coco_eval.py

#-*- coding:utf-8 -*-
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab,json
pylab.rcParams['figure.figsize'] = (10.0, 8.0)
def get_img_id(file_name):
    ls = []
    myset = []
    annos = json.load(open(file_name, 'r'))
    for anno in annos:
      ls.append(anno['image_id'])
    myset = {}.fromkeys(ls).keys()
    return myset
if __name__ == '__main__':
    annType = ['segm', 'bbox', 'keypoints']#set iouType to 'segm', 'bbox' or 'keypoints'
    annType = annType[1] # specify type here
    cocoGt_file = '/home/common/datasets/coco/annotations/instances_val2014.json'
    cocoGt = COCO(cocoGt_file)#取得标注集中coco json对象
    cocoDt_file = './results/pt_cocoapi.json'
    imgIds = get_img_id(cocoDt_file)
    print (len(imgIds))
    cocoDt = cocoGt.loadRes(cocoDt_file)#取得结果集中image json对象
    imgIds = sorted(imgIds)#按顺序排列coco标注集image_id
    imgIds = imgIds[0:5000]#标注集中的image数据
    cocoEval = COCOeval(cocoGt, cocoDt, annType)
    cocoEval.params.imgIds = imgIds#参数设置
    cocoEval.evaluate()#评价
    cocoEval.accumulate()#积累
    print("\n\n")
    print(" tips: tf-slim, 4954 samples")
    print(" nms_threshold=0.5")
    print(" score_threshold=0.001")
    print(" bbox_data_type:float32")
    cocoEval.summarize()#总结