Update faceless_processing.py

faceless_processing.py

@@ -124,6 +124,7 @@ class LineartConfig:
     rembg_model: str = ""
     transparency_threshold: float = 0.03
     detect_eyebrows: bool = True
+    is_cartoon: bool = False
 
     # Subject mask
     white_bg_threshold: int = 245
@@ -131,19 +132,19 @@ class LineartConfig:
     mask_open_iterations: int = 1
     mask_smooth_ksize: int = 5
     mask_smooth_threshold: int = 128
-    # Área mínima de silhueta. Valor menor preserva crianças e figuras pequenas.
-    min_silhouette_area: float = 80.0
-
     # Silhouette outline
     silhouette_line_thickness: int = 4
 
+    # Área mínima de silhueta. Valor menor preserva crianças e figuras pequenas.
+    min_silhouette_area: float = 90.0
+
     # --- Neural line-art parameters ---
     lineart_detect_resolution: int = 1024
     lineart_image_resolution: int = 1024
     lineart_coarse: bool = True
     
-    # Reduzido: Permite traços de corpos menores (crianças em grupos)
-    lineart_threshold: int = 120
+    # AUMENTADO: Força bruta para ignorar detalhes fracos
+    lineart_threshold: int = 115
     
     # REDUZIDO: Permite traços de corpos menores (crianças em grupos)
     lineart_min_perimeter: float = 80.0
@@ -215,6 +216,7 @@ class LineartConfig:
 
 
 
+
 def _nms_faces(faces: list, iou_thresh: float) -> list:
     if not faces:
         return []
@@ -468,7 +470,7 @@ def build_silhouette(subject_mask: np.ndarray, cfg: LineartConfig) -> np.ndarray
             cv2.drawContours(silhouette, [approx], -1, 255,
                              cfg.silhouette_line_thickness, lineType=cv2.LINE_AA)
     return silhouette
-    
+
 
 def build_face_erase_mask(rgb: np.ndarray, faces: list, cfg: LineartConfig) -> np.ndarray:
     h, w = rgb.shape[:2]
@@ -699,11 +701,64 @@ def build_interior_edges(rgb: np.ndarray, subject_mask: np.ndarray, cfg: Lineart
     return _build_interior_edges_canny(rgb, subject_mask, cfg)
 
 
+def _skeletonize(img: np.ndarray) -> np.ndarray:
+    """
+    Applies morphological skeletonization to reduce binary lines to 1-pixel wide centerlines.
+    """
+    size = np.size(img)
+    skel = np.zeros(img.shape, np.uint8)
+    element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
+    img_copy = img.copy()
+    
+    for _ in range(120): # safety limit
+        eroded = cv2.erode(img_copy, element)
+        temp = cv2.dilate(eroded, element)
+        temp = cv2.subtract(img_copy, temp)
+        skel = cv2.bitwise_or(skel, temp)
+        img_copy = eroded
+        if cv2.countNonZero(img_copy) == 0:
+            break
+            
+    return skel
+
+
 def _build_interior_edges_canny(rgb: np.ndarray, subject_mask: np.ndarray, cfg: LineartConfig) -> np.ndarray:
     gray = cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY)
     blurred = cv2.bilateralFilter(gray, cfg.bilateral_d, cfg.bilateral_sigma_color, cfg.bilateral_sigma_space)
     blurred = cv2.GaussianBlur(blurred, (cfg.gaussian_ksize, cfg.gaussian_ksize), 0)
-    edges = cv2.Canny(blurred, cfg.canny_low, cfg.canny_high)
+    
+    if getattr(cfg, 'is_cartoon', False):
+        # 1. Binarização adaptativa inteligente para extrair traços cheios (evita contorno duplo!)
+        corners = [blurred[0, 0], blurred[0, -1], blurred[-1, 0], blurred[-1, -1]]
+        bg = float(np.median(corners))
+        if bg > 127: # Fundo claro, traços escuros
+            thresh = cv2.adaptiveThreshold(
+                blurred, 255,
+                cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                cv2.THRESH_BINARY_INV,
+                21, 5
+            )
+        else: # Fundo escuro, traços claros
+            thresh = cv2.adaptiveThreshold(
+                blurred, 255,
+                cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                cv2.THRESH_BINARY,
+                21, 5
+            )
+            
+        # 2. Canny complementar para não perder pequenos detalhes de alto contraste
+        canny_edges = cv2.Canny(blurred, cfg.canny_low, cfg.canny_high)
+        # Dilata Canny levemente para fundir com os traços do limiar adaptativo
+        kernel_c = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+        canny_dilated = cv2.dilate(canny_edges, kernel_c, iterations=1)
+        
+        # Combina ambos
+        combined = cv2.bitwise_or(thresh, canny_dilated)
+        
+        # 3. Esqueletização para converter todos os traços cheios em linhas de 1 pixel centralizadas
+        edges = _skeletonize(combined)
+    else:
+        edges = cv2.Canny(blurred, cfg.canny_low, cfg.canny_high)
 
     erode_size = max(5, cfg.silhouette_line_thickness * 2 + 3)
     if erode_size % 2 == 0:
@@ -740,6 +795,7 @@ def _build_interior_edges_canny(rgb: np.ndarray, subject_mask: np.ndarray, cfg:
     return filtered
 
 
+
 _LANDMARKER_LOCK = threading.Lock()
 _LANDMARKER_CACHE = None
 _LANDMARKER_NUM_FACES = None
@@ -902,6 +958,7 @@ def draw_eyebrows(rgb: np.ndarray, cfg: LineartConfig, faces: Optional[list] = N
     return eyebrows_layer
 
 
+
 def make_faceless_lineart(img: Image.Image, cfg: LineartConfig = None) -> Image.Image:
     cfg = cfg or LineartConfig()
 
@@ -1058,6 +1115,9 @@ def make_faceless_cutout(img: Image.Image) -> Image.Image:
 
 
 
+
+
+
 def build_faceless_embroidery_assets(img: Image.Image, cfg: Optional[LineartConfig] = None, **kwargs) -> Tuple[Image.Image, Image.Image]:
     if cfg is None:
         cfg = LineartConfig()