Delete overlay.py

overlay.py

@@ -1,52 +0,0 @@
-import cv2
-import numpy as np
-
-def _read_png_rgba(path):
-    png = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-    if png is None or png.shape[2] != 4:
-        raise ValueError("Hairstyle PNG must be RGBA with transparency.")
-    return png
-
-def auto_align(png_rgba, mask, landmarks=None):
-    """Scale & position the hairstyle PNG to cover the mask area, using landmarks if available."""
-    mh, mw = mask.shape[:2]
-    ys, xs = np.where(mask > 0)
-    if len(xs) == 0 or len(ys) == 0:
-        return cv2.resize(png_rgba, (mw, mh))
-
-    x0, x1 = xs.min(), xs.max()
-    y0, y1 = ys.min(), ys.max()
-    tw, th = int((x1 - x0) * 1.2), int((y1 - y0) * 1.1)  # Slightly larger for better coverage
-    tw = max(1, min(tw, mw))
-    th = max(1, min(th, mh))
-    aligned = cv2.resize(png_rgba, (tw, th))
-
-    canvas = np.zeros((mh, mw, 4), dtype=np.uint8)
-
-    # Position: Use landmarks if available, else center on mask
-    if landmarks and "forehead_anchor" in landmarks:
-        fx, fy = landmarks["forehead_anchor"]
-        y = max(0, fy - int(0.8 * th))  # Anchor near forehead, offset up for hair
-        x = max(0, fx - int(tw / 2))
-    else:
-        y = max(0, y0 - int(0.25 * th))
-        x = max(0, x0 - int(0.05 * tw))
-
-    y2 = min(mh, y + th)
-    x2 = min(mw, x + tw)
-    crop_h, crop_w = y2 - y, x2 - x
-    canvas[y:y2, x:x2] = aligned[:crop_h, :crop_w]
-    return canvas
-
-def _alpha_blend(base_bgr, overlay_rgba):
-    bgr = base_bgr.copy()
-    alpha = overlay_rgba[:, :, 3:4] / 255.0
-    rgb = overlay_rgba[:, :, :3]
-    bgr = (alpha * rgb + (1 - alpha) * bgr).astype(np.uint8)
-    return bgr
-
-def apply_hairstyle(img_bgr, style_path, mask, landmarks=None):
-    png = _read_png_rgba(style_path)
-    aligned = auto_align(png, mask, landmarks)
-    out = _alpha_blend(img_bgr, aligned)
-    return out