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# 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.
import matplotlib.pyplot as plt
import json
import numpy as np
import argparse
from pycocotools.coco import COCO
from tqdm import tqdm
def median(data):
data.sort()
mid = len(data) // 2
median = (data[mid] + data[~mid]) / 2
return median
def draw_distribution(width, height, out_path):
w_bins = int((max(width) - min(width)) // 10)
h_bins = int((max(height) - min(height)) // 10)
plt.figure()
plt.subplot(221)
plt.hist(width, bins=w_bins, color='green')
plt.xlabel('Width rate *1000')
plt.ylabel('number')
plt.title('Distribution of Width')
plt.subplot(222)
plt.hist(height, bins=h_bins, color='blue')
plt.xlabel('Height rate *1000')
plt.title('Distribution of Height')
plt.savefig(out_path)
print(f'Distribution saved as {out_path}')
plt.show()
def get_ratio_infos(jsonfile, out_img, eval_size, small_stride):
coco = COCO(annotation_file=jsonfile)
allannjson = json.load(open(jsonfile, 'r'))
be_im_id = allannjson['annotations'][0]['image_id']
be_im_w = []
be_im_h = []
ratio_w = []
ratio_h = []
im_wid,im_hei=[],[]
for ann in tqdm(allannjson['annotations']):
if ann['iscrowd']:
continue
x0, y0, w, h = ann['bbox'][:]
if be_im_id == ann['image_id']:
be_im_w.append(w)
be_im_h.append(h)
else:
im_w = coco.imgs[be_im_id]['width']
im_h = coco.imgs[be_im_id]['height']
im_wid.append(im_w)
im_hei.append(im_h)
im_m_w = np.mean(be_im_w)
im_m_h = np.mean(be_im_h)
dis_w = im_m_w / im_w
dis_h = im_m_h / im_h
ratio_w.append(dis_w)
ratio_h.append(dis_h)
be_im_id = ann['image_id']
be_im_w = [w]
be_im_h = [h]
im_w = coco.imgs[be_im_id]['width']
im_h = coco.imgs[be_im_id]['height']
im_wid.append(im_w)
im_hei.append(im_h)
all_im_m_w = np.mean(im_wid)
all_im_m_h = np.mean(im_hei)
im_m_w = np.mean(be_im_w)
im_m_h = np.mean(be_im_h)
dis_w = im_m_w / im_w
dis_h = im_m_h / im_h
ratio_w.append(dis_w)
ratio_h.append(dis_h)
mid_w = median(ratio_w)
mid_h = median(ratio_h)
reg_ratio = []
ratio_all = ratio_h + ratio_w
for r in ratio_all:
if r < 0.2:
reg_ratio.append(r)
elif r < 0.4:
reg_ratio.append(r/2)
else:
reg_ratio.append(r/4)
reg_ratio = sorted(reg_ratio)
max_ratio = reg_ratio[int(0.95*len(reg_ratio))]
reg_max = round(max_ratio*eval_size/small_stride)
ratio_w = [i * 1000 for i in ratio_w]
ratio_h = [i * 1000 for i in ratio_h]
print(f'Suggested reg_range[1] is {reg_max+1}' )
print(f'Mean of all img_w is {all_im_m_w}')
print(f'Mean of all img_h is {all_im_m_h}')
print(f'Median of ratio_w is {mid_w}')
print(f'Median of ratio_h is {mid_h}')
print('all_img with box: ', len(ratio_h))
print('all_ann: ', len(allannjson['annotations']))
draw_distribution(ratio_w, ratio_h, out_img)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--json_path', type=str, default=None, help="Dataset json path.")
parser.add_argument(
'--eval_size', type=int, default=640, help="eval size.")
parser.add_argument(
'--small_stride', type=int, default=8, help="smallest stride.")
parser.add_argument(
'--out_img',
type=str,
default='box_distribution.jpg',
help="Name of distibution img.")
args = parser.parse_args()
get_ratio_infos(args.json_path, args.out_img, args.eval_size, args.small_stride)
if __name__ == "__main__":
main() |