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paddleOCR在Linux下自动中断程序
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#IAV1CL
Boom-Wang
Opened this issue
2024-10-05 21:20
### 🔎 Search before asking - [x] I have searched the PaddleOCR [Docs](https://paddlepaddle.github.io/PaddleOCR/) and found no similar bug report. - [x] I have searched the PaddleOCR [Issues](https://github.com/PaddlePaddle/PaddleOCR/issues) and found no similar bug report. - [x] I have searched the PaddleOCR [Discussions](https://github.com/PaddlePaddle/PaddleOCR/discussions) and found no similar bug report. ### 🐛 Bug (问题描述) 目前我正在做一个车牌号识别的项目,但是碰到了一个问题:代码在本地Windows环境下运行的时候可以正常运行,并且在测试了一个视频后可以继续测试下一个,不会自动中断。 但是当我把代码放到Linux环境中,测试完一个视频后会自动中断,需要我通过手动启动代码的方式才能继续测试下一个视频 这个问题是当我将LPRNet识别车牌模型换成PaddleOCR模型之后才出现的,之前我用LPRNet来进行车牌识别没有这个问题,但是由于LPRNet识别车牌效果很差,所以换成效果更好的PaddleOCR。 Windows环境下的cuda为11.8,Linux无cuda ### 🏃♂️ Environment (运行环境) OS Ubuntu 18 Python 3.8 PaddleOCR 2.8.1 CUDA None ### 🌰 Minimal Reproducible Example (最小可复现问题的Demo) def recognize_plate_single_image(plate_img): ocr = get_ocr() result = ocr.ocr(plate_img, det=False, cls=True) if result: text = result[0][0][0] confidence = result[0][0][1] return text, confidence else: return "未检测到", 0
### 🔎 Search before asking - [x] I have searched the PaddleOCR [Docs](https://paddlepaddle.github.io/PaddleOCR/) and found no similar bug report. - [x] I have searched the PaddleOCR [Issues](https://github.com/PaddlePaddle/PaddleOCR/issues) and found no similar bug report. - [x] I have searched the PaddleOCR [Discussions](https://github.com/PaddlePaddle/PaddleOCR/discussions) and found no similar bug report. ### 🐛 Bug (问题描述) 目前我正在做一个车牌号识别的项目,但是碰到了一个问题:代码在本地Windows环境下运行的时候可以正常运行,并且在测试了一个视频后可以继续测试下一个,不会自动中断。 但是当我把代码放到Linux环境中,测试完一个视频后会自动中断,需要我通过手动启动代码的方式才能继续测试下一个视频 这个问题是当我将LPRNet识别车牌模型换成PaddleOCR模型之后才出现的,之前我用LPRNet来进行车牌识别没有这个问题,但是由于LPRNet识别车牌效果很差,所以换成效果更好的PaddleOCR。 Windows环境下的cuda为11.8,Linux无cuda ### 🏃♂️ Environment (运行环境) OS Ubuntu 18 Python 3.8 PaddleOCR 2.8.1 CUDA None ### 🌰 Minimal Reproducible Example (最小可复现问题的Demo) def recognize_plate_single_image(plate_img): ocr = get_ocr() result = ocr.ocr(plate_img, det=False, cls=True) if result: text = result[0][0][0] confidence = result[0][0][1] return text, confidence else: return "未检测到", 0
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