Bug Fixes

Update 2024.12.08：发布 V1.2.5.2

Bug Fixes:

• Fix compatibility issue of setting environment variables on Windows systems
• Use subprocess.run instead of os.system for better cross-platform support

Bug Fixes:

• 修复在 Windows 系统下设置环境变量的兼容性问题
• 使用 subprocess.run 替代 os.system 以提供更好的跨平台支持

Update 2024.11.30：发布 V1.2.5.1

Update 2024.11.30：发布 V1.2.5.1

Major Changes:

• en_PP-OCRv3_det still uses the previous version and does not use RapidDetector

Bug Fixes:

• en_PP-OCRv3_det 依旧使用之前的版本，不使用 RapidDetector

v1.2.5: Merge pull request #88 from breezedeus/pytorch

Update 2024.11.24：发布 V1.2.5

Major Changes:

• Integrated latest PPOCRv4 text detection functionality based on RapidOCR for faster inference
  • Added support for PP-OCRv4 detection models, including standard and server versions
  • Added support for PP-OCRv3 English detection model
• Optimized model download functionality with support for domestic mirrors

主要变更：

• 基于 RapidOCR 集成 PPOCRv4 最新版文本检测功能，提供更快的推理速度
  • 新增支持 PP-OCRv4 检测模型，包括标准版和服务器版
  • 新增支持 PP-OCRv3 英文检测模型
• 优化模型下载功能，支持从国内镜像下载模型文件

新模型名称：

model_name	PyTorch 版本	ONNX 版本	支持检测的语言	模型文件大小
en_PP-OCRv3_det	X	√	英文、数字	2.3 M
ch_PP-OCRv4_det	X	√	简体中问、英文、数字	4.5 M
ch_PP-OCRv4_det_server	X	√	简体中问、英文、数字	108 M

使用方式

• 命令行：$ cnstd predict -m ch_PP-OCRv4_det_server -i examples/1_res.png -o outputs
• Python:

  from cnstd import CnStd
  
  std = CnStd(model_name="ch_PP-OCRv4_det_server")
  out = std.detect("examples/1_res.png")
  print(out)

Fix: some formats of models require fixed-size input images

Update 2024.06.22：发布 V1.2.4.2

Major Changes:

• Added a new parameter static_resized_shape when initializing YoloDetector, which is used to resize the input image to a fixed size. Some formats of models require fixed-size input images during inference, such as CoreML.

主要变更：

• YoloDetector 初始化时加入了参数 static_resized_shape, 用于把输入图片 resize 为固定大小。某些格式的模型在推理时需要固定大小的输入图片，如 CoreML。

fix: a bug in the detect method of YoloDetector

Update 2024.06.17：发布 V1.2.4.1

Major Changes:

• Fixed a bug in the detect method of YoloDetector: when the input is a single file, the output is not a double-layer nested list.

主要变更：

• 修复了 YoloDetector 中 detect 方法的一个bug：输入为单个文件时，输出不是双层嵌套的 list。

Support for YOLO Detector based on Ultralytics.

Update 2024.06.16：发布 V1.2.4

Major Changes:

• Support for YOLO Detector based on Ultralytics.

主要变更：

• 支持基于 Ultralytics 的 YOLO Detector。

V1.2.3.6: 修改默认的模型下载方式

Update 2024.04.10：发布 V1.2.3.6

主要变更：

• CN OSS 不可用了，默认下载模型地址由 CN 改为 HF。

use environment variable to determine the download source for models

Main Changes:

1. Added support for the CNSTD_DOWNLOAD_SOURCE environment variable to determine the download source for models. The default source is the Aliyun OSS address.

2. In LayoutAnalyzer, introduced two new parameters, model_categories and model_arch_yaml, to specify the list of model categories and model architectures.

adapt with the new version of ORT

• adapt with the new version of ORT
• remove direct dependency of onnxruntime from setup.py

support env variable 'HF_TOKEN' to download files from private repos

• support env variable 'HF_TOKEN' to download files from private repos
• use a fixed set of colors