# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ @File Description: # 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 python ./coco_tools/json_AnnoSta.py \ --json_path=./annotations/instances_val2017.json \ --csv_path=./anno_sta/annos.csv \ --png_shape_path=./anno_sta/annos_shape.png \ --png_shapeRate_path=./anno_sta/annos_shapeRate.png \ --png_pos_path=./anno_sta/annos_pos.png \ --png_posEnd_path=./anno_sta/annos_posEnd.png \ --png_cat_path=./anno_sta/annos_cat.png \ --png_objNum_path=./anno_sta/annos_objNum.png \ --get_relative=True """ import os import json import argparse import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt shp_rate_bins = [ 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, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.4, 2.6, 3, 3.5, 4, 5 ] def check_dir(check_path, show=True): if os.path.isdir(check_path): check_directory = check_path else: check_directory = os.path.dirname(check_path) if len(check_directory) > 0 and not os.path.exists(check_directory): os.makedirs(check_directory) if show: print('make dir:', check_directory) def js_anno_sta(js_path, csv_path, png_shape_path, png_shapeRate_path, png_pos_path, png_posEnd_path, png_cat_path, png_objNum_path, get_relative, image_keyname, anno_keyname): print('json read...\n') with open(js_path, 'r') as load_f: data = json.load(load_f) df_img = pd.DataFrame(data[image_keyname]) sns.jointplot(y='height', x='width', data=df_img, kind='hex') plt.close() df_img = df_img.rename(columns={ "id": "image_id", "height": "image_height", "width": "image_width" }) df_anno = pd.DataFrame(data[anno_keyname]) df_anno[['pox_x', 'pox_y', 'width', 'height']] = pd.DataFrame(df_anno[ 'bbox'].values.tolist()) df_anno['width'] = df_anno['width'].astype(int) df_anno['height'] = df_anno['height'].astype(int) df_merge = pd.merge(df_img, df_anno, on="image_id") if png_shape_path is not None: check_dir(png_shape_path) sns.jointplot(y='height', x='width', data=df_merge, kind='hex') plt.savefig(png_shape_path) plt.close() print('png save to', png_shape_path) if get_relative: png_shapeR_path = png_shape_path.replace('.png', '_Relative.png') df_merge['heightR'] = df_merge['height'] / df_merge['image_height'] df_merge['widthR'] = df_merge['width'] / df_merge['image_width'] sns.jointplot(y='heightR', x='widthR', data=df_merge, kind='hex') plt.savefig(png_shapeR_path) plt.close() print('png save to', png_shapeR_path) if png_shapeRate_path is not None: check_dir(png_shapeRate_path) plt.figure(figsize=(12, 8)) df_merge['shape_rate'] = (df_merge['width'] / df_merge['height']).round(1) df_merge['shape_rate'].value_counts( sort=False, bins=shp_rate_bins).plot( kind='bar', title='images shape rate') plt.xticks(rotation=20) plt.savefig(png_shapeRate_path) plt.close() print('png save to', png_shapeRate_path) if png_pos_path is not None: check_dir(png_pos_path) sns.jointplot(y='pox_y', x='pox_x', data=df_merge, kind='hex') plt.savefig(png_pos_path) plt.close() print('png save to', png_pos_path) if get_relative: png_posR_path = png_pos_path.replace('.png', '_Relative.png') df_merge['pox_yR'] = df_merge['pox_y'] / df_merge['image_height'] df_merge['pox_xR'] = df_merge['pox_x'] / df_merge['image_width'] sns.jointplot(y='pox_yR', x='pox_xR', data=df_merge, kind='hex') plt.savefig(png_posR_path) plt.close() print('png save to', png_posR_path) if png_posEnd_path is not None: check_dir(png_posEnd_path) df_merge['pox_y_end'] = df_merge['pox_y'] + df_merge['height'] df_merge['pox_x_end'] = df_merge['pox_x'] + df_merge['width'] sns.jointplot(y='pox_y_end', x='pox_x_end', data=df_merge, kind='hex') plt.savefig(png_posEnd_path) plt.close() print('png save to', png_posEnd_path) if get_relative: png_posEndR_path = png_posEnd_path.replace('.png', '_Relative.png') df_merge['pox_y_endR'] = df_merge['pox_y_end'] / df_merge[ 'image_height'] df_merge['pox_x_endR'] = df_merge['pox_x_end'] / df_merge[ 'image_width'] sns.jointplot( y='pox_y_endR', x='pox_x_endR', data=df_merge, kind='hex') plt.savefig(png_posEndR_path) plt.close() print('png save to', png_posEndR_path) if png_cat_path is not None: check_dir(png_cat_path) plt.figure(figsize=(12, 8)) df_merge['category_id'].value_counts().sort_index().plot( kind='bar', title='obj category') plt.savefig(png_cat_path) plt.close() print('png save to', png_cat_path) if png_objNum_path is not None: check_dir(png_objNum_path) plt.figure(figsize=(12, 8)) df_merge['image_id'].value_counts().value_counts().sort_index().plot( kind='bar', title='obj number per image') # df_merge['image_id'].value_counts().value_counts(bins=np.linspace(1,31,16)).sort_index().plot(kind='bar', title='obj number per image') plt.xticks(rotation=20) plt.savefig(png_objNum_path) plt.close() print('png save to', png_objNum_path) if csv_path is not None: check_dir(csv_path) df_merge.to_csv(csv_path) print('csv save to', csv_path) def get_args(): parser = argparse.ArgumentParser( description='Json Images Infomation Statistic') # Parameters parser.add_argument( '--json_path', type=str, help='json path to statistic images information') parser.add_argument( '--csv_path', type=str, default=None, help='csv path to save statistic images information, default None, do not save' ) parser.add_argument( '--png_shape_path', type=str, default=None, help='png path to save statistic images shape information, default None, do not save' ) parser.add_argument( '--png_shapeRate_path', type=str, default=None, help='png path to save statistic images shape rate information, default None, do not save' ) parser.add_argument( '--png_pos_path', type=str, default=None, help='png path to save statistic pos information, default None, do not save' ) parser.add_argument( '--png_posEnd_path', type=str, default=None, help='png path to save statistic end pos information, default None, do not save' ) parser.add_argument( '--png_cat_path', type=str, default=None, help='png path to save statistic category information, default None, do not save' ) parser.add_argument( '--png_objNum_path', type=str, default=None, help='png path to save statistic images object number information, default None, do not save' ) parser.add_argument( '--get_relative', type=bool, default=True, help='if True, get relative result') parser.add_argument( '--image_keyname', type=str, default='images', help='image key name in json, default images') parser.add_argument( '--anno_keyname', type=str, default='annotations', help='annotation key name in json, default annotations') parser.add_argument( '--Args_show', type=str, default='True', help='Args_show(default: True), if True, show args info') args = parser.parse_args() if args.Args_show.lower() == 'true': print('Args'.center(100, '-')) for k, v in vars(args).items(): print('%s = %s' % (k, v)) print() return args if __name__ == '__main__': args = get_args() js_anno_sta(args.json_path, args.csv_path, args.png_shape_path, args.png_shapeRate_path, args.png_pos_path, args.png_posEnd_path, args.png_cat_path, args.png_objNum_path, args.get_relative, args.image_keyname, args.anno_keyname)