代码拉取完成,页面将自动刷新
同步操作将从 cyahua/cnocr 强制同步,此操作会覆盖自 Fork 仓库以来所做的任何修改,且无法恢复!!!
确定后同步将在后台操作,完成时将刷新页面,请耐心等待。
# coding: utf-8
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
""" 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()
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。