#This is to convert dota-type annotations to coco positive frame annotations
#The reason is to do a vehicle detection, coco format annotations, but dota is more suitable for training
#The following is the code
#一些模块为了后面的可视化而增加
import os.path as osp
import os
from PIL import Image
import json
from matplotlib import pyplot as plt
#根据信息生成模板，此处要求bbox必须是xyxy标注
def coco_annotations(bbox,cid,bbox_id,img_id,iscrowd):
x1,y1,x2,y2=bbox
return {
'segmentation':[[x1,y1,x2,y1,x2,y2,x1,y2]],
'bbox':[x1,y1,x2-x1+1,y2-y1+1],
'category_id':cid,
'area':(y2-y1+1)*(x2-x1+1),
'iscrowd':iscrowd,
'image_id':img_id,
'id':bbox_id}
def coco_images(file_name,height,width,img_id):
return {
'file_name':file_name,
'height':height,
'width':width,
'id':img_id}
#从dota数据集中的txt格式标注中获取信息，函数中需要用到变量cls_name2id，categories
def deal_with_txt(label_path, img_id, anno_id):
annos = []
with open(label_path,'r') as gt:
gt_lines=gt.readlines()
for i in gt_lines:
iscrowd=int(i[-2])
i=i.split(' ')
cls_name=i[-2]
cid=cls_name2id[cls_name]
cood=i[:8]
cood=tuple(map(float,cood))
x1=min(cood[0],cood[2],cood[4],cood[6])
x2=max(cood[0],cood[2],cood[4],cood[6])
y1=min(cood[1],cood[3],cood[5],cood[7])
y2=max(cood[1],cood[3],cood[5],cood[7])
b=(x1,y1,x2,y2)
#下面使用coco_annotations函数把上面的读取数据输入
anno=coco_annotations(b,cid,anno_id,img_id,iscrowd)
annos.append(anno)
anno_id+=1
return annos,anno_id
def generate_coco_fmt(data_root,anno_root,categories,img_root):
'''
data_root是数据集路径
下面的路径是相对data_root的路径
anno_root是数据集的标注文件的路径
img_root是数据集的图片路径
categories需要事先得到
'''
img_id, anno_id = 0, 0
all_annos, all_images = [], []
for anno_txt in os.listdir(osp.join(data_root,anno_root)):
file_name=anno_txt.replace('txt','png')
label_path=osp.join(data_root,anno_root,anno_txt)
img_path=osp.join(data_root,img_root,file_name)
if osp.exists(img_path):
annos,anno_id=deal_with_txt(label_path,img_id,anno_id)
all_annos.extend(annos)
img=Image.open(img_path)
all_images.append(coco_images(osp.join(img_root,file_name),img.height,img.width,img_id))
img_id+=1
return{
'images':all_images,
"annotations": all_annos,
"categories": categories,
"type": "instance"
}
'''
这一部分的目的是为了获取必须提前获取的categories和cls_name2id
'''
classes=('plane', 'baseball-diamond', 'bridge', 'ground-track-field',
'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
'basketball-court', 'storage-tank', 'soccer-ball-field',
'roundabout', 'harbor', 'swimming-pool', 'helicopter')
cls_name2id={
}
categories=[]
for i in range(1,16):
cls_name2id.update({
classes[i-1]:i})
dict_cate={
'id':i,'name':classes[i-1],'supercategory':classes[i-1]}
categories.append(dict_cate)
#路径自己改一下记得
data_root='../data/DOTA1-split-1024'
anno_root='trainval1024/annfiles'
img_root='trainval1024/images'
dota_coco_fmt=generate_coco_fmt(data_root,anno_root,categories,img_root)
json.dump(dota_coco_fmt,open(osp.join(data_root,'train.json'),'a'),ensure_ascii=False)
Effect picture:
original txt contains information:
['597.0 429.0 614.0 432.0 608.0 473.0 591.0 470.0 small-vehicle 1\n', '409.0
647.0 410.0 667.0 343.0 677.0 342.0 658 .0 small-vehicle 0\n',
'434.0 959.0 505.0 955.0 507.0 978.0 436.0 980.0 large-vehicle 0\n',
'697.0 757.0 718.0 754.0 743.0 881.0 721.0 886.0 large-vehicle 0\n', '556.0 790.0 580.0 786 .0 603.0 913.0 578.0
918.0 large-vehicle 0\n',
'521.0 772.0 544.0 767.0 549.0 837.0 528.0 840.0 small-vehicle 0\n',
'456.0 790.0 479.0 789.0 505.0 939.0 483.0 940.0 large-vehicle 0\n', '480.0 781.0 503 .0 773.0 539.0 929.0 509.0
941.0 large-vehicle 0\n',
'607.0 729.0 633.0 723.0 680.0 898.0 647.0 909.0 large-vehicle 0\n',
'390.0 360.0 412.0 364.0 409.0 449.0 382.0 446.0 large-vehicle 0\n',
'751.0 21.0 747.0 43.0 584.0 0.0 669.0 0.0 large-vehicle 0\n',]
and visualize After the picture:
Of course, because the format of coco is all annotations The unified json files cannot be shown here:
so let me show you the result after the front frame is visualized.
If you want to know the visualization code, you can go to my history article.
If you write your own visualization, using opencv is much more convenient than the Image module. The Image module needs to switch between numpy and opencv.
