Delet Kategori dari Dataset Coco
from pycocotools.coco import COCO
import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
import os
from os.path import join
from tqdm import tqdm
import json
class coco_category_filter:
"""
Downloads images of one category & filters jsons
to only keep annotations of this category
"""
def __init__(self, json_path, imgs_dir, categ='person'):
self.coco = COCO(json_path) # instanciate coco class
self.json_path = json_path
self.imgs_dir = imgs_dir
self.categ = categ
self.images = self.get_imgs_from_json()
def get_imgs_from_json(self):
"""returns image names of the desired category"""
# instantiate COCO specifying the annotations json path
# Specify a list of category names of interest
catIds = self.coco.getCatIds(catNms=[self.categ])
print("catIds: ", catIds)
# Get the corresponding image ids and images using loadImgs
imgIds = self.coco.getImgIds(catIds=catIds)
images = self.coco.loadImgs(imgIds)
print(f"{len(images)} images in '{self.json_path}' with '{self.categ}' instances")
self.catIds = catIds # list
return images
def save_imgs(self):
"""saves the images of this category"""
print("Saving the images with required categories ...")
os.makedirs(self.imgs_dir, exist_ok=True)
# Save the images into a local folder
################################################# Modified lines
session = requests.Session()
retry = Retry(connect=3, backoff_factor=0.5)
adapter = HTTPAdapter(max_retries=retry)
session.mount('http://', adapter)
session.mount('https://', adapter)
#################################################
for im in tqdm(self.images):
img_data = session.get(im['coco_url']).content
with open(os.path.join(self.imgs_dir, im['file_name']), 'wb') as handler:
handler.write(img_data)
def filter_json_by_category(self, new_json_path):
"""creates a new json with the desired category"""
# {'supercategory': 'person', 'id': 1, 'name': 'person'}
### Filter images:
print("Filtering the annotations ... ")
json_parent = os.path.split(new_json_path)[0]
os.makedirs(json_parent, exist_ok=True)
imgs_ids = [x['id'] for x in self.images] # get img_ids of imgs with the category
new_imgs = [x for x in self.coco.dataset['images'] if x['id'] in imgs_ids]
catIds = self.catIds
### Filter annotations
new_annots = [x for x in self.coco.dataset['annotations'] if x['category_id'] in catIds]
### Reorganize the ids
new_imgs, annotations = self.modify_ids(new_imgs, new_annots)
### Filter categories
new_categories = [x for x in self.coco.dataset['categories'] if x['id'] in catIds]
print("new_categories: ", new_categories)
data = {
"info": self.coco.dataset['info'],
"licenses": self.coco.dataset['licenses'],
"images": new_imgs,
"annotations": new_annots,
"categories": new_categories
}
print("saving json: ")
with open(new_json_path, 'w') as f:
json.dump(data, f)
def modify_ids(self, images, annotations):
"""
creates new ids for the images. I.e., reorganizes the ids and returns the dictionaries back
images: list of images dictionaries
imId_counter: image id starting from one (each dicto will start with id of last json +1)
"""
print("Reinitialicing images and annotation IDs ...")
### Images
old_new_imgs_ids = {} # necessary for the annotations!
for n,im in enumerate(images):
old_new_imgs_ids[images[n]['id']] = n+1 # dicto with old im_ids and new im_ids
images[n]['id'] = n+1 # reorganize the ids
### Annotations
for n,ann in enumerate(annotations):
annotations[n]['id'] = n+1
old_image_id = annotations[n]['image_id']
annotations[n]['image_id'] = old_new_imgs_ids[old_image_id] # replace im_ids in the annotations as well
return images, annotations
def main(subset, year, root_dir, category='person'):
json_file = join(os.path.split(root_dir)[0], 'instances_'+subset+year+'.json') # local path
imgs_dir = join(root_dir, category + '_' + subset)
new_json_file = join(root_dir, 'annotations', subset+".json")
coco_filter = coco_category_filter(json_file, imgs_dir, categ=category) # instanciate class
coco_filter.save_imgs()
coco_filter.filter_json_by_category(new_json_file)
if __name__ == '__main__':
subset, year='train', '2017'
root_dir = './datasets/COCO/annotations'
main(subset, year, root_dir, category='person')
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