77 lines
2.6 KiB
Python
Executable File
77 lines
2.6 KiB
Python
Executable File
# Copyright (c) 2020 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 utility
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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import cv2
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import predict_system
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import copy
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import numpy as np
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import math
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import time
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import json
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if __name__ == "__main__":
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args = utility.parse_args()
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text_sys = predict_system.TextSystem(args)
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image_file_list = []
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label_file_path = "./eval_perform/gt_res/test_chinese_ic15_500_4pts.txt"
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img_set_path = "./eval_perform/"
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with open(label_file_path, "rb") as fin:
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lines = fin.readlines()
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for line in lines:
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substr = line.decode('utf-8').strip("\n").split("\t")
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if "lsvt" in substr[0]:
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continue
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image_file_list.append(substr[0])
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total_time_all = 0
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count = 0
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save_path = "./output/predict.txt"
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fout = open(save_path, "wb")
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for image_name in image_file_list:
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image_file = img_set_path + image_name
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img = cv2.imread(image_file)
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if img is None:
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logger.info("error in loading image:{}".format(image_file))
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continue
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count += 1
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total_time = 0
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starttime = time.time()
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dt_boxes, rec_res = text_sys(img)
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elapse = time.time() - starttime
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total_time_all += elapse
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print("Predict time of %s(%d): %.3fs" % (image_file, count, elapse))
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dt_num = len(dt_boxes)
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bbox_list = []
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for dno in range(dt_num):
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box = dt_boxes[dno]
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text, score = rec_res[dno]
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points = []
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for tno in range(len(box)):
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points.append([box[tno][0] * 1.0, box[tno][1] * 1.0])
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bbox_list.append({
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"transcription": text,
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"points": points,
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"scores": score * 1.0
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})
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otstr = image_name + "\t" + json.dumps(bbox_list) + "\n"
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fout.write(otstr.encode('utf-8'))
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avg_time = total_time_all / count
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logger.info("avg_time: {0}".format(avg_time))
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logger.info("avg_fps: {0}".format(1.0 / avg_time))
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fout.close()
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