coco数据集提取instance 和bbox
import os
import random,shutil
import cv2
from tqdm import tqdm
import numpy as np
from pycocotools.coco import COCO
from pycocotools import mask as coco_mask
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import imgviz
# annotation_file = 'datasets/coco2017_zip/annotations/instances_val2017.json'
# coco = COCO(annotation_file)
# catIds = coco.getCatIds()
# imgIds = coco.getImgIds()
# print(catIds)
headstr = """\
"""
objstr = """\
"""
tailstr = '''\
'''
def mkr(path):
if not os.path.exists(path):
os.makedirs(path) # 可以创建多级目录
def id2name(coco):
classes=dict()
for cls in coco.dataset['categories']:
classes[cls['id']]=cls['name']
return classes
def save_annotations_and_imgs(coco,dataset,filename,objs):
#将图片转为xml，例:COCO_train2017_000000196610.jpg-->COCO_train2017_000000196610.xml
dst_anno_dir = os.path.join(anno_dir, dataset)
mkr(dst_anno_dir)
anno_path=dst_anno_dir + '/' + filename[:-3]+'xml'
img_path=dataDir+dataset+'/'+filename
print("img_path: ", img_path)
dst_img_dir = os.path.join(img_dir, dataset)
mkr(dst_img_dir)
dst_imgpath=dst_img_dir+ '/' + filename
print("dst_imgpath: ", dst_imgpath)
img=cv2.imread(img_path)
#if (img.shape[2] == 1):
# print(filename + " not a RGB image")
# return
shutil.copy(img_path, dst_imgpath)
head=headstr % (filename, img.shape[1], img.shape[0], img.shape[2])
tail = tailstr
# write_xml(anno_path,head, objs, tail)
def showimg(coco,dataset,img,classes,cls_id,show=True):
global dataDir
I=Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name']))
#通过id，得到注释的信息
annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)
# print(annIds)
anns = coco.loadAnns(annIds)
# print(anns)
# coco.showAnns(anns)
objs = []
for ann in anns:
class_name=classes[ann['category_id']]
if class_name in classes_names:
print(class_name)
if 'bbox' in ann:
bbox=ann['bbox']
xmin = int(bbox[0])
ymin = int(bbox[1])
xmax = int(bbox[2] + bbox[0])
ymax = int(bbox[3] + bbox[1])
obj = [class_name, xmin, ymin, xmax, ymax]
objs.append(obj)
draw = ImageDraw.Draw(I)
draw.rectangle([xmin, ymin, xmax, ymax])
def save_colored_mask(mask, save_path):
lbl_pil = Image.fromarray(mask.astype(np.uint8), mode="P")
colormap = imgviz.label_colormap()
lbl_pil.putpalette(colormap.flatten())
lbl_pil.save(save_path)
if __name__ == '__main__':
savepath="datasets/coco2017_zip/my_coco/train2017/"
img_dir=savepath+'images'
anno_dir=savepath+'annotations'
mask_dir = savepath + 'mask_small'
datasets_list=['train2017']#['train2017', 'val2017'] #
#coco有80类，这里写要提取类的名字，以person为例
classes_dict = {'bottle':44,'chair':62,'couch':63,'potted plant':64,'dining table':67,'book':84,'bed':65}
classes_names = ['bottle','chair','couch','potted plant','dining table','book','bed']
# classes_names = ['bottle']
dataDir= 'datasets/coco2017_zip/'
for dataset in datasets_list:
#./COCO/annotations/instances_train2017.json
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataset)
#使用COCO API用来初始化注释数据
coco = COCO(annFile)
#获取COCO数据集中的所有类别
classes = id2name(coco)
print(classes)
#[1, 2, 3, 4, 6, 8]
classes_ids = coco.getCatIds(catNms=classes_names)
print(classes_ids)
for cls in classes_names:
#获取该类的id
cls_id=coco.getCatIds(catNms=[cls])
img_ids=coco.getImgIds(catIds=cls_id)
print(cls,len(img_ids))
# imgIds=img_ids[0:10]
for imgId in tqdm(img_ids):
img = coco.loadImgs(imgId)[0]
filename = img['file_name']
img_path=dataDir+dataset+'/'+filename
image = cv2.imread(img_path)
# print(filename)
# objs=showimg(coco, dataset, img, classes,classes_ids,show=False)
annIds = coco.getAnnIds(imgIds=img['id'], catIds=classes_ids, iscrowd=None)
if len(annIds)==0:
print(annIds)
# print('annIds:',len(annIds))
anns = coco.loadAnns(annIds)
bboxes = []
for ann in anns:
class_name=classes[ann['category_id']]
if class_name in classes_names:
# print(class_name)
if 'bbox' in ann:
bbox=ann['bbox']
xmin = int(bbox[0])
ymin = int(bbox[1])
xmax = int(bbox[2] + bbox[0])
ymax = int(bbox[3] + bbox[1])
bbox_ = [class_name, xmin, ymin, xmax, ymax]
bboxes.append(bbox_)
save_path = os.path.join(mask_dir,class_name)
if not os.path.exists(save_path):
os.mkdir(save_path)
# draw 2d bboxes
# cv2.rectangle(image, (xmin,ymin), (xmax,ymax), (255, 0, 255))
# get instance masks, and save each class instance into diffirent file path
# print('333',anns[0])
# exit()
mask = coco.annToMask(anns[0]) * anns[0]['category_id']
img_ = image.copy()
class_num = classes_dict[class_name]
img_[mask!=class_num] = 255
crop = img_[ymin:ymax,xmin:xmax]
# print([xmin,xmax,ymin,ymax])
new_filename = filename.split('.')[0]+'.png'
if os.path.isfile(os.path.join(save_path,new_filename)):
continue
else:
cv2.imwrite('{}/{}'.format(save_path,new_filename),crop)
# cv2.imwrite('datasets/coco2017_zip/my_coco/001.jpg',image)
# # get the whole one picture's instance mask
# mask = coco.annToMask(anns[0]) * anns[0]['category_id']
# for i in range(len(anns) - 1):
# mask += coco.annToMask(anns[i + 1]) * anns[i + 1]['category_id']
# save_colored_mask(mask,'{}/{}'.format('/home/yaoxing/workspace/datasets/coco2017_zip/my_coco/mask_whole',filename.split('.')[0]+'.png'))
# cv2.imwrite('{}/{}'.format('datasets/coco2017_zip/my_coco/images',filename),image)
# print(filename)
# exit()