Updated _process_saree_core

app.py

@@ -184,38 +184,62 @@ def _process_saree_core(base_image: Image.Image, pattern_image: Image.Image):
     # 3. Normalize
     mask_blurred = mask_blurred.astype(np.float32) / 255.0
 
+    # ================================
+    # NEW: SEAM-FIX UPSTREAM (3 steps)
+    # ================================
+    # (A) MASK EXPANSION / OVERLAP: expand slightly to ensure overlap across seams
+    overlap_iters = 2                # <-- tune: 1..3 (px-ish with 5x5 kernel)
+    # mask_expanded = cv2.dilate(mask_eroded, kernel, iterations=overlap_iters)   # old idea
+    # --- Better: expand the FLOAT feathered mask to preserve soft edge continuity ---
+    _mask_float = (mask_blurred * 255).astype(np.uint8)
+    _mask_expanded_u8 = cv2.dilate(_mask_float, kernel, iterations=overlap_iters)
+    mask_expanded = _mask_expanded_u8.astype(np.float32) / 255.0  # [0..1]
+
+    # (B) FEATHER AGAIN after expansion (very light) for a smooth transition band
+    mask_expanded = cv2.GaussianBlur((mask_expanded * 255).astype(np.uint8), (7, 7), sigmaX=1.5, sigmaY=1.5)
+    mask_expanded = mask_expanded.astype(np.float32) / 255.0
+
+    # (C) BLEED-COLOR FILLING in premultiplied space for near-edge pixels
+    #     - Create a thin edge band where alpha is small (e.g., up to 8%)
+    edge_upper = 0.08
     # Final RGBA
-    mask_stack = np.stack([mask_blurred] * 3, axis=-1)
-
+    # mask_stack = np.stack([mask_blurred] * 3, axis=-1)
     # pattern_final = pattern_folded * mask_stack
-    # pattern_rgb = (pattern_final * 255).astype(np.uint8)
-    # alpha_channel = (mask_blurred * 255).astype(np.uint8)
-    # pattern_rgba = np.dstack((pattern_rgb, alpha_channel))
-    # return Image.fromarray(pattern_rgba, mode="RGBA")
-
-    # --- NEW: keep premultiplied math for filtering, then un-premultiply before saving ---
-    pattern_final = pattern_folded * mask_stack  # premultiplied RGB (color * alpha)
-
-    # Optional tiny bleed to reduce edge fringing further (safe defaults). Comment out if not needed.
-    # _kernel_bleed = np.ones((3,3), np.uint8)
-    # _premul_u8 = (pattern_final * 255).astype(np.uint8)
-    # _premul_bleed = cv2.dilate(_premul_u8, _kernel_bleed, iterations=1).astype(np.float32) / 255.0
-    # _edge = (mask_blurred > 0) & (mask_blurred <= 0.02)
-    # pattern_final[_edge] = _premul_bleed[_edge]
+    # --- Replace above with expanded mask for overlap ---
+    mask_stack = np.stack([mask_expanded] * 3, axis=-1)
+    pattern_final = pattern_folded * mask_stack  # premultiplied RGB (color * alpha) with overlap
+
+    #     - Dilate premultiplied RGB slightly so edge pixels borrow nearby garment color
+    bleed_iters = 2               # <-- tune: 1..3
+    _kernel_bleed = np.ones((3, 3), np.uint8)
+    _premul_u8 = (pattern_final * 255).astype(np.uint8)
+    _premul_bleed = cv2.dilate(_premul_u8, _kernel_bleed, iterations=bleed_iters).astype(np.float32) / 255.0
+
+    #     - Replace only in the very thin edge band (alpha between 0 and edge_upper)
+    edge_band = (mask_expanded > 0.0) & (mask_expanded <= edge_upper)
+    if np.any(edge_band):
+        pattern_final[edge_band] = _premul_bleed[edge_band]
+    # ================================
+    # END NEW: SEAM-FIX UPSTREAM
+    # ================================
 
     # Premultiplied → Straight alpha
     eps = 1e-6
-    _alpha = np.clip(mask_blurred, eps, 1.0)
+    # _alpha = np.clip(mask_blurred, eps, 1.0)
+    # --- Use the expanded mask for export, with a small alpha floor to hide hairlines ---
+    alpha_floor = 0.02  # 2% floor; increase to 0.03 if a faint line persists
+    _alpha = np.clip(mask_expanded, max(alpha_floor, eps), 1.0)
     _alpha3 = _alpha[..., None]
     rgb_straight = np.clip(pattern_final / _alpha3, 0.0, 1.0)
 
     pattern_rgb = (rgb_straight * 255).astype(np.uint8)
-    alpha_channel = (mask_blurred * 255).astype(np.uint8)
+    alpha_channel = (_alpha * 255).astype(np.uint8)
     pattern_rgba = np.dstack((pattern_rgb, alpha_channel))
 
     return Image.fromarray(pattern_rgba, mode="RGBA")
 
 
+
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