print("Overall Accuracy is: %.02f\n" %(vqaEval.accuracy['overall']))
print("Per Question Type Accuracy is the following:")
for quesType in vqaEval.accuracy['perQuestionType']:
print("%s : %.02f" %(quesType, vqaEval.accuracy['perQuestionType'][quesType]))
print("\n")
print("Per Answer Type Accuracy is the following:")
for ansType in vqaEval.accuracy['perAnswerType']:
print("%s : %.02f" %(ansType, vqaEval.accuracy['perAnswerType'][ansType]))
print("\n")
# demo how to use evalQA to retrieve low score result
evals = [quesId for quesId in vqaEval.evalQA if vqaEval.evalQA[quesId]<35] #35 is per question percentage accuracy
if len(evals) > 0:
print('ground truth answers')
randomEval = random.choice(evals)
randomAnn = vqa.loadQA(randomEval)
vqa.showQA(randomAnn)
print('\n')
print('generated answer (accuracy %.02f)'%(vqaEval.evalQA[randomEval]))
ann = vqaRes.loadQA(randomEval)[0]
print("Answer: %s\n" %(ann['answer']))
imgId = randomAnn[0]['image_id']
imgFilename = 'COCO_' + dataSubType + '_'+ str(imgId).zfill(12) + '.jpg'
if os.path.isfile(imgDir + imgFilename):
I = io.imread(imgDir + imgFilename)
plt.imshow(I)
plt.axis('off')
plt.show()
for quesType in vqaEval.accuracy['perQuestionType']:
print "%s : %.02f" % (quesType,
vqaEval.accuracy['perQuestionType'][quesType])
print "\n"
print "Per Answer Type Accuracy is the following:"
for ansType in vqaEval.accuracy['perAnswerType']:
print "%s : %.02f" % (ansType, vqaEval.accuracy['perAnswerType'][ansType])
print "\n"
# demo how to use evalQA to retrieve low score result
evals = [quesId for quesId in vqaEval.evalQA if vqaEval.evalQA[quesId] < 35
] #35 is per question percentage accuracy
if len(evals) > 0:
print 'ground truth answers'
randomEval = random.choice(evals)
randomAnn = vqa.loadQA(randomEval)
vqa.showQA(randomAnn)
print '\n'
print 'generated answer (accuracy %.02f)' % (vqaEval.evalQA[randomEval])
ann = vqaRes.loadQA(randomEval)[0]
print "Answer: %s\n" % (ann['answer'])
imgId = randomAnn[0]['image_id']
imgFilename = 'COCO_' + dataSubType + '_' + str(imgId).zfill(12) + '.jpg'
# if os.path.isfile(imgDir + imgFilename):
# I = io.imread(imgDir + imgFilename)
# plt.imshow(I)
# plt.axis('off')
# plt.show()
# plot accuracy for various question types
dataType, dataSubType)
quesFile = '%s/Questions/%s%s_%s_%s_questions.json' % (
dataDir, versionType, taskType, dataType, dataSubType)
imgDir = '%s/Images/%s/%s/' % (dataDir, dataType, dataSubType)
# initialize VQA api for QA annotations
vqa = VQA(annFile, quesFile)
# load and display QA annotations for given question types
"""
All possible quesTypes for abstract and mscoco has been provided in respective text files in ../QuestionTypes/ folder.
"""
annIds = vqa.getQuesIds(quesTypes='how many')
anns = vqa.loadQA(annIds)
randomAnn = random.choice(anns)
vqa.showQA([randomAnn])
imgId = randomAnn['image_id']
imgFilename = 'COCO_' + dataSubType + '_' + str(imgId).zfill(12) + '.jpg'
if os.path.isfile(imgDir + imgFilename):
I = io.imread(imgDir + imgFilename)
plt.imshow(I)
plt.axis('off')
plt.show()
# load and display QA annotations for given answer types
"""
ansTypes can be one of the following
yes/no
number
other
"""