diff --git a/api/apps/conversation_app.py b/api/apps/conversation_app.py
index f4d3279930ed66fe02159b48ce6d0929cb74d69e..fe1047bc609ec21d5d3e85f9728cf345e7259c2e 100644
--- a/api/apps/conversation_app.py
+++ b/api/apps/conversation_app.py
@@ -58,7 +58,7 @@ def set_conversation():
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)
@@ -102,7 +102,7 @@ def rm():
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:
diff --git a/api/utils/file_utils.py b/api/utils/file_utils.py
index 1d6f15f334d1e69b46cd60f220dff3c7407d7e79..7f8145971d95cf2e24b8a76d1e1a02800e0eb54c 100644
--- a/api/utils/file_utils.py
+++ b/api/utils/file_utils.py
@@ -185,5 +185,11 @@ def thumbnail(filename, blob):
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
+
diff --git a/deepdoc/README.md b/deepdoc/README.md
index d6f397f440cc9994e30f805c33fc33db9937f30b..5c7235566f532e85e697f12d3a549022ac6ea4a9 100644
--- a/deepdoc/README.md
+++ b/deepdoc/README.md
@@ -11,7 +11,36 @@ English | [简体ä¸ć–‡](./README_zh.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
@@ -19,9 +48,14 @@ There 2 parts in *Deep*Doc so far: vision and parser.
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,
@@ -39,11 +73,18 @@ We use vision information to resolve problems as human being.
- 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:
@@ -52,8 +93,15 @@ We use vision information to resolve problems as human being.
- 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>
@@ -71,4 +119,4 @@ The résumé is a very complicated kind of document. A résumé which is compose
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.
-
+
\ No newline at end of file
diff --git a/deepdoc/vision/__init__.py b/deepdoc/vision/__init__.py
index 93eea132cc525463773191baae2bf6550c10004b..01bacbbddb0237d5148228216f59adb9d9951eb1 100644
--- a/deepdoc/vision/__init__.py
+++ b/deepdoc/vision/__init__.py
@@ -1,4 +1,49 @@
+
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
\ No newline at end of file
diff --git a/deepdoc/vision/layout_recognizer.py b/deepdoc/vision/layout_recognizer.py
index 1a5795a2e9321d24d83f9ed2a1cab2e8ddd570cd..7d0abb9d301a6b54ed265d1edde8b062faad51ec 100644
--- a/deepdoc/vision/layout_recognizer.py
+++ b/deepdoc/vision/layout_recognizer.py
@@ -1,17 +1,26 @@
+# 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",
@@ -24,7 +33,8 @@ class LayoutRecognizer(Recognizer):
"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):
@@ -37,7 +47,7 @@ class LayoutRecognizer(Recognizer):
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 = []
@@ -117,3 +127,5 @@ class LayoutRecognizer(Recognizer):
ocr_res = [b for b in ocr_res if b["text"].strip() not in garbag_set]
return ocr_res, page_layout
+
+
diff --git a/deepdoc/vision/recognizer.py b/deepdoc/vision/recognizer.py
index 932923402bf9852911253f971c2d2729589eaf39..7a1301941ccea1592e15f64d4b9f9284bac4cb98 100644
--- a/deepdoc/vision/recognizer.py
+++ b/deepdoc/vision/recognizer.py
@@ -17,7 +17,6 @@ from copy import deepcopy
import onnxruntime as ort
from huggingface_hub import snapshot_download
-from . import seeit
from .operators import *
from rag.settings import cron_logger
@@ -36,7 +35,7 @@ class Recognizer(object):
"""
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):
@@ -46,6 +45,9 @@ class Recognizer(object):
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
@@ -275,23 +277,131 @@ class Recognizer(object):
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 = []
@@ -306,22 +416,14 @@ class Recognizer(object):
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
+
+
+
diff --git a/deepdoc/vision/t_ocr.py b/deepdoc/vision/t_ocr.py
new file mode 100644
index 0000000000000000000000000000000000000000..79b3cdb7b4f9005b09236958289058241a2a2109
--- /dev/null
+++ b/deepdoc/vision/t_ocr.py
@@ -0,0 +1,47 @@
+# 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)
\ No newline at end of file
diff --git a/deepdoc/vision/t_recognizer.py b/deepdoc/vision/t_recognizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ec1f29fa2ac9a2478a7c130585f56696ae9643b
--- /dev/null
+++ b/deepdoc/vision/t_recognizer.py
@@ -0,0 +1,173 @@
+# 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)
diff --git a/deepdoc/vision/table_structure_recognizer.py b/deepdoc/vision/table_structure_recognizer.py
index 40366b1d4c30745d55729241c033d8a18333f84c..26b4a1e6d343347fcdcb483fb13f2f18d74e41e6 100644
--- a/deepdoc/vision/table_structure_recognizer.py
+++ b/deepdoc/vision/table_structure_recognizer.py
@@ -1,3 +1,15 @@
+# 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
@@ -12,15 +24,16 @@ from .recognizer import Recognizer
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/"))
@@ -79,7 +92,8 @@ class TableStructureRecognizer(Recognizer):
return True
return False
- def __blockType(self, b):
+ @staticmethod
+ def blockType(b):
patt = [
("^(20|19)[0-9]{2}[ĺą´/-][0-9]{1,2}[ćś/-][0-9]{1,2}ć—Ą*$", "Dt"),
(r"^(20|19)[0-9]{2}ĺą´$", "Dt"),
@@ -109,11 +123,12 @@ class TableStructureRecognizer(Recognizer):
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
@@ -122,14 +137,15 @@ class TableStructureRecognizer(Recognizer):
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"]
@@ -150,9 +166,9 @@ class TableStructureRecognizer(Recognizer):
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"]
@@ -313,16 +329,18 @@ class TableStructureRecognizer(Recognizer):
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:
@@ -339,8 +357,8 @@ class TableStructureRecognizer(Recognizer):
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"):
@@ -366,7 +384,8 @@ class TableStructureRecognizer(Recognizer):
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)
@@ -469,7 +488,8 @@ class TableStructureRecognizer(Recognizer):
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]
@@ -553,4 +573,3 @@ class TableStructureRecognizer(Recognizer):
tbl[rowspan[0]][colspan[0]] = arr
return tbl
-