145 lines
5.6 KiB
Python
Executable File
145 lines
5.6 KiB
Python
Executable File
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import math
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import random
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import numpy as np
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import cv2
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from ppocr.utils.utility import initial_logger
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from ppocr.utils.utility import get_image_file_list
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logger = initial_logger()
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from ppocr.data.rec.img_tools import resize_norm_img, warp
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from ppocr.data.cls.randaugment import RandAugment
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def random_crop(img):
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img_h, img_w = img.shape[:2]
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if img_w > img_h * 4:
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w = random.randint(img_h * 2, img_w)
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i = random.randint(0, img_w - w)
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img = img[:, i:i + w, :]
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return img
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class SimpleReader(object):
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def __init__(self, params):
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if params['mode'] != 'train':
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self.num_workers = 1
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else:
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self.num_workers = params['num_workers']
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if params['mode'] != 'test':
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self.img_set_dir = params['img_set_dir']
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self.label_file_path = params['label_file_path']
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self.use_gpu = params['use_gpu']
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self.image_shape = params['image_shape']
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self.mode = params['mode']
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self.infer_img = params['infer_img']
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self.use_distort = params['mode'] == 'train' and params['distort']
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self.randaug = RandAugment()
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self.label_list = params['label_list']
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if "distort" in params:
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self.use_distort = params['distort'] and params['use_gpu']
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if not params['use_gpu']:
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logger.info(
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"Distort operation can only support in GPU.Distort will be set to False."
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)
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if params['mode'] == 'train':
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self.batch_size = params['train_batch_size_per_card']
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self.drop_last = True
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else:
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self.batch_size = params['test_batch_size_per_card']
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self.drop_last = False
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self.use_distort = False
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def __call__(self, process_id):
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if self.mode != 'train':
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process_id = 0
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def get_device_num():
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if self.use_gpu:
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gpus = os.environ.get("CUDA_VISIBLE_DEVICES", "1")
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gpu_num = len(gpus.split(','))
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return gpu_num
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else:
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cpu_num = os.environ.get("CPU_NUM", 1)
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return int(cpu_num)
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def sample_iter_reader():
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if self.mode != 'train' and self.infer_img is not None:
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image_file_list = get_image_file_list(self.infer_img)
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for single_img in image_file_list:
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img = cv2.imread(single_img)
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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norm_img = resize_norm_img(img, self.image_shape)
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norm_img = norm_img[np.newaxis, :]
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yield norm_img
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else:
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with open(self.label_file_path, "rb") as fin:
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label_infor_list = fin.readlines()
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img_num = len(label_infor_list)
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img_id_list = list(range(img_num))
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random.shuffle(img_id_list)
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if sys.platform == "win32" and self.num_workers != 1:
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print("multiprocess is not fully compatible with Windows."
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"num_workers will be 1.")
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self.num_workers = 1
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if self.batch_size * get_device_num(
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) * self.num_workers > img_num:
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raise Exception(
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"The number of the whole data ({}) is smaller than the batch_size * devices_num * num_workers ({})".
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format(img_num, self.batch_size * get_device_num() *
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self.num_workers))
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for img_id in range(process_id, img_num, self.num_workers):
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label_infor = label_infor_list[img_id_list[img_id]]
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substr = label_infor.decode('utf-8').strip("\n").split("\t")
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label = self.label_list.index(substr[1])
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img_path = self.img_set_dir + "/" + substr[0]
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img = cv2.imread(img_path)
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if img is None:
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logger.info("{} does not exist!".format(img_path))
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continue
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if self.use_distort:
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img = warp(img, 10)
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img = self.randaug(img)
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norm_img = resize_norm_img(img, self.image_shape)
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norm_img = norm_img[np.newaxis, :]
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yield (norm_img, label)
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def batch_iter_reader():
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batch_outs = []
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for outs in sample_iter_reader():
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batch_outs.append(outs)
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if len(batch_outs) == self.batch_size:
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yield batch_outs
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batch_outs = []
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if not self.drop_last:
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if len(batch_outs) != 0:
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yield batch_outs
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if self.infer_img is None:
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return batch_iter_reader
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return sample_iter_reader
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