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()
# plot accuracy for various question types
f3.close()
exit(0)
#gt_mat = gt_mat.astype(int)
print(gt_mat.shape)
np.savez("gt_mat.npy", gt_mat)
exit(0)
exit(0)
exit(0)
#randomAnn = random.choice(anns)
##print("HELLO1")
#print(randomAnn)
quest = vqa.showQA([randomAnn])
print("This is quest")
print(quest)
imgId = randomAnn['image_id']
image_feat = coco_train[imgId]
print(image_feat)
print(image_feat.shape)
print(randomAnn['question_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()
exit(0)
dataSubType)
quesFile = '%s/Questions/%s_%s_%s_questions.json' % (dataDir, taskType,
dataType, dataSubType)
imgDir = '%s/%s/' % (dataDir, 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
"""
dataSubType = "train2014"
annFile = "%s/Annotations/%s_%s_annotations.json" % (dataDir, dataType, dataSubType)
quesFile = "%s/Questions/%s_%s_%s_questions.json" % (dataDir, 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
"""
