116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
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import copy
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import cv2
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import numpy as np
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from python.predict_rec import RecPredictor
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from python.predict_det import DetPredictor
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from vector_search import Graph_Index
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from utils import logger
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from utils import config
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from utils.get_image_list import get_image_list
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def split_datafile(data_file, image_root):
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gallery_images = []
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gallery_docs = []
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with open(datafile) as f:
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lines = f.readlines()
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for i, line in enumerate(lines):
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line = line.strip().split("\t")
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if line[0] == 'image_id':
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continue
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image_file = os.path.join(image_root, line[3])
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image_doc = line[1]
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gallery_images.append(image_file)
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gallery_docs.append(image_doc)
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return gallery_images, gallery_docs
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class SystemPredictor(object):
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def __init__(self, config):
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self.config = config
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self.rec_predictor = RecPredictor(config)
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self.det_predictor = DetPredictor(config)
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assert 'IndexProcess' in config.keys(), "Index config not found ... "
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self.indexer(config['IndexProcess'])
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self.return_k = self.config['IndexProcess']['infer']['return_k']
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self.search_budget = self.config['IndexProcess']['infer']['search_budget']
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def indexer(self, config):
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if 'build' in config.keys() and config['build']['enable']: # build the index from scratch
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with open(config['build']['datafile']) as f:
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lines = f.readlines()
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gallery_images, gallery_docs = split_datafile(config['build']['data_file'], config['build']['image_root'])
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# extract gallery features
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gallery_features = np.zeros([len(gallery_images), config['build']['embedding_size']], dtype=np.float32)
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for i, image_file in enumerate(gallery_images):
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img = cv2.imread(image_file)[:, :, ::-1]
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rec_feat = self.rec_predictor.predict(img)
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gallery_features[i,:] = rec_feat
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# train index
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self.Searcher = Graph_Index(dist_type=config['build']['dist_type'])
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self.Searcher.build(gallery_vectors=gallery_features, gallery_docs=gallery_docs,
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pq_size=config['build']['pq_size'], index_path=config['build']['index_path'])
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else: # load local index
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self.Searcher = Graph_Index(dist_type=config['build']['dist_type'])
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self.Searcher.load(config['infer']['index_path'])
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def predict(self, img):
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output = []
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results = self.det_predictor.predict(img)
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for result in results:
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#print(result)
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xmin, xmax, ymin, ymax = result["bbox"].astype("int")
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crop_img = img[xmin:xmax, ymin:ymax, :].copy()
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rec_results = self.rec_predictor.predict(crop_img)
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result["featrue"] = rec_results
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scores, docs = self.Searcher.search(query=rec_results, return_k=self.return_k, search_budget=self.search_budget)
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result["ret_docs"] = docs
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result["ret_scores"] = scores
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output.append(result)
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return output
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def main(config):
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system_predictor = SystemPredictor(config)
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image_list = get_image_list(config["Global"]["infer_imgs"])
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assert config["Global"]["batch_size"] == 1
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for idx, image_file in enumerate(image_list):
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img = cv2.imread(image_file)[:, :, ::-1]
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output = system_predictor.predict(img)
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#print(output)
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return
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(args.config, overrides=args.override, show=True)
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main(config)
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