cnocr_evaluate.py

# coding: utf-8
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# to you under the Apache License, Version 2.0 (the
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""" An example of predicting CAPTCHA image data with a LSTM network pre-trained with a CTC loss"""

from __future__ import print_function

import sys
import os
import time
import logging
import argparse
from operator import itemgetter
from pathlib import Path
from collections import Counter
import mxnet as mx
import Levenshtein

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from cnocr import CnOcr
from cnocr.utils import set_logger


logger = set_logger(log_level=logging.INFO)


def evaluate():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model-name", help="model name", type=str, default='densenet-lite-lstm'
    )
    parser.add_argument("--model-epoch", type=int, default=None, help="model epoch")
    parser.add_argument(
        "-i",
        "--input-fp",
        default='test.txt',
        help="the file path with image names and labels",
    )
    parser.add_argument(
        "--image-prefix-dir", default='.', help="图片所在文件夹，相对于索引文件中记录的图片位置"
    )
    parser.add_argument("--batch-size", type=int, default=128, help="batch size")
    parser.add_argument(
        "-v",
        "--verbose",
        action='store_true',
        help="whether to print details to screen",
    )

    parser.add_argument(
        "-o",
        "--output-dir",
        default=False,
        help="the output directory which records the analysis results",
    )
    args = parser.parse_args()

    ocr = CnOcr(model_name=args.model_name, model_epoch=args.model_epoch)
    alphabet = ocr._alphabet

    fn_labels_list = read_input_file(args.input_fp)

    miss_cnt, redundant_cnt = Counter(), Counter()
    model_time_cost = 0.0
    start_idx = 0
    bad_cnt = 0
    badcases = []
    while start_idx < len(fn_labels_list):
        logger.info('start_idx: %d', start_idx)
        batch = fn_labels_list[start_idx : start_idx + args.batch_size]
        batch_img_fns = []
        batch_labels = []
        batch_imgs = []
        for fn, labels in batch:
            batch_labels.append(labels)
            img_fp = os.path.join(args.image_prefix_dir, fn)
            batch_img_fns.append(img_fp)
            img = mx.image.imread(img_fp, 1).asnumpy()
            batch_imgs.append(img)

        start_time = time.time()
        batch_preds = ocr.ocr_for_single_lines(batch_imgs)
        model_time_cost += time.time() - start_time
        for bad_info in compare_preds_to_reals(
            batch_preds, batch_labels, batch_img_fns, alphabet
        ):
            if args.verbose:
                logger.info('\t'.join(bad_info))
            distance = Levenshtein.distance(bad_info[1], bad_info[2])
            bad_info.insert(0, distance)
            badcases.append(bad_info)
            miss_cnt.update(list(bad_info[-2]))
            redundant_cnt.update(list(bad_info[-1]))
            bad_cnt += 1

        start_idx += args.batch_size

    badcases.sort(key=itemgetter(0), reverse=True)

    output_dir = Path(args.output_dir)
    if not output_dir.exists():
        os.makedirs(output_dir)
    with open(output_dir / 'badcases.txt', 'w') as f:
        f.write(
            '\t'.join(
                [
                    'distance',
                    'image_fp',
                    'real_words',
                    'pred_words',
                    'miss_words',
                    'redundant_words',
                ]
            )
            + '\n'
        )
        for bad_info in badcases:
            f.write('\t'.join(map(str, bad_info)) + '\n')
    with open(output_dir / 'miss_words_stat.txt', 'w') as f:
        for word, num in miss_cnt.most_common():
            f.write('\t'.join([word, str(num)]) + '\n')
    with open(output_dir / 'redundant_words_stat.txt', 'w') as f:
        for word, num in redundant_cnt.most_common():
            f.write('\t'.join([word, str(num)]) + '\n')

    logger.info(
        "number of total cases: %d, time cost per image: %f, number of bad cases: %d"
        % (len(fn_labels_list), model_time_cost / len(fn_labels_list), bad_cnt)
    )


def read_input_file(in_fp):
    fn_labels_list = []
    with open(in_fp) as f:
        for line in f:
            fields = line.strip().split()
            fn_labels_list.append((fields[0], fields[1:]))
    return fn_labels_list


def compare_preds_to_reals(batch_preds, batch_reals, batch_img_fns, alphabet):
    for preds, reals, img_fn in zip(batch_preds, batch_reals, batch_img_fns):
        reals = [alphabet[int(_id)] for _id in reals if _id != '0']  # '0' is padding id
        if preds == reals:
            continue
        preds_set, reals_set = set(preds), set(reals)

        miss_words = reals_set.difference(preds_set)
        redundant_words = preds_set.difference(reals_set)
        yield [
            img_fn,
            ''.join(reals),
            ''.join(preds),
            ''.join(miss_words),
            ''.join(redundant_words),
        ]


if __name__ == '__main__':
    evaluate()