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256 lines
9.1 KiB
256 lines
9.1 KiB
# Copyright (c) 2022 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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''' |
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@File Description: |
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# json文件annotations信息，生成统计结果csv，对象框shape、对象看shape比例、对象框起始位置、对象结束位置、对象结束位置、对象类别、单个图像对象数量的分布 |
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python ./coco_tools/json_AnnoSta.py \ |
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--json_path=./annotations/instances_val2017.json \ |
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--csv_path=./anno_sta/annos.csv \ |
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--png_shape_path=./anno_sta/annos_shape.png \ |
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--png_shapeRate_path=./anno_sta/annos_shapeRate.png \ |
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--png_pos_path=./anno_sta/annos_pos.png \ |
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--png_posEnd_path=./anno_sta/annos_posEnd.png \ |
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--png_cat_path=./anno_sta/annos_cat.png \ |
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--png_objNum_path=./anno_sta/annos_objNum.png \ |
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--get_relative=True |
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''' |
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import os |
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import json |
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import argparse |
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import numpy as np |
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import pandas as pd |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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shp_rate_bins = [ |
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0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, |
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1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.4, 2.6, 3, 3.5, 4, 5 |
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] |
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def check_dir(check_path, show=True): |
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if os.path.isdir(check_path): |
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check_directory = check_path |
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else: |
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check_directory = os.path.dirname(check_path) |
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if not os.path.exists(check_directory): |
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os.makedirs(check_directory) |
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if show: |
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print('make dir:', check_directory) |
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def js_anno_sta(js_path, csv_path, png_shape_path, png_shapeRate_path, |
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png_pos_path, png_posEnd_path, png_cat_path, png_objNum_path, |
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get_relative, image_keyname, anno_keyname): |
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print('json read...\n') |
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with open(js_path, 'r') as load_f: |
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data = json.load(load_f) |
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df_img = pd.DataFrame(data[image_keyname]) |
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sns.jointplot('height', 'width', data=df_img, kind='hex') |
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plt.close() |
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df_img = df_img.rename(columns={ |
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"id": "image_id", |
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"height": "image_height", |
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"width": "image_width" |
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}) |
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df_anno = pd.DataFrame(data[anno_keyname]) |
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df_anno[['pox_x', 'pox_y', 'width', 'height']] = pd.DataFrame(df_anno[ |
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'bbox'].values.tolist()) |
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df_anno['width'] = df_anno['width'].astype(int) |
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df_anno['height'] = df_anno['height'].astype(int) |
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df_merge = pd.merge(df_img, df_anno, on="image_id") |
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if png_shape_path is not None: |
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check_dir(png_shape_path) |
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sns.jointplot('height', 'width', data=df_merge, kind='hex') |
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plt.savefig(png_shape_path) |
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plt.close() |
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print('png save to', png_shape_path) |
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if get_relative: |
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png_shapeR_path = png_shape_path.replace('.png', '_Relative.png') |
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df_merge['heightR'] = df_merge['height'] / df_merge['image_height'] |
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df_merge['widthR'] = df_merge['width'] / df_merge['image_width'] |
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sns.jointplot('heightR', 'widthR', data=df_merge, kind='hex') |
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plt.savefig(png_shapeR_path) |
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plt.close() |
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print('png save to', png_shapeR_path) |
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if png_shapeRate_path is not None: |
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check_dir(png_shapeRate_path) |
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plt.figure(figsize=(12, 8)) |
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df_merge['shape_rate'] = (df_merge['width'] / |
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df_merge['height']).round(1) |
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df_merge['shape_rate'].value_counts( |
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sort=False, bins=shp_rate_bins).plot( |
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kind='bar', title='images shape rate') |
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plt.xticks(rotation=20) |
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plt.savefig(png_shapeRate_path) |
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plt.close() |
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print('png save to', png_shapeRate_path) |
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if png_pos_path is not None: |
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check_dir(png_pos_path) |
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sns.jointplot('pox_y', 'pox_x', data=df_merge, kind='hex') |
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plt.savefig(png_pos_path) |
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plt.close() |
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print('png save to', png_pos_path) |
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if get_relative: |
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png_posR_path = png_pos_path.replace('.png', '_Relative.png') |
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df_merge['pox_yR'] = df_merge['pox_y'] / df_merge['image_height'] |
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df_merge['pox_xR'] = df_merge['pox_x'] / df_merge['image_width'] |
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sns.jointplot('pox_yR', 'pox_xR', data=df_merge, kind='hex') |
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plt.savefig(png_posR_path) |
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plt.close() |
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print('png save to', png_posR_path) |
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if png_posEnd_path is not None: |
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check_dir(png_posEnd_path) |
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df_merge['pox_y_end'] = df_merge['pox_y'] + df_merge['height'] |
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df_merge['pox_x_end'] = df_merge['pox_x'] + df_merge['width'] |
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sns.jointplot('pox_y_end', 'pox_x_end', data=df_merge, kind='hex') |
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plt.savefig(png_posEnd_path) |
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plt.close() |
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print('png save to', png_posEnd_path) |
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if get_relative: |
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png_posEndR_path = png_posEnd_path.replace('.png', '_Relative.png') |
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df_merge['pox_y_endR'] = df_merge['pox_y_end'] / df_merge[ |
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'image_height'] |
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df_merge['pox_x_endR'] = df_merge['pox_x_end'] / df_merge[ |
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'image_width'] |
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sns.jointplot('pox_y_endR', 'pox_x_endR', data=df_merge, kind='hex') |
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plt.savefig(png_posEndR_path) |
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plt.close() |
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print('png save to', png_posEndR_path) |
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if png_cat_path is not None: |
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check_dir(png_cat_path) |
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plt.figure(figsize=(12, 8)) |
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df_merge['category_id'].value_counts().sort_index().plot( |
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kind='bar', title='obj category') |
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plt.savefig(png_cat_path) |
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plt.close() |
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print('png save to', png_cat_path) |
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if png_objNum_path is not None: |
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check_dir(png_objNum_path) |
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plt.figure(figsize=(12, 8)) |
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df_merge['image_id'].value_counts().value_counts().sort_index().plot( |
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kind='bar', title='obj number per image') |
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# df_merge['image_id'].value_counts().value_counts(bins=np.linspace(1,31,16)).sort_index().plot(kind='bar', title='obj number per image') |
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plt.xticks(rotation=20) |
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plt.savefig(png_objNum_path) |
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plt.close() |
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print('png save to', png_objNum_path) |
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if csv_path is not None: |
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check_dir(csv_path) |
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df_merge.to_csv(csv_path) |
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print('csv save to', csv_path) |
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def get_args(): |
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parser = argparse.ArgumentParser( |
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description='Json Images Infomation Statistic') |
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# parameters |
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parser.add_argument( |
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'--json_path', |
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type=str, |
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help='json path to statistic images information') |
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parser.add_argument( |
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'--csv_path', |
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type=str, |
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default=None, |
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help='csv path to save statistic images information, default None, do not save' |
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) |
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parser.add_argument( |
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'--png_shape_path', |
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type=str, |
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default=None, |
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help='png path to save statistic images shape information, default None, do not save' |
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) |
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parser.add_argument( |
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'--png_shapeRate_path', |
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type=str, |
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default=None, |
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help='png path to save statistic images shape rate information, default None, do not save' |
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) |
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parser.add_argument( |
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'--png_pos_path', |
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type=str, |
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default=None, |
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help='png path to save statistic pos information, default None, do not save' |
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) |
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parser.add_argument( |
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'--png_posEnd_path', |
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type=str, |
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default=None, |
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help='png path to save statistic end pos information, default None, do not save' |
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) |
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parser.add_argument( |
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'--png_cat_path', |
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type=str, |
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default=None, |
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help='png path to save statistic category information, default None, do not save' |
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) |
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parser.add_argument( |
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'--png_objNum_path', |
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type=str, |
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default=None, |
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help='png path to save statistic images object number information, default None, do not save' |
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) |
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parser.add_argument( |
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'--get_relative', |
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type=bool, |
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default=True, |
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help='if True, get relative result') |
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parser.add_argument( |
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'--image_keyname', |
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type=str, |
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default='images', |
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help='image key name in json, default images') |
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parser.add_argument( |
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'--anno_keyname', |
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type=str, |
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default='annotations', |
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help='annotation key name in json, default annotations') |
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parser.add_argument( |
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'-Args_show', |
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'--Args_show', |
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type=bool, |
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default=True, |
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help='Args_show(default: True), if True, show args info') |
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args = parser.parse_args() |
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if args.Args_show: |
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print('Args'.center(100, '-')) |
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for k, v in vars(args).items(): |
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print('%s = %s' % (k, v)) |
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print() |
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return args |
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if __name__ == '__main__': |
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args = get_args() |
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js_anno_sta(args.json_path, args.csv_path, args.png_shape_path, |
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args.png_shapeRate_path, args.png_pos_path, |
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args.png_posEnd_path, args.png_cat_path, args.png_objNum_path, |
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args.get_relative, args.image_keyname, args.anno_keyname)
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