Add detection_limit to image seg

locales/zh/nodeDefs.json

@@ -29,65 +29,69 @@
     "display_name": "SAM3 图像分割",
     "inputs": {
       "sam3_model": {
-        "name": "sam3_model",
+        "name": "SAM3 模型",
         "tooltip": "从 LoadSam3Model 节点加载的 SAM3 模型（必须是 'image' 模式）"
       },
       "images": {
-        "name": "images",
+        "name": "图像",
         "tooltip": "输入要分割的图像"
       },
       "prompt": {
-        "name": "prompt",
+        "name": "提示词",
         "tooltip": "要分割的对象的文本描述（例如：'一只猫'、'人'）。支持空字符串以仅使用点/框进行分割"
       },
       "threshold": {
-        "name": "threshold",
+        "name": "置信度",
         "tooltip": "检测的置信度阈值（0.0-1.0，默认：0.40）"
       },
       "keep_model_loaded": {
-        "name": "keep_model_loaded",
+        "name": "保持模型加载",
         "tooltip": "推理后将模型保留在显存中（默认：False）"
       },
       "add_background": {
-        "name": "add_background",
+        "name": "添加背景",
         "tooltip": "为分割的图像添加背景颜色（none、black、white、grey）"
       },
       "coordinates_positive": {
-        "name": "coordinates_positive",
+        "name": "正向点坐标",
         "tooltip": "正向点坐标，JSON 字符串格式：'[{\"x\": 50, \"y\": 120}]'"
       },
       "coordinates_negative": {
-        "name": "coordinates_negative",
+        "name": "负向点坐标",
         "tooltip": "负向点坐标，JSON 字符串格式：'[{\"x\": 150, \"y\": 300}]'"
       },
       "bboxes": {
-        "name": "bboxes",
+        "name": "边界框",
         "tooltip": "边界框，格式为 (x_min, y_min, x_max, y_max) 或 (x, y, width, height)"
       },
       "mask": {
-        "name": "mask",
+        "name": "遮罩",
         "tooltip": "用于细化的输入遮罩"
+      },
+      "detection_limit": {
+        "name": "检测对象限制",
+        "tooltip": "限制每张图像检测的最大对象数量（默认：-1）"
       }
     },
     "outputs": {
       "0": {
-        "name": "masks",
+        "name": "遮罩",
         "tooltip": "合并的分割遮罩（每张图像一个遮罩，所有检测到的对象合并在一起）"
       },
       "1": {
-        "name": "images",
+        "name": "图像",
         "tooltip": "带 RGBA 透明通道的分割图像（可选背景）"
       },
       "2": {
-        "name": "obj_masks",
+        "name": "对象这种",
         "tooltip": "合并前的单个对象遮罩（用于可视化）"
       },
       "3": {
-        "name": "boxes",
+        "name": "边界框",
         "tooltip": "每个检测对象的边界框坐标 [N, 4] 格式"
       },
       "4": {
-        "name": "scores",
+        "name": "置信度分",
         "tooltip": "每个检测对象的置信度分数"
       }
     }
@@ -299,7 +303,7 @@
     },
     "outputs": {
       "0": {
-        "name": "bbox",
+        "name": "边界框",
         "tooltip": "解析后的 BBOX 格式边界框"
       }
     }

nodes.py

@@ -199,6 +199,13 @@ class Sam3ImageSegmentation(io.ComfyNode):
                     display_name="mask",
                     optional=True,
                 ),
+                io.Int.Input(
+                    "detection_limit",
+                    default=-1,
+                    min=-1,
+                    max=1000,
+                    tooltip="Advanced: Limit number of detections (-1 for no limit)"
+                )
             ],
             outputs=[
                 io.Mask.Output(
@@ -231,7 +238,7 @@ class Sam3ImageSegmentation(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, sam3_model, images, prompt, threshold=0.3, keep_model_loaded=False, add_background='none', enable_visualize=False,  coordinates_positive=None, coordinates_negative=None, bboxes=None, mask=None) -> io.NodeOutput:
+    def execute(cls, sam3_model, images, prompt, threshold=0.3, keep_model_loaded=False, add_background='none', detection_limit=-1, coordinates_positive=None, coordinates_negative=None, bboxes=None, mask=None) -> io.NodeOutput:
         offload_device = mm.unet_offload_device()
 
         processor = sam3_model.get("processor", None)
@@ -336,6 +343,11 @@ class Sam3ImageSegmentation(io.ComfyNode):
                         boxes = boxes[top_indices]
                         scores = scores[top_indices]
 
+                    if detection_limit > -1:
+                        masks = masks[:detection_limit]
+                        boxes = boxes[:detection_limit]
+                        scores = scores[:detection_limit]
+
                 output_raw_masks.append(masks)
                 # Convert masks to tensor format
                 masks_tensor = masks_to_tensor(masks)