image id of the COCO Benchmarks about coco-analyze HOT 2 OPEN

import json
with open('./coco_train2014_occlusion_crowding_benchmarks.json','r') as fp: 
    occl_benchmarks = json.load(fp)

# this will print the criteria for defining each benchmark and the number of instances it contains
# n_v: number of visible keypoints
# n_o: number of overlaps with IoU > 0.1
# n_i: number of instances in that benchmark
for bi in occl_benchmarks: 
    print("n_v: {}, n_o:{}, n_i:{}".format(bi['num_visible_keypoints'], bi['num_overlaps'], len(bi['gtIds']))

import json
with open('./coco_train2014_size_benchmarks.json','r') as fp: 
    size_benchmarks = json.load(fp)

# this will print the criteria for defining each benchmark and the number of instances it contains
# a_l: area range label
# a_s: area range size in pixels
# n_i: number of instances in that benchmark
for bi in size_benchmarks: 
    print("a_l: {}, a_s:{}, n_i:{}".format(bi['areaRngLbl'], bi['areaRng'], len(bi['gtIds'])))

# ... load the coco annotations ...
b_i = 0 # choose the benchmark index you want, for occlusion b_i is 0 to 11 for size b_i is 0 to 3
anns = coco_kps.loadAnns(size_benchmarks[b_i]['gtIds'])
image_ids = [a['image_id'] for a in anns]