Create overlay.py

overlay.py

@@ -0,0 +1,52 @@
+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