146 lines
5.5 KiB
Python
146 lines
5.5 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 copy
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import numpy as np
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import cv2
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import faiss
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import pickle
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from paddleclas.deploy.utils import logger, config
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from paddleclas.deploy.utils.get_image_list import get_image_list
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from paddleclas.deploy.utils.draw_bbox import draw_bbox_results
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from paddleclas.deploy.python.predict_rec import RecPredictor
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from paddleclas.deploy.python.predict_det import DetPredictor
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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.return_k = self.config['IndexProcess']['return_k']
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index_dir = self.config["IndexProcess"]["index_dir"]
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assert os.path.exists(os.path.join(
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index_dir, "vector.index")), "vector.index not found ..."
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assert os.path.exists(os.path.join(
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index_dir, "id_map.pkl")), "id_map.pkl not found ... "
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if config['IndexProcess'].get("dist_type") == "hamming":
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self.Searcher = faiss.read_index_binary(
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os.path.join(index_dir, "vector.index"))
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else:
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self.Searcher = faiss.read_index(
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os.path.join(index_dir, "vector.index"))
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with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
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self.id_map = pickle.load(fd)
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def append_self(self, results, shape):
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results.append({
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"class_id": 0,
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"score": 1.0,
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"bbox":
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np.array([0, 0, shape[1], shape[0]]), # xmin, ymin, xmax, ymax
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"label_name": "foreground",
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})
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return results
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def nms_to_rec_results(self, results, thresh=0.1):
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filtered_results = []
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x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
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y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
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x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
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y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
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scores = np.array([r["rec_scores"] for r in results])
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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while order.size > 0:
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i = order[0]
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= thresh)[0]
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order = order[inds + 1]
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filtered_results.append(results[i])
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return filtered_results
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def predict(self, img):
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output = []
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# st1: get all detection results
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results = self.det_predictor.predict(img)
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# st2: add the whole image for recognition to improve recall
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results = self.append_self(results, img.shape)
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# st3: recognition process, use score_thres to ensure accuracy
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for result in results:
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preds = {}
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xmin, ymin, xmax, ymax = result["bbox"].astype("int")
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crop_img = img[ymin:ymax, xmin:xmax, :].copy()
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rec_results = self.rec_predictor.predict(crop_img)
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preds["bbox"] = [xmin, ymin, xmax, ymax]
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scores, docs = self.Searcher.search(rec_results, self.return_k)
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# just top-1 result will be returned for the final
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if self.config["IndexProcess"]["dist_type"] == "hamming":
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if scores[0][0] <= self.config["IndexProcess"][
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"hamming_radius"]:
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preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
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preds["rec_scores"] = scores[0][0]
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output.append(preds)
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else:
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if scores[0][0] >= self.config["IndexProcess"]["score_thres"]:
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preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
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preds["rec_scores"] = scores[0][0]
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output.append(preds)
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# st5: nms to the final results to avoid fetching duplicate results
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output = self.nms_to_rec_results(
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output, self.config["Global"]["rec_nms_thresold"])
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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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draw_bbox_results(img, output, image_file)
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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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