diff --git a/.gitignore b/.gitignore
index fdac37d..5aefa29 100755
--- a/.gitignore
+++ b/.gitignore
@@ -141,4 +141,14 @@ dmypy.json
*.onnx
*.ttf
-*.ttc
\ No newline at end of file
+*.ttc
+
+long1.jpg
+
+*.bin
+*.mapping
+*.xml
+
+*.pdiparams
+*.pdiparams.info
+*.pdmodel
\ No newline at end of file
diff --git a/README.md b/README.md
index e8c9b9b..e4eaead 100755
--- a/README.md
+++ b/README.md
@@ -21,9 +21,12 @@
目录
- [RapidOCR (捷智OCR)](#rapidocr-捷智ocr)
+ - [TODO](#todo)
+ - [Openvino](#openvino)
- [简介](#简介)
- [近期更新](#近期更新)
- - [🔉2022-02-24 update](#2022-02-24-update)
+ - [🎧2022-04-04 update](#2022-04-04-update)
+ - [2022-02-24 update](#2022-02-24-update)
- [2021-12-18 update](#2021-12-18-update)
- [2021-11-28 update](#2021-11-28-update)
- [之前更新记录](#之前更新记录)
@@ -57,10 +60,10 @@
## TODO
-- [ ] 测试openvino推理速度，给出详细设备上推理性能对比表格
+- [x] 测试openvino推理速度，给出详细设备上推理性能对比表格
- [ ] 基于openvino的推理部署方案
-## Openvino
+## Openvino
https://blog.csdn.net/weixin_47567401/article/details/123525014
@@ -74,10 +77,15 @@ https://blog.csdn.net/weixin_47567401/article/details/123525014
- 基于百度的开源PaddleOCR 模型及训练，任何人可以使用本推理库，也可以根据自己的需求使用百度的paddlepaddle框架进行模型优化。
## 近期更新
-#### 🔉2022-02-24 update
+#### 🎧2022-04-04 update
+- 增加python下的基于OpenVINO推理引擎的支持
+- 给出OpenVINO和ONNXRuntime的性能对比表格
+- 详情参加[python/onnxruntime/README](./python/README.md)
+
+#### 2022-02-24 update
- 优化python目录下的推理代码
- 添加调用不同语言模型的推理代码示例
-- 详情参见：[python/README](./python/README.md)
+- 详情参见：[python/README](./python/onnxruntime_infer/README.md)
#### 2021-12-18 update
- 添加[Google Colab Demo](https://colab.research.google.com/github/RapidAI/RapidOCR/blob/main/RapidOCRDemo.ipynb)
diff --git a/python/README.md b/python/README.md
index d317d9c..520f97b 100755
--- a/python/README.md
+++ b/python/README.md
@@ -1,148 +1,60 @@
-### 使用步骤
-1. 下载相应模型和用于显示的字体文件
- - [百度网盘 提取码：30jv](https://pan.baidu.com/s/1qkqWK4wRdMjqGGbzR-FyWg) | [Google Drive](https://drive.google.com/drive/folders/1x_a9KpCo_1blxH1xFOfgKVkw1HYRVywY?usp=sharing)
+### 目录说明
+- `onnxruntime_infer`: 基于ONNXRuntime推理引擎推理
+- `openvino_infer`: 基于OpenVINO推理引擎推理
- - 下载之后模型和相应字体文件放在`fonts`和`models`下，最终目录结构如下：
- ```text
- models
- |-- ch_PP-OCRv2_det_infer.onnx
- |-- ch_ppocr_mobile_v2.0_cls_infer.onnx
- |-- ch_ppocr_mobile_v2.0_det_infer.onnx
- |-- ch_ppocr_server_v2.0_det_infer.onnx
- |-- ch_ppocr_server_v2.0_rec_infer.onnx
- |-- en_number_mobile_v2.0_rec_infer.onnx
- |-- korean_mobile_v2.0_rec_infer.onnx
- `-- japan_rec_crnn.onnx
+### TODO
+- [ ] 模型转INT8，尚未找到转换代码
+- [ ] 其他推理模型代码整理
+- [ ] 模型转换代码整理
- fonts
- |-- msyh.ttc
- `-- korean.ttf
- ```
+### 关于OpenVINO
+- OpenVINO可以直接推理ONNX模型，可以不用转换直接使用之前ONNX模型
+- OpenVINO推理速度更快，但是从对比来看，占用内存更多，目前正在排查原因
-2. 运行
- - 接口调用方式运行
- - 如果想要使用其他语言的识别模型，可以只对`rec_model_path`和`keys_path`作对应更改即可
- - **!!!各个不同语言的推理代码区别只在于模型和字典文件!!!**
- ```python
- from rapid_ocr_api import TextSystem, visualize
+### OpenVINO与ONNXRuntime性能对比
+- 推理设备：`Windows 64位 Intel(R) Core(TM) i5-4210M CPU @ 2.60GHz 2.59 GHz`
+- 测试图像宽高: `12119x810`
- det_model_path = 'models/ch_ppocr_mobile_v2.0_det_infer.onnx'
- cls_model_path = 'models/ch_ppocr_mobile_v2.0_cls_infer.onnx'
-
- # 中英文识别
- rec_model_path = 'models/ch_ppocr_mobile_v2.0_rec_infer.onnx'
- keys_path = 'rec_dict/ppocr_keys_v1.txt'
-
- text_sys = TextSystem(det_model_path,
- rec_model_path,
- use_angle_cls=True,
- cls_model_path=cls_model_path,
- keys_path=keys_path)
-
- image_path = r'test_images/det_images/ch_en_num.jpg'
- dt_boxes, rec_res = text_sys(image_path)
- visualize(image_path, dt_boxes, rec_res)
+| 测试模型 | 推理框架 | 占用内存(3次平均) | 推理时间(3次平均) |
+| ------------------------------------ | -------------------- | ----------------- | ----------------- |
+| `ch_PP-OCRv2_det_infer.onnx` | `ONNXRuntime=1.10.0` | 0.8G | 5.354s |
+| `ch_PP-OCRv2_det_infer.onnx` | `openvino=2022.1.0` | 3.225G | 2.53s |
+| `ch_PP-OCRv2_det_infer.xml` FP32 | `openvino=2022.1.0` | 3.175G | 2.0455s |
- # 只有中英文和数字识别
- rec_model_path = 'models/en_number_mobile_v2.0_rec_infer.onnx'
- keys_path = 'rec_dict/en_dict.txt'
+### OpenVINO与ONNXRuntime推理代码写法对比
+NOTE: 以`ch_ppocr_mobile_v2_det`中推理代码为例子
- text_sys = TextSystem(det_model_path,
- rec_model_path,
- use_angle_cls=True,
- cls_model_path=cls_model_path,
- keys_path=keys_path)
+- ONNXRuntime
+```python
+import onnxruntime
- image_path = r'test_images/det_images/en_num.png'
- dt_boxes, rec_res = text_sys(image_path)
- visualize(image_path, dt_boxes, rec_res)
+# 声明
+sess_opt = onnxruntime.SessionOptions()
+sess_opt.log_severity_level = 4
+sess_opt.enable_cpu_mem_arena = False
+self.session = onnxruntime.InferenceSession(det_model_path, sess_opt)
+self.input_name = self.session.get_inputs()[0].name
+self.output_name = self.session.get_outputs()[0].name
+# 推理
+preds = self.session.run([self.output_name],
+ {self.input_name: img})
+```
- # 日语识别
- rec_model_path = 'models/japan_rec_crnn.onnx'
- keys_path = 'rec_dict/japan_dict.txt'
+- OpenVINO
+```python
+from openvino.runtime import Core
- text_sys = TextSystem(det_model_path,
- rec_model_path,
- use_angle_cls=True,
- cls_model_path=cls_model_path,
- keys_path=keys_path)
+# 初始化
+ie = Core()
+model_onnx = ie.read_model(det_model_path)
+compile_model = ie.compile_model(model=model_onnx,
+ device_name='CPU')
+self.vino_session = compile_model.create_infer_request()
- image_path = r'test_images/det_images/japan.png'
- dt_boxes, rec_res = text_sys(image_path)
- visualize(image_path, dt_boxes, rec_res)
+# 推理
+self.vino_session.infer(inputs=[img])
+vino_preds = self.vino_session.get_output_tensor().data
+```
-
- # 韩语识别
- rec_model_path = 'models/korean_mobile_v2.0_rec_infer.onnx'
- keys_path = 'rec_dict/korean_dict.txt'
- font_path = 'fonts/korean.ttf'
-
- text_sys = TextSystem(det_model_path,
- rec_model_path,
- use_angle_cls=True,
- cls_model_path=cls_model_path,
- keys_path=keys_path)
-
- image_path = r'test_images/det_images/korean_1.jpg'
- dt_boxes, rec_res = text_sys(image_path)
- visualize(image_path, dt_boxes, rec_res, font_path)
- ```
-
- - 命令行运行
- ```shell
- $ bash test_demo.sh
- ```
-
-### 相关调节参数
-|参数名称|作用|建议取值范围|默认值|代码位置|
-|:---:|:---:|:---:|:---:|:---:|
-|box_thresh|文本检测所得框是否保留的阈值，值越大，召回率越低|[0, 1]|0.5|[text_detect.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/ch_ppocr_mobile_v2_det/text_detect.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L55)|
-|unclip_ratio|控制文本检测框的大小，值越大，检测框整体越大|[1.6, 2.0]|1.6|[text_detect.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/ch_ppocr_mobile_v2_det/text_detect.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L57)|
-|text_score|文本识别结果置信度，值越大，把握越大|[0, 1]|0.5|[rapidOCR.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/rapidOCR.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L270)|
-
-
-### onnxruntime-gpu版推理配置
-
-1. ONNXRuntime-gpu需要严格按照与cuda、cudnn版本对应来安装，具体参考[文档](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), **这一步关乎后面是否可以成功调用GPU**
-2. 推理代码中，加载onnx模型部分，用以下对应语言代码替换即可,详细参见：[官方教程](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html)
- - python版本
- ```python
- import onnxruntime as ort
-
- model_path = ''
-
- providers = [
- ('CUDAExecutionProvider', {
- 'device_id': 0,
- 'arena_extend_strategy': 'kNextPowerOfTwo',
- 'gpu_mem_limit': 2 * 1024 * 1024 * 1024,
- 'cudnn_conv_algo_search': 'EXHAUSTIVE',
- 'do_copy_in_default_stream': True,
- }),
- 'CPUExecutionProvider',
- ]
-
- session = ort.InferenceSession(model_path, providers=providers)
- ```
- - C/C++版本
- ```c++
- OrtSessionOptions* session_options = /* ... */;
-
- OrtCUDAProviderOptions options;
- options.device_id = 0;
- options.arena_extend_strategy = 0;
- options.gpu_mem_limit = 2 * 1024 * 1024 * 1024;
- options.cudnn_conv_algo_search = OrtCudnnConvAlgoSearch::EXHAUSTIVE;
- options.do_copy_in_default_stream = 1;
-
- SessionOptionsAppendExecutionProvider_CUDA(session_options, &options);
- ```
-3. 推理时间粗略对比(跑完RapidOCR整个项目)
-
- |推理方式|推理图像数目|耗费时间|
- |:---:|:---:|:---:|
- |CPU|46张图像|191 s|
- |GPU|46张图像|52.38 s|
- ||||
\ No newline at end of file
diff --git a/python/onnxruntime_infer/README.md b/python/onnxruntime_infer/README.md
new file mode 100644
index 0000000..22b78cf
--- /dev/null
+++ b/python/onnxruntime_infer/README.md
@@ -0,0 +1,148 @@
+### 使用步骤
+1. 下载相应模型和用于显示的字体文件
+ - [百度网盘 提取码：30jv](https://pan.baidu.com/s/1qkqWK4wRdMjqGGbzR-FyWg) | [Google Drive](https://drive.google.com/drive/folders/1x_a9KpCo_1blxH1xFOfgKVkw1HYRVywY?usp=sharing)
+
+ - 下载之后模型和相应字体文件放在`fonts`和`models`下，最终目录结构如下：
+ ```text
+ models
+ |-- ch_PP-OCRv2_det_infer.onnx
+ |-- ch_ppocr_mobile_v2.0_cls_infer.onnx
+ |-- ch_ppocr_mobile_v2.0_det_infer.onnx
+ |-- ch_ppocr_server_v2.0_det_infer.onnx
+ |-- ch_ppocr_server_v2.0_rec_infer.onnx
+ |-- en_number_mobile_v2.0_rec_infer.onnx
+ |-- korean_mobile_v2.0_rec_infer.onnx
+ `-- japan_rec_crnn.onnx
+
+ fonts
+ |-- msyh.ttc
+ `-- korean.ttf
+ ```
+
+2. 运行
+ - 接口调用方式运行
+ - 如果想要使用其他语言的识别模型，可以只对`rec_model_path`和`keys_path`作对应更改即可
+ - **!!!各个不同语言的推理代码区别只在于模型和字典文件!!!**
+ ```python
+ from rapid_ocr_api import TextSystem, visualize
+
+ det_model_path = 'models/ch_ppocr_mobile_v2.0_det_infer.onnx'
+ cls_model_path = 'models/ch_ppocr_mobile_v2.0_cls_infer.onnx'
+
+ # 中英文识别
+ rec_model_path = 'models/ch_ppocr_mobile_v2.0_rec_infer.onnx'
+ keys_path = 'rec_dict/ppocr_keys_v1.txt'
+
+ text_sys = TextSystem(det_model_path,
+ rec_model_path,
+ use_angle_cls=True,
+ cls_model_path=cls_model_path,
+ keys_path=keys_path)
+
+ image_path = r'test_images/det_images/ch_en_num.jpg'
+ dt_boxes, rec_res = text_sys(image_path)
+ visualize(image_path, dt_boxes, rec_res)
+
+
+ # 只有中英文和数字识别
+ rec_model_path = 'models/en_number_mobile_v2.0_rec_infer.onnx'
+ keys_path = 'rec_dict/en_dict.txt'
+
+ text_sys = TextSystem(det_model_path,
+ rec_model_path,
+ use_angle_cls=True,
+ cls_model_path=cls_model_path,
+ keys_path=keys_path)
+
+ image_path = r'test_images/det_images/en_num.png'
+ dt_boxes, rec_res = text_sys(image_path)
+ visualize(image_path, dt_boxes, rec_res)
+
+
+ # 日语识别
+ rec_model_path = 'models/japan_rec_crnn.onnx'
+ keys_path = 'rec_dict/japan_dict.txt'
+
+ text_sys = TextSystem(det_model_path,
+ rec_model_path,
+ use_angle_cls=True,
+ cls_model_path=cls_model_path,
+ keys_path=keys_path)
+
+ image_path = r'test_images/det_images/japan.png'
+ dt_boxes, rec_res = text_sys(image_path)
+ visualize(image_path, dt_boxes, rec_res)
+
+
+ # 韩语识别
+ rec_model_path = 'models/korean_mobile_v2.0_rec_infer.onnx'
+ keys_path = 'rec_dict/korean_dict.txt'
+ font_path = 'fonts/korean.ttf'
+
+ text_sys = TextSystem(det_model_path,
+ rec_model_path,
+ use_angle_cls=True,
+ cls_model_path=cls_model_path,
+ keys_path=keys_path)
+
+ image_path = r'test_images/det_images/korean_1.jpg'
+ dt_boxes, rec_res = text_sys(image_path)
+ visualize(image_path, dt_boxes, rec_res, font_path)
+ ```
+
+ - 命令行运行
+ ```shell
+ $ bash test_demo.sh
+ ```
+
+### 相关调节参数
+|参数名称|作用|建议取值范围|默认值|代码位置|
+|:---:|:---:|:---:|:---:|:---:|
+|box_thresh|文本检测所得框是否保留的阈值，值越大，召回率越低|[0, 1]|0.5|[text_detect.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/ch_ppocr_mobile_v2_det/text_detect.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L55)|
+|unclip_ratio|控制文本检测框的大小，值越大，检测框整体越大|[1.6, 2.0]|1.6|[text_detect.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/ch_ppocr_mobile_v2_det/text_detect.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L57)|
+|text_score|文本识别结果置信度，值越大，把握越大|[0, 1]|0.5|[rapidOCR.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/rapidOCR.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L270)|
+
+
+### onnxruntime-gpu版推理配置
+
+1. ONNXRuntime-gpu需要严格按照与cuda、cudnn版本对应来安装，具体参考[文档](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), **这一步关乎后面是否可以成功调用GPU**
+2. 推理代码中，加载onnx模型部分，用以下对应语言代码替换即可,详细参见：[官方教程](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html)
+ - python版本
+ ```python
+ import onnxruntime as ort
+
+ model_path = ''
+
+ providers = [
+ ('CUDAExecutionProvider', {
+ 'device_id': 0,
+ 'arena_extend_strategy': 'kNextPowerOfTwo',
+ 'gpu_mem_limit': 2 * 1024 * 1024 * 1024,
+ 'cudnn_conv_algo_search': 'EXHAUSTIVE',
+ 'do_copy_in_default_stream': True,
+ }),
+ 'CPUExecutionProvider',
+ ]
+
+ session = ort.InferenceSession(model_path, providers=providers)
+ ```
+ - C/C++版本
+ ```c++
+ OrtSessionOptions* session_options = /* ... */;
+
+ OrtCUDAProviderOptions options;
+ options.device_id = 0;
+ options.arena_extend_strategy = 0;
+ options.gpu_mem_limit = 2 * 1024 * 1024 * 1024;
+ options.cudnn_conv_algo_search = OrtCudnnConvAlgoSearch::EXHAUSTIVE;
+ options.do_copy_in_default_stream = 1;
+
+ SessionOptionsAppendExecutionProvider_CUDA(session_options, &options);
+ ```
+3. 推理时间粗略对比(跑完RapidOCR整个项目)
+
+ |推理方式|推理图像数目|耗费时间|
+ |:---:|:---:|:---:|
+ |CPU|46张图像|191 s|
+ |GPU|46张图像|52.38 s|
+ ||||
\ No newline at end of file
diff --git a/python/ch_ppocr_mobile_v2_cls/__init__.py b/python/onnxruntime_infer/ch_ppocr_mobile_v2_cls/__init__.py
old mode 100755
new mode 100644
similarity index 100%
rename from python/ch_ppocr_mobile_v2_cls/__init__.py
rename to python/onnxruntime_infer/ch_ppocr_mobile_v2_cls/__init__.py
diff --git a/python/ch_ppocr_mobile_v2_cls/run.sh b/python/onnxruntime_infer/ch_ppocr_mobile_v2_cls/run.sh
similarity index 100%
rename from python/ch_ppocr_mobile_v2_cls/run.sh
rename to python/onnxruntime_infer/ch_ppocr_mobile_v2_cls/run.sh
diff --git a/python/ch_ppocr_mobile_v2_cls/text_cls.py b/python/onnxruntime_infer/ch_ppocr_mobile_v2_cls/text_cls.py
old mode 100755
new mode 100644
similarity index 100%
rename from python/ch_ppocr_mobile_v2_cls/text_cls.py
rename to python/onnxruntime_infer/ch_ppocr_mobile_v2_cls/text_cls.py
diff --git a/python/ch_ppocr_mobile_v2_cls/utils.py b/python/onnxruntime_infer/ch_ppocr_mobile_v2_cls/utils.py
old mode 100755
new mode 100644
similarity index 100%
rename from python/ch_ppocr_mobile_v2_cls/utils.py
rename to python/onnxruntime_infer/ch_ppocr_mobile_v2_cls/utils.py
diff --git a/python/ch_ppocr_mobile_v2_det/__init__.py b/python/onnxruntime_infer/ch_ppocr_mobile_v2_det/__init__.py
old mode 100755
new mode 100644
similarity index 100%
rename from python/ch_ppocr_mobile_v2_det/__init__.py
rename to python/onnxruntime_infer/ch_ppocr_mobile_v2_det/__init__.py
diff --git a/python/ch_ppocr_mobile_v2_det/run.sh b/python/onnxruntime_infer/ch_ppocr_mobile_v2_det/run.sh
similarity index 100%
rename from python/ch_ppocr_mobile_v2_det/run.sh
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diff --git a/python/requirements.txt b/python/onnxruntime_infer/requirements.txt
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rename to python/onnxruntime_infer/requirements.txt
index a2384eb..3bccfbb
--- a/python/requirements.txt
+++ b/python/onnxruntime_infer/requirements.txt
@@ -4,4 +4,5 @@ onnxruntime==1.7.0
opencv_python==4.5.1.48
six==1.15.0
numpy==1.19.2
-Pillow==8.3.0
\ No newline at end of file
+Pillow==8.3.0
+openvino==2022.1.0
\ No newline at end of file
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diff --git a/python/test_images/rec_images/imgs_words_korean/1.jpg b/python/onnxruntime_infer/test_images/rec_images/imgs_words_korean/1.jpg
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diff --git a/python/openvino_infer/README.md b/python/openvino_infer/README.md
new file mode 100644
index 0000000..f679cc7
--- /dev/null
+++ b/python/openvino_infer/README.md
@@ -0,0 +1,6 @@
+#### 安装
+```bash
+# Windows端
+pip install openvino==2022.1.0
+pip install openvino-dev==2022.1.0
+```
\ No newline at end of file
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/__init__.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/__init__.py
new file mode 100644
index 0000000..d7a3e0b
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/__init__.py
@@ -0,0 +1,6 @@
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+# @File: __init__.py
+# @Time: 2021/01/23 15:24:27
+# @Author: Max
+from .text_detect import TextDetector
\ No newline at end of file
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/run.sh b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/run.sh
new file mode 100644
index 0000000..99fc806
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/run.sh
@@ -0,0 +1,9 @@
+#! /bin/bash
+set -e errexit
+
+root_path=$(cd $(dirname $0); pwd)
+image_path=${root_path}"/test_images/det_images/1.jpg"
+model_path=${root_path}"/models/ch_ppocr_mobile_v2.0_det_infer.onnx"
+
+python text_detect.py --image_path ${image_path} \
+ --model_path ${model_path}
\ No newline at end of file
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/text_detect.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/text_detect.py
new file mode 100644
index 0000000..2816f12
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/text_detect.py
@@ -0,0 +1,153 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+import argparse
+import time
+
+import cv2
+import numpy as np
+from openvino.runtime import Core
+
+try:
+ from .utils import (DBPostProcess, check_and_read_gif, create_operators,
+ draw_text_det_res, transform)
+except:
+ from utils import (DBPostProcess, check_and_read_gif, create_operators,
+ draw_text_det_res, transform)
+
+
+class TextDetector(object):
+ def __init__(self, det_model_path):
+ pre_process_list = [{
+ 'DetResizeForTest': {
+ 'limit_side_len': 736,
+ 'limit_type': 'min'
+ }
+ }, {
+ 'NormalizeImage': {
+ 'std': [0.229, 0.224, 0.225],
+ 'mean': [0.485, 0.456, 0.406],
+ 'scale': '1./255.',
+ 'order': 'hwc'
+ }
+ }, {
+ 'ToCHWImage': None
+ }, {
+ 'KeepKeys': {
+ 'keep_keys': ['image', 'shape']
+ }
+ }]
+
+ self.preprocess_op = create_operators(pre_process_list)
+ self.postprocess_op = DBPostProcess(thresh=0.3,
+ box_thresh=0.5,
+ max_candidates=1000,
+ unclip_ratio=1.6,
+ use_dilation=True)
+ ie = Core()
+ model_onnx = ie.read_model(det_model_path)
+ compile_model = ie.compile_model(model=model_onnx,
+ device_name='CPU')
+ self.vino_session = compile_model.create_infer_request()
+
+ def order_points_clockwise(self, pts):
+ """
+ reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+ # sort the points based on their x-coordinates
+ """
+ xSorted = pts[np.argsort(pts[:, 0]), :]
+
+ # grab the left-most and right-most points from the sorted
+ # x-roodinate points
+ leftMost = xSorted[:2, :]
+ rightMost = xSorted[2:, :]
+
+ # now, sort the left-most coordinates according to their
+ # y-coordinates so we can grab the top-left and bottom-left
+ # points, respectively
+ leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+ (tl, bl) = leftMost
+
+ rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+ (tr, br) = rightMost
+
+ rect = np.array([tl, tr, br, bl], dtype="float32")
+ return rect
+
+ def clip_det_res(self, points, img_height, img_width):
+ for pno in range(points.shape[0]):
+ points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+ points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+ return points
+
+ def filter_tag_det_res(self, dt_boxes, image_shape):
+ img_height, img_width = image_shape[0:2]
+ dt_boxes_new = []
+ for box in dt_boxes:
+ box = self.order_points_clockwise(box)
+ box = self.clip_det_res(box, img_height, img_width)
+ rect_width = int(np.linalg.norm(box[0] - box[1]))
+ rect_height = int(np.linalg.norm(box[0] - box[3]))
+ if rect_width <= 3 or rect_height <= 3:
+ continue
+ dt_boxes_new.append(box)
+ dt_boxes = np.array(dt_boxes_new)
+ return dt_boxes
+
+ def __call__(self, img):
+ ori_im = img.copy()
+ data = {'image': img}
+ data = transform(data, self.preprocess_op)
+ img, shape_list = data
+ if img is None:
+ return None, 0
+ img = np.expand_dims(img, axis=0)
+ img = img.astype(np.float32)
+ shape_list = np.expand_dims(shape_list, axis=0)
+
+ start_time = time.time()
+ self.vino_session.infer(inputs=[img])
+ vino_preds = self.vino_session.get_output_tensor().data
+
+ post_result = self.postprocess_op(vino_preds, shape_list)
+ dt_boxes = post_result[0]['points']
+ dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
+ elapse = time.time() - start_time
+
+ src_im = draw_text_det_res(dt_boxes, r'E:\PythonProjects\RapidOCR\python\test_images\long1.jpg')
+ cv2.imwrite('det_results.jpg', src_im)
+ return dt_boxes, elapse
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--image_path', type=str, help='image_path|image_dir')
+ parser.add_argument('--model_path', type=str, help='model_path')
+ args = parser.parse_args()
+
+ text_detector = TextDetector(args.model_path)
+
+ img, flag = check_and_read_gif(args.image_path)
+ if not flag:
+ img = cv2.imread(args.image_path)
+ if img is None:
+ raise ValueError(f"error in loading image:{args.image_path}")
+
+ dt_boxes, elapse = text_detector(img)
+
+ src_im = draw_text_det_res(dt_boxes, args.image_path)
+ cv2.imwrite('det_results.jpg', src_im)
+ print(f'图像已经保存为det_results.jpg了\t{elapse:.6f}s')
+
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/utils.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/utils.py
new file mode 100644
index 0000000..dfbea6c
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_ir/utils.py
@@ -0,0 +1,414 @@
+"""
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+# coding:utf-8
+from __future__ import (absolute_import, division, print_function,
+ unicode_literals)
+
+import os
+import sys
+
+import cv2
+import numpy as np
+import pyclipper
+import six
+from shapely.geometry import Polygon
+
+
+class DecodeImage(object):
+ """ decode image """
+
+ def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+ self.img_mode = img_mode
+ self.channel_first = channel_first
+
+ def __call__(self, data):
+ img = data['image']
+ if six.PY2:
+ assert type(img) is str and len(
+ img) > 0, "invalid input 'img' in DecodeImage"
+ else:
+ assert type(img) is bytes and len(
+ img) > 0, "invalid input 'img' in DecodeImage"
+ img = np.frombuffer(img, dtype='uint8')
+ img = cv2.imdecode(img, 1)
+ if img is None:
+ return None
+ if self.img_mode == 'GRAY':
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+ elif self.img_mode == 'RGB':
+ assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
+ img.shape)
+ img = img[:, :, ::-1]
+
+ if self.channel_first:
+ img = img.transpose((2, 0, 1))
+
+ data['image'] = img
+ return data
+
+
+class NormalizeImage(object):
+ """ normalize image such as substract mean, divide std
+ """
+
+ def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
+ if isinstance(scale, str):
+ scale = eval(scale)
+ self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+ mean = mean if mean is not None else [0.485, 0.456, 0.406]
+ std = std if std is not None else [0.229, 0.224, 0.225]
+
+ shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+ self.mean = np.array(mean).reshape(shape).astype('float32')
+ self.std = np.array(std).reshape(shape).astype('float32')
+
+ def __call__(self, data):
+ img = data['image']
+ from PIL import Image
+ if isinstance(img, Image.Image):
+ img = np.array(img)
+
+ assert isinstance(img,
+ np.ndarray), "invalid input 'img' in NormalizeImage"
+ data['image'] = (
+ img.astype('float32') * self.scale - self.mean) / self.std
+ return data
+
+
+class ToCHWImage(object):
+ """ convert hwc image to chw image
+ """
+
+ def __init__(self, **kwargs):
+ pass
+
+ def __call__(self, data):
+ img = data['image']
+ from PIL import Image
+ if isinstance(img, Image.Image):
+ img = np.array(img)
+ data['image'] = img.transpose((2, 0, 1))
+ return data
+
+
+class KeepKeys(object):
+ def __init__(self, keep_keys, **kwargs):
+ self.keep_keys = keep_keys
+
+ def __call__(self, data):
+ data_list = []
+ for key in self.keep_keys:
+ data_list.append(data[key])
+ return data_list
+
+
+class DetResizeForTest(object):
+ def __init__(self, **kwargs):
+ super(DetResizeForTest, self).__init__()
+ self.resize_type = 0
+ if 'image_shape' in kwargs:
+ self.image_shape = kwargs['image_shape']
+ self.resize_type = 1
+ elif 'limit_side_len' in kwargs:
+ self.limit_side_len = kwargs['limit_side_len']
+ self.limit_type = kwargs.get('limit_type', 'min')
+
+ if 'resize_long' in kwargs:
+ self.resize_type = 2
+ self.resize_long = kwargs.get('resize_long', 960)
+ else:
+ self.limit_side_len = 736
+ self.limit_type = 'min'
+
+ def __call__(self, data):
+ img = data['image']
+ src_h, src_w, _ = img.shape
+
+ if self.resize_type == 0:
+ # img, shape = self.resize_image_type0(img)
+ img, [ratio_h, ratio_w] = self.resize_image_type0(img)
+ elif self.resize_type == 2:
+ img, [ratio_h, ratio_w] = self.resize_image_type2(img)
+ else:
+ # img, shape = self.resize_image_type1(img)
+ img, [ratio_h, ratio_w] = self.resize_image_type1(img)
+ data['image'] = img
+ data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+ return data
+
+ def resize_image_type1(self, img):
+ resize_h, resize_w = self.image_shape
+ ori_h, ori_w = img.shape[:2] # (h, w, c)
+ ratio_h = float(resize_h) / ori_h
+ ratio_w = float(resize_w) / ori_w
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ # return img, np.array([ori_h, ori_w])
+ return img, [ratio_h, ratio_w]
+
+ def resize_image_type0(self, img):
+ """
+ resize image to a size multiple of 32 which is required by the network
+ args:
+ img(array): array with shape [h, w, c]
+ return(tuple):
+ img, (ratio_h, ratio_w)
+ """
+ limit_side_len = self.limit_side_len
+ h, w, _ = img.shape
+
+ # limit the max side
+ if self.limit_type == 'max':
+ if max(h, w) > limit_side_len:
+ if h > w:
+ ratio = float(limit_side_len) / h
+ else:
+ ratio = float(limit_side_len) / w
+ else:
+ ratio = 1.
+ else:
+ if min(h, w) < limit_side_len:
+ if h < w:
+ ratio = float(limit_side_len) / h
+ else:
+ ratio = float(limit_side_len) / w
+ else:
+ ratio = 1.
+ resize_h = int(h * ratio)
+ resize_w = int(w * ratio)
+
+ resize_h = int(round(resize_h / 32) * 32)
+ resize_w = int(round(resize_w / 32) * 32)
+
+ try:
+ if int(resize_w) <= 0 or int(resize_h) <= 0:
+ return None, (None, None)
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ except:
+ print(img.shape, resize_w, resize_h)
+ sys.exit(0)
+ ratio_h = resize_h / float(h)
+ ratio_w = resize_w / float(w)
+ return img, [ratio_h, ratio_w]
+
+ def resize_image_type2(self, img):
+ h, w, _ = img.shape
+
+ resize_w = w
+ resize_h = h
+
+ # Fix the longer side
+ if resize_h > resize_w:
+ ratio = float(self.resize_long) / resize_h
+ else:
+ ratio = float(self.resize_long) / resize_w
+
+ resize_h = int(resize_h * ratio)
+ resize_w = int(resize_w * ratio)
+
+ max_stride = 128
+ resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+ resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ ratio_h = resize_h / float(h)
+ ratio_w = resize_w / float(w)
+
+ return img, [ratio_h, ratio_w]
+
+
+def transform(data, ops=None):
+ """ transform """
+ if ops is None:
+ ops = []
+ for op in ops:
+ data = op(data)
+ if data is None:
+ return None
+ return data
+
+
+def check_and_read_gif(img_path):
+ if os.path.basename(img_path)[-3:] in ['gif', 'GIF']:
+ gif = cv2.VideoCapture(img_path)
+ ret, frame = gif.read()
+ if not ret:
+ print("Cannot read {}. This gif image maybe corrupted.")
+ return None, False
+ if len(frame.shape) == 2 or frame.shape[-1] == 1:
+ frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+ imgvalue = frame[:, :, ::-1]
+ return imgvalue, True
+ return None, False
+
+
+def create_operators(op_param_list, global_config=None):
+ """
+ create operators based on the config
+
+ Args:
+ params(list): a dict list, used to create some operators
+ """
+ assert isinstance(
+ op_param_list, list), ('operator config should be a list')
+ ops = []
+ for operator in op_param_list:
+ assert isinstance(operator,
+ dict) and len(operator) == 1, "yaml format error"
+ op_name = list(operator)[0]
+ param = {} if operator[op_name] is None else operator[op_name]
+ if global_config is not None:
+ param.update(global_config)
+ op = eval(op_name)(**param)
+ ops.append(op)
+ return ops
+
+
+def draw_text_det_res(dt_boxes, img_path):
+ src_im = cv2.imread(img_path)
+ for box in dt_boxes:
+ box = np.array(box).astype(np.int32).reshape(-1, 2)
+ cv2.polylines(src_im, [box], True,
+ color=(255, 255, 0), thickness=2)
+ return src_im
+
+
+class DBPostProcess(object):
+ """
+ The post process for Differentiable Binarization (DB).
+ """
+
+ def __init__(self,
+ thresh=0.3,
+ box_thresh=0.7,
+ max_candidates=1000,
+ unclip_ratio=2.0,
+ use_dilation=False,
+ **kwargs):
+ self.thresh = thresh
+ self.box_thresh = box_thresh
+ self.max_candidates = max_candidates
+ self.unclip_ratio = unclip_ratio
+ self.min_size = 3
+ self.dilation_kernel = None if not use_dilation else np.array(
+ [[1, 1], [1, 1]])
+
+ def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+ '''
+ _bitmap: single map with shape (1, H, W),
+ whose values are binarized as {0, 1}
+ '''
+
+ bitmap = _bitmap
+ height, width = bitmap.shape
+
+ outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
+ cv2.CHAIN_APPROX_SIMPLE)
+ if len(outs) == 3:
+ img, contours, _ = outs[0], outs[1], outs[2]
+ elif len(outs) == 2:
+ contours, _ = outs[0], outs[1]
+
+ num_contours = min(len(contours), self.max_candidates)
+
+ boxes = []
+ scores = []
+ for index in range(num_contours):
+ contour = contours[index]
+ points, sside = self.get_mini_boxes(contour)
+ if sside < self.min_size:
+ continue
+ points = np.array(points)
+ score = self.box_score_fast(pred, points.reshape(-1, 2))
+ if self.box_thresh > score:
+ continue
+
+ box = self.unclip(points).reshape(-1, 1, 2)
+ box, sside = self.get_mini_boxes(box)
+ if sside < self.min_size + 2:
+ continue
+ box = np.array(box)
+
+ box[:, 0] = np.clip(
+ np.round(box[:, 0] / width * dest_width), 0, dest_width)
+ box[:, 1] = np.clip(
+ np.round(box[:, 1] / height * dest_height), 0, dest_height)
+ boxes.append(box.astype(np.int16))
+ scores.append(score)
+ return np.array(boxes, dtype=np.int16), scores
+
+ def unclip(self, box):
+ unclip_ratio = self.unclip_ratio
+ poly = Polygon(box)
+ distance = poly.area * unclip_ratio / poly.length
+ offset = pyclipper.PyclipperOffset()
+ offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+ expanded = np.array(offset.Execute(distance))
+ return expanded
+
+ def get_mini_boxes(self, contour):
+ bounding_box = cv2.minAreaRect(contour)
+ points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+ index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+ if points[1][1] > points[0][1]:
+ index_1 = 0
+ index_4 = 1
+ else:
+ index_1 = 1
+ index_4 = 0
+ if points[3][1] > points[2][1]:
+ index_2 = 2
+ index_3 = 3
+ else:
+ index_2 = 3
+ index_3 = 2
+
+ box = [
+ points[index_1], points[index_2], points[index_3], points[index_4]
+ ]
+ return box, min(bounding_box[1])
+
+ def box_score_fast(self, bitmap, _box):
+ h, w = bitmap.shape[:2]
+ box = _box.copy()
+ xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
+ xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
+ ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
+ ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+
+ mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+ box[:, 0] = box[:, 0] - xmin
+ box[:, 1] = box[:, 1] - ymin
+ cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+ return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
+ def __call__(self, pred, shape_list):
+ pred = pred[:, 0, :, :]
+ segmentation = pred > self.thresh
+
+ boxes_batch = []
+ for batch_index in range(pred.shape[0]):
+ src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
+ if self.dilation_kernel is not None:
+ mask = cv2.dilate(
+ np.array(segmentation[batch_index]).astype(np.uint8),
+ self.dilation_kernel)
+ else:
+ mask = segmentation[batch_index]
+ boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
+ src_w, src_h)
+
+ boxes_batch.append({'points': boxes})
+ return boxes_batch
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/__init__.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/__init__.py
new file mode 100644
index 0000000..d7a3e0b
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/__init__.py
@@ -0,0 +1,6 @@
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+# @File: __init__.py
+# @Time: 2021/01/23 15:24:27
+# @Author: Max
+from .text_detect import TextDetector
\ No newline at end of file
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/run.sh b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/run.sh
new file mode 100644
index 0000000..99fc806
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/run.sh
@@ -0,0 +1,9 @@
+#! /bin/bash
+set -e errexit
+
+root_path=$(cd $(dirname $0); pwd)
+image_path=${root_path}"/test_images/det_images/1.jpg"
+model_path=${root_path}"/models/ch_ppocr_mobile_v2.0_det_infer.onnx"
+
+python text_detect.py --image_path ${image_path} \
+ --model_path ${model_path}
\ No newline at end of file
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/text_detect.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/text_detect.py
new file mode 100644
index 0000000..1029971
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/text_detect.py
@@ -0,0 +1,150 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+import argparse
+import time
+
+import cv2
+import numpy as np
+from openvino.runtime import Core
+
+try:
+ from .utils import (DBPostProcess, check_and_read_gif, create_operators,
+ draw_text_det_res, transform)
+except:
+ from utils import (DBPostProcess, check_and_read_gif, create_operators,
+ draw_text_det_res, transform)
+
+
+class TextDetector(object):
+ def __init__(self, det_model_path):
+ pre_process_list = [{
+ 'DetResizeForTest': {
+ 'limit_side_len': 736,
+ 'limit_type': 'min'
+ }
+ }, {
+ 'NormalizeImage': {
+ 'std': [0.229, 0.224, 0.225],
+ 'mean': [0.485, 0.456, 0.406],
+ 'scale': '1./255.',
+ 'order': 'hwc'
+ }
+ }, {
+ 'ToCHWImage': None
+ }, {
+ 'KeepKeys': {
+ 'keep_keys': ['image', 'shape']
+ }
+ }]
+
+ self.preprocess_op = create_operators(pre_process_list)
+ self.postprocess_op = DBPostProcess(thresh=0.3,
+ box_thresh=0.5,
+ max_candidates=1000,
+ unclip_ratio=1.6,
+ use_dilation=True)
+ ie = Core()
+ model_onnx = ie.read_model(det_model_path)
+ compile_model = ie.compile_model(model=model_onnx,
+ device_name='CPU')
+ self.vino_session = compile_model.create_infer_request()
+
+ def order_points_clockwise(self, pts):
+ """
+ reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+ # sort the points based on their x-coordinates
+ """
+ xSorted = pts[np.argsort(pts[:, 0]), :]
+
+ # grab the left-most and right-most points from the sorted
+ # x-roodinate points
+ leftMost = xSorted[:2, :]
+ rightMost = xSorted[2:, :]
+
+ # now, sort the left-most coordinates according to their
+ # y-coordinates so we can grab the top-left and bottom-left
+ # points, respectively
+ leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+ (tl, bl) = leftMost
+
+ rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+ (tr, br) = rightMost
+
+ rect = np.array([tl, tr, br, bl], dtype="float32")
+ return rect
+
+ def clip_det_res(self, points, img_height, img_width):
+ for pno in range(points.shape[0]):
+ points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+ points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+ return points
+
+ def filter_tag_det_res(self, dt_boxes, image_shape):
+ img_height, img_width = image_shape[0:2]
+ dt_boxes_new = []
+ for box in dt_boxes:
+ box = self.order_points_clockwise(box)
+ box = self.clip_det_res(box, img_height, img_width)
+ rect_width = int(np.linalg.norm(box[0] - box[1]))
+ rect_height = int(np.linalg.norm(box[0] - box[3]))
+ if rect_width <= 3 or rect_height <= 3:
+ continue
+ dt_boxes_new.append(box)
+ dt_boxes = np.array(dt_boxes_new)
+ return dt_boxes
+
+ def __call__(self, img):
+ ori_im = img.copy()
+ data = {'image': img}
+ data = transform(data, self.preprocess_op)
+ img, shape_list = data
+ if img is None:
+ return None, 0
+ img = np.expand_dims(img, axis=0)
+ img = img.astype(np.float32)
+ shape_list = np.expand_dims(shape_list, axis=0)
+
+ start_time = time.time()
+ self.vino_session.infer(inputs=[img])
+ vino_preds = self.vino_session.get_output_tensor().data
+
+ post_result = self.postprocess_op(vino_preds, shape_list)
+ dt_boxes = post_result[0]['points']
+ dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
+ elapse = time.time() - start_time
+ return dt_boxes, elapse
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--image_path', type=str, help='image_path|image_dir')
+ parser.add_argument('--model_path', type=str, help='model_path')
+ args = parser.parse_args()
+
+ text_detector = TextDetector(args.model_path)
+
+ img, flag = check_and_read_gif(args.image_path)
+ if not flag:
+ img = cv2.imread(args.image_path)
+ if img is None:
+ raise ValueError(f"error in loading image:{args.image_path}")
+
+ dt_boxes, elapse = text_detector(img)
+
+ src_im = draw_text_det_res(dt_boxes, args.image_path)
+ cv2.imwrite('det_results.jpg', src_im)
+ print(f'图像已经保存为det_results.jpg了\t{elapse:.6f}s')
+
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/utils.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/utils.py
new file mode 100644
index 0000000..dfbea6c
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_onnx/utils.py
@@ -0,0 +1,414 @@
+"""
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+# coding:utf-8
+from __future__ import (absolute_import, division, print_function,
+ unicode_literals)
+
+import os
+import sys
+
+import cv2
+import numpy as np
+import pyclipper
+import six
+from shapely.geometry import Polygon
+
+
+class DecodeImage(object):
+ """ decode image """
+
+ def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+ self.img_mode = img_mode
+ self.channel_first = channel_first
+
+ def __call__(self, data):
+ img = data['image']
+ if six.PY2:
+ assert type(img) is str and len(
+ img) > 0, "invalid input 'img' in DecodeImage"
+ else:
+ assert type(img) is bytes and len(
+ img) > 0, "invalid input 'img' in DecodeImage"
+ img = np.frombuffer(img, dtype='uint8')
+ img = cv2.imdecode(img, 1)
+ if img is None:
+ return None
+ if self.img_mode == 'GRAY':
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+ elif self.img_mode == 'RGB':
+ assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
+ img.shape)
+ img = img[:, :, ::-1]
+
+ if self.channel_first:
+ img = img.transpose((2, 0, 1))
+
+ data['image'] = img
+ return data
+
+
+class NormalizeImage(object):
+ """ normalize image such as substract mean, divide std
+ """
+
+ def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
+ if isinstance(scale, str):
+ scale = eval(scale)
+ self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+ mean = mean if mean is not None else [0.485, 0.456, 0.406]
+ std = std if std is not None else [0.229, 0.224, 0.225]
+
+ shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+ self.mean = np.array(mean).reshape(shape).astype('float32')
+ self.std = np.array(std).reshape(shape).astype('float32')
+
+ def __call__(self, data):
+ img = data['image']
+ from PIL import Image
+ if isinstance(img, Image.Image):
+ img = np.array(img)
+
+ assert isinstance(img,
+ np.ndarray), "invalid input 'img' in NormalizeImage"
+ data['image'] = (
+ img.astype('float32') * self.scale - self.mean) / self.std
+ return data
+
+
+class ToCHWImage(object):
+ """ convert hwc image to chw image
+ """
+
+ def __init__(self, **kwargs):
+ pass
+
+ def __call__(self, data):
+ img = data['image']
+ from PIL import Image
+ if isinstance(img, Image.Image):
+ img = np.array(img)
+ data['image'] = img.transpose((2, 0, 1))
+ return data
+
+
+class KeepKeys(object):
+ def __init__(self, keep_keys, **kwargs):
+ self.keep_keys = keep_keys
+
+ def __call__(self, data):
+ data_list = []
+ for key in self.keep_keys:
+ data_list.append(data[key])
+ return data_list
+
+
+class DetResizeForTest(object):
+ def __init__(self, **kwargs):
+ super(DetResizeForTest, self).__init__()
+ self.resize_type = 0
+ if 'image_shape' in kwargs:
+ self.image_shape = kwargs['image_shape']
+ self.resize_type = 1
+ elif 'limit_side_len' in kwargs:
+ self.limit_side_len = kwargs['limit_side_len']
+ self.limit_type = kwargs.get('limit_type', 'min')
+
+ if 'resize_long' in kwargs:
+ self.resize_type = 2
+ self.resize_long = kwargs.get('resize_long', 960)
+ else:
+ self.limit_side_len = 736
+ self.limit_type = 'min'
+
+ def __call__(self, data):
+ img = data['image']
+ src_h, src_w, _ = img.shape
+
+ if self.resize_type == 0:
+ # img, shape = self.resize_image_type0(img)
+ img, [ratio_h, ratio_w] = self.resize_image_type0(img)
+ elif self.resize_type == 2:
+ img, [ratio_h, ratio_w] = self.resize_image_type2(img)
+ else:
+ # img, shape = self.resize_image_type1(img)
+ img, [ratio_h, ratio_w] = self.resize_image_type1(img)
+ data['image'] = img
+ data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+ return data
+
+ def resize_image_type1(self, img):
+ resize_h, resize_w = self.image_shape
+ ori_h, ori_w = img.shape[:2] # (h, w, c)
+ ratio_h = float(resize_h) / ori_h
+ ratio_w = float(resize_w) / ori_w
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ # return img, np.array([ori_h, ori_w])
+ return img, [ratio_h, ratio_w]
+
+ def resize_image_type0(self, img):
+ """
+ resize image to a size multiple of 32 which is required by the network
+ args:
+ img(array): array with shape [h, w, c]
+ return(tuple):
+ img, (ratio_h, ratio_w)
+ """
+ limit_side_len = self.limit_side_len
+ h, w, _ = img.shape
+
+ # limit the max side
+ if self.limit_type == 'max':
+ if max(h, w) > limit_side_len:
+ if h > w:
+ ratio = float(limit_side_len) / h
+ else:
+ ratio = float(limit_side_len) / w
+ else:
+ ratio = 1.
+ else:
+ if min(h, w) < limit_side_len:
+ if h < w:
+ ratio = float(limit_side_len) / h
+ else:
+ ratio = float(limit_side_len) / w
+ else:
+ ratio = 1.
+ resize_h = int(h * ratio)
+ resize_w = int(w * ratio)
+
+ resize_h = int(round(resize_h / 32) * 32)
+ resize_w = int(round(resize_w / 32) * 32)
+
+ try:
+ if int(resize_w) <= 0 or int(resize_h) <= 0:
+ return None, (None, None)
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ except:
+ print(img.shape, resize_w, resize_h)
+ sys.exit(0)
+ ratio_h = resize_h / float(h)
+ ratio_w = resize_w / float(w)
+ return img, [ratio_h, ratio_w]
+
+ def resize_image_type2(self, img):
+ h, w, _ = img.shape
+
+ resize_w = w
+ resize_h = h
+
+ # Fix the longer side
+ if resize_h > resize_w:
+ ratio = float(self.resize_long) / resize_h
+ else:
+ ratio = float(self.resize_long) / resize_w
+
+ resize_h = int(resize_h * ratio)
+ resize_w = int(resize_w * ratio)
+
+ max_stride = 128
+ resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+ resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ ratio_h = resize_h / float(h)
+ ratio_w = resize_w / float(w)
+
+ return img, [ratio_h, ratio_w]
+
+
+def transform(data, ops=None):
+ """ transform """
+ if ops is None:
+ ops = []
+ for op in ops:
+ data = op(data)
+ if data is None:
+ return None
+ return data
+
+
+def check_and_read_gif(img_path):
+ if os.path.basename(img_path)[-3:] in ['gif', 'GIF']:
+ gif = cv2.VideoCapture(img_path)
+ ret, frame = gif.read()
+ if not ret:
+ print("Cannot read {}. This gif image maybe corrupted.")
+ return None, False
+ if len(frame.shape) == 2 or frame.shape[-1] == 1:
+ frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+ imgvalue = frame[:, :, ::-1]
+ return imgvalue, True
+ return None, False
+
+
+def create_operators(op_param_list, global_config=None):
+ """
+ create operators based on the config
+
+ Args:
+ params(list): a dict list, used to create some operators
+ """
+ assert isinstance(
+ op_param_list, list), ('operator config should be a list')
+ ops = []
+ for operator in op_param_list:
+ assert isinstance(operator,
+ dict) and len(operator) == 1, "yaml format error"
+ op_name = list(operator)[0]
+ param = {} if operator[op_name] is None else operator[op_name]
+ if global_config is not None:
+ param.update(global_config)
+ op = eval(op_name)(**param)
+ ops.append(op)
+ return ops
+
+
+def draw_text_det_res(dt_boxes, img_path):
+ src_im = cv2.imread(img_path)
+ for box in dt_boxes:
+ box = np.array(box).astype(np.int32).reshape(-1, 2)
+ cv2.polylines(src_im, [box], True,
+ color=(255, 255, 0), thickness=2)
+ return src_im
+
+
+class DBPostProcess(object):
+ """
+ The post process for Differentiable Binarization (DB).
+ """
+
+ def __init__(self,
+ thresh=0.3,
+ box_thresh=0.7,
+ max_candidates=1000,
+ unclip_ratio=2.0,
+ use_dilation=False,
+ **kwargs):
+ self.thresh = thresh
+ self.box_thresh = box_thresh
+ self.max_candidates = max_candidates
+ self.unclip_ratio = unclip_ratio
+ self.min_size = 3
+ self.dilation_kernel = None if not use_dilation else np.array(
+ [[1, 1], [1, 1]])
+
+ def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+ '''
+ _bitmap: single map with shape (1, H, W),
+ whose values are binarized as {0, 1}
+ '''
+
+ bitmap = _bitmap
+ height, width = bitmap.shape
+
+ outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
+ cv2.CHAIN_APPROX_SIMPLE)
+ if len(outs) == 3:
+ img, contours, _ = outs[0], outs[1], outs[2]
+ elif len(outs) == 2:
+ contours, _ = outs[0], outs[1]
+
+ num_contours = min(len(contours), self.max_candidates)
+
+ boxes = []
+ scores = []
+ for index in range(num_contours):
+ contour = contours[index]
+ points, sside = self.get_mini_boxes(contour)
+ if sside < self.min_size:
+ continue
+ points = np.array(points)
+ score = self.box_score_fast(pred, points.reshape(-1, 2))
+ if self.box_thresh > score:
+ continue
+
+ box = self.unclip(points).reshape(-1, 1, 2)
+ box, sside = self.get_mini_boxes(box)
+ if sside < self.min_size + 2:
+ continue
+ box = np.array(box)
+
+ box[:, 0] = np.clip(
+ np.round(box[:, 0] / width * dest_width), 0, dest_width)
+ box[:, 1] = np.clip(
+ np.round(box[:, 1] / height * dest_height), 0, dest_height)
+ boxes.append(box.astype(np.int16))
+ scores.append(score)
+ return np.array(boxes, dtype=np.int16), scores
+
+ def unclip(self, box):
+ unclip_ratio = self.unclip_ratio
+ poly = Polygon(box)
+ distance = poly.area * unclip_ratio / poly.length
+ offset = pyclipper.PyclipperOffset()
+ offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+ expanded = np.array(offset.Execute(distance))
+ return expanded
+
+ def get_mini_boxes(self, contour):
+ bounding_box = cv2.minAreaRect(contour)
+ points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+ index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+ if points[1][1] > points[0][1]:
+ index_1 = 0
+ index_4 = 1
+ else:
+ index_1 = 1
+ index_4 = 0
+ if points[3][1] > points[2][1]:
+ index_2 = 2
+ index_3 = 3
+ else:
+ index_2 = 3
+ index_3 = 2
+
+ box = [
+ points[index_1], points[index_2], points[index_3], points[index_4]
+ ]
+ return box, min(bounding_box[1])
+
+ def box_score_fast(self, bitmap, _box):
+ h, w = bitmap.shape[:2]
+ box = _box.copy()
+ xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
+ xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
+ ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
+ ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+
+ mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+ box[:, 0] = box[:, 0] - xmin
+ box[:, 1] = box[:, 1] - ymin
+ cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+ return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
+ def __call__(self, pred, shape_list):
+ pred = pred[:, 0, :, :]
+ segmentation = pred > self.thresh
+
+ boxes_batch = []
+ for batch_index in range(pred.shape[0]):
+ src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
+ if self.dilation_kernel is not None:
+ mask = cv2.dilate(
+ np.array(segmentation[batch_index]).astype(np.uint8),
+ self.dilation_kernel)
+ else:
+ mask = segmentation[batch_index]
+ boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
+ src_w, src_h)
+
+ boxes_batch.append({'points': boxes})
+ return boxes_batch
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/__init__.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/__init__.py
new file mode 100644
index 0000000..d7a3e0b
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/__init__.py
@@ -0,0 +1,6 @@
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+# @File: __init__.py
+# @Time: 2021/01/23 15:24:27
+# @Author: Max
+from .text_detect import TextDetector
\ No newline at end of file
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/run.sh b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/run.sh
new file mode 100644
index 0000000..99fc806
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/run.sh
@@ -0,0 +1,9 @@
+#! /bin/bash
+set -e errexit
+
+root_path=$(cd $(dirname $0); pwd)
+image_path=${root_path}"/test_images/det_images/1.jpg"
+model_path=${root_path}"/models/ch_ppocr_mobile_v2.0_det_infer.onnx"
+
+python text_detect.py --image_path ${image_path} \
+ --model_path ${model_path}
\ No newline at end of file
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/text_detect.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/text_detect.py
new file mode 100644
index 0000000..1029971
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/text_detect.py
@@ -0,0 +1,150 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+import argparse
+import time
+
+import cv2
+import numpy as np
+from openvino.runtime import Core
+
+try:
+ from .utils import (DBPostProcess, check_and_read_gif, create_operators,
+ draw_text_det_res, transform)
+except:
+ from utils import (DBPostProcess, check_and_read_gif, create_operators,
+ draw_text_det_res, transform)
+
+
+class TextDetector(object):
+ def __init__(self, det_model_path):
+ pre_process_list = [{
+ 'DetResizeForTest': {
+ 'limit_side_len': 736,
+ 'limit_type': 'min'
+ }
+ }, {
+ 'NormalizeImage': {
+ 'std': [0.229, 0.224, 0.225],
+ 'mean': [0.485, 0.456, 0.406],
+ 'scale': '1./255.',
+ 'order': 'hwc'
+ }
+ }, {
+ 'ToCHWImage': None
+ }, {
+ 'KeepKeys': {
+ 'keep_keys': ['image', 'shape']
+ }
+ }]
+
+ self.preprocess_op = create_operators(pre_process_list)
+ self.postprocess_op = DBPostProcess(thresh=0.3,
+ box_thresh=0.5,
+ max_candidates=1000,
+ unclip_ratio=1.6,
+ use_dilation=True)
+ ie = Core()
+ model_onnx = ie.read_model(det_model_path)
+ compile_model = ie.compile_model(model=model_onnx,
+ device_name='CPU')
+ self.vino_session = compile_model.create_infer_request()
+
+ def order_points_clockwise(self, pts):
+ """
+ reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+ # sort the points based on their x-coordinates
+ """
+ xSorted = pts[np.argsort(pts[:, 0]), :]
+
+ # grab the left-most and right-most points from the sorted
+ # x-roodinate points
+ leftMost = xSorted[:2, :]
+ rightMost = xSorted[2:, :]
+
+ # now, sort the left-most coordinates according to their
+ # y-coordinates so we can grab the top-left and bottom-left
+ # points, respectively
+ leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+ (tl, bl) = leftMost
+
+ rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+ (tr, br) = rightMost
+
+ rect = np.array([tl, tr, br, bl], dtype="float32")
+ return rect
+
+ def clip_det_res(self, points, img_height, img_width):
+ for pno in range(points.shape[0]):
+ points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+ points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+ return points
+
+ def filter_tag_det_res(self, dt_boxes, image_shape):
+ img_height, img_width = image_shape[0:2]
+ dt_boxes_new = []
+ for box in dt_boxes:
+ box = self.order_points_clockwise(box)
+ box = self.clip_det_res(box, img_height, img_width)
+ rect_width = int(np.linalg.norm(box[0] - box[1]))
+ rect_height = int(np.linalg.norm(box[0] - box[3]))
+ if rect_width <= 3 or rect_height <= 3:
+ continue
+ dt_boxes_new.append(box)
+ dt_boxes = np.array(dt_boxes_new)
+ return dt_boxes
+
+ def __call__(self, img):
+ ori_im = img.copy()
+ data = {'image': img}
+ data = transform(data, self.preprocess_op)
+ img, shape_list = data
+ if img is None:
+ return None, 0
+ img = np.expand_dims(img, axis=0)
+ img = img.astype(np.float32)
+ shape_list = np.expand_dims(shape_list, axis=0)
+
+ start_time = time.time()
+ self.vino_session.infer(inputs=[img])
+ vino_preds = self.vino_session.get_output_tensor().data
+
+ post_result = self.postprocess_op(vino_preds, shape_list)
+ dt_boxes = post_result[0]['points']
+ dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
+ elapse = time.time() - start_time
+ return dt_boxes, elapse
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--image_path', type=str, help='image_path|image_dir')
+ parser.add_argument('--model_path', type=str, help='model_path')
+ args = parser.parse_args()
+
+ text_detector = TextDetector(args.model_path)
+
+ img, flag = check_and_read_gif(args.image_path)
+ if not flag:
+ img = cv2.imread(args.image_path)
+ if img is None:
+ raise ValueError(f"error in loading image:{args.image_path}")
+
+ dt_boxes, elapse = text_detector(img)
+
+ src_im = draw_text_det_res(dt_boxes, args.image_path)
+ cv2.imwrite('det_results.jpg', src_im)
+ print(f'图像已经保存为det_results.jpg了\t{elapse:.6f}s')
+
diff --git a/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/utils.py b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/utils.py
new file mode 100644
index 0000000..dfbea6c
--- /dev/null
+++ b/python/openvino_infer/ch_ppocr_mobile_v2_det_openvino_paddle/utils.py
@@ -0,0 +1,414 @@
+"""
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+# coding:utf-8
+from __future__ import (absolute_import, division, print_function,
+ unicode_literals)
+
+import os
+import sys
+
+import cv2
+import numpy as np
+import pyclipper
+import six
+from shapely.geometry import Polygon
+
+
+class DecodeImage(object):
+ """ decode image """
+
+ def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+ self.img_mode = img_mode
+ self.channel_first = channel_first
+
+ def __call__(self, data):
+ img = data['image']
+ if six.PY2:
+ assert type(img) is str and len(
+ img) > 0, "invalid input 'img' in DecodeImage"
+ else:
+ assert type(img) is bytes and len(
+ img) > 0, "invalid input 'img' in DecodeImage"
+ img = np.frombuffer(img, dtype='uint8')
+ img = cv2.imdecode(img, 1)
+ if img is None:
+ return None
+ if self.img_mode == 'GRAY':
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+ elif self.img_mode == 'RGB':
+ assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
+ img.shape)
+ img = img[:, :, ::-1]
+
+ if self.channel_first:
+ img = img.transpose((2, 0, 1))
+
+ data['image'] = img
+ return data
+
+
+class NormalizeImage(object):
+ """ normalize image such as substract mean, divide std
+ """
+
+ def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
+ if isinstance(scale, str):
+ scale = eval(scale)
+ self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+ mean = mean if mean is not None else [0.485, 0.456, 0.406]
+ std = std if std is not None else [0.229, 0.224, 0.225]
+
+ shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+ self.mean = np.array(mean).reshape(shape).astype('float32')
+ self.std = np.array(std).reshape(shape).astype('float32')
+
+ def __call__(self, data):
+ img = data['image']
+ from PIL import Image
+ if isinstance(img, Image.Image):
+ img = np.array(img)
+
+ assert isinstance(img,
+ np.ndarray), "invalid input 'img' in NormalizeImage"
+ data['image'] = (
+ img.astype('float32') * self.scale - self.mean) / self.std
+ return data
+
+
+class ToCHWImage(object):
+ """ convert hwc image to chw image
+ """
+
+ def __init__(self, **kwargs):
+ pass
+
+ def __call__(self, data):
+ img = data['image']
+ from PIL import Image
+ if isinstance(img, Image.Image):
+ img = np.array(img)
+ data['image'] = img.transpose((2, 0, 1))
+ return data
+
+
+class KeepKeys(object):
+ def __init__(self, keep_keys, **kwargs):
+ self.keep_keys = keep_keys
+
+ def __call__(self, data):
+ data_list = []
+ for key in self.keep_keys:
+ data_list.append(data[key])
+ return data_list
+
+
+class DetResizeForTest(object):
+ def __init__(self, **kwargs):
+ super(DetResizeForTest, self).__init__()
+ self.resize_type = 0
+ if 'image_shape' in kwargs:
+ self.image_shape = kwargs['image_shape']
+ self.resize_type = 1
+ elif 'limit_side_len' in kwargs:
+ self.limit_side_len = kwargs['limit_side_len']
+ self.limit_type = kwargs.get('limit_type', 'min')
+
+ if 'resize_long' in kwargs:
+ self.resize_type = 2
+ self.resize_long = kwargs.get('resize_long', 960)
+ else:
+ self.limit_side_len = 736
+ self.limit_type = 'min'
+
+ def __call__(self, data):
+ img = data['image']
+ src_h, src_w, _ = img.shape
+
+ if self.resize_type == 0:
+ # img, shape = self.resize_image_type0(img)
+ img, [ratio_h, ratio_w] = self.resize_image_type0(img)
+ elif self.resize_type == 2:
+ img, [ratio_h, ratio_w] = self.resize_image_type2(img)
+ else:
+ # img, shape = self.resize_image_type1(img)
+ img, [ratio_h, ratio_w] = self.resize_image_type1(img)
+ data['image'] = img
+ data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+ return data
+
+ def resize_image_type1(self, img):
+ resize_h, resize_w = self.image_shape
+ ori_h, ori_w = img.shape[:2] # (h, w, c)
+ ratio_h = float(resize_h) / ori_h
+ ratio_w = float(resize_w) / ori_w
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ # return img, np.array([ori_h, ori_w])
+ return img, [ratio_h, ratio_w]
+
+ def resize_image_type0(self, img):
+ """
+ resize image to a size multiple of 32 which is required by the network
+ args:
+ img(array): array with shape [h, w, c]
+ return(tuple):
+ img, (ratio_h, ratio_w)
+ """
+ limit_side_len = self.limit_side_len
+ h, w, _ = img.shape
+
+ # limit the max side
+ if self.limit_type == 'max':
+ if max(h, w) > limit_side_len:
+ if h > w:
+ ratio = float(limit_side_len) / h
+ else:
+ ratio = float(limit_side_len) / w
+ else:
+ ratio = 1.
+ else:
+ if min(h, w) < limit_side_len:
+ if h < w:
+ ratio = float(limit_side_len) / h
+ else:
+ ratio = float(limit_side_len) / w
+ else:
+ ratio = 1.
+ resize_h = int(h * ratio)
+ resize_w = int(w * ratio)
+
+ resize_h = int(round(resize_h / 32) * 32)
+ resize_w = int(round(resize_w / 32) * 32)
+
+ try:
+ if int(resize_w) <= 0 or int(resize_h) <= 0:
+ return None, (None, None)
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ except:
+ print(img.shape, resize_w, resize_h)
+ sys.exit(0)
+ ratio_h = resize_h / float(h)
+ ratio_w = resize_w / float(w)
+ return img, [ratio_h, ratio_w]
+
+ def resize_image_type2(self, img):
+ h, w, _ = img.shape
+
+ resize_w = w
+ resize_h = h
+
+ # Fix the longer side
+ if resize_h > resize_w:
+ ratio = float(self.resize_long) / resize_h
+ else:
+ ratio = float(self.resize_long) / resize_w
+
+ resize_h = int(resize_h * ratio)
+ resize_w = int(resize_w * ratio)
+
+ max_stride = 128
+ resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+ resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ ratio_h = resize_h / float(h)
+ ratio_w = resize_w / float(w)
+
+ return img, [ratio_h, ratio_w]
+
+
+def transform(data, ops=None):
+ """ transform """
+ if ops is None:
+ ops = []
+ for op in ops:
+ data = op(data)
+ if data is None:
+ return None
+ return data
+
+
+def check_and_read_gif(img_path):
+ if os.path.basename(img_path)[-3:] in ['gif', 'GIF']:
+ gif = cv2.VideoCapture(img_path)
+ ret, frame = gif.read()
+ if not ret:
+ print("Cannot read {}. This gif image maybe corrupted.")
+ return None, False
+ if len(frame.shape) == 2 or frame.shape[-1] == 1:
+ frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+ imgvalue = frame[:, :, ::-1]
+ return imgvalue, True
+ return None, False
+
+
+def create_operators(op_param_list, global_config=None):
+ """
+ create operators based on the config
+
+ Args:
+ params(list): a dict list, used to create some operators
+ """
+ assert isinstance(
+ op_param_list, list), ('operator config should be a list')
+ ops = []
+ for operator in op_param_list:
+ assert isinstance(operator,
+ dict) and len(operator) == 1, "yaml format error"
+ op_name = list(operator)[0]
+ param = {} if operator[op_name] is None else operator[op_name]
+ if global_config is not None:
+ param.update(global_config)
+ op = eval(op_name)(**param)
+ ops.append(op)
+ return ops
+
+
+def draw_text_det_res(dt_boxes, img_path):
+ src_im = cv2.imread(img_path)
+ for box in dt_boxes:
+ box = np.array(box).astype(np.int32).reshape(-1, 2)
+ cv2.polylines(src_im, [box], True,
+ color=(255, 255, 0), thickness=2)
+ return src_im
+
+
+class DBPostProcess(object):
+ """
+ The post process for Differentiable Binarization (DB).
+ """
+
+ def __init__(self,
+ thresh=0.3,
+ box_thresh=0.7,
+ max_candidates=1000,
+ unclip_ratio=2.0,
+ use_dilation=False,
+ **kwargs):
+ self.thresh = thresh
+ self.box_thresh = box_thresh
+ self.max_candidates = max_candidates
+ self.unclip_ratio = unclip_ratio
+ self.min_size = 3
+ self.dilation_kernel = None if not use_dilation else np.array(
+ [[1, 1], [1, 1]])
+
+ def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+ '''
+ _bitmap: single map with shape (1, H, W),
+ whose values are binarized as {0, 1}
+ '''
+
+ bitmap = _bitmap
+ height, width = bitmap.shape
+
+ outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
+ cv2.CHAIN_APPROX_SIMPLE)
+ if len(outs) == 3:
+ img, contours, _ = outs[0], outs[1], outs[2]
+ elif len(outs) == 2:
+ contours, _ = outs[0], outs[1]
+
+ num_contours = min(len(contours), self.max_candidates)
+
+ boxes = []
+ scores = []
+ for index in range(num_contours):
+ contour = contours[index]
+ points, sside = self.get_mini_boxes(contour)
+ if sside < self.min_size:
+ continue
+ points = np.array(points)
+ score = self.box_score_fast(pred, points.reshape(-1, 2))
+ if self.box_thresh > score:
+ continue
+
+ box = self.unclip(points).reshape(-1, 1, 2)
+ box, sside = self.get_mini_boxes(box)
+ if sside < self.min_size + 2:
+ continue
+ box = np.array(box)
+
+ box[:, 0] = np.clip(
+ np.round(box[:, 0] / width * dest_width), 0, dest_width)
+ box[:, 1] = np.clip(
+ np.round(box[:, 1] / height * dest_height), 0, dest_height)
+ boxes.append(box.astype(np.int16))
+ scores.append(score)
+ return np.array(boxes, dtype=np.int16), scores
+
+ def unclip(self, box):
+ unclip_ratio = self.unclip_ratio
+ poly = Polygon(box)
+ distance = poly.area * unclip_ratio / poly.length
+ offset = pyclipper.PyclipperOffset()
+ offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+ expanded = np.array(offset.Execute(distance))
+ return expanded
+
+ def get_mini_boxes(self, contour):
+ bounding_box = cv2.minAreaRect(contour)
+ points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+ index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+ if points[1][1] > points[0][1]:
+ index_1 = 0
+ index_4 = 1
+ else:
+ index_1 = 1
+ index_4 = 0
+ if points[3][1] > points[2][1]:
+ index_2 = 2
+ index_3 = 3
+ else:
+ index_2 = 3
+ index_3 = 2
+
+ box = [
+ points[index_1], points[index_2], points[index_3], points[index_4]
+ ]
+ return box, min(bounding_box[1])
+
+ def box_score_fast(self, bitmap, _box):
+ h, w = bitmap.shape[:2]
+ box = _box.copy()
+ xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
+ xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
+ ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
+ ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+
+ mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+ box[:, 0] = box[:, 0] - xmin
+ box[:, 1] = box[:, 1] - ymin
+ cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+ return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
+ def __call__(self, pred, shape_list):
+ pred = pred[:, 0, :, :]
+ segmentation = pred > self.thresh
+
+ boxes_batch = []
+ for batch_index in range(pred.shape[0]):
+ src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
+ if self.dilation_kernel is not None:
+ mask = cv2.dilate(
+ np.array(segmentation[batch_index]).astype(np.uint8),
+ self.dilation_kernel)
+ else:
+ mask = segmentation[batch_index]
+ boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
+ src_w, src_h)
+
+ boxes_batch.append({'points': boxes})
+ return boxes_batch
diff --git a/python/openvino_infer/test_openvino.py b/python/openvino_infer/test_openvino.py
new file mode 100644
index 0000000..308838b
--- /dev/null
+++ b/python/openvino_infer/test_openvino.py
@@ -0,0 +1,59 @@
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+# @File: test_openvino.py
+# @Author: SWHL
+# @Contact: liekkaskono@163.com
+import numpy as np
+
+# Infer ONNXRuntime
+# import cv2
+
+# from ch_ppocr_mobile_v2_det import TextDetector
+
+# det_model_path = 'models/ch_PP-OCRv2_det_infer.onnx'
+
+# img_path = 'test_images/long1.jpg'
+# img = cv2.imread(img_path)
+
+# text_detect_onnx = TextDetector(det_model_path)
+# dt_boxes, elapse = text_detect_onnx(img)
+# print(f'onnxruntime: {elapse}')
+
+
+# # Infer ONNX
+# import cv2
+# from ch_ppocr_mobile_v2_det_openvino_onnx import TextDetector as TextDetectorVino
+
+# det_model_path = 'models/ch_PP-OCRv2_det_infer.onnx'
+# img_path = 'test_images/det_images/ch_en_num.jpg'
+# img = cv2.imread(img_path)
+
+# text_detect_vino = TextDetectorVino(det_model_path)
+# dt_boxes, elapse = text_detect_vino(img)
+# print(f'openvino ONNX: {elapse}\tdt_boxes:{np.mean(dt_boxes)}')
+
+# # Infer IR
+import cv2
+from ch_ppocr_mobile_v2_det_openvino_ir import TextDetector as TextDetectorIR
+
+det_model_path = 'models/IR/FP16/ch_PP-OCRv2_det_infer.xml'
+img_path = 'test_images/long1.jpg'
+img = cv2.imread(img_path)
+
+text_detect_ir = TextDetectorIR(det_model_path)
+dt_boxes, elapse = text_detect_ir(img)
+print(f'FP16 openvino IR: {elapse}\tdt_boxes:{np.mean(dt_boxes)}')
+
+# Infer paddle 有误
+# RuntimeError: Can't SetBlob with name: x, because model input (shape={?,3,960,960}) and blob (shape=(1.3.12128.800)) are incompatible
+# import cv2
+# from ch_ppocr_mobile_v2_det_openvino_paddle import TextDetector as TextDetectorPaddle
+
+# det_model_path = 'models/ch_PP-OCRv2_det_infer/ch_PP-OCRv2_det_infer/inference.pdmodel'
+# img_path = 'test_images/long1.jpg'
+# img = cv2.imread(img_path)
+
+# text_detect_ir = TextDetectorPaddle(det_model_path)
+# dt_boxes, elapse = text_detect_ir(img)
+# print(f'openvino IR: {elapse}')
+