import io import math import random import os import cv2 import lmdb import numpy as np from PIL import Image from torch.utils.data import Dataset from openrec.preprocess import create_operators, transform class RatioDataSet(Dataset): def __init__(self, config, mode, logger, seed=None, epoch=1): super(RatioDataSet, self).__init__() self.ds_width = config[mode]['dataset'].get('ds_width', True) global_config = config['Global'] dataset_config = config[mode]['dataset'] loader_config = config[mode]['loader'] max_ratio = loader_config.get('max_ratio', 10) min_ratio = loader_config.get('min_ratio', 1) syn = dataset_config.get('syn', False) if syn: data_dir_list = [] data_dir = '../training_aug_lmdb_noerror/ep' + str(epoch) for dir_syn in os.listdir(data_dir): data_dir_list.append(data_dir + '/' + dir_syn) else: data_dir_list = dataset_config['data_dir_list'] self.padding = dataset_config.get('padding', True) self.padding_rand = dataset_config.get('padding_rand', False) self.padding_doub = dataset_config.get('padding_doub', False) self.do_shuffle = loader_config['shuffle'] self.seed = epoch data_source_num = len(data_dir_list) ratio_list = dataset_config.get('ratio_list', 1.0) if isinstance(ratio_list, (float, int)): ratio_list = [float(ratio_list)] * int(data_source_num) assert ( len(ratio_list) == data_source_num ), 'The length of ratio_list should be the same as the file_list.' self.lmdb_sets = self.load_hierarchical_lmdb_dataset( data_dir_list, ratio_list) for data_dir in data_dir_list: logger.info('Initialize indexs of datasets:%s' % data_dir) self.logger = logger self.data_idx_order_list = self.dataset_traversal() wh_ratio = np.around(np.array(self.get_wh_ratio())) self.wh_ratio = np.clip(wh_ratio, a_min=min_ratio, a_max=max_ratio) for i in range(max_ratio + 1): logger.info((1 * (self.wh_ratio == i)).sum()) self.wh_ratio_sort = np.argsort(self.wh_ratio) self.ops = create_operators(dataset_config['transforms'], global_config) self.need_reset = True in [x < 1 for x in ratio_list] self.error = 0 self.base_shape = dataset_config.get( 'base_shape', [[64, 64], [96, 48], [112, 40], [128, 32]]) self.base_h = 32 def get_wh_ratio(self): wh_ratio = [] for idx in range(self.data_idx_order_list.shape[0]): lmdb_idx, file_idx = self.data_idx_order_list[idx] lmdb_idx = int(lmdb_idx) file_idx = int(file_idx) wh_key = 'wh-%09d'.encode() % file_idx wh = self.lmdb_sets[lmdb_idx]['txn'].get(wh_key) if wh is None: img_key = f'image-{file_idx:09d}'.encode() img = self.lmdb_sets[lmdb_idx]['txn'].get(img_key) buf = io.BytesIO(img) w, h = Image.open(buf).size else: wh = wh.decode('utf-8') w, h = wh.split('_') wh_ratio.append(float(w) / float(h)) return wh_ratio def load_hierarchical_lmdb_dataset(self, data_dir_list, ratio_list): lmdb_sets = {} dataset_idx = 0 for dirpath, ratio in zip(data_dir_list, ratio_list): 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, 'ratio_num_samples': int(ratio * num_samples) } dataset_idx += 1 return lmdb_sets def dataset_traversal(self): lmdb_num = len(self.lmdb_sets) total_sample_num = 0 for lno in range(lmdb_num): total_sample_num += self.lmdb_sets[lno]['ratio_num_samples'] data_idx_order_list = np.zeros((total_sample_num, 2)) beg_idx = 0 for lno in range(lmdb_num): tmp_sample_num = self.lmdb_sets[lno]['ratio_num_samples'] end_idx = beg_idx + tmp_sample_num data_idx_order_list[beg_idx:end_idx, 0] = lno data_idx_order_list[beg_idx:end_idx, 1] = list( random.sample(range(1, self.lmdb_sets[lno]['num_samples'] + 1), self.lmdb_sets[lno]['ratio_num_samples'])) beg_idx = beg_idx + tmp_sample_num return data_idx_order_list def get_img_data(self, value): """get_img_data.""" if not value: return None imgdata = np.frombuffer(value, dtype='uint8') if imgdata is None: return None imgori = cv2.imdecode(imgdata, 1) if imgori is None: return None return imgori def resize_norm_img(self, data, gen_ratio, padding=True): img = data['image'] h = img.shape[0] w = img.shape[1] if self.padding_rand and random.random() < 0.5: padding = not padding imgW, imgH = self.base_shape[gen_ratio - 1] if gen_ratio <= 4 else [ self.base_h * gen_ratio, self.base_h ] use_ratio = imgW // imgH if use_ratio >= (w // h) + 2: self.error += 1 return None if not padding: resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) resized_w = imgW else: ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int( math.ceil(imgH * ratio * (random.random() + 0.5))) resized_w = min(imgW, resized_w) resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype('float32') resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 padding_im = np.zeros((3, imgH, imgW), dtype=np.float32) if self.padding_doub and random.random() < 0.5: padding_im[:, :, -resized_w:] = resized_image else: padding_im[:, :, :resized_w] = resized_image valid_ratio = min(1.0, float(resized_w / imgW)) data['image'] = padding_im data['valid_ratio'] = valid_ratio data['real_ratio'] = round(w / h) return data 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) return imgbuf, label def __getitem__(self, properties): img_width = properties[0] img_height = properties[1] idx = properties[2] ratio = properties[3] lmdb_idx, file_idx = self.data_idx_order_list[idx] lmdb_idx = int(lmdb_idx) file_idx = int(file_idx) sample_info = self.get_lmdb_sample_info( self.lmdb_sets[lmdb_idx]['txn'], file_idx) if sample_info is None: ratio_ids = np.where(self.wh_ratio == ratio)[0].tolist() ids = random.sample(ratio_ids, 1) return self.__getitem__([img_width, img_height, ids[0], ratio]) img, label = sample_info data = {'image': img, 'label': label} outs = transform(data, self.ops[:-1]) if outs is not None: outs = self.resize_norm_img(outs, ratio, padding=self.padding) if outs is None: ratio_ids = np.where(self.wh_ratio == ratio)[0].tolist() ids = random.sample(ratio_ids, 1) return self.__getitem__([img_width, img_height, ids[0], ratio]) outs = transform(outs, self.ops[-1:]) if outs is None: ratio_ids = np.where(self.wh_ratio == ratio)[0].tolist() ids = random.sample(ratio_ids, 1) return self.__getitem__([img_width, img_height, ids[0], ratio]) return outs def __len__(self): return self.data_idx_order_list.shape[0]