Add WAS_Text_Compare
This commit is contained in:
Jordan Thompson
@@ -154,6 +154,7 @@
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- Tensor Batch to Image: Select a single image out of a latent batch for post processing with filters
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- Text Add Tokens: Add custom tokens to parse in filenames or other text.
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- Text Add Token by Input: Add custom token by inputs representing single **single line** name and value of the token
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- Text Compare: Compare two strings. Returns a boolean if they are the same, a score of similarity, and the similarity or difference text.
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- Text Concatenate: Merge two strings
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- Text Dictionary Update: Merge two dictionaries
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- Text File History: Show previously opened text files *(requires restart to show last sessions files at this time)*
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+121
-2
@@ -5462,6 +5462,126 @@ class WAS_Text_String:
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return (text, text_b, text_c, text_d)
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# Text Compare Strings
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class WAS_Text_Compare:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"text_a": (TEXT_TYPE,),
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"text_b": (TEXT_TYPE,),
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"mode": (["similarity","difference"],),
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"tolerance": ("FLOAT", {"default":0.0,"min":0.0,"max":1.0,"step":0.01}),
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}
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}
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RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE,"NUMBER","NUMBER",TEXT_TYPE)
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RETURN_NAMES = ("TEXT_A_PASS","TEXT_B_PASS","BOOL_NUMBER","SCORE_NUMBER","COMPARISON_TEXT")
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FUNCTION = "text_compare"
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CATEGORY = "WAS Suite/Text/Search"
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def text_compare(self, text_a='', text_b='', mode='similarity', tolerance=0.0):
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boolean = ( 1 if text_a == text_b else 0 )
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sim = self.string_compare(text_a, text_b, tolerance, ( True if mode == 'difference' else False ))
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score = float(sim[0])
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sim_result = ' '.join(sim[1][::-1])
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return (text_a, text_b, boolean, score, sim_result)
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def string_compare(self, str1, str2, threshold=1.0, difference_mode=False):
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m = len(str1)
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n = len(str2)
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if difference_mode:
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dp = [[0 for x in range(n+1)] for x in range(m+1)]
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for i in range(m+1):
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for j in range(n+1):
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if i == 0:
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dp[i][j] = j
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elif j == 0:
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dp[i][j] = i
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elif str1[i-1] == str2[j-1]:
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dp[i][j] = dp[i-1][j-1]
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else:
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dp[i][j] = 1 + min(dp[i][j-1], # Insert
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dp[i-1][j], # Remove
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dp[i-1][j-1]) # Replace
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diff_indices = []
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i, j = m, n
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while i > 0 and j > 0:
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if str1[i-1] == str2[j-1]:
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i -= 1
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j -= 1
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else:
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diff_indices.append(i-1)
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i, j = min((i, j-1), (i-1, j))
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diff_indices.reverse()
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words = []
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start_idx = 0
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for i in diff_indices:
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if str1[i] == " ":
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words.append(str1[start_idx:i])
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start_idx = i+1
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words.append(str1[start_idx:m])
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difference_score = 1 - ((dp[m][n] - len(words)) / max(m, n))
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return (difference_score, words[::-1])
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else:
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dp = [[0 for x in range(n+1)] for x in range(m+1)]
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similar_words = set()
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for i in range(m+1):
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for j in range(n+1):
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if i == 0:
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dp[i][j] = j
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elif j == 0:
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dp[i][j] = i
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elif str1[i-1] == str2[j-1]:
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dp[i][j] = dp[i-1][j-1]
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if i > 1 and j > 1 and str1[i-2] == ' ' and str2[j-2] == ' ':
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word1_start = i-2
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word2_start = j-2
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while word1_start > 0 and str1[word1_start-1] != " ":
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word1_start -= 1
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while word2_start > 0 and str2[word2_start-1] != " ":
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word2_start -= 1
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word1 = str1[word1_start:i-1]
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word2 = str2[word2_start:j-1]
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if word1 in str2 or word2 in str1:
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if word1 not in similar_words:
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similar_words.add(word1)
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if word2 not in similar_words:
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similar_words.add(word2)
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else:
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dp[i][j] = 1 + min(dp[i][j-1], # Insert
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dp[i-1][j], # Remove
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dp[i-1][j-1]) # Replace
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if dp[i][j] <= threshold and i > 0 and j > 0:
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word1_start = max(0, i-dp[i][j])
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word2_start = max(0, j-dp[i][j])
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word1_end = i
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word2_end = j
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while word1_start > 0 and str1[word1_start-1] != " ":
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word1_start -= 1
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while word2_start > 0 and str2[word2_start-1] != " ":
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word2_start -= 1
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while word1_end < m and str1[word1_end] != " ":
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word1_end += 1
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while word2_end < n and str2[word2_end] != " ":
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word2_end += 1
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word1 = str1[word1_start:word1_end]
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word2 = str2[word2_start:word2_end]
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if word1 in str2 or word2 in str1:
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if word1 not in similar_words:
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similar_words.add(word1)
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if word2 not in similar_words:
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similar_words.add(word2)
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similarity_score = 1 - (dp[m][n]/max(m,n))
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return (similarity_score, list(similar_words))
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# Text Random Line
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class WAS_Text_Random_Line:
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@@ -5704,8 +5824,6 @@ class WAS_Text_Parse_NSP:
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new_text = replace_wildcards(text, (None if seed == 0 else seed), noodle_key)
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print('\033[34mWAS NS\033[0m CLIPTextEncode Wildcards:\n', new_text)
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return (new_text, )
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@@ -7984,6 +8102,7 @@ NODE_CLASS_MAPPINGS = {
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"Text Dictionary Update": WAS_Dictionary_Update,
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"Text Add Tokens": WAS_Text_Add_Tokens,
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"Text Add Token by Input": WAS_Text_Add_Token_Input,
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"Text Compare": WAS_Text_Compare,
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"Text Concatenate": WAS_Text_Concatenate,
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"Text File History Loader": WAS_Text_File_History,
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"Text Find and Replace by Dictionary": WAS_Search_and_Replace_Dictionary,
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