YogeLiu/TexTeller
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

1) 实现了文本-公式混排识别; 2) 重构了项目结构

Browse Source
This commit is contained in:
三洋三洋
2024-04-21 00:05:14 +08:00
parent eab6e4c85d
commit 185b2e3db6
19 changed files with 753 additions and 296 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
src/models/utils/__init__.py Normal file
View File

@@ -0,0 +1 @@
from .mix_inference import mix_inference

257
src/models/utils/mix_inference.py Normal file
View File

@@ -0,0 +1,257 @@
import re
import heapq
import cv2
import numpy as np
from onnxruntime import InferenceSession
from collections import Counter
from typing import List
from PIL import Image
from surya.ocr import run_ocr
from surya.detection import batch_text_detection
from surya.input.processing import slice_polys_from_image, slice_bboxes_from_image
from surya.recognition import batch_recognition
from surya.model.detection import segformer
from surya.model.recognition.model import load_model
from surya.model.recognition.processor import load_processor
from ..det_model.inference import PredictConfig
from ..det_model.inference import predict as latex_det_predict
from ..det_model.Bbox import Bbox, draw_bboxes
from ..ocr_model.model.TexTeller import TexTeller
from ..ocr_model.utils.inference import inference as latex_rec_predict
from ..ocr_model.utils.to_katex import to_katex
MAXV = 999999999
def mask_img(img, bboxes: List[Bbox], bg_color: np.ndarray) -> np.ndarray:
mask_img = img.copy()
for bbox in bboxes:
mask_img[bbox.p.y:bbox.p.y + bbox.h, bbox.p.x:bbox.p.x + bbox.w] = bg_color
return mask_img
def bbox_merge(sorted_bboxes: List[Bbox]) -> List[Bbox]:
if (len(sorted_bboxes) == 0):
return []
bboxes = sorted_bboxes.copy()
guard = Bbox(MAXV, bboxes[-1].p.y, -1, -1, label="guard")
bboxes.append(guard)
res = []
prev = bboxes[0]
for curr in bboxes:
if prev.ur_point.x <= curr.p.x or not prev.same_row(curr):
res.append(prev)
prev = curr
else:
prev.w = max(prev.w, curr.ur_point.x - prev.p.x)
return res
def split_conflict(ocr_bboxes: List[Bbox], latex_bboxes: List[Bbox]) -> List[Bbox]:
if latex_bboxes == []:
return ocr_bboxes
if ocr_bboxes == [] or len(ocr_bboxes) == 1:
return ocr_bboxes
bboxes = sorted(ocr_bboxes + latex_bboxes)
######## debug #########
for idx, bbox in enumerate(bboxes):
bbox.content = str(idx)
draw_bboxes(Image.fromarray(img), bboxes, name="before_split_confict.png")
######## debug ###########
assert len(bboxes) > 1
heapq.heapify(bboxes)
res = []
candidate = heapq.heappop(bboxes)
curr = heapq.heappop(bboxes)
idx = 0
while (len(bboxes) > 0):
idx += 1
assert candidate.p.x < curr.p.x or not candidate.same_row(curr)
if candidate.ur_point.x <= curr.p.x or not candidate.same_row(curr):
res.append(candidate)
candidate = curr
curr = heapq.heappop(bboxes)
elif candidate.ur_point.x < curr.ur_point.x:
assert not (candidate.label != "text" and curr.label != "text")
if candidate.label == "text" and curr.label == "text":
candidate.w = curr.ur_point.x - candidate.p.x
curr = heapq.heappop(bboxes)
elif candidate.label != curr.label:
if candidate.label == "text":
candidate.w = curr.p.x - candidate.p.x
res.append(candidate)
candidate = curr
curr = heapq.heappop(bboxes)
else:
curr.w = curr.ur_point.x - candidate.ur_point.x
curr.p.x = candidate.ur_point.x
heapq.heappush(bboxes, curr)
curr = heapq.heappop(bboxes)
elif candidate.ur_point.x >= curr.ur_point.x:
assert not (candidate.label != "text" and curr.label != "text")
if candidate.label == "text":
assert curr.label != "text"
heapq.heappush(
bboxes,
Bbox(
curr.ur_point.x,
candidate.p.y,
candidate.h,
candidate.ur_point.x - curr.ur_point.x,
label="text",
confidence=candidate.confidence,
content=None
)
)
candidate.w = curr.p.x - candidate.p.x
res.append(candidate)
candidate = curr
curr = heapq.heappop(bboxes)
else:
assert curr.label == "text"
curr = heapq.heappop(bboxes)
else:
assert False
res.append(candidate)
res.append(curr)
######## debug #########
for idx, bbox in enumerate(res):
bbox.content = str(idx)
draw_bboxes(Image.fromarray(img), res, name="after_split_confict.png")
######## debug ###########
return res
def mix_inference(
img_path: str,
language: str,
infer_config,
latex_det_model,
lang_ocr_models,
latex_rec_models,
accelerator="cpu",
num_beams=1
) -> str:
'''
Input a mixed image of formula text and output str (in markdown syntax)
'''
global img
img = cv2.imread(img_path)
corners = [tuple(img[0, 0]), tuple(img[0, -1]),
tuple(img[-1, 0]), tuple(img[-1, -1])]
bg_color = np.array(Counter(corners).most_common(1)[0][0])
latex_bboxes = latex_det_predict(img_path, latex_det_model, infer_config)
latex_bboxes = sorted(latex_bboxes)
draw_bboxes(Image.fromarray(img), latex_bboxes, name="latex_bboxes(unmerged).png")
latex_bboxes = bbox_merge(latex_bboxes)
draw_bboxes(Image.fromarray(img), latex_bboxes, name="latex_bboxes(merged).png")
masked_img = mask_img(img, latex_bboxes, bg_color)
det_model, det_processor, rec_model, rec_processor = lang_ocr_models
images = [Image.fromarray(masked_img)]
det_prediction = batch_text_detection(images, det_model, det_processor)[0]
draw_bboxes(Image.fromarray(img), latex_bboxes, name="ocr_bboxes(unmerged).png")
lang = [language]
slice_map = []
all_slices = []
all_langs = []
ocr_bboxes = [
Bbox(
p.bbox[0], p.bbox[1], p.bbox[3] - p.bbox[1], p.bbox[2] - p.bbox[0],
label="text",
confidence=p.confidence,
content=None
)
for p in det_prediction.bboxes
]
ocr_bboxes = sorted(ocr_bboxes)
ocr_bboxes = bbox_merge(ocr_bboxes)
draw_bboxes(Image.fromarray(img), ocr_bboxes, name="ocr_bboxes(merged).png")
ocr_bboxes = split_conflict(ocr_bboxes, latex_bboxes)
ocr_bboxes = list(filter(lambda x: x.label == "text", ocr_bboxes))
polygons = [
[
[bbox.ul_point.x, bbox.ul_point.y],
[bbox.ur_point.x, bbox.ur_point.y],
[bbox.lr_point.x, bbox.lr_point.y],
[bbox.ll_point.x, bbox.ll_point.y]
]
for bbox in ocr_bboxes
]
slices = slice_polys_from_image(images[0], polygons)
slice_map.append(len(slices))
all_slices.extend(slices)
all_langs.extend([lang] * len(slices))
rec_predictions, _ = batch_recognition(all_slices, all_langs, rec_model, rec_processor)
assert len(rec_predictions) == len(ocr_bboxes)
for content, bbox in zip(rec_predictions, ocr_bboxes):
bbox.content = content
latex_imgs =[]
for bbox in latex_bboxes:
latex_imgs.append(img[bbox.p.y:bbox.p.y + bbox.h, bbox.p.x:bbox.p.x + bbox.w])
latex_rec_res = latex_rec_predict(*latex_rec_models, latex_imgs, accelerator, num_beams, max_tokens=200)
for bbox, content in zip(latex_bboxes, latex_rec_res):
bbox.content = to_katex(content)
if bbox.label == "embedding":
bbox.content = " $" + bbox.content + "$ "
elif bbox.label == "isolated":
bbox.content = '\n' + r"$$" + bbox.content + r"$$" + '\n'
bboxes = sorted(ocr_bboxes + latex_bboxes)
if bboxes == []:
return ""
md = ""
prev = Bbox(bboxes[0].p.x, bboxes[0].p.y, -1, -1, label="guard")
# prev = bboxes[0]
for curr in bboxes:
if not prev.same_row(curr):
md += "\n"
md += curr.content
if (
prev.label == "isolated"
and curr.label == "text"
and bool(re.fullmatch(r"\([1-9]\d*?\)", curr.content))
):
md += '\n'
prev = curr
return md
if __name__ == '__main__':
img_path = "/Users/Leehy/Code/TexTeller/test3.png"
# latex_det_model = InferenceSession("/Users/Leehy/Code/TexTeller/src/models/det_model/model/rtdetr_r50vd_6x_coco_trained_on_IBEM_en_papers.onnx")
latex_det_model = InferenceSession("/Users/Leehy/Code/TexTeller/src/models/det_model/model/rtdetr_r50vd_6x_coco.onnx")
infer_config = PredictConfig("/Users/Leehy/Code/TexTeller/src/models/det_model/model/infer_cfg.yml")
det_processor, det_model = segformer.load_processor(), segformer.load_model()
rec_model, rec_processor = load_model(), load_processor()
lang_ocr_models = (det_model, det_processor, rec_model, rec_processor)
texteller = TexTeller.from_pretrained()
tokenizer = TexTeller.get_tokenizer()
latex_rec_models = (texteller, tokenizer)
res = mix_inference(img_path, "zh", infer_config, latex_det_model, lang_ocr_models, latex_rec_models)
print(res)
pause = 1