54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
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"""Basic retouching tools: healing / inpainting and skin softening."""
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from __future__ import annotations
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from typing import Tuple
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import numpy as np
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from PIL import Image
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def heal_spot(
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img: Image.Image,
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center: Tuple[int, int],
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radius: int,
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) -> Image.Image:
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"""Remove blemish at *center* using OpenCV Telea inpainting."""
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import cv2
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arr = np.asarray(img.convert("RGB"))
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bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
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mask = np.zeros(arr.shape[:2], dtype=np.uint8)
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cv2.circle(mask, center, radius, 255, -1)
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result = cv2.inpaint(bgr, mask, inpaintRadius=radius * 2, flags=cv2.INPAINT_TELEA)
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result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
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return Image.fromarray(result)
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def soften_skin(
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rgb_float01: np.ndarray,
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mask: np.ndarray,
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strength: float,
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) -> np.ndarray:
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"""Edge-preserving skin softening (bilateral filter) within *mask* area.
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*strength*: 0..100. 0 = no effect.
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*mask*: float01 (H, W) – typically the person/face mask.
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"""
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if strength <= 0:
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return rgb_float01
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import cv2
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img8 = np.clip(rgb_float01 * 255, 0, 255).astype(np.uint8)
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d = max(3, int(strength / 10))
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sigma_color = 30 + strength * 0.7
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sigma_space = 30 + strength * 0.7
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smoothed = cv2.bilateralFilter(img8, d, sigma_color, sigma_space)
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smoothed_f = smoothed.astype(np.float32) / 255.0
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alpha = np.clip(mask * min(strength / 100.0, 1.0), 0.0, 1.0)[..., None]
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result = rgb_float01 * (1.0 - alpha) + smoothed_f * alpha
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return np.clip(result, 0.0, 1.0).astype(np.float32)
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