forked from pradana.aumars/videocr
Update model to use PaddleOCR results
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@ -17,25 +17,15 @@ class PredictedFrame:
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confidence: int # total confidence of all words
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text: str
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def __init__(self, index: int, pred_data: str, conf_threshold: int):
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def __init__(self, index: int, pred_data: list[list], conf_threshold: int):
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self.index = index
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self.words = []
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block = 0 # keep track of line breaks
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for l in pred_data.splitlines()[1:]:
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word_data = l.split()
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if len(word_data) < 12:
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# no word is predicted
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for l in pred_data:
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if len(l) < 2:
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continue
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_, _, block_num, *_, conf, text = word_data
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block_num, conf = int(block_num), int(conf)
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# handle line breaks
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if block < block_num:
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block = block_num
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if self.words and self.words[-1].text != '\n':
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self.words.append(PredictedWord(0, '\n'))
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text = l[1][0]
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conf = int(l[1][1] * 100)
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# word predictions with low confidence will be filtered out
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if conf >= conf_threshold:
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