feat: ImpactCount_Elts_in_SEGS

improve: ImpactLogger

README.md

@@ -119,6 +119,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
     * `Dilate SEG_ELT` - Dilate the mask of SEG_ELT.
     * `From SEG_ELT` bbox - Extract coordinate from bbox in SEG_ELT
     * `From SEG_ELT` crop_region - Extract coordinate from crop_region in SEG_ELT
+  * `Count Elt in SEGS` - Number of Elts ins SEGS
 
 ### Pipe nodes
    * `ToDetailerPipe`, `FromDetailerPipe` - These nodes are used to bundle multiple inputs used in the detailer, such as models and vae, ..., into a single DETAILER_PIPE or extract the elements that are bundled in the DETAILER_PIPE.

__init__.py

@@ -195,6 +195,7 @@ def do_install():
     "ImpactScaleBy_BBOX_SEG_ELT": SEG_ELT_BBOX_ScaleBy,
     "ImpactFrom_SEG_ELT_bbox": From_SEG_ELT_bbox,
     "ImpactFrom_SEG_ELT_crop_region": From_SEG_ELT_crop_region,
+    "ImpactCount_Elts_in_SEGS": Count_Elts_in_SEGS,
 
     "BboxDetectorCombined_v2": BboxDetectorCombined,
     "SegmDetectorCombined_v2": SegmDetectorCombined,
@@ -380,6 +381,7 @@ def do_install():
     "ImpactFrom_SEG_ELT_crop_region": "From SEG_ELT crop_region",
     "ImpactDilate_Mask_SEG_ELT": "Dilate Mask (SEG_ELT)",
     "ImpactScaleBy_BBOX_SEG_ELT": "ScaleBy BBOX (SEG_ELT)",
+    "ImpactCount_Elts_in_SEGS": "Count Elts in SEGS",
     "ImpactDilateMask": "Dilate Mask",
     "ImpactGaussianBlurMask": "Gaussian Blur Mask",
     "ImpactDilateMaskInSEGS": "Dilate Mask (SEGS)",

modules/impact/config.py

@@ -1,7 +1,7 @@
 import configparser
 import os
 
-version_code = [5, 11, 5]
+version_code = [5, 12]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 
 dependency_version = 21

modules/impact/segs_nodes.py

@@ -730,6 +730,23 @@ def doit(self, **kwargs):
             return ((dim, res), )
 
 
+class Count_Elts_in_SEGS:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "segs": ("SEGS", ),
+                     },
+                }
+
+    RETURN_TYPES = ("INT",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, segs):
+        return (len(segs[1]), )
+
+
 class DecomposeSEGS:
     @classmethod
     def INPUT_TYPES(s):

modules/impact/util_nodes.py

@@ -6,6 +6,7 @@
 import sys
 import nodes
 import re
+from server import PromptServer
 
 
 class GeneralSwitch:
@@ -196,8 +197,9 @@ class ImpactLogger:
     def INPUT_TYPES(s):
         return {"required": {
                         "data": (any_typ, ""),
+                        "text": ("STRING", {"multiline": True}),
                     },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
                 }
 
     CATEGORY = "ImpactPack/Debug"
@@ -207,7 +209,7 @@ def INPUT_TYPES(s):
     RETURN_TYPES = ()
     FUNCTION = "doit"
 
-    def doit(self, data, prompt, extra_pnginfo):
+    def doit(self, data, text, prompt, extra_pnginfo, unique_id):
         shape = ""
         if hasattr(data, "shape"):
             shape = f"{data.shape} / "
@@ -218,12 +220,13 @@ def doit(self, data, prompt, extra_pnginfo):
 
         # for x in prompt:
         #     if 'inputs' in x and 'populated_text' in x['inputs']:
-        #         print(f"PROMP: {x['10']['inputs']['populated_text']}")
+        #         print(f"PROMPT: {x['10']['inputs']['populated_text']}")
         #
         # for x in extra_pnginfo['workflow']['nodes']:
         #     if x['type'] == 'ImpactWildcardProcessor':
         #         print(f" WV : {x['widgets_values'][1]}\n")
 
+        PromptServer.instance.send_sync("impact-node-feedback", {"node_id": unique_id, "widget_name": "text", "type": "TEXT", "value": f"{data}"})
         return {}
 
 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-impact-pack"
 description = "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
-version = "5.11.5"
+version = "5.12"
 license = "LICENSE"
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]