detect a table part from entire image in python

I have an image of size 3500×5000, now I want to detect only the table part from the entire image and crop and rotate it if it is not straight for OCR processing. After all search, I get an idea for cropping each cell in an image using https://medium.com/coinmonks/a-box-detection-algorithm-for-any-image-containing-boxes-756c15d7ed26 , but don’t know how to crop the table part in an image.

The image I used here:

now I want only this part like this:(manually cropped)

Thanks in advance!..

Answer

@user:12894468, @user:5404226 After a long search I came to found a best approch to find only table in images using DETECTRON2 https://github.com/facebookresearch/detectron2 but it works only in lunix environment, I used windows subsystem for lunix, here I used this code for several images placed in folder and create each folder(may image contains one or more table) with the file name

import uuid 
# Some basic setup:
# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()

# import some common libraries
import numpy as np
import os, json, cv2, random
#from google.colab.patches import cv2_imshow

# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog

from detectron2.engine import DefaultTrainer
from detectron2.structures import BoxMode

from detectron2.utils.visualizer import ColorMode


ROOT_DIR = "./"
ipdir = ROOT_DIR + "nswtable_input/image/"
opdir = ROOT_DIR + "results_nswtable/"



def predict(im, item):
    fileName=item
    outputs = predictor(im)
    v = Visualizer(im[:, :, ::-1],
                   metadata=balloon_metadata, 
                   scale=0.8, 
                   instance_mode=ColorMode.IMAGE_BW   # remove the colors of unsegmented pixels
    )
    v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
    #print(outputs["instances"].pred_boxes.tensor.numpy())
    path = "/root/images/"
    path1="/root/tblImg/"
    cv2.imwrite(path1 + fileName + ".png", v.get_image()[:, :, ::-1])
    boxes = {}
    
    file = os.path.join(path,fileName)
    try:
        f=os.makedirs(file,exist_ok=True)
        print("Directory '%s' created " % file)
    except OSError as error:
        print("cannot create"%directory)
    i=1
    coords=[]
    for coordinates in outputs["instances"].to("cpu").pred_boxes:
        
        coordinates_array = []
        for k in coordinates:
            coordinates_array.append(int(k))
        boxes[uuid.uuid4().hex[:].upper()] = coordinates_array
        coords.append(coordinates_array)
    
    for k,v in boxes.items():

        crop_img = im[v[1]:v[3], v[0]:v[2], :]
        #print(v[1],v[3], v[0],v[2])
        #cv2_imshow(crop_img)
        crop_width,crop_height=crop_img.shape[0],crop_img.shape[1]
        if crop_width>crop_height:
            img_rot=cv2.rotate(crop_img,cv2.ROTATE_90_CLOCKWISE)
            
        #------for naming the images------#v[1]=y,v[3]=y+h, v[0]=x,v[2]=x+w
            margin = 0
        
            ymin = max(v[1]-margin,0)
            ymax =v[3]+margin
            xmin = max(v[0] - margin,0)
            xmax = v[2]+margin
            #print(ymin,ymax,xmin,xmax)
            cv2.imwrite(file+'/'+str(i)+'_'+str(xmin)+'_'+str(ymin)+'_'+str(xmin)+'_'+str(ymax)+'_'+str(xmax)+'_'+str(ymin)+'_'+str(xmax)+'_'+str(ymax)+ '.png', img_rot)
            i=i+1

    
    return outputs


dirs = os.listdir(ipdir)


for item in dirs:
    if os.path.isfile(ipdir+item):
        im = cv2.imread(ipdir+item)
        print(item)
        f, e = os.path.splitext(ipdir+item)
        #width,height = im.shape[1],im.shape[0]
        item = item[:-4]
        predict(im, item)

the output I get as below:

https://ibb.co/0Q16Gyv


        
    

Use the above mentioned link for how to train the sample and others