add ocr and recognizer demo, update README (#74)

api/apps/conversation_app.py

         conv = {
             "id": get_uuid(),
             "dialog_id": req["dialog_id"],
-            "name": "New conversation",
+            "name": req.get("name", "New conversation"),
             "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
         }
         ConversationService.save(**conv)
 def list_convsersation():
     dialog_id = request.args["dialog_id"]
     try:
-        convs = ConversationService.query(dialog_id=dialog_id)
+        convs = ConversationService.query(dialog_id=dialog_id, order_by=ConversationService.model.create_time, reverse=True)
         convs = [d.to_dict() for d in convs]
         return get_json_result(data=convs)
     except Exception as e:

api/utils/file_utils.py

             pass
 
 
+def traversal_files(base):
+    for root, ds, fs in os.walk(base):
+        for f in fs:
+            fullname = os.path.join(root, f)
+            yield fullname
+
 
 

deepdoc/README.md

 
 With a bunch of documents from various domains with various formats and along with diverse retrieval requirements, 
 an accurate analysis becomes a very challenge task. *Deep*Doc is born for that purpose.
-There 2 parts in *Deep*Doc so far: vision and parser.
+There are 2 parts in *Deep*Doc so far: vision and parser. 
+You can run the flowing test programs if you're interested in our results of OCR, layout recognition and TSR.
+```bash
+python deepdoc/vision/t_ocr.py -h
+usage: t_ocr.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR]
+
+options:
+  -h, --help            show this help message and exit
+  --inputs INPUTS       Directory where to store images or PDFs, or a file path to a single image or PDF
+  --output_dir OUTPUT_DIR
+                        Directory where to store the output images. Default: './ocr_outputs'
+```
+```bash
+python deepdoc/vision/t_recognizer.py -h
+usage: t_recognizer.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR] [--threshold THRESHOLD] [--mode {layout,tsr}]
+
+options:
+  -h, --help            show this help message and exit
+  --inputs INPUTS       Directory where to store images or PDFs, or a file path to a single image or PDF
+  --output_dir OUTPUT_DIR
+                        Directory where to store the output images. Default: './layouts_outputs'
+  --threshold THRESHOLD
+                        A threshold to filter out detections. Default: 0.5
+  --mode {layout,tsr}   Task mode: layout recognition or table structure recognition
+```
+
+Our models are served on HuggingFace. If you have trouble downloading HuggingFace models, this might help!!
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+```
 
 <a name="2"></a>
 ## 2. Vision
 We use vision information to resolve problems as human being.
   - OCR. Since a lot of documents presented as images or at least be able to transform to image, 
     OCR is a very essential and fundamental or even universal solution for text extraction.
-    
+    ```bash
+        python deepdoc/vision/t_ocr.py --inputs=path_to_images_or_pdfs --output_dir=path_to_store_result
+     ```
+    The inputs could be directory to images or PDF, or a image or PDF. 
+    You can look into the folder 'path_to_store_result' where has images which demonstrate the positions of results,
+    txt files which contain the OCR text.
     <div align="center" style="margin-top:20px;margin-bottom:20px;">
-    <img src="https://lh6.googleusercontent.com/2xdiSjaGWkZ71YdORc71Ujf7jCHmO6G-6ONklzGiUYEh3QZpjPo6MQ9eqEFX20am_cdW4Ck0YRraXEetXWnM08kJd99yhik13Cy0_YKUAq2zVGR15LzkovRAmK9iT4o3hcJ8dTpspaJKUwt6R4gN7So" width="300"/>
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/f25bee3d-aaf7-4102-baf5-d5208361d110" width="900"/>
     </div>
 
   - Layout recognition. Documents from different domain may have various layouts, 
       - Footer
       - Reference
       - Equation
+      
+     Have a try on the following command to see the layout detection results.
+     ```bash
+        python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=layout --output_dir=path_to_store_result
+     ```
+    The inputs could be directory to images or PDF, or a image or PDF. 
+    You can look into the folder 'path_to_store_result' where has images which demonstrate the detection results as following:
     <div align="center" style="margin-top:20px;margin-bottom:20px;">
-    <img src="https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/ppstructure/docs/layout/layout.png?raw=true" width="900"/>
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/07e0f625-9b28-43d0-9fbb-5bf586cd286f" width="1000"/>
     </div>
   
-  - Table Structure Recognition(TSR). Data table is a frequently used structure present data including numbers or text.
+  - Table Structure Recognition(TSR). Data table is a frequently used structure to present data including numbers or text.
     And the structure of a table might be very complex, like hierarchy headers, spanning cells and projected row headers.
     Along with TSR, we also reassemble the content into sentences which could be well comprehended by LLM. 
     We have five labels for TSR task:
       - Column header
       - Projected row header
       - Spanning cell
+      
+    Have a try on the following command to see the layout detection results.
+     ```bash
+        python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=tsr --output_dir=path_to_store_result
+     ```
+    The inputs could be directory to images or PDF, or a image or PDF. 
+    You can look into the folder 'path_to_store_result' where has both images and html pages which demonstrate the detection results as following:
     <div align="center" style="margin-top:20px;margin-bottom:20px;">
-    <img src="https://user-images.githubusercontent.com/10793386/139559159-cd23c972-8731-48ed-91df-f3f27e9f4d79.jpg" width="900"/>
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/cb24e81b-f2ba-49f3-ac09-883d75606f4c" width="1000"/>
     </div>
         
 <a name="3"></a>
 with various layouts could be resolved into structured data composed of nearly a hundred of fields.
 We haven't opened the parser yet, as we open the processing method after parsing procedure.
 
-    
+    

deepdoc/vision/__init__.py

+
 from .ocr import OCR
 from .recognizer import Recognizer
 from .layout_recognizer import LayoutRecognizer
 from .table_structure_recognizer import TableStructureRecognizer
+
+def init_in_out(args):
+    from PIL import Image
+    import fitz
+    import os
+    import traceback
+    from api.utils.file_utils import traversal_files
+    images = []
+    outputs = []
+
+    if not os.path.exists(args.output_dir):
+        os.mkdir(args.output_dir)
+
+    def pdf_pages(fnm, zoomin=3):
+        nonlocal outputs, images
+        pdf = fitz.open(fnm)
+        mat = fitz.Matrix(zoomin, zoomin)
+        for i, page in enumerate(pdf):
+            pix = page.get_pixmap(matrix=mat)
+            img = Image.frombytes("RGB", [pix.width, pix.height],
+                                  pix.samples)
+            images.append(img)
+            outputs.append(os.path.split(fnm)[-1] + f"_{i}.jpg")
+
+    def images_and_outputs(fnm):
+        nonlocal outputs, images
+        if fnm.split(".")[-1].lower() == "pdf":
+            pdf_pages(fnm)
+            return
+        try:
+            images.append(Image.open(fnm))
+            outputs.append(os.path.split(fnm)[-1])
+        except Exception as e:
+            traceback.print_exc()
+
+    if os.path.isdir(args.inputs):
+        for fnm in traversal_files(args.inputs):
+            images_and_outputs(fnm)
+    else:
+        images_and_outputs(args.inputs)
+
+    for i in range(len(outputs)): outputs[i] = os.path.join(args.output_dir, outputs[i])
+
+    return images, outputs

deepdoc/vision/layout_recognizer.py

+#  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
 import re
 from collections import Counter
 from copy import deepcopy
-
 import numpy as np
-
 from api.utils.file_utils import get_project_base_directory
-from .recognizer import Recognizer
+from deepdoc.vision import Recognizer
 
 
 class LayoutRecognizer(Recognizer):
-    def __init__(self, domain):
-        self.layout_labels = [
+    labels = [
              "_background_",
              "Text",
              "Title",
              "Reference",
              "Equation",
         ]
-        super().__init__(self.layout_labels, domain,
+    def __init__(self, domain):
+        super().__init__(self.labels, domain,
                          os.path.join(get_project_base_directory(), "rag/res/deepdoc/"))
 
     def __call__(self, image_list, ocr_res, scale_factor=3, thr=0.7, batch_size=16):
             return any([re.search(p, b["text"]) for p in patt])
 
         layouts = super().__call__(image_list, thr, batch_size)
-        # save_results(image_list, layouts, self.layout_labels, output_dir='output/', threshold=0.7)
+        # save_results(image_list, layouts, self.labels, output_dir='output/', threshold=0.7)
         assert len(image_list) == len(ocr_res)
         # Tag layout type
         boxes = []
         ocr_res = [b for b in ocr_res if b["text"].strip() not in garbag_set]
         return ocr_res, page_layout
 
+
+

deepdoc/vision/recognizer.py

 import onnxruntime as ort
 from huggingface_hub import snapshot_download
 
-from . import seeit
 from .operators import *
 from rag.settings import cron_logger
 
 
         """
         if not model_dir:
-            model_dir = snapshot_download(repo_id="InfiniFlow/ocr")
+            model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc")
 
         model_file_path = os.path.join(model_dir, task_name + ".onnx")
         if not os.path.exists(model_file_path):
             self.ort_sess = ort.InferenceSession(model_file_path, providers=['CUDAExecutionProvider'])
         else:
             self.ort_sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider'])
+        self.input_names = [node.name for node in self.ort_sess.get_inputs()]
+        self.output_names = [node.name for node in self.ort_sess.get_outputs()]
+        self.input_shape = self.ort_sess.get_inputs()[0].shape[2:4]
         self.label_list = label_list
 
     @staticmethod
         return max_overlaped_i
 
     def preprocess(self, image_list):
-        preprocess_ops = []
-        for op_info in [
-            {'interp': 2, 'keep_ratio': False, 'target_size': [800, 608], 'type': 'LinearResize'},
-            {'is_scale': True, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'type': 'StandardizeImage'},
-            {'type': 'Permute'},
-            {'stride': 32, 'type': 'PadStride'}
-        ]:
-            new_op_info = op_info.copy()
-            op_type = new_op_info.pop('type')
-            preprocess_ops.append(eval(op_type)(**new_op_info))
-
         inputs = []
-        for im_path in image_list:
-            im, im_info = preprocess(im_path, preprocess_ops)
-            inputs.append({"image": np.array((im,)).astype('float32'), "scale_factor": np.array((im_info["scale_factor"],)).astype('float32')})
+        if "scale_factor" in self.input_names:
+            preprocess_ops = []
+            for op_info in [
+                {'interp': 2, 'keep_ratio': False, 'target_size': [800, 608], 'type': 'LinearResize'},
+                {'is_scale': True, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'type': 'StandardizeImage'},
+                {'type': 'Permute'},
+                {'stride': 32, 'type': 'PadStride'}
+            ]:
+                new_op_info = op_info.copy()
+                op_type = new_op_info.pop('type')
+                preprocess_ops.append(eval(op_type)(**new_op_info))
+
+            for im_path in image_list:
+                im, im_info = preprocess(im_path, preprocess_ops)
+                inputs.append({"image": np.array((im,)).astype('float32'),
+                               "scale_factor": np.array((im_info["scale_factor"],)).astype('float32')})
+        else:
+            hh, ww = self.input_shape
+            for img in image_list:
+                h, w = img.shape[:2]
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+                img = cv2.resize(np.array(img).astype('float32'), (ww, hh))
+                # Scale input pixel values to 0 to 1
+                img /= 255.0
+                img = img.transpose(2, 0, 1)
+                img = img[np.newaxis, :, :, :].astype(np.float32)
+                inputs.append({self.input_names[0]: img, "scale_factor": [w/ww, h/hh]})
         return inputs
 
+    def postprocess(self, boxes, inputs, thr):
+        if "scale_factor" in self.input_names:
+            bb = []
+            for b in boxes:
+                clsid, bbox, score = int(b[0]), b[2:], b[1]
+                if score < thr:
+                    continue
+                if clsid >= len(self.label_list):
+                    cron_logger.warning(f"bad category id")
+                    continue
+                bb.append({
+                    "type": self.label_list[clsid].lower(),
+                    "bbox": [float(t) for t in bbox.tolist()],
+                    "score": float(score)
+                })
+            return bb
+
+        def xywh2xyxy(x):
+            # [x, y, w, h] to [x1, y1, x2, y2]
+            y = np.copy(x)
+            y[:, 0] = x[:, 0] - x[:, 2] / 2
+            y[:, 1] = x[:, 1] - x[:, 3] / 2
+            y[:, 2] = x[:, 0] + x[:, 2] / 2
+            y[:, 3] = x[:, 1] + x[:, 3] / 2
+            return y
+
+        def compute_iou(box, boxes):
+            # Compute xmin, ymin, xmax, ymax for both boxes
+            xmin = np.maximum(box[0], boxes[:, 0])
+            ymin = np.maximum(box[1], boxes[:, 1])
+            xmax = np.minimum(box[2], boxes[:, 2])
+            ymax = np.minimum(box[3], boxes[:, 3])
+
+            # Compute intersection area
+            intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
+
+            # Compute union area
+            box_area = (box[2] - box[0]) * (box[3] - box[1])
+            boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+            union_area = box_area + boxes_area - intersection_area
+
+            # Compute IoU
+            iou = intersection_area / union_area
+
+            return iou
+
+        def iou_filter(boxes, scores, iou_threshold):
+            sorted_indices = np.argsort(scores)[::-1]
+
+            keep_boxes = []
+            while sorted_indices.size > 0:
+                # Pick the last box
+                box_id = sorted_indices[0]
+                keep_boxes.append(box_id)
+
+                # Compute IoU of the picked box with the rest
+                ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
+
+                # Remove boxes with IoU over the threshold
+                keep_indices = np.where(ious < iou_threshold)[0]
+
+                # print(keep_indices.shape, sorted_indices.shape)
+                sorted_indices = sorted_indices[keep_indices + 1]
+
+            return keep_boxes
+
+        boxes = np.squeeze(boxes).T
+        # Filter out object confidence scores below threshold
+        scores = np.max(boxes[:, 4:], axis=1)
+        boxes = boxes[scores > thr, :]
+        scores = scores[scores > thr]
+        if len(boxes) == 0: return []
+
+        # Get the class with the highest confidence
+        class_ids = np.argmax(boxes[:, 4:], axis=1)
+        boxes = boxes[:, :4]
+        input_shape = np.array([inputs["scale_factor"][0], inputs["scale_factor"][1], inputs["scale_factor"][0], inputs["scale_factor"][1]])
+        boxes = np.multiply(boxes, input_shape, dtype=np.float32)
+        boxes = xywh2xyxy(boxes)
+
+        unique_class_ids = np.unique(class_ids)
+        indices = []
+        for class_id in unique_class_ids:
+            class_indices = np.where(class_ids == class_id)[0]
+            class_boxes = boxes[class_indices, :]
+            class_scores = scores[class_indices]
+            class_keep_boxes = iou_filter(class_boxes, class_scores, 0.2)
+            indices.extend(class_indices[class_keep_boxes])
+
+        return [{
+            "type": self.label_list[class_ids[i]].lower(),
+            "bbox": [float(t) for t in boxes[i].tolist()],
+            "score": float(scores[i])
+        } for i in indices]
+
     def __call__(self, image_list, thr=0.7, batch_size=16):
         res = []
         imgs = []
             end_index = min((i + 1) * batch_size, len(imgs))
             batch_image_list = imgs[start_index:end_index]
             inputs = self.preprocess(batch_image_list)
+            print("preprocess")
             for ins in inputs:
-                bb = []
-                for b in self.ort_sess.run(None, ins)[0]:
-                    clsid, bbox, score = int(b[0]), b[2:], b[1]
-                    if score < thr:
-                        continue
-                    if clsid >= len(self.label_list):
-                        cron_logger.warning(f"bad category id")
-                        continue
-                    bb.append({
-                        "type": self.label_list[clsid].lower(),
-                        "bbox": [float(t) for t in bbox.tolist()],
-                        "score": float(score)
-                    })
+                bb = self.postprocess(self.ort_sess.run(None, {k:v for k,v in ins.items() if k in self.input_names})[0], ins, thr)
                 res.append(bb)
 
         #seeit.save_results(image_list, res, self.label_list, threshold=thr)
 
         return res
+
+
+

deepdoc/vision/t_ocr.py

+#  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, sys
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../')))
+import numpy as np
+import argparse
+from deepdoc.vision import OCR, init_in_out
+from deepdoc.vision.seeit import draw_box
+
+def main(args):
+    ocr = OCR()
+    images, outputs = init_in_out(args)
+
+    for i, img in enumerate(images):
+        bxs = ocr(np.array(img))
+        bxs = [(line[0], line[1][0]) for line in bxs]
+        bxs = [{
+                "text": t,
+                "bbox": [b[0][0], b[0][1],  b[1][0], b[-1][1]],
+                "type": "ocr",
+                "score": 1} for b, t in bxs if b[0][0] <= b[1][0] and b[0][1] <= b[-1][1]]
+        img = draw_box(images[i], bxs, ["ocr"], 1.)
+        img.save(outputs[i], quality=95)
+        with open(outputs[i] + ".txt", "w+") as f: f.write("\n".join([o["text"] for o in bxs]))
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--inputs',
+                        help="Directory where to store images or PDFs, or a file path to a single image or PDF",
+                        required=True)
+    parser.add_argument('--output_dir', help="Directory where to store the output images. Default: './ocr_outputs'",
+                         default="./ocr_outputs")
+    args = parser.parse_args()
+    main(args)

deepdoc/vision/t_recognizer.py

+#  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, sys
+import re
+
+import numpy as np
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../')))
+
+import argparse
+from api.utils.file_utils import get_project_base_directory
+from deepdoc.vision import Recognizer, LayoutRecognizer, TableStructureRecognizer, OCR, init_in_out
+from deepdoc.vision.seeit import draw_box
+
+
+def main(args):
+    images, outputs = init_in_out(args)
+    if args.mode.lower() == "layout":
+        labels = LayoutRecognizer.labels
+        detr = Recognizer(labels, "layout.paper", os.path.join(get_project_base_directory(), "rag/res/deepdoc/"))
+    if args.mode.lower() == "tsr":
+        labels = TableStructureRecognizer.labels
+        detr = TableStructureRecognizer()
+        ocr = OCR()
+
+    layouts = detr(images, float(args.threshold))
+    for i, lyt in enumerate(layouts):
+        if args.mode.lower() == "tsr":
+            #lyt = [t for t in lyt if t["type"] == "table column"]
+            html = get_table_html(images[i], lyt, ocr)
+            with open(outputs[i]+".html", "w+") as f: f.write(html)
+            lyt = [{
+                "type": t["label"],
+                "bbox": [t["x0"], t["top"], t["x1"], t["bottom"]],
+                "score": t["score"]
+            } for t in lyt]
+        img = draw_box(images[i], lyt, labels, float(args.threshold))
+        img.save(outputs[i], quality=95)
+        print("save result to: " + outputs[i])
+
+
+def get_table_html(img, tb_cpns, ocr):
+    boxes = ocr(np.array(img))
+    boxes = Recognizer.sort_Y_firstly(
+        [{"x0": b[0][0], "x1": b[1][0],
+          "top": b[0][1], "text": t[0],
+          "bottom": b[-1][1],
+          "layout_type": "table",
+          "page_number": 0} for b, t in boxes if b[0][0] <= b[1][0] and b[0][1] <= b[-1][1]],
+        np.mean([b[-1][1]-b[0][1] for b,_ in boxes]) / 3
+    )
+
+    def gather(kwd, fzy=10, ption=0.6):
+        nonlocal boxes
+        eles = Recognizer.sort_Y_firstly(
+            [r for r in tb_cpns if re.match(kwd, r["label"])], fzy)
+        eles = Recognizer.layouts_cleanup(boxes, eles, 5, ption)
+        return Recognizer.sort_Y_firstly(eles, 0)
+
+    headers = gather(r".*header$")
+    rows = gather(r".* (row|header)")
+    spans = gather(r".*spanning")
+    clmns = sorted([r for r in tb_cpns if re.match(
+        r"table column$", r["label"])], key=lambda x: x["x0"])
+    clmns = Recognizer.layouts_cleanup(boxes, clmns, 5, 0.5)
+    for b in boxes:
+        ii = Recognizer.find_overlapped_with_threashold(b, rows, thr=0.3)
+        if ii is not None:
+            b["R"] = ii
+            b["R_top"] = rows[ii]["top"]
+            b["R_bott"] = rows[ii]["bottom"]
+
+        ii = Recognizer.find_overlapped_with_threashold(b, headers, thr=0.3)
+        if ii is not None:
+            b["H_top"] = headers[ii]["top"]
+            b["H_bott"] = headers[ii]["bottom"]
+            b["H_left"] = headers[ii]["x0"]
+            b["H_right"] = headers[ii]["x1"]
+            b["H"] = ii
+
+        ii = Recognizer.find_overlapped_with_threashold(b, clmns, thr=0.3)
+        if ii is not None:
+            b["C"] = ii
+            b["C_left"] = clmns[ii]["x0"]
+            b["C_right"] = clmns[ii]["x1"]
+
+        ii = Recognizer.find_overlapped_with_threashold(b, spans, thr=0.3)
+        if ii is not None:
+            b["H_top"] = spans[ii]["top"]
+            b["H_bott"] = spans[ii]["bottom"]
+            b["H_left"] = spans[ii]["x0"]
+            b["H_right"] = spans[ii]["x1"]
+            b["SP"] = ii
+    html = """
+    <html>
+    <head>
+    <style>
+    ._table_1nkzy_11 {
+      margin: auto;
+      width: 70%%;
+      padding: 10px;
+    }
+    ._table_1nkzy_11 p {
+      margin-bottom: 50px;
+      border: 1px solid #e1e1e1;
+    }
+    
+    caption {
+      color: #6ac1ca;
+      font-size: 20px;
+      height: 50px;
+      line-height: 50px;
+      font-weight: 600;
+      margin-bottom: 10px;
+    }
+    
+    ._table_1nkzy_11 table {
+      width: 100%%;
+      border-collapse: collapse;
+    }
+    
+    th {
+      color: #fff;
+      background-color: #6ac1ca;
+    }
+    
+    td:hover {
+      background: #c1e8e8;
+    }
+    
+    tr:nth-child(even) {
+      background-color: #f2f2f2;
+    }
+    
+    ._table_1nkzy_11 th,
+    ._table_1nkzy_11 td {
+      text-align: center;
+      border: 1px solid #ddd;
+      padding: 8px;
+    }
+    </style>
+    </head>
+    <body>
+    %s
+    </body>
+    </html>
+"""% TableStructureRecognizer.construct_table(boxes, html=True)
+    return html
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--inputs',
+                        help="Directory where to store images or PDFs, or a file path to a single image or PDF",
+                        required=True)
+    parser.add_argument('--output_dir', help="Directory where to store the output images. Default: './layouts_outputs'",
+                        default="./layouts_outputs")
+    parser.add_argument('--threshold', help="A threshold to filter out detections. Default: 0.5", default=0.5)
+    parser.add_argument('--mode', help="Task mode: layout recognition or table structure recognition", choices=["layout", "tsr"],
+                        default="layout")
+    args = parser.parse_args()
+    main(args)

deepdoc/vision/table_structure_recognizer.py

+#  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 re
 
 
 class TableStructureRecognizer(Recognizer):
+    labels = [
+        "table",
+        "table column",
+        "table row",
+        "table column header",
+        "table projected row header",
+        "table spanning cell",
+    ]
+
     def __init__(self):
-        self.labels = [
-            "table",
-            "table column",
-            "table row",
-            "table column header",
-            "table projected row header",
-            "table spanning cell",
-        ]
         super().__init__(self.labels, "tsr",
                          os.path.join(get_project_base_directory(), "rag/res/deepdoc/"))
 
             return True
         return False
 
-    def __blockType(self, b):
+    @staticmethod
+    def blockType(b):
         patt = [
             ("^(20|19)[0-9]{2}[年/-][0-9]{1,2}[月/-][0-9]{1,2}日*$", "Dt"),
             (r"^(20|19)[0-9]{2}年$", "Dt"),
 
         return "Ot"
 
-    def construct_table(self, boxes, is_english=False, html=False):
+    @staticmethod
+    def construct_table(boxes, is_english=False, html=False):
         cap = ""
         i = 0
         while i < len(boxes):
-            if self.is_caption(boxes[i]):
+            if TableStructureRecognizer.is_caption(boxes[i]):
                 cap += boxes[i]["text"]
                 boxes.pop(i)
                 i -= 1
         if not boxes:
             return []
         for b in boxes:
-            b["btype"] = self.__blockType(b)
+            b["btype"] = TableStructureRecognizer.blockType(b)
         max_type = Counter([b["btype"] for b in boxes]).items()
         max_type = max(max_type, key=lambda x: x[1])[0] if max_type else ""
         logging.debug("MAXTYPE: " + max_type)
 
         rowh = [b["R_bott"] - b["R_top"] for b in boxes if "R" in b]
         rowh = np.min(rowh) if rowh else 0
-        boxes = self.sort_R_firstly(boxes, rowh / 2)
+        boxes = Recognizer.sort_R_firstly(boxes, rowh / 2)
+        #for b in boxes:print(b)
         boxes[0]["rn"] = 0
         rows = [[boxes[0]]]
         btm = boxes[0]["bottom"]
         colwm = np.min(colwm) if colwm else 0
         crosspage = len(set([b["page_number"] for b in boxes])) > 1
         if crosspage:
-            boxes = self.sort_X_firstly(boxes, colwm / 2, False)
+            boxes = Recognizer.sort_X_firstly(boxes, colwm / 2, False)
         else:
-            boxes = self.sort_C_firstly(boxes, colwm / 2)
+            boxes = Recognizer.sort_C_firstly(boxes, colwm / 2)
         boxes[0]["cn"] = 0
         cols = [[boxes[0]]]
         right = boxes[0]["x1"]
                 hdset.add(i)
 
         if html:
-            return [self.__html_table(cap, hdset,
-                                      self.__cal_spans(boxes, rows,
-                                                       cols, tbl, True)
-                                      )]
+            return TableStructureRecognizer.__html_table(cap, hdset,
+                                                         TableStructureRecognizer.__cal_spans(boxes, rows,
+                                                                                              cols, tbl, True)
+                                                         )
 
-        return self.__desc_table(cap, hdset,
-                                 self.__cal_spans(boxes, rows, cols, tbl, False),
-                                 is_english)
+        return TableStructureRecognizer.__desc_table(cap, hdset,
+                                                     TableStructureRecognizer.__cal_spans(boxes, rows, cols, tbl,
+                                                                                          False),
+                                                     is_english)
 
-    def __html_table(self, cap, hdset, tbl):
+    @staticmethod
+    def __html_table(cap, hdset, tbl):
         # constrcut HTML
         html = "<table>"
         if cap:
                 txt = ""
                 if arr:
                     h = min(np.min([c["bottom"] - c["top"] for c in arr]) / 2, 10)
-                    txt = "".join([c["text"]
-                                   for c in self.sort_Y_firstly(arr, h)])
+                    txt = " ".join([c["text"]
+                                   for c in Recognizer.sort_Y_firstly(arr, h)])
                 txts.append(txt)
                 sp = ""
                 if arr[0].get("colspan"):
         html += "\n</table>"
         return html
 
-    def __desc_table(self, cap, hdr_rowno, tbl, is_english):
+    @staticmethod
+    def __desc_table(cap, hdr_rowno, tbl, is_english):
         # get text of every colomn in header row to become header text
         clmno = len(tbl[0])
         rowno = len(tbl)
             row_txt = [t + f"\t——{from_}“{cap}”" for t in row_txt]
         return row_txt
 
-    def __cal_spans(self, boxes, rows, cols, tbl, html=True):
+    @staticmethod
+    def __cal_spans(boxes, rows, cols, tbl, html=True):
         # caculate span
         clft = [np.mean([c.get("C_left", c["x0"]) for c in cln])
                 for cln in cols]
                 tbl[rowspan[0]][colspan[0]] = arr
 
         return tbl
-