""" Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 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 numpy as np import copy import pickle import lmdb # install lmdb by "pip install lmdb" import base64 class ImageFeaturesH5Reader(object): """ Reader class """ def __init__(self, features_path): self.features_path = features_path self.env = lmdb.open(self.features_path, max_readers=1, readonly=True, lock=False, readahead=False, meminit=False) with self.env.begin(write=False) as txn: self._image_ids = pickle.loads(txn.get('keys'.encode())) self.features = [None] * len(self._image_ids) self.num_boxes = [None] * len(self._image_ids) self.boxes = [None] * len(self._image_ids) self.boxes_ori = [None] * len(self._image_ids) def __len__(self): return len(self._image_ids) def __getitem__(self, image_id): image_id = str(image_id).encode() index = self._image_ids.index(image_id) # Read chunk from file everytime if not loaded in memory. with self.env.begin(write=False) as txn: item = pickle.loads(txn.get(image_id)) image_id = item['image_id'] image_h = int(item['image_h']) image_w = int(item['image_w']) num_boxes = int(item['num_boxes']) features = np.frombuffer(base64.b64decode(item["features"]), dtype=np.float32).reshape(num_boxes, 2048) boxes = np.frombuffer(base64.b64decode(item['boxes']), dtype=np.float32).reshape(num_boxes, 4) g_feat = np.sum(features, axis=0) / num_boxes num_boxes = num_boxes + 1 features = np.concatenate([np.expand_dims(g_feat, axis=0), features], axis=0) image_location = np.zeros((boxes.shape[0], 5), dtype=np.float32) image_location[:, :4] = boxes image_location[:, 4] = (image_location[:, 3] - image_location[:, 1]) * \ (image_location[:, 2] - image_location[:, 0]) / (float(image_w) * float(image_h)) image_location_ori = copy.deepcopy(image_location) image_location[:, 0] = image_location[:, 0] / float(image_w) image_location[:, 1] = image_location[:, 1] / float(image_h) image_location[:, 2] = image_location[:, 2] / float(image_w) image_location[:, 3] = image_location[:, 3] / float(image_h) g_location = np.array([0, 0, 1, 1, 1]) image_location = np.concatenate([np.expand_dims(g_location, axis=0), image_location], axis=0) g_location_ori = np.array([0, 0, image_w, image_h, image_w * image_h]) image_location_ori = np.concatenate([np.expand_dims(g_location_ori, axis=0), image_location_ori], axis=0) data_json = {"features": features, "num_boxes": num_boxes, "image_location": image_location, "image_location_ori": image_location_ori } return data_json