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"""
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主程序作者：王昱博
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车牌识别系统：
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使用OCR技术对车牌号码进行识别
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使用图像分类AI对车牌种类进行区分
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"""
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import cv2
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from ocr import OCR
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from cut_image import ImageCutter
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from classification_ai import ClassificationAI
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classify_models = ['.\\classify_model\\0.0625.pkl', '.\\classify_model\\0.0625-2.pkl', '.\\classify_model\\0.125.pkl']
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def train(train_set_path: str, export_path: str) -> None:
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ClassificationAI.TrainAI(train_set_path, export_path)
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def main(classify_model_index: int, image_path: str) -> None:
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global classify_models
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origin_image, gray_image = ImageCutter.ImagePreProcess(image_path)
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lpr_text, lpr_conf, cut_image = OCR.RecognizeLicensePlate2(origin_image)
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ocr_text, ocr_type = OCR.RecognizeLicensePlate(cut_image)
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ai_type, ai_conf = ClassificationAI.PredictImage(cut_image, classify_models[classify_model_index])
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print(f'识别完成，以下为识别结果：\n车牌号：{lpr_text} [置信度：{lpr_conf}]\n车牌类型：\n\t{ocr_type}(OCR推测)\n\t{ai_type}(AI分类识别)\n\tAI识别置信度：{ai_conf}')
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cv2.waitKey(0)
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if __name__ == '__main__':
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result = input('请选择运行模式(训练(y)/识别(n)): ')
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if result == 'y' or result == 'Y':
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data_path = input('输入训练集路径: ')
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export_path = input('输入模型保存路径: ')
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try:
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train(data_path, export_path)
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except Exception as e:
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print(f'训练过程中发生错误: {e}')
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else:
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print('模型已成功训练')
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finally:
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print('训练结束')
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elif result == 'n' or result == 'N':
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model_index = input('选择使用的识别模型(1/2/3): ')
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image_path = input('输入图片路径: ')
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if (not model_index.isdigit()) or (int(model_index) < 1) or (int(model_index) > 3):
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print('输入有误')
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else:
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main(int(model_index), image_path)
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else:
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print('输入有误')
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