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from typing import Tuple, List, Sequence, Optional, Union
from torchvision import transforms
from torch import nn, Tensor
from PIL import Image
from pathlib import Path
from bs4 import BeautifulSoup as bs
import numpy as np
import numpy.typing as npt
from numpy import uint8
ImageType = npt.NDArray[uint8]
from transformers import AutoModelForObjectDetection
import torch
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.patches import Patch
from utils import draw_only_box
from unitable import UnitableFullPredictor
from ultralyticsplus import YOLO, render_result
"""
USES YOLO FOR DETECITON INSTEAD OF TABLE TRANSFORMER
Table TransFORMER
"""
class DetectionAndOcrTable4():
#This components can take in entire pdf page as input , scan for tables and return the table in html format
#Uses the full unitable model - different to DetectionAndOcrTable1
def __init__(self):
self.unitableFullPredictor = UnitableFullPredictor()
self.detector = YOLO('foduucom/table-detection-and-extraction')
# set model parameters
self.detector.overrides['conf'] = 0.25 # NMS confidence threshold
self.detector.overrides['iou'] = 0.45 # NMS IoU threshold
self.detector.overrides['agnostic_nms'] = False # NMS class-agnostic
self.detector.overrides['max_det'] = 1000 # maximum number of detections per image
@staticmethod
def save_detection(detected_lines_images:List[ImageType], prefix = './res/test1/res_'):
i = 0
for img in detected_lines_images:
pilimg = Image.fromarray(img)
pilimg.save(prefix+str(i)+'.png')
i=i+1
"""
Valid 'Boxes' object attributes and properties are:
Attributes:
boxes (torch.Tensor) or (numpy.ndarray): A tensor or numpy array containing the detection boxes,
with shape (num_boxes, 6).
orig_shape (torch.Tensor) or (numpy.ndarray): Original image size, in the format (height, width).
Properties:
xyxy (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format.
conf (torch.Tensor) or (numpy.ndarray): The confidence values of the boxes.
cls (torch.Tensor) or (numpy.ndarray): The class values of the boxes.
xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format.
xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size.
xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size.
"""
# Image is page image
def predict(self,image:Image.Image,debugfolder_filename_page_name = None):
results = self.detector.predict(image)
#Array of bboxes
bbxs = results[0].boxes.xyxy.int().tolist()
#Array of confidences
conf = results[0].boxes.conf.float().tolist()
print(bbxs)
print(conf)
#images_to_recognizer = cropImage(bxs, img)
img_to_save = draw_only_box(image, bbxs)
img_to_save.save(debugfolder_filename_page_name+"detectionBoxRes.png", quality=95)
# we need something to draw the detection
cropped_tables =[]
for i in range (len(bbxs)):
# TODO: find the right confidence and padding values
if conf[i]< 0.65:
continue
padded = [bbxs[i][0]-10,bbxs[i][1]-10,bbxs[i][2]+10,bbxs[i][3]+10]
cropped_table = image.convert("RGB").crop(padded)
cropped_table.save(debugfolder_filename_page_name +"yolo_cropped_table_"+str(i)+".png")
cropped_tables.append(cropped_table)
print("number of cropped tables found: "+str(len(cropped_tables)))
# Step 1: Unitable
#This take PIL Images as input
if cropped_tables != []:
table_codes = self.unitableFullPredictor.predict(cropped_tables,debugfolder_filename_page_name)
return table_codes
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