CA and WA

evaluate.py

import re
import sys
import os
import tempfile
import subprocess
import pdb
import pandas as pd
import numpy as np
from collections import defaultdict
from ocr.baselines.base_config import *



def calculate_word_accuracy(**kwargs):
    """ Calculates the word level accuracy of the OCR result using corrected result as ground truth. """

    path = kwargs['path']
    acc = kwargs['accuracy']
    files = list(map(lambda f: path+'/' + f, os.listdir(path)))
    def clean(base_name):
        base_name = base_name.split('.')[0]
        return base_name + '_ocr.txt'
    count = 0
    ch_acc = defaultdict(float)
    for file_ in files:
        # pdb.set_trace()
        if '_ocr' not in file_:
            gt_file = file_
            pr_file = clean(file_)
            count+=1
            print(count)
            try:
                cmd = ['ocr-evaluation-tools/dist/bin/ocrevalutf8.fix', '{}'.format(acc), '{}'.format(gt_file), '{}'.format(pr_file)]
                process = subprocess.run(cmd, stdout=subprocess.PIPE)
                accuracy = process.stdout.decode().splitlines()[4].strip().split()[0].replace('%', '')
                ch_acc[file_] = float(accuracy)
            except Exception as e:
                print(e)
                pass
    df = pd.DataFrame(list(ch_acc.items()), columns=['file', 'accuracy'])
    df.to_csv('ocr/stats/{}.csv'.format('rahul'))
    print(np.mean(list(ch_acc.values())))        
   

def main(**kwargs):
    opt = Config()
    opt._parse(kwargs)
    dir_ = opt.path
    accuracy = opt.accuracy
    
    calculate_word_accuracy(path=dir_,
                            accuracy=accuracy)

if __name__=='__main__':
    import fire
    fire.Fire(main)    
    
# python -m ocr.baselines.evaluate --path=<path>