 1
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Highlight intensity.
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@param temperature float
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@range -100 100
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@default 0
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Color temperature (-100 = cool/blue, +100 = warm/orange).
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@param tint float
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@range -100 100
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@default 0
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Green/magenta tint (-100 = green, +100 = magenta).
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@example
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(effect color_grade :temperature 30 :shadows [0 0 20])
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@example
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;; Cinematic teal-orange look
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(effect color_grade :shadows [0 10 20] :highlights [20 10 0])
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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 color grading 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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shadows = params.get("shadows", [0, 0, 0])
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midtones = params.get("midtones", [0, 0, 0])
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highlights = params.get("highlights", [0, 0, 0])
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lift = params.get("lift", 0)
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gamma = max(0.5, min(params.get("gamma", 1), 2))
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gain = max(0.5, min(params.get("gain", 1), 2))
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temperature = params.get("temperature", 0)
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tint = params.get("tint", 0)
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if state is None:
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state = {}
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result = frame.astype(np.float32) / 255.0
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# Apply lift (shadows)
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result = result + lift
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# Apply gamma (midtones)
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result = np.power(np.clip(result, 0.001, 1), 1 / gamma)
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# Apply gain (highlights)
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result = result * gain
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# Convert tints to float
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if isinstance(shadows, (list, tuple)) and len(shadows) >= 3:
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shadows = np.array(shadows[:3], dtype=np.float32) / 255.0
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else:
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shadows = np.zeros(3, dtype=np.float32)
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if isinstance(midtones, (list, tuple)) and len(midtones) >= 3:
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midtones = np.array(midtones[:3], dtype=np.float32) / 255.0
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else:
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midtones = np.zeros(3, dtype=np.float32)
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if isinstance(highlights, (list, tuple)) and len(highlights) >= 3:
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highlights = np.array(highlights[:3], dtype=np.float32) / 255.0
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else:
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highlights = np.zeros(3, dtype=np.float32)
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# Calculate luminance for zone-based grading
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lum = 0.299 * result[:, :, 0] + 0.587 * result[:, :, 1] + 0.114 * result[:, :, 2]
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# Create zone masks
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shadow_mask = np.clip(1 - lum * 3, 0, 1)[:, :, np.newaxis]
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highlight_mask = np.clip((lum - 0.67) * 3, 0, 1)[:, :, np.newaxis]
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midtone_mask = 1 - shadow_mask - highlight_mask
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# Apply zone tints
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for c in range(3):
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result[:, :, c] += shadows[c] * shadow_mask[:, :, 0]
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result[:, :, c] += midtones[c] * midtone_mask[:, :, 0]
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result[:, :, c] += highlights[c] * highlight_mask[:, :, 0]
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# Apply temperature (blue <-> orange)
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if temperature != 0:
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temp_shift = temperature / 100.0
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result[:, :, 0] += temp_shift * 0.3 # Red
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result[:, :, 2] -= temp_shift * 0.3 # Blue
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# Apply tint (green <-> magenta)
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if tint != 0:
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tint_shift = tint / 100.0
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result[:, :, 1] -= tint_shift * 0.2 # Green
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return (np.clip(result, 0, 1) * 255).astype(np.uint8), state
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