def compute_IoU_mask(prediction, gt, overlap_threshold):
IoU = jaccard(prediction, gt)
# for each prediction select gt with the largest IoU and ignore the others
for i in range(len(prediction)):
maxj = IoU[i, :].argmax()
IoU[i, :maxj] = 0
IoU[i, (maxj + 1):] = 0
# make a mask of all "matched" predictions vs gt
return IoU >= overlap_threshold
def compute_IoU_mask(prediction, gt, overlap_threshold):
IoU = jaccard(prediction, gt)
# for each prediction select gt with the largest IoU and ignore the others
for i in range(len(prediction)):
maxj = IoU[i, :].argmax()
IoU[i, :maxj] = 0
IoU[i, (maxj + 1):] = 0
# make a mask of all "matched" predictions vs gt
return IoU >= overlap_threshold
def compute_IoU(prediction, gt, confidence, confidence_threshold):
IoU = jaccard(prediction, gt)
IoU[confidence < confidence_threshold, :] = 0
return IoU