evaluate.py

from pycocotools.coco import COCO
from pycocoevalcap.eval import COCOEvalCap
import matplotlib.pyplot as plt
import skimage.io as io
import pylab
import sys  # hadie
pylab.rcParams['figure.figsize'] = (10.0, 8.0)

import json
from json import encoder
encoder.FLOAT_REPR = lambda o: format(o, '.3f')

# create coco object and cocoRes object
coco = COCO(sys.argv[2])  # load the original annotation file # edited by hadie 
cocoRes = coco.loadRes(sys.argv[1])  # edited by hadie
# create cocoEval object by taking coco and cocoRes
cocoEval = COCOEvalCap(coco, cocoRes)

# evaluate on a subset of images by setting
# cocoEval.params['image_id'] = cocoRes.getImgIds()
# please remove this line when evaluating the full validation set
cocoEval.params['image_id'] = cocoRes.getImgIds()

# evaluate results
# SPICE will take a few minutes the first time, but speeds up due to caching
cocoEval.evaluate()
# print output evaluation scores
for metric, score in cocoEval.eval.items():
    print('%s: %.3f' % (metric, score))