chore: Optim code logic and update README

README.md

@@ -13,17 +13,17 @@
 </div>
 
 ### 简介
-主要是做文档类图像的版面分析。具体来说，就是分析给定的文档类别图像（论文截图等），定位其中类别和位置，如标题、段落、表格和图片等各个部分。
+主要是做文档类图像的版面分析。具体来说，就是分析给定的文档类别图像（论文截图、研报等），定位其中类别和位置，如标题、段落、表格和图片等各个部分。
 
 目前支持三种类别的版面分析模型：中文、英文和表格版面分析模型，具体可参见下面表格：
 
 |`model_type`| 版面类型 |        模型名称          |  支持类别|
 | :------ | :----- | :------ | :----- |
-|`pp_layout_table`|   表格   |   `layout_table.onnx`     |`table` |
-| `pp_layout_publaynet`|   英文   | `layout_publaynet.onnx`   |`text title list table figure` |
-| `pp_layout_table`|   中文   |   `layout_cdla.onnx`    | `text title figure  figure_caption table table_caption` <br> `header footer reference equation` |
-| `yolov8n_layout_paper`|   论文   |   `yolov8n_layout_paper.onnx`    | `text title figure figure_caption table table_caption` <br> `header footer reference equation` |
-| `yolov8n_layout_report`|   研报   |   `yolov8n_layout_report.onnx`    | `text title header footer figure figure_caption table table_caption` <br> `toc` |
+|`pp_layout_table`|   表格   |   `layout_table.onnx`     |`["table"]` |
+| `pp_layout_publaynet`|   英文   | `layout_publaynet.onnx`   |`["text", "title", "list", "table", "figure"]` |
+| `pp_layout_table`|   中文   |   `layout_cdla.onnx`    | `['text', 'title', 'figure', 'figure_caption', 'table', 'table_caption', 'header', 'footer', 'reference', 'equation']` |
+| `yolov8n_layout_paper`|   论文   |   `yolov8n_layout_paper.onnx`    | `['text', 'title', 'figure', 'figure_caption', 'table', 'table_caption', 'header', 'footer', 'reference', 'equation']` |
+| `yolov8n_layout_report`|   研报   |   `yolov8n_layout_report.onnx`    | `['text', 'title', 'header', 'footer', 'figure', 'figure_caption', 'table', 'table_caption', 'toc']` |
 
 PP模型来源：[PaddleOCR 版面分析](https://github.com/PaddlePaddle/PaddleOCR/blob/133d67f27dc8a241d6b2e30a9f047a0fb75bebbe/ppstructure/layout/README_ch.md)
 

docs/doc_whl_rapid_layout.md

@@ -1 +1 @@
-See [link](https://github.com/RapidAI/RapidStructure) for details.
+See [link](https://github.com/RapidAI/RapidLayout) for details.

rapid_layout/main.py

@@ -13,7 +13,7 @@
     DownloadModel,
     LoadImage,
     OrtInferSession,
-    PicoDetPostProcess,
+    PPPostProcess,
     PPPreProcess,
     VisLayout,
     YOLOv8PostProcess,
@@ -36,32 +36,39 @@
 
 
 class RapidLayout:
-
     def __init__(
         self,
         model_type: str = "pp_layout_cdla",
         model_path: Union[str, Path, None] = None,
         conf_thres: float = 0.5,
         iou_thres: float = 0.5,
         use_cuda: bool = False,
+        use_dml: bool = False,
     ):
+        if not self.check_of(conf_thres):
+            raise ValueError(f"conf_thres {conf_thres} is outside of range [0, 1]")
+
+        if not self.check_of(iou_thres):
+            raise ValueError(f"iou_thres {conf_thres} is outside of range [0, 1]")
+
         self.model_type = model_type
         config = {
             "model_path": self.get_model_path(model_type, model_path),
             "use_cuda": use_cuda,
+            "use_dml": use_dml,
         }
         self.session = OrtInferSession(config)
         labels = self.session.get_character_list()
         logger.info("%s contains %s", model_type, labels)
 
         # pp
         self.pp_preprocess = PPPreProcess(img_size=(800, 608))
-        self.pp_postprocess = PicoDetPostProcess(labels, conf_thres, iou_thres)
+        self.pp_postprocess = PPPostProcess(labels, conf_thres, iou_thres)
 
         # yolov8
         self.yolov8_input_shape = (640, 640)
-        self.yolo_preprocess = YOLOv8PreProcess(img_size=self.yolov8_input_shape)
-        self.yolo_postprocess = YOLOv8PostProcess(labels, conf_thres, iou_thres)
+        self.yolov8_preprocess = YOLOv8PreProcess(img_size=self.yolov8_input_shape)
+        self.yolov8_postprocess = YOLOv8PostProcess(labels, conf_thres, iou_thres)
 
         self.load_img = LoadImage()
 
@@ -97,9 +104,9 @@ def pp_layout(self, img: np.ndarray, ori_img_shape: Tuple[int, int]):
         return boxes, scores, class_names, elapse
 
     def yolov8_layout(self, img: np.ndarray, ori_img_shape: Tuple[int, int]):
-        input_tensor = self.yolo_preprocess(img)
+        input_tensor = self.yolov8_preprocess(img)
         outputs = self.session(input_tensor)
-        boxes, scores, class_names = self.yolo_postprocess(
+        boxes, scores, class_names = self.yolov8_postprocess(
             outputs, ori_img_shape, self.yolov8_input_shape
         )
         return boxes, scores, class_names
@@ -117,6 +124,12 @@ def get_model_path(model_type: str, model_path: Union[str, Path, None]) -> str:
         logger.info("model url is None, using the default model %s", DEFAULT_MODEL_PATH)
         return DEFAULT_MODEL_PATH
 
+    @staticmethod
+    def check_of(thres: float) -> bool:
+        if 0 <= thres <= 1.0:
+            return True
+        return False
+
 
 def main():
     parser = argparse.ArgumentParser()

rapid_layout/utils/__init__.py

@@ -1,18 +1,10 @@
 # -*- encoding: utf-8 -*-
 # @Author: SWHL
 # @Contact: [email protected]
-import yaml
-
 from .download_model import DownloadModel
 from .infer_engine import OrtInferSession
-from .load_image import LoadImage
+from .load_image import LoadImage, LoadImageError
 from .logger import get_logger
-from .post_prepross import PicoDetPostProcess, YOLOv8PostProcess
+from .post_prepross import PPPostProcess, YOLOv8PostProcess
 from .pre_procss import PPPreProcess, YOLOv8PreProcess
 from .vis_res import VisLayout
-
-
-def read_yaml(yaml_path):
-    with open(yaml_path, "rb") as f:
-        data = yaml.load(f, Loader=yaml.Loader)
-    return data

rapid_layout/utils/post_prepross.py

@@ -6,7 +6,7 @@
 import numpy as np
 
 
-class PicoDetPostProcess:
+class PPPostProcess:
     def __init__(self, labels, conf_thres=0.4, iou_thres=0.5):
         self.labels = labels
         self.strides = [8, 16, 32, 64]
@@ -247,7 +247,6 @@ def area_of(left_top, right_bottom):
 
 
 class YOLOv8PostProcess:
-
     def __init__(self, labels: List[str], conf_thres=0.7, iou_thres=0.5):
         self.labels = labels
         self.conf_threshold = conf_thres
@@ -297,7 +296,6 @@ def extract_boxes(self, predictions):
         return boxes
 
     def rescale_boxes(self, boxes):
-
         # Rescale boxes to original image dimensions
         input_shape = np.array(
             [self.input_width, self.input_height, self.input_width, self.input_height]
@@ -332,7 +330,6 @@ def nms(boxes, scores, iou_threshold):
 
 
 def multiclass_nms(boxes, scores, class_ids, iou_threshold):
-
     unique_class_ids = np.unique(class_ids)
 
     keep_boxes = []

requirements.txt

@@ -1,5 +1,4 @@
 onnxruntime>=1.7.0
-PyYAML>=6.0
 opencv_python>=4.5.1.48
 numpy>=1.21.6,<2
 Pillow

setup.py

@@ -49,7 +49,7 @@ def get_readme():
     include_package_data=True,
     install_requires=read_txt("requirements.txt"),
     packages=[MODULE_NAME, f"{MODULE_NAME}.models", f"{MODULE_NAME}.utils"],
-    package_data={"": ["layout_cdla.onnx", "*.yaml"]},
+    package_data={"": ["layout_cdla.onnx"]},
     keywords=["ppstructure,layout,rapidocr,rapid_layout"],
     classifiers=[
         "Programming Language :: Python :: 3.6",

tests/test_layout.py

@@ -13,17 +13,37 @@
 sys.path.append(str(root_dir))
 
 from rapid_layout import RapidLayout
+from rapid_layout.utils import LoadImageError
 
 test_file_dir = cur_dir / "test_files"
 img_path = test_file_dir / "layout.png"
 
 img = cv2.imread(str(img_path))
 
 
+def test_iou_outside_thres():
+    with pytest.raises(ValueError) as exc:
+        engine = RapidLayout(iou_thres=1.2)
+    assert exc.type is ValueError
+
+
+def test_conf_outside_thres():
+    with pytest.raises(ValueError) as exc:
+        engine = RapidLayout(conf_thres=1.2)
+    assert exc.type is ValueError
+
+
+def test_empty():
+    with pytest.raises(LoadImageError) as exc:
+        engine = RapidLayout()
+        engine(None)
+    assert exc.type is LoadImageError
+
+
 @pytest.mark.parametrize(
     "img_content", [img_path, str(img_path), open(img_path, "rb").read(), img]
 )
-def test_multi_input(img_content):
+def test_pp_layout(img_content):
     engine = RapidLayout()
     boxes, scores, class_names, *elapse = engine(img_content)
     assert len(boxes) == 15
@@ -32,7 +52,7 @@ def test_multi_input(img_content):
 @pytest.mark.parametrize(
     "img_content", [img_path, str(img_path), open(img_path, "rb").read(), img]
 )
-def test_yolov8_input(img_content):
+def test_yolov8_layout(img_content):
     engine = RapidLayout(model_type="yolov8n_layout_paper")
     boxes, scores, class_names, *elapse = engine(img_content)
     assert len(boxes) == 11