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109 lines
4.1 KiB
109 lines
4.1 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 numpy as np
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from src.text_connector.detector import detect
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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("--result_path", type=str, default="", help="Image path.")
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parser.add_argument("--label_path", type=str, default="", help="label path.")
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args_opt = parser.parse_args()
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def get_pred(img_file, result_path):
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file_name = img_file.split('.')[0]
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proposal_file = os.path.join(result_path, file_name + "_0.bin")
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mask_file = os.path.join(result_path, file_name + "_1.bin")
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proposal = np.fromfile(proposal_file, dtype=np.float16).reshape(1000, 5)
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proposal_mask = np.fromfile(mask_file, dtype=np.int8).reshape(1000)
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return proposal, proposal_mask
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def get_img_metas(imgSize):
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org_width, org_height = imgSize
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h_scale = 576 / org_height
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w_scale = 960 / org_width
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return np.array([576, 960, h_scale, w_scale])
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def get_gt_box(img_file, label_path):
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label_file = os.path.join(label_path, img_file.replace("jpg", "txt"))
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file = open(label_file)
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lines = file.readlines()
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gt_boxs = []
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for line in lines:
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label_info = line.split(",")
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print(label_info)
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gt_boxs.append([int(label_info[0]), int(label_info[1]), int(label_info[2]), int(label_info[3])])
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#print(line)
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#print(gt_boxs)
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return gt_boxs
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def ctpn_infer_test(dataset_path='', result_path='', label_path=''):
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output_dir = "./output/"
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output_img_dir = "./output_img/"
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img_files = os.listdir(dataset_path)
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for file in img_files:
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print("processing image: ", file)
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from PIL import Image, ImageDraw
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img = Image.open(dataset_path + '/' + file)
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proposal, proposal_mask = get_pred(file, result_path)
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img_size = img.size
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img_metas = get_img_metas(img_size)
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all_box_tmp = proposal
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all_mask_tmp = np.expand_dims(proposal_mask, 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[:2].astype(np.int32)
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bboxes = detect(textsegs, scores[:, np.newaxis], shape)
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draw = ImageDraw.Draw(img)
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image_h = img_metas[2]
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image_w = img_metas[3]
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gt_boxs = get_gt_box(file, label_path)
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for gt_box in gt_boxs:
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gt_x1 = gt_box[0]
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gt_y1 = gt_box[1]
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gt_x2 = gt_box[2]
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gt_y2 = gt_box[3]
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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_" + file.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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img.save(output_img_dir + file)
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if __name__ == '__main__':
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ctpn_infer_test(args_opt.dataset_path, args_opt.result_path, args_opt.label_path)
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