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119 lines
5.2 KiB
119 lines
5.2 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""Evaluation for CTPN"""
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import os
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import argparse
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import time
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import numpy as np
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.common import set_seed
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from src.ctpn import CTPN
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from src.config import config
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from src.dataset import create_ctpn_dataset
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from src.text_connector.detector import detect
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set_seed(1)
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parser = argparse.ArgumentParser(description="CTPN evaluation")
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parser.add_argument("--dataset_path", type=str, default="", help="Dataset path.")
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parser.add_argument("--image_path", type=str, default="", help="Image path.")
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parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path.")
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
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def ctpn_infer_test(dataset_path='', ckpt_path='', img_dir=''):
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"""ctpn infer."""
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print("ckpt path is {}".format(ckpt_path))
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ds = create_ctpn_dataset(dataset_path, batch_size=config.test_batch_size, repeat_num=1, is_training=False)
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config.batch_size = config.test_batch_size
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total = ds.get_dataset_size()
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print("*************total dataset size is {}".format(total))
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net = CTPN(config, is_training=False)
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param_dict = load_checkpoint(ckpt_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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eval_iter = 0
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print("\n========================================\n")
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print("Processing, please wait a moment.")
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img_basenames = []
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output_dir = os.path.join(os.getcwd(), "submit")
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if not os.path.exists(output_dir):
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os.mkdir(output_dir)
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for file in os.listdir(img_dir):
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img_basenames.append(os.path.basename(file))
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for data in ds.create_dict_iterator():
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img_data = data['image']
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img_metas = data['image_shape']
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gt_bboxes = data['box']
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gt_labels = data['label']
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gt_num = data['valid_num']
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start = time.time()
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# run net
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output = net(img_data, gt_bboxes, gt_labels, gt_num)
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gt_bboxes = gt_bboxes.asnumpy()
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gt_labels = gt_labels.asnumpy()
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gt_num = gt_num.asnumpy().astype(bool)
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end = time.time()
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proposal = output[0]
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proposal_mask = output[1]
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print("start to draw pic")
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for j in range(config.test_batch_size):
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img = img_basenames[config.test_batch_size * eval_iter + j]
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all_box_tmp = proposal[j].asnumpy()
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all_mask_tmp = np.expand_dims(proposal_mask[j].asnumpy(), axis=1)
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using_boxes_mask = all_box_tmp * all_mask_tmp
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textsegs = using_boxes_mask[:, 0:4].astype(np.float32)
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scores = using_boxes_mask[:, 4].astype(np.float32)
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shape = img_metas.asnumpy()[0][:2].astype(np.int32)
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bboxes = detect(textsegs, scores[:, np.newaxis], shape)
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from PIL import Image, ImageDraw
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im = Image.open(img_dir + '/' + img)
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draw = ImageDraw.Draw(im)
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image_h = img_metas.asnumpy()[j][2]
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image_w = img_metas.asnumpy()[j][3]
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gt_boxs = gt_bboxes[j][gt_num[j], :]
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for gt_box in gt_boxs:
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gt_x1 = gt_box[0] / image_w
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gt_y1 = gt_box[1] / image_h
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gt_x2 = gt_box[2] / image_w
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gt_y2 = gt_box[3] / image_h
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draw.line([(gt_x1, gt_y1), (gt_x1, gt_y2), (gt_x2, gt_y2), (gt_x2, gt_y1), (gt_x1, gt_y1)],\
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fill='green', width=2)
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file_name = "res_" + img.replace("jpg", "txt")
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output_file = os.path.join(output_dir, file_name)
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f = open(output_file, 'w')
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for bbox in bboxes:
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x1 = bbox[0] / image_w
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y1 = bbox[1] / image_h
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x2 = bbox[2] / image_w
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y2 = bbox[3] / image_h
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draw.line([(x1, y1), (x1, y2), (x2, y2), (x2, y1), (x1, y1)], fill='red', width=2)
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str_tmp = str(int(x1)) + "," + str(int(y1)) + "," + str(int(x2)) + "," + str(int(y2))
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f.write(str_tmp)
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f.write("\n")
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f.close()
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im.save(img)
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percent = round(eval_iter / total * 100, 2)
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eval_iter = eval_iter + 1
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print("Iter {} cost time {}".format(eval_iter, end - start))
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print(' %s [%d/%d]' % (str(percent) + '%', eval_iter, total), end='\r')
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if __name__ == '__main__':
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ctpn_infer_test(args_opt.dataset_path, args_opt.checkpoint_path, img_dir=args_opt.image_path)
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