Updated Core Process

app.py

@@ -53,8 +53,20 @@ def depth_to_normal(depth):
 # CORE PROCESSING FUNCTION
 # ===============================
 def _process_saree_core(base_image: Image.Image, pattern_image: Image.Image):
-    img_pil = base_image.convert("RGB")
-    img_np = np.array(img_pil)
+    # img_pil = base_image.convert("RGB")  # <-- COMMENTED: this injects black behind transparency
+    # img_np = np.array(img_pil)
+
+    # --- NEW: alpha-aware RGB (composite RGBA over white to avoid black fringe) ---
+    base_rgba = base_image.convert("RGBA")
+    _arr = np.array(base_rgba).astype(np.float32)
+    _rgb = _arr[..., :3]
+    _a = (_arr[..., 3:4] / 255.0)
+    _rgb_over_white = _rgb * _a + (1.0 - _a) * 255.0
+    _rgb_over_white = _rgb_over_white.astype(np.uint8)
+
+    img_pil = Image.fromarray(_rgb_over_white, mode="RGB")
+    img_np = _rgb_over_white
+    # --- end NEW ---
 
     # Prepare tensor
     img_resized = img_pil.resize((384, 384))
@@ -129,7 +141,6 @@ def _process_saree_core(base_image: Image.Image, pattern_image: Image.Image):
     mask_binary = (mask_alpha > k/100).astype(np.uint8) * 255  # slightly stricter threshold
     mask_eroded = cv2.erode(mask_binary, kernel, iterations=3)  # balanced erosion
 
-
     # 2. Feather edges (blur)
     mask_blurred = cv2.GaussianBlur(mask_eroded, (15, 15), sigmaX=3, sigmaY=3)
 
@@ -138,13 +149,36 @@ def _process_saree_core(base_image: Image.Image, pattern_image: Image.Image):
 
     # Final RGBA
     mask_stack = np.stack([mask_blurred] * 3, axis=-1)
-    pattern_final = pattern_folded * mask_stack
-    pattern_rgb = (pattern_final * 255).astype(np.uint8)
+
+    # 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]
+
+    # Premultiplied → Straight alpha
+    eps = 1e-6
+    _alpha = np.clip(mask_blurred, 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)
     pattern_rgba = np.dstack((pattern_rgb, alpha_channel))
 
     return Image.fromarray(pattern_rgba, mode="RGBA")
 
+
 # ===============================
 # WRAPPER: ACCEPT BYTES OR BASE64
 # ===============================