# 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 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('height', '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('height', '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('heightR', '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('pox_y', '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('pox_yR', '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('pox_y_end', '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('pox_y_endR', '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',
'--Args_show',
type=bool,
default=True,
help='Args_show(default: True), if True, show args info')
args = parser.parse_args()
if args.Args_show:
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)