tidy up and remove some logging outputs

Author: Jonas Krauss

js/openposeadv.js

@@ -529,16 +529,7 @@ export class OpenPosePanel {
             this.undo_history.push(JSON.stringify(canvas));
             this.redo_history.length = 0;
             this.saveToNode()
-        });
-
-        // const json_observer = new MutationObserver((m) => {
-        //     if(gradioApp().querySelector('#tab_openpose_editor').style.display!=='block') return;
-        //     try {
-        //         const raw = gradioApp().querySelector("#jsonbox").querySelector("textarea").value
-        //         if(raw.length!==0) detectImage(raw);
-        //     } catch(e){console.log(e)}
-        // })
-        // json_observer.observe(gradioApp().querySelector("#jsonbox"), { "attributes": true })
+        })
 
         return canvas;
     }

js/ultools.js

@@ -1,7 +1,7 @@
 import { app } from "/scripts/app.js"
 import './exif-reader.js'
-import "./fabric.min.js";
-import { OpenPosePanel, loadImageAsync } from "./openposeadv.js";
+import "./fabric.min.js"
+import { OpenPosePanel, loadImageAsync } from "./openposeadv.js"
 
 app.registerExtension({
 

nodes/OpenPoseEditorAdv.py

@@ -1,5 +1,3 @@
-import os.path
-import folder_paths
 import torch
 import json
 from nodes import LoadImage
@@ -23,13 +21,7 @@ def INPUT_TYPES(s):
     def load_image(self, image, savedPose):
         image, mask = LoadImage.load_image(self, image)
         savedPose = json.loads(savedPose)
-        masks = []
-        #for mask in json.loads(masksJson):
-        #masks.append(torch.full((100, 100), 1.0, dtype=torch.float32, device="cpu"))
-        #mask = torch.full((savedPose['width'], savedPose['height']), 0.0, dtype=torch.float32, device="cpu")
-        #mask[20:220, 20:120] = 1.0
-        #masks.append(mask)
-        print(savedPose['width'], savedPose['height'])
+        # print(savedPose['width'], savedPose['height'])
 
         width = savedPose['width']
         height = savedPose['height']
@@ -42,9 +34,8 @@ def load_image(self, image, savedPose):
                 if point[1] > maxCoordinates[1]: maxCoordinates[1] = min(int(point[1]), height)
                 if point[0] < minCoordinates[0]: minCoordinates[0] = max(int(point[0]), 0)
                 if point[1] < minCoordinates[1]: minCoordinates[1] = max(int(point[1]), 0)
-            print(minCoordinates, maxCoordinates)
+            # print(minCoordinates, maxCoordinates)
             mask = torch.full((savedPose['height'], savedPose['width']), 0.0, dtype=torch.float32, device="cpu")
-            #mask[minCoordinates[1]:minCoordinates[1]+maxCoordinates[1], minCoordinates[0]:minCoordinates[0]+maxCoordinates[0]] = 1.0
             mask[minCoordinates[1]:maxCoordinates[1], minCoordinates[0]:maxCoordinates[0]] = 1.0
             retVal.append(mask)