Merge pull request 'yolorestart' (#1) from yolopart_restart into main
Reviewed-on: https://gitea.spdis.top/spdis/License_plate_recognition/pulls/1
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@@ -1,36 +1,17 @@
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import numpy as np
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from paddleocr import TextRecognition
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import cv2
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def initialize_ocr_model():
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"""
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初始化OCR模型
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返回:
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bool: 初始化是否成功
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"""
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# OCR模型初始化代码
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# 例如: 加载预训练模型、设置参数等
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model = TextRecognition(model_name="PP-OCRv5_server_rec")
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print("OCR模型初始化完成(占位)")
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return True
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return model
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def ocr_predict(image_array):
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"""
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OCR车牌号识别接口函数
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参数:
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image_array: numpy数组格式的车牌图像,已经过矫正处理
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返回:
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list: 包含7个字符的列表,代表车牌号的每个字符
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例如: ['京', 'A', '1', '2', '3', '4', '5']
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"""
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# 这是OCR部分的占位函数
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# 实际实现时,这里应该包含:
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# 1. 图像预处理
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# 2. OCR模型推理
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# 3. 后处理和字符识别
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# 临时返回占位结果
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placeholder_result = ['待', '识', '别', '0', '0', '0', '0']
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# 保持原有模型调用方式
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output = initialize_ocr_model().predict(input=image_array)
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# 结构化输出结果
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results = output[0]["rec_text"]
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placeholder_result = results.split(',')
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return placeholder_result
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