Merge branch 'PaddlePaddle:dygraph' into dygraph
去除部分冗余代码
This commit is contained in:
Wei-JL
Alchemist_W
commit
6ded97c7df
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@ -6,3 +6,4 @@ recursive-include ppocr/data *.py
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recursive-include ppocr/postprocess *.py
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recursive-include tools/infer *.py
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recursive-include ppocr/utils/e2e_utils *.py
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recursive-include ppstructure *.py
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@ -819,12 +819,10 @@ class MainWindow(QMainWindow, WindowMixin):
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if self.itemsToShapesbox:
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self.rotateImgWarn()
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else:
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_value = True
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self.rotateImg(filename=filename, k=1, _value=_value)
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self.rotateImg(filename=filename, k=1, _value=True)
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else:
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_value = False
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self.rotateImgWarn()
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self.actions.rotateRight.setEnabled(_value)
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self.actions.rotateRight.setEnabled(False)
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def rotateRightImg(self, _value=False):
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@ -834,12 +832,10 @@ class MainWindow(QMainWindow, WindowMixin):
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if self.itemsToShapesbox:
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self.rotateImgWarn()
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else:
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_value = True
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self.rotateImg(filename=filename, k=-1, _value=_value)
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self.rotateImg(filename=filename, k=-1, _value=True)
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else:
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_value = False
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self.rotateImgWarn()
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self.actions.rotateRight.setEnabled(_value)
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self.actions.rotateRight.setEnabled(False)
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def toggleDrawingSensitive(self, drawing=True):
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"""In the middle of drawing, toggling between modes should be disabled."""
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@ -1582,7 +1578,7 @@ class MainWindow(QMainWindow, WindowMixin):
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self.actions.reRec.setEnabled(True)
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self.actions.open_dataset_dir.setEnabled(True)
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self.actions.rotateLeft.setEnabled(True)
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self.actions.rotateRight.setEnabled (True)
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self.actions.rotateRight.setEnabled(True)
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@ -11,7 +11,8 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddleocr
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from .paddleocr import *
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__all__ = ['PaddleOCR', 'draw_ocr']
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from .paddleocr import PaddleOCR
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from .tools.infer.utility import draw_ocr
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__version__ = paddleocr.VERSION
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__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res','download_with_progressbar']
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@ -14,7 +14,7 @@ PaddleOCR在Windows 平台下基于`Visual Studio 2019 Community` 进行了测
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### Step1: 下载PaddlePaddle C++ 预测库 fluid_inference
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PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/windows_cpp_inference.html)
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PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#windows)
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解压后`D:\projects\fluid_inference`目录包含内容为：
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```
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@ -5,23 +5,29 @@
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### 1.1 安装whl包
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pip安装
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```bash
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pip install "paddleocr>=2.0.1" # 推荐使用2.0.1+版本
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```
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本地构建并安装
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```bash
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python3 setup.py bdist_wheel
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pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x是paddleocr的版本号
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```
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## 2 使用
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### 2.1 代码使用
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paddleocr whl包会自动下载ppocr轻量级模型作为默认模型，可以根据第3节**自定义模型**进行自定义更换。
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* 检测+方向分类器+识别全流程
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```python
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from paddleocr import PaddleOCR, draw_ocr
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# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
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# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
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ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
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@ -32,6 +38,7 @@ for line in result:
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# 显示结果
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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@ -40,31 +47,36 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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结果是一个list，每个item包含了文本框，文字和识别置信度
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```bash
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[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
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[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
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[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
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......
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```
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结果可视化
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<div align="center">
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<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
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</div>
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* 检测+识别
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```python
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from paddleocr import PaddleOCR, draw_ocr
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ocr = PaddleOCR() # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs/11.jpg'
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result = ocr.ocr(img_path,cls=False)
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result = ocr.ocr(img_path, cls=False)
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for line in result:
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print(line)
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# 显示结果
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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@ -73,37 +85,45 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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结果是一个list，每个item包含了文本框，文字和识别置信度
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```bash
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[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
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[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
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[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
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......
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```
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结果可视化
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<div align="center">
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<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
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</div>
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* 方向分类器+识别
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
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result = ocr.ocr(img_path, det=False, cls=True)
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for line in result:
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print(line)
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```
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结果是一个list，每个item只包含识别结果和识别置信度
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```bash
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['韩国小馆', 0.9907421]
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```
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* 单独执行检测
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```python
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from paddleocr import PaddleOCR, draw_ocr
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ocr = PaddleOCR() # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs/11.jpg'
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result = ocr.ocr(img_path, rec=False)
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@ -118,13 +138,16 @@ im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/Pa
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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结果是一个list，每个item只包含文本框
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```bash
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[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
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[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
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[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
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......
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```
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结果可视化
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@ -133,29 +156,37 @@ im_show.save('result.jpg')
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</div>
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* 单独执行识别
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR() # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
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result = ocr.ocr(img_path, det=False)
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for line in result:
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print(line)
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```
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结果是一个list，每个item只包含识别结果和识别置信度
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```bash
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['韩国小馆', 0.9907421]
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```
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* 单独执行方向分类器
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
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result = ocr.ocr(img_path, det=False, rec=False, cls=True)
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for line in result:
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print(line)
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```
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结果是一个list，每个item只包含分类结果和分类置信度
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```bash
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['0', 0.9999924]
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```
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@ -163,15 +194,19 @@ for line in result:
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### 2.2 通过命令行使用
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查看帮助信息
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```bash
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paddleocr -h
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```
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* 检测+方向分类器+识别全流程
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
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```
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结果是一个list，每个item包含了文本框，文字和识别置信度
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|
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```bash
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[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
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[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
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@ -180,10 +215,13 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
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```
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* 检测+识别
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
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```
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结果是一个list，每个item包含了文本框，文字和识别置信度
|
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|
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```bash
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[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
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[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
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@ -192,20 +230,25 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
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```
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|
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* 方向分类器+识别
|
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|
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```bash
|
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paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false
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```
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结果是一个list，每个item只包含识别结果和识别置信度
|
||||
|
||||
```bash
|
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['韩国小馆', 0.9907421]
|
||||
```
|
||||
|
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* 单独执行检测
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
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||||
```
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|
||||
结果是一个list，每个item只包含文本框
|
||||
|
||||
```bash
|
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[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
|
||||
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
|
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|
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@ -214,34 +257,42 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
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|||
```
|
||||
|
||||
* 单独执行识别
|
||||
|
||||
```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含识别结果和识别置信度
|
||||
|
||||
```bash
|
||||
['韩国小馆', 0.9907421]
|
||||
```
|
||||
|
||||
* 单独执行方向分类器
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false --rec false
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含分类结果和分类置信度
|
||||
|
||||
```bash
|
||||
['0', 0.9999924]
|
||||
```
|
||||
|
||||
## 3 自定义模型
|
||||
当内置模型无法满足需求时，需要使用到自己训练的模型。
|
||||
首先，参照[inference.md](./inference.md) 第一节转换将检测、分类和识别模型转换为inference模型，然后按照如下方式使用
|
||||
|
||||
当内置模型无法满足需求时，需要使用到自己训练的模型。 首先，参照[inference.md](./inference.md) 第一节转换将检测、分类和识别模型转换为inference模型，然后按照如下方式使用
|
||||
|
||||
### 3.1 代码使用
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
|
||||
# 模型路径下必须含有model和params文件
|
||||
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_model_dir}', rec_char_dict_path='{your_rec_char_dict_path}', cls_model_dir='{your_cls_model_dir}', use_angle_cls=True)
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||||
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_model_dir}',
|
||||
rec_char_dict_path='{your_rec_char_dict_path}', cls_model_dir='{your_cls_model_dir}',
|
||||
use_angle_cls=True)
|
||||
img_path = 'PaddleOCR/doc/imgs/11.jpg'
|
||||
result = ocr.ocr(img_path, cls=True)
|
||||
for line in result:
|
||||
|
|
@ -249,6 +300,7 @@ for line in result:
|
|||
|
||||
# 显示结果
|
||||
from PIL import Image
|
||||
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
|
|
@ -269,8 +321,10 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
|
|||
### 4.1 网络图片
|
||||
|
||||
- 代码使用
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
from paddleocr import PaddleOCR, draw_ocr, download_with_progressbar
|
||||
|
||||
# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
|
||||
# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
|
|
@ -281,7 +335,9 @@ for line in result:
|
|||
|
||||
# 显示结果
|
||||
from PIL import Image
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
|
||||
download_with_progressbar(img_path, 'tmp.jpg')
|
||||
image = Image.open('tmp.jpg').convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
scores = [line[1][1] for line in result]
|
||||
|
|
@ -289,15 +345,21 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
|
|||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
|
||||
- 命令行模式
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true
|
||||
```
|
||||
|
||||
### 4.2 numpy数组
|
||||
|
||||
仅通过代码使用时支持numpy数组作为输入
|
||||
|
||||
```python
|
||||
import cv2
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
|
||||
# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
|
||||
# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
|
|
@ -310,6 +372,7 @@ for line in result:
|
|||
|
||||
# 显示结果
|
||||
from PIL import Image
|
||||
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
|
|
@ -356,3 +419,4 @@ im_show.save('result.jpg')
|
|||
| rec | 前向时是否启动识别 | TRUE |
|
||||
| cls | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类) | FALSE |
|
||||
| show_log | 是否打印det和rec等信息 | FALSE |
|
||||
| type | 执行ocr或者表格结构化, 值可选['ocr','structure'] | ocr |
|
||||
|
|
|
|||
|
|
@ -305,7 +305,8 @@ paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-f
|
|||
Support numpy array as input only when used by code
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
import cv2
|
||||
from paddleocr import PaddleOCR, draw_ocr, download_with_progressbar
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs/11.jpg'
|
||||
img = cv2.imread(img_path)
|
||||
|
|
@ -316,7 +317,9 @@ for line in result:
|
|||
|
||||
# show result
|
||||
from PIL import Image
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
|
||||
download_with_progressbar(img_path, 'tmp.jpg')
|
||||
image = Image.open('tmp.jpg').convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
scores = [line[1][1] for line in result]
|
||||
|
|
@ -362,5 +365,5 @@ im_show.save('result.jpg')
|
|||
| det | Enable detction when `ppocr.ocr` func exec | TRUE |
|
||||
| rec | Enable recognition when `ppocr.ocr` func exec | TRUE |
|
||||
| cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE |
|
||||
| show_log | Whether to print log in det and rec
|
||||
| FALSE |
|
||||
| show_log | Whether to print log in det and rec | FALSE |
|
||||
| type | Perform ocr or table structuring, the value is selected in ['ocr','structure'] | ocr |
|
||||
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146
paddleocr.py
146
paddleocr.py
|
|
@ -29,16 +29,19 @@ from ppocr.utils.logging import get_logger
|
|||
logger = get_logger()
|
||||
from ppocr.utils.utility import check_and_read_gif, get_image_file_list
|
||||
from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url
|
||||
from tools.infer.utility import draw_ocr, init_args, str2bool
|
||||
from tools.infer.utility import draw_ocr, str2bool
|
||||
from ppstructure.utility import init_args, draw_structure_result
|
||||
from ppstructure.predict_system import OCRSystem, save_structure_res
|
||||
|
||||
__all__ = ['PaddleOCR']
|
||||
__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res','download_with_progressbar']
|
||||
|
||||
model_urls = {
|
||||
'det': {
|
||||
'ch':
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
|
||||
'en':
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar'
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
|
||||
'structure': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
|
||||
},
|
||||
'rec': {
|
||||
'ch': {
|
||||
|
|
@ -110,14 +113,21 @@ model_urls = {
|
|||
'url':
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
|
||||
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
|
||||
},
|
||||
'structure': {
|
||||
'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
|
||||
'dict_path': 'ppocr/utils/dict/table_dict.txt'
|
||||
}
|
||||
},
|
||||
'cls':
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
|
||||
'cls': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
|
||||
'table': {
|
||||
'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
|
||||
'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
|
||||
}
|
||||
}
|
||||
|
||||
SUPPORT_DET_MODEL = ['DB']
|
||||
VERSION = '2.1'
|
||||
VERSION = '2.2'
|
||||
SUPPORT_REC_MODEL = ['CRNN']
|
||||
BASE_DIR = os.path.expanduser("~/.paddleocr/")
|
||||
|
||||
|
|
@ -129,9 +139,10 @@ def parse_args(mMain=True):
|
|||
parser.add_argument("--lang", type=str, default='ch')
|
||||
parser.add_argument("--det", type=str2bool, default=True)
|
||||
parser.add_argument("--rec", type=str2bool, default=True)
|
||||
parser.add_argument("--type", type=str, default='ocr')
|
||||
|
||||
for action in parser._actions:
|
||||
if action.dest == 'rec_char_dict_path':
|
||||
if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
|
||||
action.default = None
|
||||
if mMain:
|
||||
return parser.parse_args()
|
||||
|
|
@ -142,19 +153,7 @@ def parse_args(mMain=True):
|
|||
return argparse.Namespace(**inference_args_dict)
|
||||
|
||||
|
||||
class PaddleOCR(predict_system.TextSystem):
|
||||
def __init__(self, **kwargs):
|
||||
"""
|
||||
paddleocr package
|
||||
args:
|
||||
**kwargs: other params show in paddleocr --help
|
||||
"""
|
||||
params = parse_args(mMain=False)
|
||||
params.__dict__.update(**kwargs)
|
||||
if not params.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
self.use_angle_cls = params.use_angle_cls
|
||||
lang = params.lang
|
||||
def parse_lang(lang):
|
||||
latin_lang = [
|
||||
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
|
||||
'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
|
||||
|
|
@ -183,23 +182,36 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
model_urls['rec'].keys(), lang)
|
||||
if lang == "ch":
|
||||
det_lang = "ch"
|
||||
elif lang == 'structure':
|
||||
det_lang = 'structure'
|
||||
else:
|
||||
det_lang = "en"
|
||||
use_inner_dict = False
|
||||
if params.rec_char_dict_path is None:
|
||||
use_inner_dict = True
|
||||
params.rec_char_dict_path = model_urls['rec'][lang][
|
||||
'dict_path']
|
||||
return lang, det_lang
|
||||
|
||||
|
||||
class PaddleOCR(predict_system.TextSystem):
|
||||
def __init__(self, **kwargs):
|
||||
"""
|
||||
paddleocr package
|
||||
args:
|
||||
**kwargs: other params show in paddleocr --help
|
||||
"""
|
||||
params = parse_args(mMain=False)
|
||||
params.__dict__.update(**kwargs)
|
||||
if not params.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
self.use_angle_cls = params.use_angle_cls
|
||||
lang, det_lang = parse_lang(params.lang)
|
||||
|
||||
# init model dir
|
||||
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'det', det_lang),
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
|
||||
model_urls['det'][det_lang])
|
||||
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'rec', lang),
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
|
||||
model_urls['rec'][lang]['url'])
|
||||
params.cls_model_dir, cls_url = confirm_model_dir_url(params.cls_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'cls'),
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
|
||||
model_urls['cls'])
|
||||
# download model
|
||||
maybe_download(params.det_model_dir, det_url)
|
||||
|
|
@ -212,9 +224,9 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
if params.rec_algorithm not in SUPPORT_REC_MODEL:
|
||||
logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
|
||||
sys.exit(0)
|
||||
if use_inner_dict:
|
||||
params.rec_char_dict_path = str(
|
||||
Path(__file__).parent / params.rec_char_dict_path)
|
||||
|
||||
if params.rec_char_dict_path is None:
|
||||
params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
|
||||
|
||||
print(params)
|
||||
# init det_model and rec_model
|
||||
|
|
@ -272,6 +284,59 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
return rec_res
|
||||
|
||||
|
||||
class PPStructure(OCRSystem):
|
||||
def __init__(self, **kwargs):
|
||||
params = parse_args(mMain=False)
|
||||
params.__dict__.update(**kwargs)
|
||||
if not params.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
lang, det_lang = parse_lang(params.lang)
|
||||
|
||||
# init model dir
|
||||
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
|
||||
model_urls['det'][det_lang])
|
||||
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
|
||||
model_urls['rec'][lang]['url'])
|
||||
params.table_model_dir, table_url = confirm_model_dir_url(params.table_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),
|
||||
model_urls['table']['url'])
|
||||
# download model
|
||||
maybe_download(params.det_model_dir, det_url)
|
||||
maybe_download(params.rec_model_dir, rec_url)
|
||||
maybe_download(params.table_model_dir, table_url)
|
||||
|
||||
if params.rec_char_dict_path is None:
|
||||
params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
|
||||
if params.table_char_dict_path is None:
|
||||
params.table_char_dict_path = str(Path(__file__).parent / model_urls['table']['dict_path'])
|
||||
|
||||
print(params)
|
||||
super().__init__(params)
|
||||
|
||||
def __call__(self, img):
|
||||
if isinstance(img, str):
|
||||
# download net image
|
||||
if img.startswith('http'):
|
||||
download_with_progressbar(img, 'tmp.jpg')
|
||||
img = 'tmp.jpg'
|
||||
image_file = img
|
||||
img, flag = check_and_read_gif(image_file)
|
||||
if not flag:
|
||||
with open(image_file, 'rb') as f:
|
||||
np_arr = np.frombuffer(f.read(), dtype=np.uint8)
|
||||
img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
|
||||
if img is None:
|
||||
logger.error("error in loading image:{}".format(image_file))
|
||||
return None
|
||||
if isinstance(img, np.ndarray) and len(img.shape) == 2:
|
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
||||
|
||||
res = super().__call__(img)
|
||||
return res
|
||||
|
||||
|
||||
def main():
|
||||
# for cmd
|
||||
args = parse_args(mMain=True)
|
||||
|
|
@ -284,14 +349,29 @@ def main():
|
|||
if len(image_file_list) == 0:
|
||||
logger.error('no images find in {}'.format(args.image_dir))
|
||||
return
|
||||
if args.type == 'ocr':
|
||||
engine = PaddleOCR(**(args.__dict__))
|
||||
elif args.type == 'structure':
|
||||
engine = PPStructure(**(args.__dict__))
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
ocr_engine = PaddleOCR(**(args.__dict__))
|
||||
for img_path in image_file_list:
|
||||
img_name = os.path.basename(img_path).split('.')[0]
|
||||
logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
|
||||
result = ocr_engine.ocr(img_path,
|
||||
if args.type == 'ocr':
|
||||
result = engine.ocr(img_path,
|
||||
det=args.det,
|
||||
rec=args.rec,
|
||||
cls=args.use_angle_cls)
|
||||
if result is not None:
|
||||
for line in result:
|
||||
logger.info(line)
|
||||
elif args.type == 'structure':
|
||||
result = engine(img_path)
|
||||
save_structure_res(result, args.output, img_name)
|
||||
|
||||
for item in result:
|
||||
item.pop('img')
|
||||
logger.info(item)
|
||||
|
||||
|
|
|
|||
|
|
@ -19,6 +19,7 @@ from __future__ import unicode_literals
|
|||
|
||||
import numpy as np
|
||||
import string
|
||||
import json
|
||||
|
||||
|
||||
class ClsLabelEncode(object):
|
||||
|
|
@ -39,7 +40,6 @@ class DetLabelEncode(object):
|
|||
pass
|
||||
|
||||
def __call__(self, data):
|
||||
import json
|
||||
label = data['label']
|
||||
label = json.loads(label)
|
||||
nBox = len(label)
|
||||
|
|
@ -53,6 +53,8 @@ class DetLabelEncode(object):
|
|||
txt_tags.append(True)
|
||||
else:
|
||||
txt_tags.append(False)
|
||||
if len(boxes) == 0:
|
||||
return None
|
||||
boxes = self.expand_points_num(boxes)
|
||||
boxes = np.array(boxes, dtype=np.float32)
|
||||
txt_tags = np.array(txt_tags, dtype=np.bool)
|
||||
|
|
@ -352,19 +354,22 @@ class SRNLabelEncode(BaseRecLabelEncode):
|
|||
% beg_or_end
|
||||
return idx
|
||||
|
||||
|
||||
class TableLabelEncode(object):
|
||||
""" Convert between text-label and text-index """
|
||||
|
||||
def __init__(self,
|
||||
max_text_length,
|
||||
max_elem_length,
|
||||
max_cell_num,
|
||||
character_dict_path,
|
||||
span_weight = 1.0,
|
||||
span_weight=1.0,
|
||||
**kwargs):
|
||||
self.max_text_length = max_text_length
|
||||
self.max_elem_length = max_elem_length
|
||||
self.max_cell_num = max_cell_num
|
||||
list_character, list_elem = self.load_char_elem_dict(character_dict_path)
|
||||
list_character, list_elem = self.load_char_elem_dict(
|
||||
character_dict_path)
|
||||
list_character = self.add_special_char(list_character)
|
||||
list_elem = self.add_special_char(list_elem)
|
||||
self.dict_character = {}
|
||||
|
|
@ -380,14 +385,15 @@ class TableLabelEncode(object):
|
|||
list_elem = []
|
||||
with open(character_dict_path, "rb") as fin:
|
||||
lines = fin.readlines()
|
||||
substr = lines[0].decode('utf-8').strip("\n").split("\t")
|
||||
substr = lines[0].decode('utf-8').strip("\r\n").split("\t")
|
||||
character_num = int(substr[0])
|
||||
elem_num = int(substr[1])
|
||||
for cno in range(1, 1+character_num):
|
||||
character = lines[cno].decode('utf-8').strip("\n")
|
||||
|
||||
for cno in range(1, 1 + character_num):
|
||||
character = lines[cno].decode('utf-8').strip("\r\n")
|
||||
list_character.append(character)
|
||||
for eno in range(1+character_num, 1+character_num+elem_num):
|
||||
elem = lines[eno].decode('utf-8').strip("\n")
|
||||
for eno in range(1 + character_num, 1 + character_num + elem_num):
|
||||
elem = lines[eno].decode('utf-8').strip("\r\n")
|
||||
list_elem.append(elem)
|
||||
return list_character, list_elem
|
||||
|
||||
|
|
@ -412,18 +418,22 @@ class TableLabelEncode(object):
|
|||
return None
|
||||
elem_num = len(structure)
|
||||
structure = [0] + structure + [len(self.dict_elem) - 1]
|
||||
structure = structure + [0] * (self.max_elem_length + 2 - len(structure))
|
||||
structure = structure + [0] * (self.max_elem_length + 2 - len(structure)
|
||||
)
|
||||
structure = np.array(structure)
|
||||
data['structure'] = structure
|
||||
elem_char_idx1 = self.dict_elem['<td>']
|
||||
elem_char_idx2 = self.dict_elem['<td']
|
||||
span_idx_list = self.get_span_idx_list()
|
||||
td_idx_list = np.logical_or(structure == elem_char_idx1, structure == elem_char_idx2)
|
||||
td_idx_list = np.logical_or(structure == elem_char_idx1,
|
||||
structure == elem_char_idx2)
|
||||
td_idx_list = np.where(td_idx_list)[0]
|
||||
|
||||
structure_mask = np.ones((self.max_elem_length + 2, 1), dtype=np.float32)
|
||||
structure_mask = np.ones(
|
||||
(self.max_elem_length + 2, 1), dtype=np.float32)
|
||||
bbox_list = np.zeros((self.max_elem_length + 2, 4), dtype=np.float32)
|
||||
bbox_list_mask = np.zeros((self.max_elem_length + 2, 1), dtype=np.float32)
|
||||
bbox_list_mask = np.zeros(
|
||||
(self.max_elem_length + 2, 1), dtype=np.float32)
|
||||
img_height, img_width, img_ch = data['image'].shape
|
||||
if len(span_idx_list) > 0:
|
||||
span_weight = len(td_idx_list) * 1.0 / len(span_idx_list)
|
||||
|
|
@ -450,9 +460,11 @@ class TableLabelEncode(object):
|
|||
char_end_idx = self.get_beg_end_flag_idx('end', 'char')
|
||||
elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem')
|
||||
elem_end_idx = self.get_beg_end_flag_idx('end', 'elem')
|
||||
data['sp_tokens'] = np.array([char_beg_idx, char_end_idx, elem_beg_idx,
|
||||
elem_end_idx, elem_char_idx1, elem_char_idx2, self.max_text_length,
|
||||
self.max_elem_length, self.max_cell_num, elem_num])
|
||||
data['sp_tokens'] = np.array([
|
||||
char_beg_idx, char_end_idx, elem_beg_idx, elem_end_idx,
|
||||
elem_char_idx1, elem_char_idx2, self.max_text_length,
|
||||
self.max_elem_length, self.max_cell_num, elem_num
|
||||
])
|
||||
return data
|
||||
|
||||
def encode(self, text, char_or_elem):
|
||||
|
|
@ -509,4 +521,3 @@ class TableLabelEncode(object):
|
|||
assert False, "Unsupport type %s in char_or_elem" \
|
||||
% char_or_elem
|
||||
return idx
|
||||
|
||||
|
|
@ -346,14 +346,14 @@ class TableLabelDecode(object):
|
|||
list_elem = []
|
||||
with open(character_dict_path, "rb") as fin:
|
||||
lines = fin.readlines()
|
||||
substr = lines[0].decode('utf-8').strip("\n").split("\t")
|
||||
substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split("\t")
|
||||
character_num = int(substr[0])
|
||||
elem_num = int(substr[1])
|
||||
for cno in range(1, 1 + character_num):
|
||||
character = lines[cno].decode('utf-8').strip("\n")
|
||||
character = lines[cno].decode('utf-8').strip("\n").strip("\r\n")
|
||||
list_character.append(character)
|
||||
for eno in range(1 + character_num, 1 + character_num + elem_num):
|
||||
elem = lines[eno].decode('utf-8').strip("\n")
|
||||
elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n")
|
||||
list_elem.append(elem)
|
||||
return list_character, list_elem
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +0,0 @@
|
|||
include LICENSE
|
||||
include README.md
|
||||
|
||||
recursive-include ppocr/utils *.txt utility.py logging.py network.py
|
||||
recursive-include ppocr/data *.py
|
||||
recursive-include ppocr/postprocess *.py
|
||||
recursive-include tools/infer *.py
|
||||
recursive-include ppstructure *.py
|
||||
|
||||
|
|
@ -1,64 +1,116 @@
|
|||
# PaddleStructure
|
||||
English | [简体中文](README_ch.md)
|
||||
|
||||
PaddleStructure is an OCR toolkit for complex layout analysis. It can divide document data in the form of pictures into **text, table, title, picture and list** 5 types of areas, and extract the table area as excel
|
||||
## 1. Quick start
|
||||
# PP-Structure
|
||||
|
||||
### install
|
||||
PP-Structure is an OCR toolkit that can be used for complex documents analysis. The main features are as follows:
|
||||
- Support the layout analysis of documents, divide the documents into 5 types of areas **text, title, table, image and list** (conjunction with Layout-Parser)
|
||||
- Support to extract the texts from the text, title, picture and list areas (used in conjunction with PP-OCR)
|
||||
- Support to extract excel files from the table areas
|
||||
- Support python whl package and command line usage, easy to use
|
||||
- Support custom training for layout analysis and table structure tasks
|
||||
|
||||
**install layoutparser**
|
||||
```sh
|
||||
pip3 install https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
**install paddlestructure**
|
||||
## 1. Visualization
|
||||
|
||||
install by pypi
|
||||
<img src="../doc/table/ppstructure.GIF" width="100%"/>
|
||||
|
||||
|
||||
|
||||
## 2. Installation
|
||||
|
||||
### 2.1 Install requirements
|
||||
|
||||
- **（1) Install PaddlePaddle**
|
||||
|
||||
```bash
|
||||
pip install paddlestructure
|
||||
pip3 install --upgrade pip
|
||||
|
||||
# GPU
|
||||
python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/simple
|
||||
|
||||
# CPU
|
||||
python3 -m pip install paddlepaddle==2.1.1 -i https://mirror.baidu.com/pypi/simple
|
||||
|
||||
# For more，refer[Installation](https://www.paddlepaddle.org.cn/install/quick)。
|
||||
```
|
||||
|
||||
build own whl package and install
|
||||
```bash
|
||||
python3 setup.py bdist_wheel
|
||||
pip3 install dist/paddlestructure-x.x.x-py3-none-any.whl # x.x.x is the version of paddlestructure
|
||||
```
|
||||
|
||||
### 1.2 Use
|
||||
|
||||
#### 1.2.1 Use by command line
|
||||
- **(2) Install Layout-Parser**
|
||||
|
||||
```bash
|
||||
paddlestructure --image_dir=../doc/table/1.png
|
||||
pip3 install -U premailer paddleocr https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
#### 1.2.2 Use by code
|
||||
### 2.2 Install PaddleOCR（including PP-OCR and PP-Structure）
|
||||
|
||||
- **（1) PIP install PaddleOCR whl package（inference only）**
|
||||
|
||||
```bash
|
||||
pip install "paddleocr>=2.2"
|
||||
```
|
||||
|
||||
- **（2) Clone PaddleOCR（Inference+training）**
|
||||
|
||||
```bash
|
||||
git clone https://github.com/PaddlePaddle/PaddleOCR
|
||||
```
|
||||
|
||||
|
||||
## 3. Quick Start
|
||||
|
||||
### 3.1 Use by command line
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir=../doc/table/1.png --type=structure
|
||||
```
|
||||
|
||||
### 3.2 Use by python API
|
||||
|
||||
```python
|
||||
import os
|
||||
import cv2
|
||||
from paddlestructure import PaddleStructure,draw_result,save_res
|
||||
from paddleocr import PPStructure,draw_structure_result,save_structure_res
|
||||
|
||||
table_engine = PaddleStructure(show_log=True)
|
||||
table_engine = PPStructure(show_log=True)
|
||||
|
||||
save_folder = './output/table'
|
||||
img_path = '../doc/table/1.png'
|
||||
img = cv2.imread(img_path)
|
||||
result = table_engine(img)
|
||||
save_res(result, save_folder,os.path.basename(img_path).split('.')[0])
|
||||
save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
|
||||
|
||||
for line in result:
|
||||
line.pop('img')
|
||||
print(line)
|
||||
|
||||
from PIL import Image
|
||||
|
||||
font_path = '../doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
|
||||
font_path = '../doc/fonts/simfang.ttf'
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
im_show = draw_result(image, result,font_path=font_path)
|
||||
im_show = draw_structure_result(image, result,font_path=font_path)
|
||||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
### 3.3 Returned results format
|
||||
The returned results of PP-Structure is a list composed of a dict, an example is as follows
|
||||
|
||||
#### 1.2.3 Parameter Description：
|
||||
```shell
|
||||
[
|
||||
{ 'type': 'Text',
|
||||
'bbox': [34, 432, 345, 462],
|
||||
'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
|
||||
[('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent ', 0.465441)])
|
||||
}
|
||||
]
|
||||
```
|
||||
The description of each field in dict is as follows
|
||||
|
||||
| Parameter | Description |
|
||||
| --------------- | -------------|
|
||||
|type|Type of image area|
|
||||
|bbox|The coordinates of the image area in the original image, respectively [left upper x, left upper y, right bottom x, right bottom y]|
|
||||
|res|OCR or table recognition result of image area。<br> Table: HTML string of the table; <br> OCR: A tuple containing the detection coordinates and recognition results of each single line of text|
|
||||
|
||||
|
||||
### 3.4 Parameter description：
|
||||
|
||||
| Parameter | Description | Default value |
|
||||
| --------------- | ---------------------------------------- | ------------------------------------------- |
|
||||
|
|
@ -69,37 +121,69 @@ im_show.save('result.jpg')
|
|||
|
||||
Most of the parameters are consistent with the paddleocr whl package, see [doc of whl](../doc/doc_en/whl_en.md)
|
||||
|
||||
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel, and the excel file name will be the coordinates of the table in the image.
|
||||
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
|
||||
|
||||
## 2. PaddleStructure Pipeline
|
||||
## 4. PP-Structure Pipeline
|
||||
|
||||
the process is as follows
|
||||
|
||||
|
||||
In PaddleStructure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, including **text, title, image, list and table** 5 categories. For the first 4 types of areas, directly use the PP-OCR to complete the text detection and recognition. The table area will be converted to an excel file of the same table style via Table OCR.
|
||||
In PP-Structure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, including **text, title, image, list and table** 5 categories. For the first 4 types of areas, directly use the PP-OCR to complete the text detection and recognition. The table area will be converted to an excel file of the same table style via Table OCR.
|
||||
|
||||
### 2.1 LayoutParser
|
||||
### 4.1 LayoutParser
|
||||
|
||||
Layout analysis divides the document data into regions, including the use of Python scripts for layout analysis tools, extraction of special category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README.md).
|
||||
Layout analysis divides the document data into regions, including the use of Python scripts for layout analysis tools, extraction of special category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README_en.md).
|
||||
|
||||
### 2.2 Table OCR
|
||||
### 4.2 Table Recognition
|
||||
|
||||
Table OCR converts table image into excel documents, which include the detection and recognition of table text and the prediction of table structure and cell coordinates. For detailed, please refer to [document](table/README.md)
|
||||
Table Recognition converts table image into excel documents, which include the detection and recognition of table text and the prediction of table structure and cell coordinates. For detailed, please refer to [document](table/README.md)
|
||||
|
||||
### 3. Predictive by inference engine
|
||||
## 5. Prediction by inference engine
|
||||
|
||||
Use the following commands to complete the inference.
|
||||
|
||||
```python
|
||||
python3 table/predict_system.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
|
||||
```
|
||||
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel, and the excel file name will be the coordinates of the table in the image.
|
||||
cd PaddleOCR/ppstructure
|
||||
|
||||
# 3. Model List
|
||||
# download model
|
||||
mkdir inference && cd inference
|
||||
# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
|
||||
# Download the table structure model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
|
||||
cd ..
|
||||
|
||||
python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
|
||||
```
|
||||
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
|
||||
|
||||
**Model List**
|
||||
|
||||
|
||||
|model name|description|config|model size|download|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|en_ppocr_mobile_v2.0_table_det|Text detection in English table scene|[ch_det_mv3_db_v2.0.yml](../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)| 4.7M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_rec|Text recognition in English table scene|[rec_chinese_lite_train_v2.0.yml](..//configs/rec/rec_mv3_none_bilstm_ctc.yml)|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction for English table scenarios|[table_mv3.yml](../configs/table/table_mv3.yml)|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|
||||
|
||||
**Model List**
|
||||
|
||||
LayoutParser model
|
||||
|
||||
|model name|description|download|
|
||||
| --- | --- | --- |
|
||||
| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis model trained on the PubLayNet data set can be divided into 5 types of areas **text, title, table, picture and list** | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) |
|
||||
| ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset can only detect tables | [TableBank Word](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) |
|
||||
| ppyolov2_r50vd_dcn_365e_tableBank_latex | The layout analysis model trained on the TableBank Latex dataset can only detect tables | [TableBank Latex](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) |
|
||||
|
||||
OCR and table recognition model
|
||||
|
||||
|model name|description|model size|download|
|
||||
| --- | --- | --- | --- |
|
||||
|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|
||||
|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_det|Text detection of English table scenes trained on PubLayNet dataset|4.7M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_rec|Text recognition of English table scene trained on PubLayNet dataset|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|
||||
|
||||
If you need to use other models, you can download the model in [model_list](../doc/doc_en/models_list_en.md) or use your own trained model to configure it to the three fields of `det_model_dir`, `rec_model_dir`, `table_model_dir` .
|
||||
|
|
|
|||
|
|
@ -1,65 +1,122 @@
|
|||
# PaddleStructure
|
||||
[English](README.md) | 简体中文
|
||||
|
||||
PaddleStructure是一个用于复杂版面分析的OCR工具包，其能够对图片形式的文档数据划分**文字、表格、标题、图片以及列表**5类区域，并将表格区域提取为excel
|
||||
# PP-Structure
|
||||
|
||||
## 1. 快速开始
|
||||
PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包，主要特性如下：
|
||||
- 支持对图片形式的文档进行版面分析，可以划分**文字、标题、表格、图片以及列表**5类区域（与Layout-Parser联合使用）
|
||||
- 支持文字、标题、图片以及列表区域提取为文字字段（与PP-OCR联合使用）
|
||||
- 支持表格区域进行结构化分析，最终结果输出Excel文件
|
||||
- 支持python whl包和命令行两种方式，简单易用
|
||||
- 支持版面分析和表格结构化两类任务自定义训练
|
||||
|
||||
### 1.1 安装
|
||||
## 1. 效果展示
|
||||
|
||||
**安装 layoutparser**
|
||||
```sh
|
||||
pip3 install https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
**安装 paddlestructure**
|
||||
<img src="../doc/table/ppstructure.GIF" width="100%"/>
|
||||
|
||||
pip安装
|
||||
```bash
|
||||
pip install paddlestructure
|
||||
```
|
||||
|
||||
本地构建并安装
|
||||
```bash
|
||||
python3 setup.py bdist_wheel
|
||||
pip3 install dist/paddlestructure-x.x.x-py3-none-any.whl # x.x.x是 paddlestructure 的版本号
|
||||
```
|
||||
|
||||
### 1.2 PaddleStructure whl包使用
|
||||
## 2. 安装
|
||||
|
||||
#### 1.2.1 命令行使用
|
||||
### 2.1 安装依赖
|
||||
|
||||
- **（1) 安装PaddlePaddle**
|
||||
|
||||
```bash
|
||||
paddlestructure --image_dir=../doc/table/1.png
|
||||
pip3 install --upgrade pip
|
||||
|
||||
# GPU安装
|
||||
python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/simple
|
||||
|
||||
# CPU安装
|
||||
python3 -m pip install paddlepaddle==2.1.1 -i https://mirror.baidu.com/pypi/simple
|
||||
|
||||
# 更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
|
||||
```
|
||||
|
||||
#### 1.2.2 Python脚本使用
|
||||
- **(2) 安装 Layout-Parser**
|
||||
|
||||
```bash
|
||||
pip3 install -U premailer paddleocr https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
### 2.2 安装PaddleOCR（包含PP-OCR和PP-Structure）
|
||||
|
||||
- **（1) PIP快速安装PaddleOCR whl包（仅预测）**
|
||||
|
||||
```bash
|
||||
pip install "paddleocr>=2.2" # 推荐使用2.2+版本
|
||||
```
|
||||
|
||||
- **（2) 完整克隆PaddleOCR源码（预测+训练）**
|
||||
|
||||
```bash
|
||||
【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
|
||||
|
||||
#如果因为网络问题无法pull成功，也可选择使用码云上的托管：
|
||||
git clone https://gitee.com/paddlepaddle/PaddleOCR
|
||||
|
||||
#注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
|
||||
```
|
||||
|
||||
|
||||
## 3. PP-Structure 快速开始
|
||||
|
||||
### 3.1 命令行使用（默认参数，极简）
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir=../doc/table/1.png --type=structure
|
||||
```
|
||||
|
||||
### 3.2 Python脚本使用（自定义参数，灵活）
|
||||
|
||||
```python
|
||||
import os
|
||||
import cv2
|
||||
from paddlestructure import PaddleStructure,draw_result,save_res
|
||||
from paddleocr import PPStructure,draw_structure_result,save_structure_res
|
||||
|
||||
table_engine = PaddleStructure(show_log=True)
|
||||
table_engine = PPStructure(show_log=True)
|
||||
|
||||
save_folder = './output/table'
|
||||
img_path = '../doc/table/1.png'
|
||||
img = cv2.imread(img_path)
|
||||
result = table_engine(img)
|
||||
save_res(result, save_folder,os.path.basename(img_path).split('.')[0])
|
||||
save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
|
||||
|
||||
for line in result:
|
||||
line.pop('img')
|
||||
print(line)
|
||||
|
||||
from PIL import Image
|
||||
|
||||
font_path = '../doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
im_show = draw_result(image, result,font_path=font_path)
|
||||
im_show = draw_structure_result(image, result,font_path=font_path)
|
||||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
|
||||
### 3.3 返回结果说明
|
||||
PP-Structure的返回结果为一个dict组成的list，示例如下
|
||||
|
||||
#### 1.2.3 参数说明
|
||||
```shell
|
||||
[
|
||||
{ 'type': 'Text',
|
||||
'bbox': [34, 432, 345, 462],
|
||||
'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
|
||||
[('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent ', 0.465441)])
|
||||
}
|
||||
]
|
||||
```
|
||||
dict 里各个字段说明如下
|
||||
|
||||
| 字段 | 说明 |
|
||||
| --------------- | -------------|
|
||||
|type|图片区域的类型|
|
||||
|bbox|图片区域的在原图的坐标，分别[左上角x，左上角y，右下角x，右下角y]|
|
||||
|res|图片区域的OCR或表格识别结果。<br> 表格: 表格的HTML字符串; <br> OCR: 一个包含各个单行文字的检测坐标和识别结果的元组|
|
||||
|
||||
|
||||
### 3.4 参数说明
|
||||
|
||||
| 字段 | 说明 | 默认值 |
|
||||
| --------------- | ---------------------------------------- | ------------------------------------------- |
|
||||
|
|
@ -70,38 +127,62 @@ im_show.save('result.jpg')
|
|||
|
||||
大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
|
||||
|
||||
运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，excel文件名为表格在图片里的坐标。
|
||||
运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
|
||||
|
||||
|
||||
## 2. PaddleStructure Pipeline
|
||||
## 4. PP-Structure Pipeline介绍
|
||||
|
||||
流程如下
|
||||
|
||||
|
||||
在PaddleStructure中，图片会先经由layoutparser进行版面分析，在版面分析中，会对图片里的区域进行分类，包括**文字、标题、图片、列表和表格**5类。对于前4类区域，直接使用PP-OCR完成对应区域文字检测与识别。对于表格类区域，经过Table OCR处理后，表格图片转换为相同表格样式的Excel文件。
|
||||
在PP-Structure中，图片会先经由Layout-Parser进行版面分析，在版面分析中，会对图片里的区域进行分类，包括**文字、标题、图片、列表和表格**5类。对于前4类区域，直接使用PP-OCR完成对应区域文字检测与识别。对于表格类区域，经过表格结构化处理后，表格图片转换为相同表格样式的Excel文件。
|
||||
|
||||
### 2.1 LayoutParser
|
||||
### 4.1 版面分析
|
||||
|
||||
版面分析对文档数据进行区域分类，其中包括版面分析工具的Python脚本使用、提取指定类别检测框、性能指标以及自定义训练版面分析模型，详细内容可以参考[文档](layout/README.md)。
|
||||
版面分析对文档数据进行区域分类，其中包括版面分析工具的Python脚本使用、提取指定类别检测框、性能指标以及自定义训练版面分析模型，详细内容可以参考[文档](layout/README_ch.md)。
|
||||
|
||||
### 2.2 Table OCR
|
||||
### 4.2 表格识别
|
||||
|
||||
Table OCR将表格图片转换为excel文档，其中包含对于表格文本的检测和识别以及对于表格结构和单元格坐标的预测，详细说明参考[文档](table/README_ch.md)
|
||||
表格识别将表格图片转换为excel文档，其中包含对于表格文本的检测和识别以及对于表格结构和单元格坐标的预测，详细说明参考[文档](table/README_ch.md)
|
||||
|
||||
### 3. 预测引擎推理
|
||||
## 5. 预测引擎推理（与whl包效果相同）
|
||||
|
||||
使用如下命令即可完成预测引擎的推理
|
||||
|
||||
```python
|
||||
python3 table/predict_system.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
|
||||
cd ppstructure
|
||||
|
||||
# 下载模型
|
||||
mkdir inference && cd inference
|
||||
# 下载超轻量级中文OCR模型的检测模型并解压
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
# 下载超轻量级中文OCR模型的识别模型并解压
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
|
||||
# 下载超轻量级英文表格英寸模型并解压
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
|
||||
cd ..
|
||||
|
||||
python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
|
||||
```
|
||||
运行完成后，每张图片会output字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，excel文件名为表格在图片里的坐标。
|
||||
运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
|
||||
|
||||
# 3. Model List
|
||||
**Model List**
|
||||
|
||||
LayoutParser 模型
|
||||
|
||||
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|en_ppocr_mobile_v2.0_table_det|英文表格场景的文字检测|[ch_det_mv3_db_v2.0.yml](../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)| 4.7M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_rec|英文表格场景的文字识别|[rec_chinese_lite_train_v2.0.yml](..//configs/rec/rec_mv3_none_bilstm_ctc.yml)|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_structure|英文表格场景的表格结构预测|[table_mv3.yml](../configs/table/table_mv3.yml)|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|
||||
|模型名称|模型简介|下载地址|
|
||||
| --- | --- | --- |
|
||||
| ppyolov2_r50vd_dcn_365e_publaynet | PubLayNet 数据集训练的版面分析模型，可以划分**文字、标题、表格、图片以及列表**5类区域 | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) |
|
||||
| ppyolov2_r50vd_dcn_365e_tableBank_word | TableBank Word 数据集训练的版面分析模型，只能检测表格 | [TableBank Word](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) |
|
||||
| ppyolov2_r50vd_dcn_365e_tableBank_latex | TableBank Latex 数据集训练的版面分析模型，只能检测表格 | [TableBank Latex](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) |
|
||||
|
||||
OCR和表格识别模型
|
||||
|
||||
|模型名称|模型简介|推理模型大小|下载地址|
|
||||
| --- | --- | --- | --- |
|
||||
|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|
||||
|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_det|PubLayNet数据集训练的英文表格场景的文字检测|4.7M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_rec|PubLayNet数据集训练的英文表格场景的文字识别|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|
||||
|
||||
如需要使用其他模型，可以在 [model_list](../doc/doc_ch/models_list.md) 下载模型或者使用自己训练好的模型配置到`det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。
|
||||
|
|
|
|||
|
|
@ -11,7 +11,3 @@
|
|||
# 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.
|
||||
|
||||
from .paddlestructure import PaddleStructure, draw_result, save_res
|
||||
|
||||
__all__ = ['PaddleStructure', 'draw_result', 'save_res']
|
||||
|
|
|
|||
|
|
@ -1,64 +1,72 @@
|
|||
# 版面分析使用说明
|
||||
English | [简体中文](README_ch.md)
|
||||
|
||||
* [1. 安装whl包](#安装whl包)
|
||||
* [2. 使用](#使用)
|
||||
* [3. 后处理](#后处理)
|
||||
* [4. 指标](#指标)
|
||||
* [5. 训练版面分析模型](#训练版面分析模型)
|
||||
|
||||
<a name="安装whl包"></a>
|
||||
# Getting Started
|
||||
|
||||
## 1. 安装whl包
|
||||
[1. Install whl package](#Install)
|
||||
|
||||
[2. Quick Start](#QuickStart)
|
||||
|
||||
[3. PostProcess](#PostProcess)
|
||||
|
||||
[4. Results](#Results)
|
||||
|
||||
[5. Training](#Training)
|
||||
|
||||
<a name="Install"></a>
|
||||
|
||||
## 1. Install whl package
|
||||
```bash
|
||||
wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
pip install -U layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
<a name="使用"></a>
|
||||
<a name="QuickStart"></a>
|
||||
|
||||
## 2. 使用
|
||||
## 2. Quick Start
|
||||
|
||||
使用layoutparser识别给定文档的布局：
|
||||
Use LayoutParser to identify the layout of a document:
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import layoutparser as lp
|
||||
image = cv2.imread("imags/paper-image.jpg")
|
||||
image = cv2.imread("doc/table/layout.jpg")
|
||||
image = image[..., ::-1]
|
||||
|
||||
# 加载模型
|
||||
# load model
|
||||
model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
|
||||
threshold=0.5,
|
||||
label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
|
||||
enforce_cpu=False,
|
||||
enable_mkldnn=True)
|
||||
# 检测
|
||||
# detect
|
||||
layout = model.detect(image)
|
||||
|
||||
# 显示结果
|
||||
lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
||||
# show result
|
||||
show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
||||
show_img.show()
|
||||
```
|
||||
|
||||
下图展示了结果，不同颜色的检测框表示不同的类别，并通过`show_element_type`在框的左上角显示具体类别：
|
||||
The following figure shows the result, with different colored detection boxes representing different categories and displaying specific categories in the upper left corner of the box with `show_element_type`
|
||||
|
||||
<div align="center">
|
||||
<img src="../../doc/table/result_all.jpg" width = "600" />
|
||||
</div>
|
||||
`PaddleDetectionLayoutModel`parameters are described as follows:
|
||||
|
||||
`PaddleDetectionLayoutModel`函数参数说明如下:
|
||||
| parameter | description | default | remark |
|
||||
| :------------: | :------------------------------------------------------: | :---------: | :----------------------------------------------------------: |
|
||||
| config_path | model config path | None | Specify config_ path will automatically download the model (only for the first time,the model will exist and will not be downloaded again) |
|
||||
| model_path | model path | None | local model path, config_ path and model_ path must be set to one, cannot be none at the same time |
|
||||
| threshold | threshold of prediction score | 0.5 | \ |
|
||||
| input_shape | picture size of reshape | [3,640,640] | \ |
|
||||
| batch_size | testing batch size | 1 | \ |
|
||||
| label_map | category mapping table | None | Setting config_ path, it can be none, and the label is automatically obtained according to the dataset name_ map |
|
||||
| enforce_cpu | whether to use CPU | False | False to use GPU, and True to force the use of CPU |
|
||||
| enforce_mkldnn | whether mkldnn acceleration is enabled in CPU prediction | True | \ |
|
||||
| thread_num | the number of CPU threads | 10 | \ |
|
||||
|
||||
| 参数 | 含义 | 默认值 | 备注 |
|
||||
| :------------: | :-------------------------: | :---------: | :----------------------------------------------------------: |
|
||||
| config_path | 模型配置路径 | None | 指定config_path会自动下载模型(仅第一次，之后模型存在，不会再下载) |
|
||||
| model_path | 模型路径 | None | 本地模型路径，config_path和model_path必须设置一个，不能同时为None |
|
||||
| threshold | 预测得分的阈值 | 0.5 | \ |
|
||||
| input_shape | reshape之后图片尺寸 | [3,640,640] | \ |
|
||||
| batch_size | 测试batch size | 1 | \ |
|
||||
| label_map | 类别映射表 | None | 设置config_path时，可以为None，根据数据集名称自动获取label_map |
|
||||
| enforce_cpu | 代码是否使用CPU运行 | False | 设置为False表示使用GPU，True表示强制使用CPU |
|
||||
| enforce_mkldnn | CPU预测中是否开启MKLDNN加速 | True | \ |
|
||||
| thread_num | 设置CPU线程数 | 10 | \ |
|
||||
|
||||
目前支持以下几种模型配置和label map，您可以通过修改 `--config_path`和 `--label_map`使用这些模型，从而检测不同类型的内容：
|
||||
The following model configurations and label maps are currently supported, which you can use by modifying '--config_path' and '--label_map' to detect different types of content:
|
||||
|
||||
| dataset | config_path | label_map |
|
||||
| ------------------------------------------------------------ | ------------------------------------------------------------ | --------------------------------------------------------- |
|
||||
|
|
@ -66,25 +74,26 @@ lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
|||
| TableBank latex | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_latex/config | {0:"Table"} |
|
||||
| [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) | lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"} |
|
||||
|
||||
* TableBank word和TableBank latex分别在word文档、latex文档数据集训练；
|
||||
* 下载TableBank数据集同时包含word和latex。
|
||||
* TableBank word and TableBank latex are trained on datasets of word documents and latex documents respectively;
|
||||
* Download TableBank dataset contains both word and latex。
|
||||
|
||||
<a name="后处理"></a>
|
||||
<a name="PostProcess"></a>
|
||||
|
||||
## 3. 后处理
|
||||
## 3. PostProcess
|
||||
|
||||
版面分析检测包含多个类别，如果只想获取指定类别(如"Text"类别)的检测框、可以使用下述代码：
|
||||
Layout parser contains multiple categories, if you only want to get the detection box for a specific category (such as the "Text" category), you can use the following code:
|
||||
|
||||
```python
|
||||
# 首先过滤特定文本类型的区域
|
||||
# follow the above code
|
||||
# filter areas for a specific text type
|
||||
text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
|
||||
figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
|
||||
|
||||
# 因为在图像区域内可能检测到文本区域，所以只需要删除它们
|
||||
# text areas may be detected within the image area, delete these areas
|
||||
text_blocks = lp.Layout([b for b in text_blocks \
|
||||
if not any(b.is_in(b_fig) for b_fig in figure_blocks)])
|
||||
|
||||
# 对文本区域排序并分配id
|
||||
# sort text areas and assign ID
|
||||
h, w = image.shape[:2]
|
||||
|
||||
left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)
|
||||
|
|
@ -95,24 +104,24 @@ left_blocks.sort(key = lambda b:b.coordinates[1])
|
|||
right_blocks = [b for b in text_blocks if b not in left_blocks]
|
||||
right_blocks.sort(key = lambda b:b.coordinates[1])
|
||||
|
||||
# 最终合并两个列表，并按顺序添加索引
|
||||
# the two lists are merged and the indexes are added in order
|
||||
text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])
|
||||
|
||||
# 显示结果
|
||||
lp.draw_box(image, text_blocks,
|
||||
# display result
|
||||
show_img = lp.draw_box(image, text_blocks,
|
||||
box_width=3,
|
||||
show_element_id=True)
|
||||
show_img.show()
|
||||
```
|
||||
|
||||
显示只有"Text"类别的结果：
|
||||
Displays results with only the "Text" category：
|
||||
|
||||
<div align="center">
|
||||
<img src="../../doc/table/result_text.jpg" width = "600" />
|
||||
</div>
|
||||
<a name="Results"></a>
|
||||
|
||||
<a name="指标"></a>
|
||||
|
||||
## 4. 指标
|
||||
## 4. Results
|
||||
|
||||
| Dataset | mAP | CPU time cost | GPU time cost |
|
||||
| --------- | ---- | ------------- | ------------- |
|
||||
|
|
@ -125,9 +134,8 @@ lp.draw_box(image, text_blocks,
|
|||
|
||||
**GPU：** a single NVIDIA Tesla P40
|
||||
|
||||
<a name="训练版面分析模型"></a>
|
||||
<a name="Training"></a>
|
||||
|
||||
## 5. 训练版面分析模型
|
||||
|
||||
上述模型基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) 训练，如果您想训练自己的版面分析模型，请参考：[train_layoutparser_model](train_layoutparser_model.md)
|
||||
## 5. Training
|
||||
|
||||
The above model is based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). If you want to train your own layout parser model，please refer to：[train_layoutparser_model](train_layoutparser_model.md)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,141 @@
|
|||
[English](README.md) | 简体中文
|
||||
|
||||
# 版面分析使用说明
|
||||
|
||||
[1. 安装whl包](#安装whl包)
|
||||
|
||||
[2. 使用](#使用)
|
||||
|
||||
[3. 后处理](#后处理)
|
||||
|
||||
[4. 指标](#指标)
|
||||
|
||||
[5. 训练版面分析模型](#训练版面分析模型)
|
||||
|
||||
<a name="安装whl包"></a>
|
||||
|
||||
## 1. 安装whl包
|
||||
```bash
|
||||
pip install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
<a name="使用"></a>
|
||||
|
||||
## 2. 使用
|
||||
|
||||
使用layoutparser识别给定文档的布局：
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import layoutparser as lp
|
||||
image = cv2.imread("doc/table/layout.jpg")
|
||||
image = image[..., ::-1]
|
||||
|
||||
# 加载模型
|
||||
model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
|
||||
threshold=0.5,
|
||||
label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
|
||||
enforce_cpu=False,
|
||||
enable_mkldnn=True)
|
||||
# 检测
|
||||
layout = model.detect(image)
|
||||
|
||||
# 显示结果
|
||||
show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
||||
show_img.show()
|
||||
```
|
||||
|
||||
下图展示了结果，不同颜色的检测框表示不同的类别，并通过`show_element_type`在框的左上角显示具体类别：
|
||||
|
||||
<div align="center">
|
||||
<img src="../../doc/table/result_all.jpg" width = "600" />
|
||||
</div>
|
||||
|
||||
`PaddleDetectionLayoutModel`函数参数说明如下:
|
||||
|
||||
| 参数 | 含义 | 默认值 | 备注 |
|
||||
| :------------: | :-------------------------: | :---------: | :----------------------------------------------------------: |
|
||||
| config_path | 模型配置路径 | None | 指定config_path会自动下载模型(仅第一次，之后模型存在，不会再下载) |
|
||||
| model_path | 模型路径 | None | 本地模型路径，config_path和model_path必须设置一个，不能同时为None |
|
||||
| threshold | 预测得分的阈值 | 0.5 | \ |
|
||||
| input_shape | reshape之后图片尺寸 | [3,640,640] | \ |
|
||||
| batch_size | 测试batch size | 1 | \ |
|
||||
| label_map | 类别映射表 | None | 设置config_path时，可以为None，根据数据集名称自动获取label_map |
|
||||
| enforce_cpu | 代码是否使用CPU运行 | False | 设置为False表示使用GPU，True表示强制使用CPU |
|
||||
| enforce_mkldnn | CPU预测中是否开启MKLDNN加速 | True | \ |
|
||||
| thread_num | 设置CPU线程数 | 10 | \ |
|
||||
|
||||
目前支持以下几种模型配置和label map，您可以通过修改 `--config_path`和 `--label_map`使用这些模型，从而检测不同类型的内容：
|
||||
|
||||
| dataset | config_path | label_map |
|
||||
| ------------------------------------------------------------ | ------------------------------------------------------------ | --------------------------------------------------------- |
|
||||
| [TableBank](https://doc-analysis.github.io/tablebank-page/index.html) word | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_word/config | {0:"Table"} |
|
||||
| TableBank latex | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_latex/config | {0:"Table"} |
|
||||
| [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) | lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"} |
|
||||
|
||||
* TableBank word和TableBank latex分别在word文档、latex文档数据集训练；
|
||||
* 下载的TableBank数据集里同时包含word和latex。
|
||||
|
||||
<a name="后处理"></a>
|
||||
|
||||
## 3. 后处理
|
||||
|
||||
版面分析检测包含多个类别，如果只想获取指定类别(如"Text"类别)的检测框、可以使用下述代码：
|
||||
|
||||
```python
|
||||
# 接上面代码
|
||||
# 首先过滤特定文本类型的区域
|
||||
text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
|
||||
figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
|
||||
|
||||
# 因为在图像区域内可能检测到文本区域，所以只需要删除它们
|
||||
text_blocks = lp.Layout([b for b in text_blocks \
|
||||
if not any(b.is_in(b_fig) for b_fig in figure_blocks)])
|
||||
|
||||
# 对文本区域排序并分配id
|
||||
h, w = image.shape[:2]
|
||||
|
||||
left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)
|
||||
|
||||
left_blocks = text_blocks.filter_by(left_interval, center=True)
|
||||
left_blocks.sort(key = lambda b:b.coordinates[1])
|
||||
|
||||
right_blocks = [b for b in text_blocks if b not in left_blocks]
|
||||
right_blocks.sort(key = lambda b:b.coordinates[1])
|
||||
|
||||
# 最终合并两个列表，并按顺序添加索引
|
||||
text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])
|
||||
|
||||
# 显示结果
|
||||
show_img = lp.draw_box(image, text_blocks,
|
||||
box_width=3,
|
||||
show_element_id=True)
|
||||
show_img.show()
|
||||
```
|
||||
|
||||
显示只有"Text"类别的结果：
|
||||
|
||||
<div align="center">
|
||||
<img src="../../doc/table/result_text.jpg" width = "600" />
|
||||
</div>
|
||||
|
||||
<a name="指标"></a>
|
||||
|
||||
## 4. 指标
|
||||
|
||||
| Dataset | mAP | CPU time cost | GPU time cost |
|
||||
| --------- | ---- | ------------- | ------------- |
|
||||
| PubLayNet | 93.6 | 1713.7ms | 66.6ms |
|
||||
| TableBank | 96.2 | 1968.4ms | 65.1ms |
|
||||
|
||||
**Envrionment：**
|
||||
|
||||
**CPU：** Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
|
||||
|
||||
**GPU：** a single NVIDIA Tesla P40
|
||||
|
||||
<a name="训练版面分析模型"></a>
|
||||
|
||||
## 5. 训练版面分析模型
|
||||
|
||||
上述模型基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) 训练，如果您想训练自己的版面分析模型，请参考：[train_layoutparser_model](train_layoutparser_model_ch.md)
|
||||
|
|
@ -1,23 +1,32 @@
|
|||
# 训练版面分析
|
||||
# Training layout-parse
|
||||
|
||||
* [1. 安装](#安装)
|
||||
* [1.1 环境要求](#环境要求)
|
||||
* [1.2 安装PaddleDetection](#安装PaddleDetection)
|
||||
* [2. 准备数据](#准备数据)
|
||||
* [3. 配置文件改动和说明](#配置文件改动和说明)
|
||||
* [4. PaddleDetection训练](#训练)
|
||||
* [5. PaddleDetection预测](#预测)
|
||||
* [6. 预测部署](#预测部署)
|
||||
* [6.1 模型导出](#模型导出)
|
||||
* [6.2 layout parser预测](#layout_parser预测)
|
||||
[1. Installation](#Installation)
|
||||
|
||||
<a name="安装"></a>
|
||||
[1.1 Requirements](#Requirements)
|
||||
|
||||
## 1. 安装
|
||||
[1.2 Install PaddleDetection](#Install PaddleDetection)
|
||||
|
||||
<a name="环境要求"></a>
|
||||
[2. Data preparation](#Data preparation)
|
||||
|
||||
### 1.1 环境要求
|
||||
[3. Configuration](#Configuration)
|
||||
|
||||
[4. Training](#Training)
|
||||
|
||||
[5. Prediction](#Prediction)
|
||||
|
||||
[6. Deployment](#Deployment)
|
||||
|
||||
[6.1 Export model](#Export model)
|
||||
|
||||
[6.2 Inference](#Inference)
|
||||
|
||||
<a name="Installation"></a>
|
||||
|
||||
## 1. Installation
|
||||
|
||||
<a name="Requirements"></a>
|
||||
|
||||
### 1.1 Requirements
|
||||
|
||||
- PaddlePaddle 2.1
|
||||
- OS 64 bit
|
||||
|
|
@ -26,152 +35,159 @@
|
|||
- CUDA >= 10.1
|
||||
- cuDNN >= 7.6
|
||||
|
||||
<a name="安装PaddleDetection"></a>
|
||||
<a name="Install PaddleDetection"></a>
|
||||
|
||||
### 1.2 安装PaddleDetection
|
||||
### 1.2 Install PaddleDetection
|
||||
|
||||
```bash
|
||||
# 克隆PaddleDetection仓库
|
||||
# Clone PaddleDetection repository
|
||||
cd <path/to/clone/PaddleDetection>
|
||||
git clone https://github.com/PaddlePaddle/PaddleDetection.git
|
||||
|
||||
cd PaddleDetection
|
||||
# 安装其他依赖
|
||||
# Install other dependencies
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
更多安装教程，请参考: [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
|
||||
For more installation tutorials, please refer to： [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
|
||||
|
||||
<a name="数据准备"></a>
|
||||
<a name="Data preparation"></a>
|
||||
|
||||
## 2. 准备数据
|
||||
## 2. Data preparation
|
||||
|
||||
下载 [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) 数据集：
|
||||
Download the [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) dataset
|
||||
|
||||
```bash
|
||||
cd PaddleDetection/dataset/
|
||||
mkdir publaynet
|
||||
# 执行命令，下载
|
||||
# execute the command，download PubLayNet
|
||||
wget -O publaynet.tar.gz https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz?_ga=2.104193024.1076900768.1622560733-649911202.1622560733
|
||||
# 解压
|
||||
# unpack
|
||||
tar -xvf publaynet.tar.gz
|
||||
```
|
||||
|
||||
解压之后PubLayNet目录结构：
|
||||
PubLayNet directory structure after decompressing ：
|
||||
|
||||
| File or Folder | Description | num |
|
||||
| :------------- | :----------------------------------------------- | ------- |
|
||||
| `train/` | Images in the training subset | 335,703 |
|
||||
| `val/` | Images in the validation subset | 11,245 |
|
||||
| `test/` | Images in the testing subset | 11,405 |
|
||||
| `train.json` | Annotations for training images | |
|
||||
| `val.json` | Annotations for validation images | |
|
||||
| `LICENSE.txt` | Plaintext version of the CDLA-Permissive license | |
|
||||
| `README.txt` | Text file with the file names and description | |
|
||||
| `train.json` | Annotations for training images | 1 |
|
||||
| `val.json` | Annotations for validation images | 1 |
|
||||
| `LICENSE.txt` | Plaintext version of the CDLA-Permissive license | 1 |
|
||||
| `README.txt` | Text file with the file names and description | 1 |
|
||||
|
||||
如果使用其它数据集，请参考[准备训练数据](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md)
|
||||
For other datasets，please refer to [the PrepareDataSet]((https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md) )
|
||||
|
||||
<a name="配置文件改动和说明"></a>
|
||||
<a name="Configuration"></a>
|
||||
|
||||
## 3. 配置文件改动和说明
|
||||
## 3. Configuration
|
||||
|
||||
我们使用 `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml`配置进行训练，配置文件摘要如下：
|
||||
|
||||
<div align='center'>
|
||||
<img src='../../doc/table/PaddleDetection_config.png' width='600px'/>
|
||||
</div>
|
||||
|
||||
从上图看到 `ppyolov2_r50vd_dcn_365e_coco.yml` 配置需要依赖其他的配置文件，在该例子中需要依赖:
|
||||
|
||||
```
|
||||
coco_detection.yml：主要说明了训练数据和验证数据的路径
|
||||
|
||||
runtime.yml：主要说明了公共的运行参数，比如是否使用GPU、每多少个epoch存储checkpoint等
|
||||
|
||||
optimizer_365e.yml：主要说明了学习率和优化器的配置
|
||||
|
||||
ppyolov2_r50vd_dcn.yml：主要说明模型和主干网络的情况
|
||||
|
||||
ppyolov2_reader.yml：主要说明数据读取器配置，如batch size，并发加载子进程数等，同时包含读取后预处理操作，如resize、数据增强等等
|
||||
```
|
||||
|
||||
根据实际情况，修改上述文件，比如数据集路径、batch size等。
|
||||
|
||||
<a name="训练"></a>
|
||||
|
||||
## 4. PaddleDetection训练
|
||||
|
||||
PaddleDetection提供了单卡/多卡训练模式，满足用户多种训练需求
|
||||
|
||||
* GPU 单卡训练
|
||||
We use the `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml` configuration for training，the configuration file is as follows
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0 #windows和Mac下不需要执行该命令
|
||||
_BASE_: [
|
||||
'../datasets/coco_detection.yml',
|
||||
'../runtime.yml',
|
||||
'./_base_/ppyolov2_r50vd_dcn.yml',
|
||||
'./_base_/optimizer_365e.yml',
|
||||
'./_base_/ppyolov2_reader.yml',
|
||||
]
|
||||
|
||||
snapshot_epoch: 8
|
||||
weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final
|
||||
```
|
||||
The `ppyolov2_r50vd_dcn_365e_coco.yml` configuration depends on other configuration files, in this case:
|
||||
|
||||
- coco_detection.yml：mainly explains the path of training data and verification data
|
||||
|
||||
- runtime.yml：mainly describes the common parameters, such as whether to use the GPU and how many epoch to save model etc.
|
||||
|
||||
- optimizer_365e.yml：mainly explains the learning rate and optimizer configuration
|
||||
|
||||
- ppyolov2_r50vd_dcn.yml：mainly describes the model and the network
|
||||
|
||||
- ppyolov2_reader.yml：mainly describes the configuration of data readers, such as batch size and number of concurrent loading child processes, and also includes post preprocessing, such as resize and data augmention etc.
|
||||
|
||||
|
||||
Modify the preceding files, such as the dataset path and batch size etc.
|
||||
|
||||
<a name="Training"></a>
|
||||
|
||||
## 4. Training
|
||||
|
||||
PaddleDetection provides single-card/multi-card training mode to meet various training needs of users:
|
||||
|
||||
* GPU single card training
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0 #Don't need to run this command on Windows and Mac
|
||||
python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml
|
||||
```
|
||||
|
||||
* GPU多卡训练
|
||||
* GPU multi-card training
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval
|
||||
```
|
||||
|
||||
--eval：表示边训练边验证
|
||||
--eval: training while verifying
|
||||
|
||||
* 模型恢复训练
|
||||
* Model recovery training
|
||||
|
||||
在日常训练过程中，有的用户由于一些原因导致训练中断，用户可以使用-r的命令恢复训练:
|
||||
During the daily training, if training is interrupted due to some reasons, you can use the -r command to resume the training:
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval -r output/ppyolov2_r50vd_dcn_365e_coco/10000
|
||||
```
|
||||
|
||||
注意：如果遇到 "`Out of memory error`" 问题, 尝试在 `ppyolov2_reader.yml` 文件中调小`batch_size`
|
||||
Note: If you encounter "`Out of memory error`" , try reducing `batch_size` in the `ppyolov2_reader.yml` file
|
||||
|
||||
<a name="预测"></a>
|
||||
prediction<a name="Prediction"></a>
|
||||
|
||||
## 5. PaddleDetection预测
|
||||
## 5. Prediction
|
||||
|
||||
设置参数，使用PaddleDetection预测：
|
||||
Set parameters and use PaddleDetection to predict：
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0
|
||||
python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer_img=images/paper-image.jpg --output_dir=infer_output/ --draw_threshold=0.5 -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final --use_vdl=Ture
|
||||
```
|
||||
|
||||
`--draw_threshold` 是个可选参数. 根据 [NMS](https://ieeexplore.ieee.org/document/1699659) 的计算，不同阈值会产生不同的结果 `keep_top_k`表示设置输出目标的最大数量，默认值为100，用户可以根据自己的实际情况进行设定。
|
||||
`--draw_threshold` is an optional parameter. According to the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659), different threshold will produce different results, ` keep_top_k ` represent the maximum amount of output target, the default value is 10. You can set different value according to your own actual situation。
|
||||
|
||||
<a name="预测部署"></a>
|
||||
<a name="Deployment"></a>
|
||||
|
||||
## 6. 预测部署
|
||||
## 6. Deployment
|
||||
|
||||
在layout parser中使用自己训练好的模型，
|
||||
Use your trained model in Layout Parser
|
||||
|
||||
<a name="模型导出"></a>
|
||||
<a name="Export model"></a>
|
||||
|
||||
### 6.1 模型导出
|
||||
### 6.1 Export model
|
||||
|
||||
在模型训练过程中保存的模型文件是包含前向预测和反向传播的过程，在实际的工业部署则不需要反向传播，因此需要将模型进行导成部署需要的模型格式。 在PaddleDetection中提供了 `tools/export_model.py`脚本来导出模型。
|
||||
n the process of model training, the model file saved contains the process of forward prediction and back propagation. In the actual industrial deployment, there is no need for back propagation. Therefore, the model should be translated into the model format required by the deployment. The `tools/export_model.py` script is provided in PaddleDetection to export the model.
|
||||
|
||||
导出模型名称默认是`model.*`，layout parser代码模型名称是`inference.*`, 所以修改[PaddleDetection/ppdet/engine/trainer.py ](https://github.com/PaddlePaddle/PaddleDetection/blob/b87a1ea86fa18ce69e44a17ad1b49c1326f19ff9/ppdet/engine/trainer.py#L512) (点开链接查看详细代码行)，将`model`改为`inference`即可。
|
||||
The exported model name defaults to `model.*`, Layout Parser's code model is `inference.*`, So change [PaddleDetection/ppdet/engine/trainer. Py ](https://github.com/PaddlePaddle/PaddleDetection/blob/b87a1ea86fa18ce69e44a17ad1b49c1326f19ff9/ppdet/engine/trainer.py# L512) (click on the link to see the detailed line of code), change 'model' to 'inference'.
|
||||
|
||||
执行导出模型脚本：
|
||||
Execute the script to export model:
|
||||
|
||||
```bash
|
||||
python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams
|
||||
```
|
||||
|
||||
预测模型会导出到`inference/ppyolov2_r50vd_dcn_365e_coco`目录下，分别为`infer_cfg.yml`(预测不需要), `inference.pdiparams`, `inference.pdiparams.info`,`inference.pdmodel` 。
|
||||
The prediction model is exported to `inference/ppyolov2_r50vd_dcn_365e_coco` ,including:`infer_cfg.yml`(prediction not required), `inference.pdiparams`, `inference.pdiparams.info`,`inference.pdmodel`
|
||||
|
||||
更多模型导出教程，请参考：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
|
||||
More model export tutorials, please refer to：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
|
||||
|
||||
<a name="layout parser预测"></a>
|
||||
<a name="Inference"></a>
|
||||
|
||||
### 6.2 layout_parser预测
|
||||
### 6.2 Inference
|
||||
|
||||
`model_path`指定训练好的模型路径，使用layout parser进行预测：
|
||||
`model_path` represent the trained model path, and layoutparser is used to predict:
|
||||
|
||||
```bash
|
||||
import layoutparser as lp
|
||||
|
|
@ -182,7 +198,6 @@ model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_3
|
|||
|
||||
***
|
||||
|
||||
更多PaddleDetection训练教程，请参考：[PaddleDetection训练](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
|
||||
More PaddleDetection training tutorials，please reference：[PaddleDetection Training](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
|
||||
|
||||
***
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,203 @@
|
|||
# 训练版面分析
|
||||
|
||||
[1. 安装](#安装)
|
||||
|
||||
[1.1 环境要求](#环境要求)
|
||||
|
||||
[1.2 安装PaddleDetection](#安装PaddleDetection)
|
||||
|
||||
[2. 准备数据](#准备数据)
|
||||
|
||||
[3. 配置文件改动和说明](#配置文件改动和说明)
|
||||
|
||||
[4. PaddleDetection训练](#训练)
|
||||
|
||||
[5. PaddleDetection预测](#预测)
|
||||
|
||||
[6. 预测部署](#预测部署)
|
||||
|
||||
[6.1 模型导出](#模型导出)
|
||||
|
||||
[6.2 layout parser预测](#layout_parser预测)
|
||||
|
||||
<a name="安装"></a>
|
||||
|
||||
## 1. 安装
|
||||
|
||||
<a name="环境要求"></a>
|
||||
|
||||
### 1.1 环境要求
|
||||
|
||||
- PaddlePaddle 2.1
|
||||
- OS 64 bit
|
||||
- Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64 bit
|
||||
- pip/pip3(9.0.1+), 64 bit
|
||||
- CUDA >= 10.1
|
||||
- cuDNN >= 7.6
|
||||
|
||||
<a name="安装PaddleDetection"></a>
|
||||
|
||||
### 1.2 安装PaddleDetection
|
||||
|
||||
```bash
|
||||
# 克隆PaddleDetection仓库
|
||||
cd <path/to/clone/PaddleDetection>
|
||||
git clone https://github.com/PaddlePaddle/PaddleDetection.git
|
||||
|
||||
cd PaddleDetection
|
||||
# 安装其他依赖
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
更多安装教程，请参考: [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
|
||||
|
||||
<a name="数据准备"></a>
|
||||
|
||||
## 2. 准备数据
|
||||
|
||||
下载 [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) 数据集：
|
||||
|
||||
```bash
|
||||
cd PaddleDetection/dataset/
|
||||
mkdir publaynet
|
||||
# 执行命令，下载
|
||||
wget -O publaynet.tar.gz https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz?_ga=2.104193024.1076900768.1622560733-649911202.1622560733
|
||||
# 解压
|
||||
tar -xvf publaynet.tar.gz
|
||||
```
|
||||
|
||||
解压之后PubLayNet目录结构：
|
||||
|
||||
| File or Folder | Description | num |
|
||||
| :------------- | :----------------------------------------------- | ------- |
|
||||
| `train/` | Images in the training subset | 335,703 |
|
||||
| `val/` | Images in the validation subset | 11,245 |
|
||||
| `test/` | Images in the testing subset | 11,405 |
|
||||
| `train.json` | Annotations for training images | 1 |
|
||||
| `val.json` | Annotations for validation images | 1 |
|
||||
| `LICENSE.txt` | Plaintext version of the CDLA-Permissive license | 1 |
|
||||
| `README.txt` | Text file with the file names and description | 1 |
|
||||
|
||||
如果使用其它数据集，请参考[准备训练数据](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md)
|
||||
|
||||
<a name="配置文件改动和说明"></a>
|
||||
|
||||
## 3. 配置文件改动和说明
|
||||
|
||||
我们使用 `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml`配置进行训练，配置文件摘要如下：
|
||||
|
||||
```bash
|
||||
_BASE_: [
|
||||
'../datasets/coco_detection.yml',
|
||||
'../runtime.yml',
|
||||
'./_base_/ppyolov2_r50vd_dcn.yml',
|
||||
'./_base_/optimizer_365e.yml',
|
||||
'./_base_/ppyolov2_reader.yml',
|
||||
]
|
||||
|
||||
snapshot_epoch: 8
|
||||
weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final
|
||||
```
|
||||
从中可以看到 `ppyolov2_r50vd_dcn_365e_coco.yml` 配置需要依赖其他的配置文件，在该例子中需要依赖:
|
||||
|
||||
- coco_detection.yml：主要说明了训练数据和验证数据的路径
|
||||
|
||||
- runtime.yml：主要说明了公共的运行参数，比如是否使用GPU、每多少个epoch存储checkpoint等
|
||||
|
||||
- optimizer_365e.yml：主要说明了学习率和优化器的配置
|
||||
|
||||
- ppyolov2_r50vd_dcn.yml：主要说明模型和主干网络的情况
|
||||
|
||||
- ppyolov2_reader.yml：主要说明数据读取器配置，如batch size，并发加载子进程数等，同时包含读取后预处理操作，如resize、数据增强等等
|
||||
|
||||
|
||||
根据实际情况，修改上述文件，比如数据集路径、batch size等。
|
||||
|
||||
<a name="训练"></a>
|
||||
|
||||
## 4. PaddleDetection训练
|
||||
|
||||
PaddleDetection提供了单卡/多卡训练模式，满足用户多种训练需求
|
||||
|
||||
* GPU 单卡训练
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0 #windows和Mac下不需要执行该命令
|
||||
python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml
|
||||
```
|
||||
|
||||
* GPU多卡训练
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval
|
||||
```
|
||||
|
||||
--eval：表示边训练边验证
|
||||
|
||||
* 模型恢复训练
|
||||
|
||||
在日常训练过程中，有的用户由于一些原因导致训练中断，用户可以使用-r的命令恢复训练:
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval -r output/ppyolov2_r50vd_dcn_365e_coco/10000
|
||||
```
|
||||
|
||||
注意：如果遇到 "`Out of memory error`" 问题, 尝试在 `ppyolov2_reader.yml` 文件中调小`batch_size`
|
||||
|
||||
<a name="预测"></a>
|
||||
|
||||
## 5. PaddleDetection预测
|
||||
|
||||
设置参数，使用PaddleDetection预测：
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0
|
||||
python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer_img=images/paper-image.jpg --output_dir=infer_output/ --draw_threshold=0.5 -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final --use_vdl=Ture
|
||||
```
|
||||
|
||||
`--draw_threshold` 是个可选参数. 根据 [NMS](https://ieeexplore.ieee.org/document/1699659) 的计算，不同阈值会产生不同的结果 `keep_top_k`表示设置输出目标的最大数量，默认值为100，用户可以根据自己的实际情况进行设定。
|
||||
|
||||
<a name="预测部署"></a>
|
||||
|
||||
## 6. 预测部署
|
||||
|
||||
在layout parser中使用自己训练好的模型。
|
||||
|
||||
<a name="模型导出"></a>
|
||||
|
||||
### 6.1 模型导出
|
||||
|
||||
在模型训练过程中保存的模型文件是包含前向预测和反向传播的过程，在实际的工业部署则不需要反向传播，因此需要将模型进行导成部署需要的模型格式。 在PaddleDetection中提供了 `tools/export_model.py`脚本来导出模型。
|
||||
|
||||
导出模型名称默认是`model.*`，layout parser代码模型名称是`inference.*`, 所以修改[PaddleDetection/ppdet/engine/trainer.py ](https://github.com/PaddlePaddle/PaddleDetection/blob/b87a1ea86fa18ce69e44a17ad1b49c1326f19ff9/ppdet/engine/trainer.py#L512) (点开链接查看详细代码行)，将`model`改为`inference`即可。
|
||||
|
||||
执行导出模型脚本：
|
||||
|
||||
```bash
|
||||
python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams
|
||||
```
|
||||
|
||||
预测模型会导出到`inference/ppyolov2_r50vd_dcn_365e_coco`目录下，分别为`infer_cfg.yml`(预测不需要), `inference.pdiparams`, `inference.pdiparams.info`,`inference.pdmodel` 。
|
||||
|
||||
更多模型导出教程，请参考：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
|
||||
|
||||
<a name="layout parser预测"></a>
|
||||
|
||||
### 6.2 layout_parser预测
|
||||
|
||||
`model_path`指定训练好的模型路径，使用layout parser进行预测：
|
||||
|
||||
```bash
|
||||
import layoutparser as lp
|
||||
model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_365e_coco", threshold=0.5,label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},enforce_cpu=True,enable_mkldnn=True)
|
||||
```
|
||||
|
||||
|
||||
|
||||
***
|
||||
|
||||
更多PaddleDetection训练教程，请参考：[PaddleDetection训练](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
|
||||
|
||||
***
|
||||
|
|
@ -1,147 +0,0 @@
|
|||
# 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.
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
|
||||
__dir__ = os.path.dirname(__file__)
|
||||
sys.path.append(__dir__)
|
||||
sys.path.append(os.path.join(__dir__, '..'))
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
from ppocr.utils.logging import get_logger
|
||||
from ppstructure.predict_system import OCRSystem, save_res
|
||||
from ppstructure.utility import init_args, draw_result
|
||||
|
||||
logger = get_logger()
|
||||
from ppocr.utils.utility import check_and_read_gif, get_image_file_list
|
||||
from ppocr.utils.network import maybe_download, download_with_progressbar, confirm_model_dir_url, is_link
|
||||
|
||||
__all__ = ['PaddleStructure', 'draw_result', 'save_res']
|
||||
|
||||
VERSION = '2.1'
|
||||
BASE_DIR = os.path.expanduser("~/.paddlestructure/")
|
||||
|
||||
model_urls = {
|
||||
'det': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar',
|
||||
'rec': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
|
||||
'table': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar'
|
||||
|
||||
}
|
||||
|
||||
|
||||
def parse_args(mMain=True):
|
||||
import argparse
|
||||
parser = init_args()
|
||||
parser.add_help = mMain
|
||||
|
||||
for action in parser._actions:
|
||||
if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
|
||||
action.default = None
|
||||
if mMain:
|
||||
return parser.parse_args()
|
||||
else:
|
||||
inference_args_dict = {}
|
||||
for action in parser._actions:
|
||||
inference_args_dict[action.dest] = action.default
|
||||
return argparse.Namespace(**inference_args_dict)
|
||||
|
||||
|
||||
class PaddleStructure(OCRSystem):
|
||||
def __init__(self, **kwargs):
|
||||
params = parse_args(mMain=False)
|
||||
params.__dict__.update(**kwargs)
|
||||
if not params.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
params.use_angle_cls = False
|
||||
# init model dir
|
||||
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'det'),
|
||||
model_urls['det'])
|
||||
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'rec'),
|
||||
model_urls['rec'])
|
||||
params.table_model_dir, table_url = confirm_model_dir_url(params.table_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'table'),
|
||||
model_urls['table'])
|
||||
# download model
|
||||
maybe_download(params.det_model_dir, det_url)
|
||||
maybe_download(params.rec_model_dir, rec_url)
|
||||
maybe_download(params.table_model_dir, table_url)
|
||||
|
||||
if params.rec_char_dict_path is None:
|
||||
params.rec_char_type = 'EN'
|
||||
if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')):
|
||||
params.rec_char_dict_path = str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')
|
||||
else:
|
||||
params.rec_char_dict_path = str(Path(__file__).parent.parent / 'ppocr/utils/dict/table_dict.txt')
|
||||
if params.table_char_dict_path is None:
|
||||
if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')):
|
||||
params.table_char_dict_path = str(
|
||||
Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')
|
||||
else:
|
||||
params.table_char_dict_path = str(
|
||||
Path(__file__).parent.parent / 'ppocr/utils/dict/table_structure_dict.txt')
|
||||
|
||||
print(params)
|
||||
super().__init__(params)
|
||||
|
||||
def __call__(self, img):
|
||||
if isinstance(img, str):
|
||||
# download net image
|
||||
if img.startswith('http'):
|
||||
download_with_progressbar(img, 'tmp.jpg')
|
||||
img = 'tmp.jpg'
|
||||
image_file = img
|
||||
img, flag = check_and_read_gif(image_file)
|
||||
if not flag:
|
||||
with open(image_file, 'rb') as f:
|
||||
np_arr = np.frombuffer(f.read(), dtype=np.uint8)
|
||||
img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
|
||||
if img is None:
|
||||
logger.error("error in loading image:{}".format(image_file))
|
||||
return None
|
||||
if isinstance(img, np.ndarray) and len(img.shape) == 2:
|
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
||||
|
||||
res = super().__call__(img)
|
||||
return res
|
||||
|
||||
|
||||
def main():
|
||||
# for cmd
|
||||
args = parse_args(mMain=True)
|
||||
image_dir = args.image_dir
|
||||
save_folder = args.output
|
||||
if image_dir.startswith('http'):
|
||||
download_with_progressbar(image_dir, 'tmp.jpg')
|
||||
image_file_list = ['tmp.jpg']
|
||||
else:
|
||||
image_file_list = get_image_file_list(args.image_dir)
|
||||
if len(image_file_list) == 0:
|
||||
logger.error('no images find in {}'.format(args.image_dir))
|
||||
return
|
||||
|
||||
structure_engine = PaddleStructure(**(args.__dict__))
|
||||
for img_path in image_file_list:
|
||||
img_name = os.path.basename(img_path).split('.')[0]
|
||||
logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
|
||||
result = structure_engine(img_path)
|
||||
for item in result:
|
||||
logger.info(item['res'])
|
||||
save_res(result, save_folder, img_name)
|
||||
logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
|
||||
|
|
@ -26,26 +26,33 @@ import numpy as np
|
|||
import time
|
||||
import logging
|
||||
|
||||
import layoutparser as lp
|
||||
|
||||
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
|
||||
from ppocr.utils.logging import get_logger
|
||||
from tools.infer.predict_system import TextSystem
|
||||
from ppstructure.table.predict_table import TableSystem, to_excel
|
||||
from ppstructure.utility import parse_args, draw_result
|
||||
from ppstructure.utility import parse_args, draw_structure_result
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
class OCRSystem(object):
|
||||
def __init__(self, args):
|
||||
args.det_limit_type = 'resize_long'
|
||||
import layoutparser as lp
|
||||
# args.det_limit_type = 'resize_long'
|
||||
args.drop_score = 0
|
||||
if not args.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
self.text_system = TextSystem(args)
|
||||
self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer)
|
||||
self.table_layout = lp.PaddleDetectionLayoutModel("lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
|
||||
|
||||
config_path = None
|
||||
model_path = None
|
||||
if os.path.isdir(args.layout_path_model):
|
||||
model_path = args.layout_path_model
|
||||
else:
|
||||
config_path = args.layout_path_model
|
||||
self.table_layout = lp.PaddleDetectionLayoutModel(config_path=config_path,
|
||||
model_path=model_path,
|
||||
threshold=0.5, enable_mkldnn=args.enable_mkldnn,
|
||||
enforce_cpu=not args.use_gpu, thread_num=args.cpu_threads)
|
||||
self.use_angle_cls = args.use_angle_cls
|
||||
|
|
@ -65,24 +72,35 @@ class OCRSystem(object):
|
|||
filter_boxes, filter_rec_res = self.text_system(roi_img)
|
||||
filter_boxes = [x + [x1, y1] for x in filter_boxes]
|
||||
filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes]
|
||||
|
||||
res = (filter_boxes, filter_rec_res)
|
||||
res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'res': res})
|
||||
# remove style char
|
||||
style_token = ['<strike>', '<strike>', '<sup>', '</sub>', '<b>', '</b>', '<sub>', '</sup>',
|
||||
'<overline>', '</overline>', '<underline>', '</underline>', '<i>', '</i>']
|
||||
filter_rec_res_tmp = []
|
||||
for rec_res in filter_rec_res:
|
||||
rec_str, rec_conf = rec_res
|
||||
for token in style_token:
|
||||
if token in rec_str:
|
||||
rec_str = rec_str.replace(token, '')
|
||||
filter_rec_res_tmp.append((rec_str, rec_conf))
|
||||
res = (filter_boxes, filter_rec_res_tmp)
|
||||
res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'img': roi_img, 'res': res})
|
||||
return res_list
|
||||
|
||||
|
||||
def save_res(res, save_folder, img_name):
|
||||
def save_structure_res(res, save_folder, img_name):
|
||||
excel_save_folder = os.path.join(save_folder, img_name)
|
||||
os.makedirs(excel_save_folder, exist_ok=True)
|
||||
# save res
|
||||
with open(os.path.join(excel_save_folder, 'res.txt'), 'w', encoding='utf8') as f:
|
||||
for region in res:
|
||||
if region['type'] == 'Table':
|
||||
excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
|
||||
to_excel(region['res'], excel_path)
|
||||
elif region['type'] == 'Figure':
|
||||
pass
|
||||
if region['type'] == 'Figure':
|
||||
roi_img = region['img']
|
||||
img_path = os.path.join(excel_save_folder, '{}.jpg'.format(region['bbox']))
|
||||
cv2.imwrite(img_path, roi_img)
|
||||
else:
|
||||
with open(os.path.join(excel_save_folder, 'res.txt'), 'a', encoding='utf8') as f:
|
||||
for box, rec_res in zip(region['res'][0], region['res'][1]):
|
||||
f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res))
|
||||
|
||||
|
|
@ -108,8 +126,8 @@ def main(args):
|
|||
continue
|
||||
starttime = time.time()
|
||||
res = structure_sys(img)
|
||||
save_res(res, save_folder, img_name)
|
||||
draw_img = draw_result(img, res, args.vis_font_path)
|
||||
save_structure_res(res, save_folder, img_name)
|
||||
draw_img = draw_structure_result(img, res, args.vis_font_path)
|
||||
cv2.imwrite(os.path.join(save_folder, img_name, 'show.jpg'), draw_img)
|
||||
logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
|
||||
elapse = time.time() - starttime
|
||||
|
|
|
|||
|
|
@ -1,70 +0,0 @@
|
|||
# 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.
|
||||
import os
|
||||
|
||||
from setuptools import setup
|
||||
from io import open
|
||||
import shutil
|
||||
|
||||
with open('../requirements.txt', encoding="utf-8-sig") as f:
|
||||
requirements = f.readlines()
|
||||
requirements.append('tqdm')
|
||||
|
||||
|
||||
def readme():
|
||||
with open('README_ch.md', encoding="utf-8-sig") as f:
|
||||
README = f.read()
|
||||
return README
|
||||
|
||||
|
||||
shutil.copytree('./table', './ppstructure/table')
|
||||
shutil.copyfile('./predict_system.py', './ppstructure/predict_system.py')
|
||||
shutil.copyfile('./utility.py', './ppstructure/utility.py')
|
||||
shutil.copytree('../ppocr', './ppocr')
|
||||
shutil.copytree('../tools', './tools')
|
||||
shutil.copyfile('../LICENSE', './LICENSE')
|
||||
|
||||
setup(
|
||||
name='paddlestructure',
|
||||
packages=['paddlestructure'],
|
||||
package_dir={'paddlestructure': ''},
|
||||
include_package_data=True,
|
||||
entry_points={"console_scripts": ["paddlestructure= paddlestructure.paddlestructure:main"]},
|
||||
version='1.0',
|
||||
install_requires=requirements,
|
||||
license='Apache License 2.0',
|
||||
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
|
||||
long_description=readme(),
|
||||
long_description_content_type='text/markdown',
|
||||
url='https://github.com/PaddlePaddle/PaddleOCR',
|
||||
download_url='https://github.com/PaddlePaddle/PaddleOCR.git',
|
||||
keywords=[
|
||||
'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition'
|
||||
],
|
||||
classifiers=[
|
||||
'Intended Audience :: Developers', 'Operating System :: OS Independent',
|
||||
'Natural Language :: Chinese (Simplified)',
|
||||
'Programming Language :: Python :: 3',
|
||||
'Programming Language :: Python :: 3.2',
|
||||
'Programming Language :: Python :: 3.3',
|
||||
'Programming Language :: Python :: 3.4',
|
||||
'Programming Language :: Python :: 3.5',
|
||||
'Programming Language :: Python :: 3.6',
|
||||
'Programming Language :: Python :: 3.7', 'Topic :: Utilities'
|
||||
], )
|
||||
|
||||
shutil.rmtree('ppocr')
|
||||
shutil.rmtree('tools')
|
||||
shutil.rmtree('ppstructure')
|
||||
os.remove('LICENSE')
|
||||
|
|
@ -1,12 +1,12 @@
|
|||
# Table structure and content prediction
|
||||
# Table Recognition
|
||||
|
||||
## 1. pipeline
|
||||
The ocr of the table mainly contains three models
|
||||
The table recognition mainly contains three models
|
||||
1. Single line text detection-DB
|
||||
2. Single line text recognition-CRNN
|
||||
3. Table structure and cell coordinate prediction-RARE
|
||||
|
||||
The table ocr flow chart is as follows
|
||||
The table recognition flow chart is as follows
|
||||
|
||||
|
||||
|
||||
|
|
@ -15,10 +15,39 @@ The table ocr flow chart is as follows
|
|||
3. The recognition result of the cell is combined by the coordinates, recognition result of the single line and the coordinates of the cell.
|
||||
4. The cell recognition result and the table structure together construct the html string of the table.
|
||||
|
||||
## 2. How to use
|
||||
## 2. Performance
|
||||
We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the performance is as follows:
|
||||
|
||||
|
||||
### 2.1 Train
|
||||
|Method|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
|
||||
| --- | --- |
|
||||
| EDD<sup>[2]</sup> | 88.3 |
|
||||
| Ours | 93.32 |
|
||||
|
||||
## 3. How to use
|
||||
|
||||
### 3.1 quick start
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
|
||||
# download model
|
||||
mkdir inference && cd inference
|
||||
# Download the detection model of the ultra-lightweight table English OCR model and unzip it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar
|
||||
# Download the recognition model of the ultra-lightweight table English OCR model and unzip it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar
|
||||
# Download the ultra-lightweight English table inch model and unzip it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
|
||||
cd ..
|
||||
# run
|
||||
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --det_limit_side_len=736 --det_limit_type=min --output ../output/table
|
||||
```
|
||||
Note: The above model is trained on the PubLayNet dataset and only supports English scanning scenarios. If you need to identify other scenarios, you need to train the model yourself and replace the three fields `det_model_dir`, `rec_model_dir`, `table_model_dir`.
|
||||
|
||||
After running, the excel sheet of each picture will be saved in the directory specified by the output field
|
||||
|
||||
### 3.2 Train
|
||||
|
||||
In this chapter, we only introduce the training of the table structure model, For model training of [text detection](../../doc/doc_en/detection_en.md) and [text recognition](../../doc/doc_en/recognition_en.md), please refer to the corresponding documents
|
||||
|
||||
|
|
@ -48,29 +77,40 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
|
|||
|
||||
**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
|
||||
|
||||
### 2.2 Eval
|
||||
First cd to the PaddleOCR/ppstructure directory
|
||||
### 3.3 Eval
|
||||
|
||||
The table uses TEDS (Tree-Edit-Distance-based Similarity) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
|
||||
The table uses [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
|
||||
```json
|
||||
{"PMC4289340_004_00.png": [["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"], [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]]}
|
||||
{"PMC4289340_004_00.png": [
|
||||
["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"],
|
||||
[[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
|
||||
[["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
|
||||
]}
|
||||
```
|
||||
In gt json, the key is the image name, the value is the corresponding gt, and gt is a list composed of four items, and each item is
|
||||
1. HTML string list of table structure
|
||||
2. The coordinates of each cell (not including the empty text in the cell)
|
||||
3. The text information in each cell (not including the empty text in the cell)
|
||||
4. The text information in each cell (including the empty text in the cell)
|
||||
|
||||
Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
|
||||
```
|
||||
|
||||
If the PubLatNet eval dataset is used, it will be output
|
||||
```bash
|
||||
teds: 93.32
|
||||
```
|
||||
|
||||
### 2.3 Inference
|
||||
First cd to the PaddleOCR/ppstructure directory
|
||||
### 3.4 Inference
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
|
||||
```
|
||||
After running, the excel sheet of each picture will be saved in the directory specified by the output field
|
||||
|
||||
Reference
|
||||
1. https://github.com/ibm-aur-nlp/PubTabNet
|
||||
2. https://arxiv.org/pdf/1911.10683
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
# 表格结构和内容预测
|
||||
# 表格识别
|
||||
|
||||
## 1. pipeline
|
||||
表格的ocr主要包含三个模型
|
||||
## 1. 表格识别 pipeline
|
||||
表格识别主要包含三个模型
|
||||
1. 单行文本检测-DB
|
||||
2. 单行文本识别-CRNN
|
||||
3. 表格结构和cell坐标预测-RARE
|
||||
|
|
@ -10,18 +10,50 @@
|
|||
|
||||
|
||||
|
||||
1. 图片由单行文字检测检测模型到单行文字的坐标，然后送入识别模型拿到识别结果。
|
||||
流程说明:
|
||||
|
||||
1. 图片由单行文字检测模型检测到单行文字的坐标，然后送入识别模型拿到识别结果。
|
||||
2. 图片由表格结构和cell坐标预测模型拿到表格的结构信息和单元格的坐标信息。
|
||||
3. 由单行文字的坐标、识别结果和单元格的坐标一起组合出单元格的识别结果。
|
||||
4. 单元格的识别结果和表格结构一起构造表格的html字符串。
|
||||
|
||||
## 2. 使用
|
||||
## 2. 性能
|
||||
我们在 PubTabNet<sup>[1]</sup> 评估数据集上对算法进行了评估，性能如下
|
||||
|
||||
### 2.1 训练
|
||||
|
||||
|算法|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
|
||||
| --- | --- |
|
||||
| EDD<sup>[2]</sup> | 88.3 |
|
||||
| Ours | 93.32 |
|
||||
|
||||
## 3. 使用
|
||||
|
||||
### 3.1 快速开始
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
|
||||
# 下载模型
|
||||
mkdir inference && cd inference
|
||||
# 下载超轻量级表格英文OCR模型的检测模型并解压
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar
|
||||
# 下载超轻量级表格英文OCR模型的识别模型并解压
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar
|
||||
# 下载超轻量级英文表格英寸模型并解压
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
|
||||
cd ..
|
||||
# 执行预测
|
||||
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --det_limit_side_len=736 --det_limit_type=min --output ../output/table
|
||||
```
|
||||
运行完成后，每张图片的excel表格会保存到output字段指定的目录下
|
||||
|
||||
note: 上述模型是在 PubLayNet 数据集上训练的表格识别模型，仅支持英文扫描场景，如需识别其他场景需要自己训练模型后替换 `det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。
|
||||
|
||||
### 3.2 训练
|
||||
在这一章节中，我们仅介绍表格结构模型的训练，[文字检测](../../doc/doc_ch/detection.md)和[文字识别](../../doc/doc_ch/recognition.md)的模型训练请参考对应的文档。
|
||||
|
||||
#### 数据准备
|
||||
训练数据使用公开数据集[PubTabNet](https://arxiv.org/abs/1911.10683)，可以从[官网](https://github.com/ibm-aur-nlp/PubTabNet)下载。PubTabNet数据集包含约50万张表格数据的图像，以及图像对应的html格式的注释。
|
||||
训练数据使用公开数据集PubTabNet ([论文](https://arxiv.org/abs/1911.10683)，[下载地址](https://github.com/ibm-aur-nlp/PubTabNet))。PubTabNet数据集包含约50万张表格数据的图像，以及图像对应的html格式的注释。
|
||||
|
||||
#### 启动训练
|
||||
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
|
||||
|
|
@ -44,29 +76,38 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
|
|||
**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
|
||||
|
||||
|
||||
### 2.2 评估
|
||||
先cd到PaddleOCR/ppstructure目录下
|
||||
### 3.3 评估
|
||||
|
||||
表格使用 TEDS(Tree-Edit-Distance-based Similarity) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
|
||||
表格使用 [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
|
||||
```json
|
||||
{"PMC4289340_004_00.png": [["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"], [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]]}
|
||||
{"PMC4289340_004_00.png": [
|
||||
["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"],
|
||||
[[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
|
||||
[["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
|
||||
]}
|
||||
```
|
||||
json 中，key为图片名，value为对于的gt，gt是一个由四个item组成的list，每个item分别为
|
||||
json 中，key为图片名，value为对应的gt，gt是一个由三个item组成的list，每个item分别为
|
||||
1. 表格结构的html字符串list
|
||||
2. 每个cell的坐标 (不包括cell里文字为空的)
|
||||
3. 每个cell里的文字信息 (不包括cell里文字为空的)
|
||||
4. 每个cell里的文字信息 (包括cell里文字为空的)
|
||||
|
||||
准备完成后使用如下命令进行评估，评估完成后会输出teds指标。
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
|
||||
```
|
||||
如使用PubLatNet评估数据集，将会输出
|
||||
```bash
|
||||
teds: 93.32
|
||||
```
|
||||
|
||||
|
||||
### 2.3 预测
|
||||
先cd到PaddleOCR/ppstructure目录下
|
||||
### 3.4 预测
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
|
||||
```
|
||||
运行完成后，每张图片的excel表格会保存到output字段指定的目录下
|
||||
|
||||
Reference
|
||||
1. https://github.com/ibm-aur-nlp/PubTabNet
|
||||
2. https://arxiv.org/pdf/1911.10683
|
||||
|
|
@ -46,20 +46,20 @@ def main(gt_path, img_root, args):
|
|||
pred_html = text_sys(img)
|
||||
pred_htmls.append(pred_html)
|
||||
|
||||
gt_structures, gt_bboxes, gt_contents, contents_with_block = jsons_gt[img_name]
|
||||
gt_html, gt = get_gt_html(gt_structures, contents_with_block)
|
||||
gt_structures, gt_bboxes, gt_contents = jsons_gt[img_name]
|
||||
gt_html, gt = get_gt_html(gt_structures, gt_contents)
|
||||
gt_htmls.append(gt_html)
|
||||
scores = teds.batch_evaluate_html(gt_htmls, pred_htmls)
|
||||
logger.info('teds:', sum(scores) / len(scores))
|
||||
|
||||
|
||||
def get_gt_html(gt_structures, contents_with_block):
|
||||
def get_gt_html(gt_structures, gt_contents):
|
||||
end_html = []
|
||||
td_index = 0
|
||||
for tag in gt_structures:
|
||||
if '</td>' in tag:
|
||||
if contents_with_block[td_index] != []:
|
||||
end_html.extend(contents_with_block[td_index])
|
||||
if gt_contents[td_index] != []:
|
||||
end_html.extend(gt_contents[td_index])
|
||||
end_html.append(tag)
|
||||
td_index += 1
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ def init_args():
|
|||
parser.add_argument("--table_model_dir", type=str)
|
||||
parser.add_argument("--table_char_type", type=str, default='en')
|
||||
parser.add_argument("--table_char_dict_path", type=str, default="../ppocr/utils/dict/table_structure_dict.txt")
|
||||
|
||||
parser.add_argument("--layout_path_model", type=str, default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
|
||||
return parser
|
||||
|
||||
|
||||
|
|
@ -36,15 +36,13 @@ def parse_args():
|
|||
return parser.parse_args()
|
||||
|
||||
|
||||
def draw_result(image, result, font_path):
|
||||
def draw_structure_result(image, result, font_path):
|
||||
if isinstance(image, np.ndarray):
|
||||
image = Image.fromarray(image)
|
||||
boxes, txts, scores = [], [], []
|
||||
for region in result:
|
||||
if region['type'] == 'Table':
|
||||
pass
|
||||
elif region['type'] == 'Figure':
|
||||
pass
|
||||
else:
|
||||
for box, rec_res in zip(region['res'][0], region['res'][1]):
|
||||
boxes.append(np.array(box).reshape(-1, 2))
|
||||
|
|
|
|||
3
setup.py
3
setup.py
|
|
@ -14,6 +14,7 @@
|
|||
|
||||
from setuptools import setup
|
||||
from io import open
|
||||
from paddleocr import VERSION
|
||||
|
||||
with open('requirements.txt', encoding="utf-8-sig") as f:
|
||||
requirements = f.readlines()
|
||||
|
|
@ -32,7 +33,7 @@ setup(
|
|||
package_dir={'paddleocr': ''},
|
||||
include_package_data=True,
|
||||
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
|
||||
version='2.0.6',
|
||||
version=VERSION,
|
||||
install_requires=requirements,
|
||||
license='Apache License 2.0',
|
||||
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
|
||||
|
|
|
|||
|
|
@ -1,35 +0,0 @@
|
|||
model_name:ocr_det
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.auto_cast:null
|
||||
Global.epoch_num:10
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:
|
||||
Global.use_gpu:
|
||||
Global.pretrained_model:null
|
||||
|
||||
trainer:norm|pact
|
||||
norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
quant_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
|
||||
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
|
||||
|
||||
Global.save_inference_dir:./output/
|
||||
Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
|
||||
quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
|
||||
fpgm_export:deploy/slim/prune/export_prune_model.py
|
||||
distill_export:null
|
||||
|
||||
inference:tools/infer/predict_det.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:True|False
|
||||
--precision:fp32|fp16|int8
|
||||
--det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
|
||||
--image_dir:./inference/ch_det_data_50/all-sum-510/
|
||||
--save_log_path:./test/output/
|
||||
|
|
@ -1,35 +0,0 @@
|
|||
model_name:ocr_rec
|
||||
python:python
|
||||
gpu_list:0|0,1
|
||||
Global.auto_cast:null
|
||||
Global.epoch_num:10
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:
|
||||
Global.use_gpu:
|
||||
Global.pretrained_model:null
|
||||
|
||||
trainer:norm|pact
|
||||
norm_train:tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
|
||||
quant_train:deploy/slim/quantization/quant.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
|
||||
eval:tools/eval.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o
|
||||
|
||||
Global.save_inference_dir:./output/
|
||||
Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o
|
||||
quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
|
||||
inference:tools/infer/predict_rec.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:True|False
|
||||
--precision:fp32|fp16|int8
|
||||
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
|
||||
--image_dir:./inference/rec_inference
|
||||
--save_log_path:./test/output/
|
||||
146
test/prepare.sh
146
test/prepare.sh
|
|
@ -1,146 +0,0 @@
|
|||
#!/bin/bash
|
||||
FILENAME=$1
|
||||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
|
||||
MODE=$2
|
||||
|
||||
dataline=$(cat ${FILENAME})
|
||||
|
||||
# parser params
|
||||
IFS=$'\n'
|
||||
lines=(${dataline})
|
||||
function func_parser_key(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
tmp=${array[0]}
|
||||
echo ${tmp}
|
||||
}
|
||||
function func_parser_value(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
tmp=${array[1]}
|
||||
echo ${tmp}
|
||||
}
|
||||
IFS=$'\n'
|
||||
# The training params
|
||||
model_name=$(func_parser_value "${lines[0]}")
|
||||
train_model_list=$(func_parser_value "${lines[0]}")
|
||||
|
||||
trainer_list=$(func_parser_value "${lines[10]}")
|
||||
|
||||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
|
||||
MODE=$2
|
||||
# prepare pretrained weights and dataset
|
||||
if [ ${train_model_list[*]} = "ocr_det" ]; then
|
||||
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
|
||||
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
|
||||
cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
|
||||
fi
|
||||
if [ ${MODE} = "lite_train_infer" ];then
|
||||
# pretrain lite train data
|
||||
rm -rf ./train_data/icdar2015
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos
|
||||
|
||||
cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar
|
||||
ln -s ./icdar2015_lite ./icdar2015
|
||||
cd ../
|
||||
epoch=10
|
||||
eval_batch_step=10
|
||||
elif [ ${MODE} = "whole_train_infer" ];then
|
||||
rm -rf ./train_data/icdar2015
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
|
||||
cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
|
||||
epoch=500
|
||||
eval_batch_step=200
|
||||
elif [ ${MODE} = "whole_infer" ];then
|
||||
rm -rf ./train_data/icdar2015
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
|
||||
cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
|
||||
ln -s ./icdar2015_infer ./icdar2015
|
||||
cd ../
|
||||
epoch=10
|
||||
eval_batch_step=10
|
||||
else
|
||||
rm -rf ./train_data/icdar2015
|
||||
wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
|
||||
if [ ${model_name} = "ocr_det" ]; then
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
else
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
fi
|
||||
fi
|
||||
|
||||
|
||||
IFS='|'
|
||||
for train_model in ${train_model_list[*]}; do
|
||||
if [ ${train_model} = "ocr_det" ];then
|
||||
model_name="ocr_det"
|
||||
yml_file="configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
|
||||
cd ./inference && tar xf ch_det_data_50.tar && cd ../
|
||||
img_dir="./inference/ch_det_data_50/all-sum-510"
|
||||
data_dir=./inference/ch_det_data_50/
|
||||
data_label_file=[./inference/ch_det_data_50/test_gt_50.txt]
|
||||
elif [ ${train_model} = "ocr_rec" ];then
|
||||
model_name="ocr_rec"
|
||||
yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
|
||||
cd ./inference && tar xf rec_inference.tar && cd ../
|
||||
img_dir="./inference/rec_inference/"
|
||||
data_dir=./inference/rec_inference
|
||||
data_label_file=[./inference/rec_inference/rec_gt_test.txt]
|
||||
fi
|
||||
|
||||
# eval
|
||||
for slim_trainer in ${trainer_list[*]}; do
|
||||
if [ ${slim_trainer} = "norm" ]; then
|
||||
if [ ${model_name} = "det" ]; then
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_det_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
else
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
fi
|
||||
elif [ ${slim_trainer} = "pact" ]; then
|
||||
if [ ${model_name} = "det" ]; then
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_det_quant_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
else
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_rec_quant_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_quant_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
fi
|
||||
elif [ ${slim_trainer} = "distill" ]; then
|
||||
if [ ${model_name} = "det" ]; then
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_det_distill_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
else
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_rec_distill_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_distill_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
fi
|
||||
elif [ ${slim_trainer} = "fpgm" ]; then
|
||||
if [ ${model_name} = "det" ]; then
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
else
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_rec_prune_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_prune_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
fi
|
||||
fi
|
||||
done
|
||||
done
|
||||
237
test/test.sh
237
test/test.sh
|
|
@ -1,237 +0,0 @@
|
|||
#!/bin/bash
|
||||
FILENAME=$1
|
||||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
|
||||
MODE=$2
|
||||
|
||||
dataline=$(cat ${FILENAME})
|
||||
|
||||
# parser params
|
||||
IFS=$'\n'
|
||||
lines=(${dataline})
|
||||
function func_parser_key(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
tmp=${array[0]}
|
||||
echo ${tmp}
|
||||
}
|
||||
function func_parser_value(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
tmp=${array[1]}
|
||||
echo ${tmp}
|
||||
}
|
||||
function status_check(){
|
||||
last_status=$1 # the exit code
|
||||
run_command=$2
|
||||
run_log=$3
|
||||
if [ $last_status -eq 0 ]; then
|
||||
echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
|
||||
else
|
||||
echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
|
||||
fi
|
||||
}
|
||||
|
||||
IFS=$'\n'
|
||||
# The training params
|
||||
model_name=$(func_parser_value "${lines[0]}")
|
||||
python=$(func_parser_value "${lines[1]}")
|
||||
gpu_list=$(func_parser_value "${lines[2]}")
|
||||
autocast_list=$(func_parser_value "${lines[3]}")
|
||||
autocast_key=$(func_parser_key "${lines[3]}")
|
||||
epoch_key=$(func_parser_key "${lines[4]}")
|
||||
epoch_num=$(func_parser_value "${lines[4]}")
|
||||
save_model_key=$(func_parser_key "${lines[5]}")
|
||||
train_batch_key=$(func_parser_key "${lines[6]}")
|
||||
train_use_gpu_key=$(func_parser_key "${lines[7]}")
|
||||
pretrain_model_key=$(func_parser_key "${lines[8]}")
|
||||
pretrain_model_value=$(func_parser_value "${lines[8]}")
|
||||
|
||||
trainer_list=$(func_parser_value "${lines[9]}")
|
||||
norm_trainer=$(func_parser_value "${lines[10]}")
|
||||
pact_trainer=$(func_parser_value "${lines[11]}")
|
||||
fpgm_trainer=$(func_parser_value "${lines[12]}")
|
||||
distill_trainer=$(func_parser_value "${lines[13]}")
|
||||
|
||||
eval_py=$(func_parser_value "${lines[14]}")
|
||||
|
||||
save_infer_key=$(func_parser_key "${lines[15]}")
|
||||
export_weight=$(func_parser_key "${lines[16]}")
|
||||
norm_export=$(func_parser_value "${lines[17]}")
|
||||
pact_export=$(func_parser_value "${lines[18]}")
|
||||
fpgm_export=$(func_parser_value "${lines[19]}")
|
||||
distill_export=$(func_parser_value "${lines[20]}")
|
||||
|
||||
inference_py=$(func_parser_value "${lines[21]}")
|
||||
use_gpu_key=$(func_parser_key "${lines[22]}")
|
||||
use_gpu_list=$(func_parser_value "${lines[22]}")
|
||||
use_mkldnn_key=$(func_parser_key "${lines[23]}")
|
||||
use_mkldnn_list=$(func_parser_value "${lines[23]}")
|
||||
cpu_threads_key=$(func_parser_key "${lines[24]}")
|
||||
cpu_threads_list=$(func_parser_value "${lines[24]}")
|
||||
batch_size_key=$(func_parser_key "${lines[25]}")
|
||||
batch_size_list=$(func_parser_value "${lines[25]}")
|
||||
use_trt_key=$(func_parser_key "${lines[26]}")
|
||||
use_trt_list=$(func_parser_value "${lines[26]}")
|
||||
precision_key=$(func_parser_key "${lines[27]}")
|
||||
precision_list=$(func_parser_value "${lines[27]}")
|
||||
infer_model_key=$(func_parser_key "${lines[28]}")
|
||||
infer_model=$(func_parser_value "${lines[28]}")
|
||||
image_dir_key=$(func_parser_key "${lines[29]}")
|
||||
infer_img_dir=$(func_parser_value "${lines[29]}")
|
||||
save_log_key=$(func_parser_key "${lines[30]}")
|
||||
|
||||
LOG_PATH="./test/output"
|
||||
mkdir -p ${LOG_PATH}
|
||||
status_log="${LOG_PATH}/results.log"
|
||||
|
||||
|
||||
function func_inference(){
|
||||
IFS='|'
|
||||
_python=$1
|
||||
_script=$2
|
||||
_model_dir=$3
|
||||
_log_path=$4
|
||||
_img_dir=$5
|
||||
|
||||
# inference
|
||||
for use_gpu in ${use_gpu_list[*]}; do
|
||||
if [ ${use_gpu} = "False" ]; then
|
||||
for use_mkldnn in ${use_mkldnn_list[*]}; do
|
||||
for threads in ${cpu_threads_list[*]}; do
|
||||
for batch_size in ${batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
|
||||
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
|
||||
eval $command
|
||||
status_check $? "${command}" "${status_log}"
|
||||
done
|
||||
done
|
||||
done
|
||||
else
|
||||
for use_trt in ${use_trt_list[*]}; do
|
||||
for precision in ${precision_list[*]}; do
|
||||
if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
|
||||
continue
|
||||
fi
|
||||
for batch_size in ${batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
|
||||
eval $command
|
||||
status_check $? "${command}" "${status_log}"
|
||||
done
|
||||
done
|
||||
done
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
if [ ${MODE} != "infer" ]; then
|
||||
|
||||
IFS="|"
|
||||
for gpu in ${gpu_list[*]}; do
|
||||
use_gpu=True
|
||||
if [ ${gpu} = "-1" ];then
|
||||
use_gpu=False
|
||||
env=""
|
||||
elif [ ${#gpu} -le 1 ];then
|
||||
env="export CUDA_VISIBLE_DEVICES=${gpu}"
|
||||
eval ${env}
|
||||
elif [ ${#gpu} -le 15 ];then
|
||||
IFS=","
|
||||
array=(${gpu})
|
||||
env="export CUDA_VISIBLE_DEVICES=${array[0]}"
|
||||
IFS="|"
|
||||
else
|
||||
IFS=";"
|
||||
array=(${gpu})
|
||||
ips=${array[0]}
|
||||
gpu=${array[1]}
|
||||
IFS="|"
|
||||
env=" "
|
||||
fi
|
||||
for autocast in ${autocast_list[*]}; do
|
||||
for trainer in ${trainer_list[*]}; do
|
||||
if [ ${trainer} = "pact" ]; then
|
||||
run_train=${pact_trainer}
|
||||
run_export=${pact_export}
|
||||
elif [ ${trainer} = "fpgm" ]; then
|
||||
run_train=${fpgm_trainer}
|
||||
run_export=${fpgm_export}
|
||||
elif [ ${trainer} = "distill" ]; then
|
||||
run_train=${distill_trainer}
|
||||
run_export=${distill_export}
|
||||
else
|
||||
run_train=${norm_trainer}
|
||||
run_export=${norm_export}
|
||||
fi
|
||||
|
||||
if [ ${run_train} = "null" ]; then
|
||||
continue
|
||||
fi
|
||||
if [ ${run_export} = "null" ]; then
|
||||
continue
|
||||
fi
|
||||
|
||||
# not set autocast when autocast is null
|
||||
if [ ${autocast} = "null" ]; then
|
||||
set_autocast=" "
|
||||
else
|
||||
set_autocast="${autocast_key}=${autocast}"
|
||||
fi
|
||||
# not set epoch when whole_train_infer
|
||||
if [ ${MODE} != "whole_train_infer" ]; then
|
||||
set_epoch="${epoch_key}=${epoch_num}"
|
||||
else
|
||||
set_epoch=" "
|
||||
fi
|
||||
# set pretrain
|
||||
if [ ${pretrain_model_value} != "null" ]; then
|
||||
set_pretrain="${pretrain_model_key}=${pretrain_model_value}"
|
||||
else
|
||||
set_pretrain=" "
|
||||
fi
|
||||
|
||||
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
|
||||
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
|
||||
cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
|
||||
elif [ ${#gpu} -le 15 ];then # train with multi-gpu
|
||||
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
|
||||
else # train with multi-machine
|
||||
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_pretrain} ${set_epoch} ${set_autocast}"
|
||||
fi
|
||||
# run train
|
||||
eval $cmd
|
||||
status_check $? "${cmd}" "${status_log}"
|
||||
|
||||
# run eval
|
||||
eval_cmd="${python} ${eval_py} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/latest"
|
||||
eval $eval_cmd
|
||||
status_check $? "${eval_cmd}" "${status_log}"
|
||||
|
||||
# run export model
|
||||
save_infer_path="${save_log}"
|
||||
export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${export_weight}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
|
||||
eval $export_cmd
|
||||
status_check $? "${export_cmd}" "${status_log}"
|
||||
|
||||
#run inference
|
||||
eval $env
|
||||
save_infer_path="${save_log}"
|
||||
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
|
||||
eval "unset CUDA_VISIBLE_DEVICES"
|
||||
done
|
||||
done
|
||||
done
|
||||
|
||||
else
|
||||
GPUID=$3
|
||||
if [ ${#GPUID} -le 0 ];then
|
||||
env=" "
|
||||
else
|
||||
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
|
||||
fi
|
||||
echo $env
|
||||
#run inference
|
||||
func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}"
|
||||
fi
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
===========================train_params===========================
|
||||
model_name:ocr_det
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:null
|
||||
Global.epoch_num:lite_train_infer=2|whole_train_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train|pact_train
|
||||
norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
|
||||
quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
|
||||
fpgm_export:deploy/slim/prune/export_prune_model.py
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
##
|
||||
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_det.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|fp16|int8
|
||||
--det_model_dir:
|
||||
--image_dir:./inference/ch_det_data_50/all-sum-510/
|
||||
--save_log_path:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
#!/bin/bash
|
||||
FILENAME=$1
|
||||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
|
||||
MODE=$2
|
||||
|
||||
dataline=$(cat ${FILENAME})
|
||||
|
||||
# parser params
|
||||
IFS=$'\n'
|
||||
lines=(${dataline})
|
||||
function func_parser_key(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
tmp=${array[0]}
|
||||
echo ${tmp}
|
||||
}
|
||||
function func_parser_value(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
tmp=${array[1]}
|
||||
echo ${tmp}
|
||||
}
|
||||
IFS=$'\n'
|
||||
# The training params
|
||||
model_name=$(func_parser_value "${lines[1]}")
|
||||
|
||||
trainer_list=$(func_parser_value "${lines[14]}")
|
||||
|
||||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
|
||||
MODE=$2
|
||||
|
||||
if [ ${MODE} = "lite_train_infer" ];then
|
||||
# pretrain lite train data
|
||||
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
|
||||
rm -rf ./train_data/icdar2015
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
|
||||
cd ./train_data/ && tar xf icdar2015_lite.tar
|
||||
ln -s ./icdar2015_lite ./icdar2015
|
||||
cd ../
|
||||
elif [ ${MODE} = "whole_train_infer" ];then
|
||||
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
|
||||
rm -rf ./train_data/icdar2015
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
|
||||
cd ./train_data/ && tar xf icdar2015.tar && cd ../
|
||||
elif [ ${MODE} = "whole_infer" ];then
|
||||
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
|
||||
rm -rf ./train_data/icdar2015
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
|
||||
cd ./train_data/ && tar xf icdar2015_infer.tar
|
||||
ln -s ./icdar2015_infer ./icdar2015
|
||||
cd ../
|
||||
else
|
||||
rm -rf ./train_data/icdar2015
|
||||
if [[ ${model_name} = "ocr_det" ]]; then
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
|
||||
else
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
|
||||
fi
|
||||
fi
|
||||
|
|
@ -0,0 +1,362 @@
|
|||
#!/bin/bash
|
||||
FILENAME=$1
|
||||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
|
||||
MODE=$2
|
||||
|
||||
dataline=$(cat ${FILENAME})
|
||||
|
||||
# parser params
|
||||
IFS=$'\n'
|
||||
lines=(${dataline})
|
||||
|
||||
function func_parser_key(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
tmp=${array[0]}
|
||||
echo ${tmp}
|
||||
}
|
||||
function func_parser_value(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
tmp=${array[1]}
|
||||
echo ${tmp}
|
||||
}
|
||||
function func_set_params(){
|
||||
key=$1
|
||||
value=$2
|
||||
if [ ${key} = "null" ];then
|
||||
echo " "
|
||||
elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
|
||||
echo " "
|
||||
else
|
||||
echo "${key}=${value}"
|
||||
fi
|
||||
}
|
||||
function func_parser_params(){
|
||||
strs=$1
|
||||
IFS=":"
|
||||
array=(${strs})
|
||||
key=${array[0]}
|
||||
tmp=${array[1]}
|
||||
IFS="|"
|
||||
res=""
|
||||
for _params in ${tmp[*]}; do
|
||||
IFS="="
|
||||
array=(${_params})
|
||||
mode=${array[0]}
|
||||
value=${array[1]}
|
||||
if [[ ${mode} = ${MODE} ]]; then
|
||||
IFS="|"
|
||||
#echo $(func_set_params "${mode}" "${value}")
|
||||
echo $value
|
||||
break
|
||||
fi
|
||||
IFS="|"
|
||||
done
|
||||
echo ${res}
|
||||
}
|
||||
function status_check(){
|
||||
last_status=$1 # the exit code
|
||||
run_command=$2
|
||||
run_log=$3
|
||||
if [ $last_status -eq 0 ]; then
|
||||
echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
|
||||
else
|
||||
echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
|
||||
fi
|
||||
}
|
||||
|
||||
IFS=$'\n'
|
||||
# The training params
|
||||
model_name=$(func_parser_value "${lines[1]}")
|
||||
python=$(func_parser_value "${lines[2]}")
|
||||
gpu_list=$(func_parser_value "${lines[3]}")
|
||||
train_use_gpu_key=$(func_parser_key "${lines[4]}")
|
||||
train_use_gpu_value=$(func_parser_value "${lines[4]}")
|
||||
autocast_list=$(func_parser_value "${lines[5]}")
|
||||
autocast_key=$(func_parser_key "${lines[5]}")
|
||||
epoch_key=$(func_parser_key "${lines[6]}")
|
||||
epoch_num=$(func_parser_params "${lines[6]}")
|
||||
save_model_key=$(func_parser_key "${lines[7]}")
|
||||
train_batch_key=$(func_parser_key "${lines[8]}")
|
||||
train_batch_value=$(func_parser_params "${lines[8]}")
|
||||
pretrain_model_key=$(func_parser_key "${lines[9]}")
|
||||
pretrain_model_value=$(func_parser_value "${lines[9]}")
|
||||
train_model_name=$(func_parser_value "${lines[10]}")
|
||||
train_infer_img_dir=$(func_parser_value "${lines[11]}")
|
||||
train_param_key1=$(func_parser_key "${lines[12]}")
|
||||
train_param_value1=$(func_parser_value "${lines[12]}")
|
||||
|
||||
trainer_list=$(func_parser_value "${lines[14]}")
|
||||
trainer_norm=$(func_parser_key "${lines[15]}")
|
||||
norm_trainer=$(func_parser_value "${lines[15]}")
|
||||
pact_key=$(func_parser_key "${lines[16]}")
|
||||
pact_trainer=$(func_parser_value "${lines[16]}")
|
||||
fpgm_key=$(func_parser_key "${lines[17]}")
|
||||
fpgm_trainer=$(func_parser_value "${lines[17]}")
|
||||
distill_key=$(func_parser_key "${lines[18]}")
|
||||
distill_trainer=$(func_parser_value "${lines[18]}")
|
||||
trainer_key1=$(func_parser_key "${lines[19]}")
|
||||
trainer_value1=$(func_parser_value "${lines[19]}")
|
||||
trainer_key2=$(func_parser_key "${lines[20]}")
|
||||
trainer_value2=$(func_parser_value "${lines[20]}")
|
||||
|
||||
eval_py=$(func_parser_value "${lines[23]}")
|
||||
eval_key1=$(func_parser_key "${lines[24]}")
|
||||
eval_value1=$(func_parser_value "${lines[24]}")
|
||||
|
||||
save_infer_key=$(func_parser_key "${lines[27]}")
|
||||
export_weight=$(func_parser_key "${lines[28]}")
|
||||
norm_export=$(func_parser_value "${lines[29]}")
|
||||
pact_export=$(func_parser_value "${lines[30]}")
|
||||
fpgm_export=$(func_parser_value "${lines[31]}")
|
||||
distill_export=$(func_parser_value "${lines[32]}")
|
||||
export_key1=$(func_parser_key "${lines[33]}")
|
||||
export_value1=$(func_parser_value "${lines[33]}")
|
||||
export_key2=$(func_parser_key "${lines[34]}")
|
||||
export_value2=$(func_parser_value "${lines[34]}")
|
||||
|
||||
# parser inference model
|
||||
infer_model_dir_list=$(func_parser_value "${lines[36]}")
|
||||
infer_export_list=$(func_parser_value "${lines[37]}")
|
||||
infer_is_quant=$(func_parser_value "${lines[38]}")
|
||||
# parser inference
|
||||
inference_py=$(func_parser_value "${lines[39]}")
|
||||
use_gpu_key=$(func_parser_key "${lines[40]}")
|
||||
use_gpu_list=$(func_parser_value "${lines[40]}")
|
||||
use_mkldnn_key=$(func_parser_key "${lines[41]}")
|
||||
use_mkldnn_list=$(func_parser_value "${lines[41]}")
|
||||
cpu_threads_key=$(func_parser_key "${lines[42]}")
|
||||
cpu_threads_list=$(func_parser_value "${lines[42]}")
|
||||
batch_size_key=$(func_parser_key "${lines[43]}")
|
||||
batch_size_list=$(func_parser_value "${lines[43]}")
|
||||
use_trt_key=$(func_parser_key "${lines[44]}")
|
||||
use_trt_list=$(func_parser_value "${lines[44]}")
|
||||
precision_key=$(func_parser_key "${lines[45]}")
|
||||
precision_list=$(func_parser_value "${lines[45]}")
|
||||
infer_model_key=$(func_parser_key "${lines[46]}")
|
||||
image_dir_key=$(func_parser_key "${lines[47]}")
|
||||
infer_img_dir=$(func_parser_value "${lines[47]}")
|
||||
save_log_key=$(func_parser_key "${lines[48]}")
|
||||
benchmark_key=$(func_parser_key "${lines[49]}")
|
||||
benchmark_value=$(func_parser_value "${lines[49]}")
|
||||
infer_key1=$(func_parser_key "${lines[50]}")
|
||||
infer_value1=$(func_parser_value "${lines[50]}")
|
||||
|
||||
LOG_PATH="./tests/output"
|
||||
mkdir -p ${LOG_PATH}
|
||||
status_log="${LOG_PATH}/results.log"
|
||||
|
||||
|
||||
function func_inference(){
|
||||
IFS='|'
|
||||
_python=$1
|
||||
_script=$2
|
||||
_model_dir=$3
|
||||
_log_path=$4
|
||||
_img_dir=$5
|
||||
_flag_quant=$6
|
||||
# inference
|
||||
for use_gpu in ${use_gpu_list[*]}; do
|
||||
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
|
||||
for use_mkldnn in ${use_mkldnn_list[*]}; do
|
||||
if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
|
||||
continue
|
||||
fi
|
||||
for threads in ${cpu_threads_list[*]}; do
|
||||
for batch_size in ${batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
|
||||
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
|
||||
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
|
||||
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
|
||||
set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
|
||||
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
|
||||
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
|
||||
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
eval "cat ${_save_log_path}"
|
||||
status_check $last_status "${command}" "${status_log}"
|
||||
done
|
||||
done
|
||||
done
|
||||
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
|
||||
for use_trt in ${use_trt_list[*]}; do
|
||||
for precision in ${precision_list[*]}; do
|
||||
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
|
||||
continue
|
||||
fi
|
||||
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
|
||||
continue
|
||||
fi
|
||||
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
|
||||
continue
|
||||
fi
|
||||
for batch_size in ${batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
|
||||
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
|
||||
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
|
||||
set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
|
||||
set_precision=$(func_set_params "${precision_key}" "${precision}")
|
||||
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
|
||||
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
|
||||
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
eval "cat ${_save_log_path}"
|
||||
status_check $last_status "${command}" "${status_log}"
|
||||
|
||||
done
|
||||
done
|
||||
done
|
||||
else
|
||||
echo "Does not support hardware other than CPU and GPU Currently!"
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
if [ ${MODE} = "infer" ]; then
|
||||
GPUID=$3
|
||||
if [ ${#GPUID} -le 0 ];then
|
||||
env=" "
|
||||
else
|
||||
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
|
||||
fi
|
||||
# set CUDA_VISIBLE_DEVICES
|
||||
eval $env
|
||||
export Count=0
|
||||
IFS="|"
|
||||
infer_run_exports=(${infer_export_list})
|
||||
infer_quant_flag=(${infer_is_quant})
|
||||
for infer_model in ${infer_model_dir_list[*]}; do
|
||||
# run export
|
||||
if [ ${infer_run_exports[Count]} != "null" ];then
|
||||
set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
|
||||
set_save_infer_key=$(func_set_params "${save_infer_key}" "${infer_model}")
|
||||
export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}"
|
||||
eval $export_cmd
|
||||
status_export=$?
|
||||
if [ ${status_export} = 0 ];then
|
||||
status_check $status_export "${export_cmd}" "${status_log}"
|
||||
fi
|
||||
fi
|
||||
#run inference
|
||||
is_quant=${infer_quant_flag[Count]}
|
||||
func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
|
||||
Count=$(($Count + 1))
|
||||
done
|
||||
|
||||
else
|
||||
IFS="|"
|
||||
export Count=0
|
||||
USE_GPU_KEY=(${train_use_gpu_value})
|
||||
for gpu in ${gpu_list[*]}; do
|
||||
use_gpu=${USE_GPU_KEY[Count]}
|
||||
Count=$(($Count + 1))
|
||||
if [ ${gpu} = "-1" ];then
|
||||
env=""
|
||||
elif [ ${#gpu} -le 1 ];then
|
||||
env="export CUDA_VISIBLE_DEVICES=${gpu}"
|
||||
eval ${env}
|
||||
elif [ ${#gpu} -le 15 ];then
|
||||
IFS=","
|
||||
array=(${gpu})
|
||||
env="export CUDA_VISIBLE_DEVICES=${array[0]}"
|
||||
IFS="|"
|
||||
else
|
||||
IFS=";"
|
||||
array=(${gpu})
|
||||
ips=${array[0]}
|
||||
gpu=${array[1]}
|
||||
IFS="|"
|
||||
env=" "
|
||||
fi
|
||||
for autocast in ${autocast_list[*]}; do
|
||||
for trainer in ${trainer_list[*]}; do
|
||||
flag_quant=False
|
||||
if [ ${trainer} = ${pact_key} ]; then
|
||||
run_train=${pact_trainer}
|
||||
run_export=${pact_export}
|
||||
flag_quant=True
|
||||
elif [ ${trainer} = "${fpgm_key}" ]; then
|
||||
run_train=${fpgm_trainer}
|
||||
run_export=${fpgm_export}
|
||||
elif [ ${trainer} = "${distill_key}" ]; then
|
||||
run_train=${distill_trainer}
|
||||
run_export=${distill_export}
|
||||
elif [ ${trainer} = ${trainer_key1} ]; then
|
||||
run_train=${trainer_value1}
|
||||
run_export=${export_value1}
|
||||
elif [[ ${trainer} = ${trainer_key2} ]]; then
|
||||
run_train=${trainer_value2}
|
||||
run_export=${export_value2}
|
||||
else
|
||||
run_train=${norm_trainer}
|
||||
run_export=${norm_export}
|
||||
fi
|
||||
|
||||
if [ ${run_train} = "null" ]; then
|
||||
continue
|
||||
fi
|
||||
|
||||
set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
|
||||
set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
|
||||
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
|
||||
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
|
||||
set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
|
||||
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
|
||||
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
|
||||
|
||||
# load pretrain from norm training if current trainer is pact or fpgm trainer
|
||||
if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
|
||||
set_pretrain="${load_norm_train_model}"
|
||||
fi
|
||||
|
||||
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
|
||||
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
|
||||
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
|
||||
elif [ ${#gpu} -le 15 ];then # train with multi-gpu
|
||||
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
|
||||
else # train with multi-machine
|
||||
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
|
||||
fi
|
||||
# run train
|
||||
eval "unset CUDA_VISIBLE_DEVICES"
|
||||
eval $cmd
|
||||
status_check $? "${cmd}" "${status_log}"
|
||||
|
||||
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
|
||||
# save norm trained models to set pretrain for pact training and fpgm training
|
||||
if [ ${trainer} = ${trainer_norm} ]; then
|
||||
load_norm_train_model=${set_eval_pretrain}
|
||||
fi
|
||||
# run eval
|
||||
if [ ${eval_py} != "null" ]; then
|
||||
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
|
||||
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
|
||||
eval $eval_cmd
|
||||
status_check $? "${eval_cmd}" "${status_log}"
|
||||
fi
|
||||
# run export model
|
||||
if [ ${run_export} != "null" ]; then
|
||||
# run export model
|
||||
save_infer_path="${save_log}"
|
||||
set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
|
||||
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
|
||||
export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
|
||||
eval $export_cmd
|
||||
status_check $? "${export_cmd}" "${status_log}"
|
||||
|
||||
#run inference
|
||||
eval $env
|
||||
save_infer_path="${save_log}"
|
||||
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
|
||||
eval "unset CUDA_VISIBLE_DEVICES"
|
||||
fi
|
||||
done # done with: for trainer in ${trainer_list[*]}; do
|
||||
done # done with: for autocast in ${autocast_list[*]}; do
|
||||
done # done with: for gpu in ${gpu_list[*]}; do
|
||||
fi # end if [ ${MODE} = "infer" ]; then
|
||||
|
|
@ -106,7 +106,7 @@ class TextDetector(object):
|
|||
model_precision=args.precision,
|
||||
batch_size=1,
|
||||
data_shape="dynamic",
|
||||
save_path=args.save_log_path,
|
||||
save_path=None,
|
||||
inference_config=self.config,
|
||||
pids=pid,
|
||||
process_name=None,
|
||||
|
|
@ -114,7 +114,8 @@ class TextDetector(object):
|
|||
time_keys=[
|
||||
'preprocess_time', 'inference_time', 'postprocess_time'
|
||||
],
|
||||
warmup=10)
|
||||
warmup=2,
|
||||
logger=logger)
|
||||
|
||||
def order_points_clockwise(self, pts):
|
||||
"""
|
||||
|
|
@ -236,7 +237,7 @@ if __name__ == "__main__":
|
|||
|
||||
if args.warmup:
|
||||
img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
|
||||
for i in range(10):
|
||||
for i in range(2):
|
||||
res = text_detector(img)
|
||||
|
||||
if not os.path.exists(draw_img_save):
|
||||
|
|
|
|||
|
|
@ -73,7 +73,7 @@ class TextRecognizer(object):
|
|||
model_precision=args.precision,
|
||||
batch_size=args.rec_batch_num,
|
||||
data_shape="dynamic",
|
||||
save_path=args.save_log_path,
|
||||
save_path=None, #args.save_log_path,
|
||||
inference_config=self.config,
|
||||
pids=pid,
|
||||
process_name=None,
|
||||
|
|
@ -81,7 +81,8 @@ class TextRecognizer(object):
|
|||
time_keys=[
|
||||
'preprocess_time', 'inference_time', 'postprocess_time'
|
||||
],
|
||||
warmup=10)
|
||||
warmup=2,
|
||||
logger=logger)
|
||||
|
||||
def resize_norm_img(self, img, max_wh_ratio):
|
||||
imgC, imgH, imgW = self.rec_image_shape
|
||||
|
|
@ -272,10 +273,10 @@ def main(args):
|
|||
valid_image_file_list = []
|
||||
img_list = []
|
||||
|
||||
# warmup 10 times
|
||||
# warmup 2 times
|
||||
if args.warmup:
|
||||
img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8)
|
||||
for i in range(10):
|
||||
for i in range(2):
|
||||
res = text_recognizer([img])
|
||||
|
||||
for image_file in image_file_list:
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ from paddle import inference
|
|||
import time
|
||||
from ppocr.utils.logging import get_logger
|
||||
|
||||
|
||||
def str2bool(v):
|
||||
return v.lower() in ("true", "t", "1")
|
||||
|
||||
|
|
@ -47,8 +48,8 @@ def init_args():
|
|||
|
||||
# DB parmas
|
||||
parser.add_argument("--det_db_thresh", type=float, default=0.3)
|
||||
parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
|
||||
parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
|
||||
parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
|
||||
parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
|
||||
parser.add_argument("--max_batch_size", type=int, default=10)
|
||||
parser.add_argument("--use_dilation", type=bool, default=False)
|
||||
parser.add_argument("--det_db_score_mode", type=str, default="fast")
|
||||
|
|
@ -168,46 +169,67 @@ def create_predictor(args, mode, logger):
|
|||
if mode == "det":
|
||||
min_input_shape = {
|
||||
"x": [1, 3, 50, 50],
|
||||
"conv2d_92.tmp_0": [1, 96, 20, 20],
|
||||
"conv2d_91.tmp_0": [1, 96, 10, 10],
|
||||
"conv2d_92.tmp_0": [1, 120, 20, 20],
|
||||
"conv2d_91.tmp_0": [1, 24, 10, 10],
|
||||
"conv2d_59.tmp_0": [1, 96, 20, 20],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
|
||||
"conv2d_124.tmp_0": [1, 96, 20, 20],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
|
||||
"conv2d_124.tmp_0": [1, 256, 20, 20],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
|
||||
"elementwise_add_7": [1, 56, 2, 2],
|
||||
"nearest_interp_v2_0.tmp_0": [1, 96, 2, 2]
|
||||
"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
|
||||
}
|
||||
max_input_shape = {
|
||||
"x": [1, 3, 2000, 2000],
|
||||
"conv2d_92.tmp_0": [1, 96, 400, 400],
|
||||
"conv2d_91.tmp_0": [1, 96, 200, 200],
|
||||
"conv2d_92.tmp_0": [1, 120, 400, 400],
|
||||
"conv2d_91.tmp_0": [1, 24, 200, 200],
|
||||
"conv2d_59.tmp_0": [1, 96, 400, 400],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
|
||||
"conv2d_124.tmp_0": [1, 256, 400, 400],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 24, 400, 400],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
|
||||
"elementwise_add_7": [1, 56, 400, 400],
|
||||
"nearest_interp_v2_0.tmp_0": [1, 96, 400, 400]
|
||||
"nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
|
||||
}
|
||||
opt_input_shape = {
|
||||
"x": [1, 3, 640, 640],
|
||||
"conv2d_92.tmp_0": [1, 96, 160, 160],
|
||||
"conv2d_91.tmp_0": [1, 96, 80, 80],
|
||||
"conv2d_92.tmp_0": [1, 120, 160, 160],
|
||||
"conv2d_91.tmp_0": [1, 24, 80, 80],
|
||||
"conv2d_59.tmp_0": [1, 96, 160, 160],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
|
||||
"conv2d_124.tmp_0": [1, 256, 160, 160],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
|
||||
"elementwise_add_7": [1, 56, 40, 40],
|
||||
"nearest_interp_v2_0.tmp_0": [1, 96, 40, 40]
|
||||
"nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
|
||||
}
|
||||
min_pact_shape = {
|
||||
"nearest_interp_v2_26.tmp_0":[1,256,20,20],
|
||||
"nearest_interp_v2_27.tmp_0":[1,64,20,20],
|
||||
"nearest_interp_v2_28.tmp_0":[1,64,20,20],
|
||||
"nearest_interp_v2_29.tmp_0":[1,64,20,20]
|
||||
}
|
||||
max_pact_shape = {
|
||||
"nearest_interp_v2_26.tmp_0":[1,256,400,400],
|
||||
"nearest_interp_v2_27.tmp_0":[1,64,400,400],
|
||||
"nearest_interp_v2_28.tmp_0":[1,64,400,400],
|
||||
"nearest_interp_v2_29.tmp_0":[1,64,400,400]
|
||||
}
|
||||
opt_pact_shape = {
|
||||
"nearest_interp_v2_26.tmp_0":[1,256,160,160],
|
||||
"nearest_interp_v2_27.tmp_0":[1,64,160,160],
|
||||
"nearest_interp_v2_28.tmp_0":[1,64,160,160],
|
||||
"nearest_interp_v2_29.tmp_0":[1,64,160,160]
|
||||
}
|
||||
min_input_shape.update(min_pact_shape)
|
||||
max_input_shape.update(max_pact_shape)
|
||||
opt_input_shape.update(opt_pact_shape)
|
||||
elif mode == "rec":
|
||||
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
|
||||
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
|
||||
|
|
|
|||
Loading…
Reference in New Issue