示例#1
0
 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
示例#3
0
 def compute_IoU(prediction, gt, confidence, confidence_threshold):
     IoU = jaccard(prediction, gt)
     IoU[confidence < confidence_threshold, :] = 0
     return IoU