用的onnx模型是在您提供的网盘下载的ch_ppocr_mobile_v2.0_rec_infer.onnx 转换的命令是 python "C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\model_optimizer\mo.py" --input_model=ch_ppocr_mobile_v2.0_rec_infer.onnx --output_dir=. --model_name=model_rec --data_type=FP32 出现如下错误 请问这错误是啥意思?该怎么解决呢？

pip install https://github.com/RapidAI/RapidOCR/raw/main/release/python_sdk/sdk_rapidocr_v1.0.0/rapidocr-1.0.0-py3-none-any.whl -i https://pypi.douban.com/simple/ Looking in indexes: https://pypi.douban.com/simple/ Collecting rapidocr==1.0.0 Using cached https://github.com/RapidAI/RapidOCR/raw/main/release/python_sdk/sdk_rapidocr_v1.0.0/rapidocr-1.0.0-py3-none-any.whl (18 kB) Collecting six>=1.15.0 Downloading https://pypi.doubanio.com/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl (11 kB) Requirement already satisfied: numpy>=1.19.3 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from rapidocr==1.0.0) (1.21.4) Collecting pyclipper>=1.2.1 Downloading https://pypi.doubanio.com/packages/24/6e/b7b4d05383cb654560d63247ddeaf8b4847b69b68d8bc6c832cd7678dab1/pyclipper-1.3.0.zip (142 kB) |████████████████████████████████| 142 kB 2.7 MB/s Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Requirement already satisfied: Shapely>=1.7.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from rapidocr==1.0.0) (1.8.0) ERROR: Could not find a version that satisfies the requirement onnxruntime>=1.7.0 (from rapidocr) (from versions: none) ERROR: No matching distribution found for onnxruntime>=1.7.0

But I do have onnxruntime on my Mac.

🍺 /opt/homebrew/Cellar/onnxruntime/1.9.1: 77 files, 11.9MB

操作系统WIN10 x64 语言C++ 编译RapidOCR 如果仅仅使用onnxruntime-1.7.0-shared.7z和 opencv-3.4.13-sharedLib.7z, 直接运行build.bat会报错，提示cmake找不到onnxruntime，所以只能使用onnxruntime-1.6.0-sharedLib.7z，

环境： windows 工具： Anaconda3-2020.11-Windows-x86_64 在Anaconda里面： conda create -n base37 python=3.7 然后：在base37里面安装了 requirements.txt 然后，windows下面使用 base37下面的执行 rapidOCR.py

报错： C:\ProgramData\Anaconda3\python.exe E:/comm_Item/Item_doing/ocr_recog_py/RapidOCR/python/rapidOCR.py dt_boxes num : 17, elapse : 0.11702466011047363 cls num : 17, elapse : 0.016003131866455078 2021-06-06 17:06:33.2157753 [E:onnxruntime:, sequential_executor.cc:339 onnxruntime::SequentialExecutor::Execute] Non-zero status code returned while running ScatterND node. Name:'ScatterND@1' Status Message: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: {1}, indices shape: {1}, data shape: {3} Traceback (most recent call last): File "E:/comm_Item/Item_doing/ocr_recog_py/RapidOCR/python/rapidOCR.py", line 271, in dt_boxes, rec_res = text_sys(args.image_path) File "E:/comm_Item/Item_doing/ocr_recog_py/RapidOCR/python/rapidOCR.py", line 195, in call rec_res, elapse = self.text_recognizer(img_crop_list) File "E:\comm_Item\Item_doing\ocr_recog_py\RapidOCR\python\ch_ppocr_mobile_v2_rec\text_recognize.py", line 115, in call preds = self.session.run(None, onnx_inputs)[0] File "C:\ProgramData\Anaconda3\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py", line 188, in run return self._sess.run(output_names, input_feed, run_options) onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Non-zero status code returned while running ScatterND node. Name:'ScatterND@1' Status Message: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: {1}, indices shape: {1}, data shape: {3}

Process finished with exit code 1

I built onnxruntime with TensorRT to see if there could be any performance improvements with RapidOCR but unfortunately, the inference returned an empty array. Here's the log:

C:\Users\samay\Documents\RapidOCR\python>python rapidOCR.py
2021-07-08 00:09:15.7074765 [E:onnxruntime:Default, tensorrt_execution_provider.h:51 onnxruntime::TensorrtLogger::log] [2021-07-08 05:09:15   ERROR] Parameter check failed at: engine.cpp::nvinfer1::rt::ExecutionContext::setBindingDimensions::1136, condition: profileMaxDims.d[i] >= dimensions.d[i]
Traceback (most recent call last):
  File "C:\Users\samay\Documents\RapidOCR\python\rapidOCR.py", line 257, in <module>
    dt_boxes, rec_res = text_sys(args.image_path)
  File "C:\Users\samay\Documents\RapidOCR\python\rapidOCR.py", line 177, in __call__
    dt_boxes, elapse = self.text_detector(img)
  File "C:\Users\samay\Documents\RapidOCR\python\ch_ppocr_mobile_v2_det\text_detect.py", line 136, in __call__
    dt_boxes = post_result[0]['points']
IndexError: list index out of range

I'm by no means an expert in model conversion so I'm guessing tensorrt simply doesn't support the converted onnx model ? Is there a way to make it work ?

您好，我在测试的时候，发现python+onnx+onnxRuntime的推理速度慢于python+paddle+mkl的时间，想问下是我某些设置没有开启嘛？我将两个代码的预处理参数统一了。 我的cpu是Intel(R) Core(TM) i5-1035G1 CPU @ 1.00GHz 1.19 GHz。

从文档里看，目前的返回结果是这样的：

[['0', '香港深圳抽血', '0.93583983'], ['1', '专业查性别', '0.89865875'], ['2', '专业鉴定B超单', '0.9955703'], ['3', 'b超仪器查性别', '0.99489486'], ['4', '加微信eee', '0.99073666'], ['5', '可邮寄', '0.99923944']]

坐标其实还是比较重要的，可以用在后期对内容进行分段处理。

下面的数据格式是我在项目里做的一个api结果的例子。

lines：提供了一个合并后的文本结果。方便直接显示使用。 regions：识别到的区域，每个区域的文字和坐标。

代码在这里 https://github.com/cuiliang/RapidOCR/blob/Quicker/ocrweb/api_task.py
不太懂py，代码只是跟随感觉拼凑的😂，供大佬参考。

{
  "result": {
    "lines": "Filters Is:issueis:open\n\n10pen 35Closed\n\n建议：将ppocr_keys等信息直接存储到onnx模型\n#42 opened 4daysagoby AutumnSun1996",
    "regions": [
      {
        "text": "Filters",
        "confidence": 0.9966548,
        "rect": {
          "left": 57,
          "top": 0,
          "right": 116,
          "bottom": 2
        }
      },
      {
        "text": "Is:issueis:open",
        "confidence": 0.84303313,
        "rect": {
          "left": 210,
          "top": 2,
          "right": 347,
          "bottom": 3
        }
      },
      {
        "text": "10pen",
        "confidence": 0.976416,
        "rect": {
          "left": 89,
          "top": 88,
          "right": 160,
          "bottom": 88
        }
      },
      {
        "text": "35Closed",
        "confidence": 0.9819431,
        "rect": {
          "left": 213,
          "top": 89,
          "right": 305,
          "bottom": 89
        }
      },
      {
        "text": "建议：将ppocr_keys等信息直接存储到onnx模型",
        "confidence": 0.97398514,
        "rect": {
          "left": 90,
          "top": 158,
          "right": 594,
          "bottom": 158
        }
      },
      {
        "text": "#42 opened 4daysagoby AutumnSun1996",
        "confidence": 0.9657532,
        "rect": {
          "left": 91,
          "top": 199,
          "right": 442,
          "bottom": 198
        }
      }
    ]
  },
  "info": {
    "total_elapse": 0.45919999999999994,
    "elapse_part": {
      "det_elapse": "0.3858",
      "cls_elapse": "0.0011",
      "rec_elapse": "0.0723"
    }
  }
}

建议将ppocr_keys, rec_img_shape等信息直接存储到onnx模型

目前, ppocr_keys是单独存放在txt文件, 然后在config.yaml中配置文件路径; rec_img_shape是在config.yaml中配置 这两个参数是和onnx模型强相关的, 可以直接作为元数据存储到onnx模型内, 减少配置的需求. 尤其是ppocr_keys, 目前通过另一个文件来分发, 容易出现两边不一致的情况. ONNX本身支持自定义元信息的存储. 使用这种方式, 部署相关的配置应该会更简单.

参考代码:

# 添加meta信息
import onnx

model = onnx.load_model('/path/to/model.onnx')
meta = model.metadata_props.add()
meta.key = 'dictionary'
meta.value = open('/path/to/ppocr_keys_v1.txt', 'r', -1, 'u8').read()

meta = model.metadata_props.add()
meta.key = 'shape'
meta.value = '[3,48,320]'

onnx.save_model(model, '/path/to/model.onnx')

# 获取meta信息
import json
import onnxruntime as ort

sess = ort.InferenceSession('/path/to/model.onnx')
metamap = sess.get_modelmeta().custom_metadata_map
chars = metamap['dictionary'].splitlines()
input_shape = json.loads(metamap['shape'])

转换公开的base64格式的违规词汇数据（https://gitee.com/xstudio/badwords/tree/master）为utf-8格式，并采用AC自动机对识别文本进行违规词汇检测。同时，将先前js / css / url注入的安全检测功能合并到一个类中（detection.py / class Detection()），在task.py中设置函数detection对上述安全检测功能选择性使用（默认值全为true，即全部使用）

Hi,

I want to use .net tool to compare models, but I couldn't download the files, because of they shared on QQ. I couldn't register it. Can someone share this file with me, please? Via googledrive, wetransfer, telegram etc. Thanks for all.

Links:

https://github.com/RapidAI/RapidOCR/blob/main/docs/README_en.md#demo https://github.com/RapidAI/RapidOCR/tree/main/cpp#demo%E4%B8%8B%E8%BD%BDwinmaclinux

参考OCRWeb实现的多语言部署

• 同时支持多种语言，可通过接口参数配置语言及预测过程中的其他参数
• 调整结果展示方式为基于canvas, 减少后端处理和接口数据传输
• 预测接口添加Token验证支持
• 添加pyinstaller打包脚本，简化安装步骤

示例打包结果: https://github.com/AutumnSun1996/RapidOCR/releases/tag/v1.1.1-ocrweb-multi