213 lines
8.1 KiB
Python
Executable File
213 lines
8.1 KiB
Python
Executable File
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
#Licensed 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.
|
|
|
|
import os
|
|
import math
|
|
import random
|
|
import numpy as np
|
|
import cv2
|
|
|
|
import string
|
|
import lmdb
|
|
|
|
from ppocr.utils.utility import initial_logger
|
|
from ppocr.utils.utility import get_image_file_list
|
|
logger = initial_logger()
|
|
|
|
from .img_tools import process_image, get_img_data
|
|
|
|
|
|
class LMDBReader(object):
|
|
def __init__(self, params):
|
|
if params['mode'] != 'train':
|
|
self.num_workers = 1
|
|
else:
|
|
self.num_workers = params['num_workers']
|
|
self.lmdb_sets_dir = params['lmdb_sets_dir']
|
|
self.char_ops = params['char_ops']
|
|
self.image_shape = params['image_shape']
|
|
self.loss_type = params['loss_type']
|
|
self.max_text_length = params['max_text_length']
|
|
self.mode = params['mode']
|
|
if params['mode'] == 'train':
|
|
self.batch_size = params['train_batch_size_per_card']
|
|
else:
|
|
self.batch_size = params['test_batch_size_per_card']
|
|
|
|
def load_hierarchical_lmdb_dataset(self):
|
|
lmdb_sets = {}
|
|
dataset_idx = 0
|
|
for dirpath, dirnames, filenames in os.walk(self.lmdb_sets_dir + '/'):
|
|
if not dirnames:
|
|
env = lmdb.open(
|
|
dirpath,
|
|
max_readers=32,
|
|
readonly=True,
|
|
lock=False,
|
|
readahead=False,
|
|
meminit=False)
|
|
txn = env.begin(write=False)
|
|
num_samples = int(txn.get('num-samples'.encode()))
|
|
lmdb_sets[dataset_idx] = {"dirpath":dirpath, "env":env, \
|
|
"txn":txn, "num_samples":num_samples}
|
|
dataset_idx += 1
|
|
return lmdb_sets
|
|
|
|
def print_lmdb_sets_info(self, lmdb_sets):
|
|
lmdb_info_strs = []
|
|
for dataset_idx in range(len(lmdb_sets)):
|
|
tmp_str = " %s:%d," % (lmdb_sets[dataset_idx]['dirpath'],
|
|
lmdb_sets[dataset_idx]['num_samples'])
|
|
lmdb_info_strs.append(tmp_str)
|
|
lmdb_info_strs = ''.join(lmdb_info_strs)
|
|
logger.info("DataSummary:" + lmdb_info_strs)
|
|
return
|
|
|
|
def close_lmdb_dataset(self, lmdb_sets):
|
|
for dataset_idx in lmdb_sets:
|
|
lmdb_sets[dataset_idx]['env'].close()
|
|
return
|
|
|
|
def get_lmdb_sample_info(self, txn, index):
|
|
label_key = 'label-%09d'.encode() % index
|
|
label = txn.get(label_key)
|
|
if label is None:
|
|
return None
|
|
label = label.decode('utf-8')
|
|
img_key = 'image-%09d'.encode() % index
|
|
imgbuf = txn.get(img_key)
|
|
img = get_img_data(imgbuf)
|
|
if img is None:
|
|
return None
|
|
return img, label
|
|
|
|
def __call__(self, process_id):
|
|
if self.mode != 'train':
|
|
process_id = 0
|
|
|
|
def sample_iter_reader():
|
|
lmdb_sets = self.load_hierarchical_lmdb_dataset()
|
|
if process_id == 0:
|
|
self.print_lmdb_sets_info(lmdb_sets)
|
|
cur_index_sets = [1 + process_id] * len(lmdb_sets)
|
|
while True:
|
|
finish_read_num = 0
|
|
for dataset_idx in range(len(lmdb_sets)):
|
|
cur_index = cur_index_sets[dataset_idx]
|
|
if cur_index > lmdb_sets[dataset_idx]['num_samples']:
|
|
finish_read_num += 1
|
|
else:
|
|
sample_info = self.get_lmdb_sample_info(
|
|
lmdb_sets[dataset_idx]['txn'], cur_index)
|
|
cur_index_sets[dataset_idx] += self.num_workers
|
|
if sample_info is None:
|
|
continue
|
|
img, label = sample_info
|
|
outs = process_image(img, self.image_shape, label,
|
|
self.char_ops, self.loss_type,
|
|
self.max_text_length)
|
|
if outs is None:
|
|
continue
|
|
yield outs
|
|
|
|
if finish_read_num == len(lmdb_sets):
|
|
break
|
|
self.close_lmdb_dataset(lmdb_sets)
|
|
|
|
def batch_iter_reader():
|
|
batch_outs = []
|
|
for outs in sample_iter_reader():
|
|
batch_outs.append(outs)
|
|
if len(batch_outs) == self.batch_size:
|
|
yield batch_outs
|
|
batch_outs = []
|
|
if len(batch_outs) != 0:
|
|
yield batch_outs
|
|
|
|
return batch_iter_reader
|
|
|
|
|
|
class SimpleReader(object):
|
|
def __init__(self, params):
|
|
if params['mode'] != 'train':
|
|
self.num_workers = 1
|
|
else:
|
|
self.num_workers = params['num_workers']
|
|
if params['mode'] != 'test':
|
|
self.img_set_dir = params['img_set_dir']
|
|
self.label_file_path = params['label_file_path']
|
|
self.char_ops = params['char_ops']
|
|
self.image_shape = params['image_shape']
|
|
self.loss_type = params['loss_type']
|
|
self.max_text_length = params['max_text_length']
|
|
self.mode = params['mode']
|
|
if params['mode'] == 'train':
|
|
self.batch_size = params['train_batch_size_per_card']
|
|
elif params['mode'] == 'eval':
|
|
self.batch_size = params['test_batch_size_per_card']
|
|
else:
|
|
self.batch_size = 1
|
|
self.infer_img = params['infer_img']
|
|
|
|
def __call__(self, process_id):
|
|
if self.mode != 'train':
|
|
process_id = 0
|
|
|
|
def sample_iter_reader():
|
|
if self.mode == 'test':
|
|
image_file_list = get_image_file_list(self.infer_img)
|
|
for single_img in image_file_list:
|
|
img = cv2.imread(single_img)
|
|
if img.shape[-1]==1 or len(list(img.shape))==2:
|
|
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
|
norm_img = process_image(img, self.image_shape)
|
|
yield norm_img
|
|
else:
|
|
with open(self.label_file_path, "rb") as fin:
|
|
label_infor_list = fin.readlines()
|
|
img_num = len(label_infor_list)
|
|
img_id_list = list(range(img_num))
|
|
random.shuffle(img_id_list)
|
|
for img_id in range(process_id, img_num, self.num_workers):
|
|
label_infor = label_infor_list[img_id_list[img_id]]
|
|
substr = label_infor.decode('utf-8').strip("\n").split("\t")
|
|
img_path = self.img_set_dir + "/" + substr[0]
|
|
img = cv2.imread(img_path)
|
|
if img.shape[-1]==1 or len(list(img.shape))==2:
|
|
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
|
if img is None:
|
|
logger.info("{} does not exist!".format(img_path))
|
|
continue
|
|
label = substr[1]
|
|
outs = process_image(img, self.image_shape, label,
|
|
self.char_ops, self.loss_type,
|
|
self.max_text_length)
|
|
if outs is None:
|
|
continue
|
|
yield outs
|
|
|
|
def batch_iter_reader():
|
|
batch_outs = []
|
|
for outs in sample_iter_reader():
|
|
batch_outs.append(outs)
|
|
if len(batch_outs) == self.batch_size:
|
|
yield batch_outs
|
|
batch_outs = []
|
|
if len(batch_outs) != 0:
|
|
yield batch_outs
|
|
|
|
if self.mode != 'test':
|
|
return batch_iter_reader
|
|
return sample_iter_reader
|