提示优化，自适应分割阈值

__init__.py

@@ -479,13 +479,6 @@ class VideoCutFromDir:
         return {
             "required": {
                 "frame_dir": ("STRING", {"default": None}),
-                "threshold": ("FLOAT", {
-                    "default": 0.60,
-                    "min": 0.1,
-                    "max": 1.0,
-                    "step": 0.01,
-                    "display": "number"
-                }),
                 "min_frame": ("INT", {
                     "default": 16,
                     "min": 1,
@@ -508,8 +501,8 @@ class VideoCutFromDir:
 
     CATEGORY = "badger"
 
-    def video_cut_from_dir(self, frame_dir, threshold, min_frame, max_frame):
-        cutList = getCutList(frame_dir, threshold, min_frame, max_frame)
+    def video_cut_from_dir(self, frame_dir, min_frame, max_frame):
+        cutList = getCutList(frame_dir, min_frame, max_frame)
         dirPathString = cutToDir(frame_dir, cutList)
 
         return (dirPathString,)

videoCut.py

@@ -39,7 +39,7 @@ def calculate_image_similarity(img_path1, img_path2):
     return combined_score
 
 
-def getCutList(imagePath, threshold, min_frame, max_frame):
+def getCutList(imagePath, min_frame, max_frame):
     pngList = os.listdir(imagePath)
     cutList = []
     indexList = []
@@ -51,9 +51,14 @@ def getCutList(imagePath, threshold, min_frame, max_frame):
         imgPath0 = os.path.join(imagePath, pngList[i])
         imgPath1 = os.path.join(imagePath, pngList[i + 1])
         similarity = calculate_image_similarity(imgPath0, imgPath1)
-        print("切割画面（" + str(i + 1) + "/" + str(len(pngList) - 1))
+        print("切割画面(" + str(i + 1) + "/" + str(len(pngList) - 1) +") 相似度:"+ str(similarity))
         indexList.append(i)
         resList.append(similarity)
+        i += 1
+
+    i = min_frame - 1
+    threshold = sum(resList)/len(resList)
+    while i < len(pngList) - 1:
         if num >= max_frame:
             num = 0
             cutList.append(pngList[i])