From 181b2933c1350158213403339d6a68cc1e29f873 Mon Sep 17 00:00:00 2001 From: weishengyu Date: Tue, 15 Dec 2020 15:38:01 +0800 Subject: [PATCH] change config; add doc --- doc/doc_ch/style_text_rec.md | 109 ++++++++++++++++++++++++ doc/doc_en/style_text_rec_en.md | 106 +++++++++++++++++++++++ tools/style_text_rec/configs/config.yml | 6 +- 3 files changed, 218 insertions(+), 3 deletions(-) create mode 100644 doc/doc_ch/style_text_rec.md create mode 100644 doc/doc_en/style_text_rec_en.md diff --git a/doc/doc_ch/style_text_rec.md b/doc/doc_ch/style_text_rec.md new file mode 100644 index 00000000..89cf9d4a --- /dev/null +++ b/doc/doc_ch/style_text_rec.md @@ -0,0 +1,109 @@ +### 快速上手 + +Style-Text是对百度自研文本编辑算法《Editing Text in the Wild》中提出的SRNet网络的改进，不同于常用的GAN的方法只选择一个分支，该工具将文本合成任务分解为三个子模块，文本风格迁移模块、背景抽取模块和前背景融合模块，来提升合成数据的效果。下图显示了一些示例结果。 + + +此外，在实际铭牌文本识别场景和韩语文本识别场景，验证了该合成工具的有效性，具体如下。 + + + +#### 环境配置 + +1. 参考[快速安装](./installation.md)，安装PaddlePaddle并准备环境。 +2. 进入`style_text_rec`目录，下载模型，并解压： + +```bash +cd tools/style_text_rec +wget /path/to/style_text_models.zip +unzip style_text_models.zip +``` + +您可以在[此处]()下载模型文件。如果您选择了其他下载位置，请在`configs/config.yml`中修改模型文件的地址，修改时需要同时修改这三个配置： + +``` +bg_generator: + pretrain: style_text_models/bg_generator +... +text_generator: + pretrain: style_text_models/text_generator +... +fusion_generator: + pretrain: style_text_models/fusion_generator +``` + + + +#### 合成单张图片 + +1. 运行tools/synth_image，生成示例图片： + +```python +python -m tools.synth_image -c configs/config.yml +``` + +1. 运行后，会生成`fake_busion.jpg`，即为最终结果。除此之外，程序还会生成并保存中间结果： + * `fake_bg.jpg`：为风格参考图去掉文字后的背景； + * `fake_text.jpg`：是用提供的字符串，仿照风格参考图中文字的风格，生成在灰色背景上的文字图片。 + +2. 如果您想尝试其他风格图像和文字的效果，可以在`tools/synth_image.py`中修改： + * `img = cv2.imread("examples/style_images/1.jpg")`：请在此处修改风格图像的目录； + * `corpus = "PaddleOCR"`：请在此处修改要使用的语料文本 + * 注意：请修改语言选项（`language = "en"`）和语料相对应，目前我们支持英文、简体中文和韩语。 + +3. 在`tools/synth_image.py`中，我们还提供了一个`batch_synth_images`方法，可以两两组合语料和图片，批量生成一批数据。 + +### 高级使用 + +#### 组件介绍 + +`Style Text Rec`主要包含以下组件： + +* `style_samplers`：风格图片采样器，负责返回风格图片。目前我们提供了`DatasetSampler`，可以从一个有标注的数据集中采样。 + +* `corpus_generators`：语料生成器，负责生成语料。目前提供了两种语料成生成器： + * `EnNumCorpus`：根据给定的长度生成随机字符串，字符可能是大小写英文字母、数字和空格。 + * `FileCorpus`：读取指定的文本文件，并随机返回其中的单词. + +* `text_drawers`：标准字体图片生成器，负责根据输入的语料，生成标准字体的图片。注意，使用该组件时，一定要根据语料修改对应的语言信息，否则可能会书写失败。 + +* `predictors`：预测器，根据给定的风格图片和标准字体图片，调用深度学习模型，生成新的数据。`predictor`是整个算法的核心模块。 + +* `writers`：文件输出器，负责将合成的图片与标签文件写入硬盘。 + +* `synthesisers`：合成器，负责调用各个模块，完成数据合成。 + +### 合成数据集 + +在开始合成数据集前，需要准备一些素材。 + +首先，需要风格图片作为合成图片的参考依据，这些数据可以是用作训练OCR识别模型的数据集。本例中使用带有标注文件的数据集作为风格图片. + +1. 在`configs/dataset_config.yml`中配置输入数据路径。 + * `StyleSamplerl`： + * `method`：使用的风格图片采样方法； + * `image_home`：风格图片目录； + * `label_file`：风格图片路径列表文件，如果所用数据集有label，则label_file为label文件路径； + * `with_label`：标志`label_file`是否为label文件。 + + * `CorpusGenerator`： + * `method`：语料生成方法，目前有`FileCorpus`和`EnNumCorpus`可选。如果使用`EnNumCorpus`，则不需要填写其他配置，否则需要修改`corpus_file`和`language`； + * `language`：语料的语种； + * `corpus_file`: 语料文件路径。 + +2. 运行`tools/synth_dataset`合成数据： + + ``` bash + python -m tools.synth_dataset -c configs/dataset_config.yml + ``` + +3. 如果您想使用并行方式来快速合成数据，可以通过启动多个进程，在启动时需要指定不同的`tag`（`-t`），如下所示: + + ```bash + python -m tools.synth_dataset -t 0 -c configs/dataset_config.yml + python -m tools.synth_dataset -t 1 -c configs/dataset_config.yml + ``` + + +### 使用合成数据集进行OCR识别训练 + +在完成上述操作后，即可得到用于OCR识别的合成数据集，接下来请参考[OCR识别文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/recognition.md#%E5%90%AF%E5%8A%A8%E8%AE%AD%E7%BB%83)，完成训练。 \ No newline at end of file diff --git a/doc/doc_en/style_text_rec_en.md b/doc/doc_en/style_text_rec_en.md new file mode 100644 index 00000000..48438132 --- /dev/null +++ b/doc/doc_en/style_text_rec_en.md @@ -0,0 +1,106 @@ +### Quick Start + +`Style-Text` is an improvement of the SRNet network proposed in Baidu's self-developed text editing algorithm "Editing Text in the Wild". It is different from the commonly used GAN methods. This tool decomposes the text synthesis task into three sub-modules to improve the effect of synthetic data: text style transfer module, background extraction module and fusion module. + +The following figure shows some example results. In addition, the actual `nameplate text recognition` scene and `the Korean text recognition` scene verify the effectiveness of the synthesis tool, as follows. + + +#### Preparation + +1. Please refer the [QUICK INSTALLATION](./installation_en.md) to install PaddlePaddle. +2. Download the pretrained models and unzip: + +```bash +cd tools/style_text_rec +wget /path/to/style_text_models.zip +unzip style_text_models.zip +``` + +You can dowload models [here](). If you save the model files in other folders, please edit the three model paths in `configs/config.yml`: + +``` +bg_generator: + pretrain: style_text_rec/bg_generator +... +text_generator: + pretrain: style_text_models/text_generator +... +fusion_generator: + pretrain: style_text_models/fusion_generator +``` + + + +#### Demo + +1. You can use the following commands to run a demo： + +```bash +python -m tools.synth_image -c configs/config.yml +``` + +2. The results are `fake_bg.jpg`, `fake_text.jpg` and `fake_fusion.jpg` as shown in the figure above. Above them: + * `fake_text.jpg` is the generated image with the same font style as `Style Input`; + * `fake_bg.jpg` is the generated image of `Style Input` after removing foreground. + * `fake_fusion.jpg` is the final result, that is synthesised by `fake_text.jpg` and `fake_bg.jpg`. + +3. If want to generate image by other `Style Input` or `Text Input`, you can modify the `tools/synth_image.py`: + * `img = cv2.imread("examples/style_images/1.jpg")`: the path of `Style Input`; + * `corpus = "PaddleOCR"`: the `Text Input`; + * Notice：modify the language option(`language = "en"`) to adapt `Text Input`, that support `en`, `ch`, `ko`. + +4. We also provide `batch_synth_images` mothod, that can combine corpus and pictures in pairs to generate a batch of data. + +### Advanced Usage + +#### Components + +`Style Text Rec` mainly contains the following components： + +* `style_samplers`: It can sample `Style Input` from a dataset. Now, We only provide `DatasetSampler`. + +* `corpus_generators`: It can generate corpus. Now, wo only provide two `corpus_generators`: + * `EnNumCorpus`: It can generate a random string according to a given length, including uppercase and lowercase English letters, numbers and spaces. + * `FileCorpus`: It can read a text file and randomly return the words in it. + +* `text_drawers`: It can generate `Text Input`(text picture in standard font according to the input corpus). Note that when using, you have to modify the language information according to the corpus. + +* `predictors`: It can call the deep learning model to generate new data based on the `Style Input` and `Text Input`. + +* `writers`: It can write the generated pictures(`fake_bg.jpg`, `fake_text.jpg` and `fake_fusion.jpg`) and label information to the disk. + +* `synthesisers`: It can call the all modules to complete the work. + +### Generate Dataset + +Before the start, you need to prepare some data as material. +First, you should have the style reference data for synthesis tasks, which are generally used as datasets for OCR recognition tasks. + +1. The referenced dataset can be specifed in `configs/dataset_config.yml`: + * `StyleSampler`: + * `method`: The method of `StyleSampler`. + * `image_home`: The directory of pictures. + * `label_file`: The list of pictures path if `with_label` is `false`, otherwise, the label file path. + * `with_label`: The `label_file` is label file or not. + + * `CorpusGenerator`: + * `method`: The mothod of `CorpusGenerator`. If `FileCorpus` used, you need modify `corpus_file` and `language` accordingly, if `EnNumCorpus`, other configurations is not needed. + * `language`: The language of the corpus. Needed if method is not `EnNumCorpus`. + * `corpus_file`: The corpus file path. Needed if method is not `EnNumCorpus`. + +2. You can run the following command to start synthesis task: + + ``` bash + python -m tools.synth_dataset.py -c configs/dataset_config.yml + ``` + +3. You can using the following command to start multiple synthesis tasks in a multi-threaded manner, which needed to specifying tags by `-t`: + + ```bash + python -m tools.synth_dataset.py -t 0 -c configs/dataset_config.yml + python -m tools.synth_dataset.py -t 1 -c configs/dataset_config.yml + ``` + +### OCR Recognition Training + +After completing the above operations, you can get the synthetic data set for OCR recognition. Next, please complete the training by refering to [OCR Recognition Document](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/recognition. md#%E5%90%AF%E5%8A%A8%E8%AE%AD%E7%BB%83). \ No newline at end of file diff --git a/tools/style_text_rec/configs/config.yml b/tools/style_text_rec/configs/config.yml index 25bc697a..3b10b3d2 100644 --- a/tools/style_text_rec/configs/config.yml +++ b/tools/style_text_rec/configs/config.yml @@ -23,7 +23,7 @@ Predictor: - 0.5 expand_result: false bg_generator: - pretrain: models/style_text_rec/bg_generator + pretrain: style_text_models/bg_generator module_name: bg_generator generator_type: BgGeneratorWithMask encode_dim: 64 @@ -33,7 +33,7 @@ Predictor: conv_block_dilation: true output_factor: 1.05 text_generator: - pretrain: models/style_text_rec/text_generator + pretrain: style_text_models/text_generator module_name: text_generator generator_type: TextGenerator encode_dim: 64 @@ -42,7 +42,7 @@ Predictor: conv_block_dropout: false conv_block_dilation: true fusion_generator: - pretrain: models/style_text_rec/fusion_generator + pretrain: style_text_models/fusion_generator module_name: fusion_generator generator_type: FusionGeneratorSimple encode_dim: 64 -- GitLab