class_docstr: Streaming interface to allow for any source of predictions. 
  Initialize it, count predictions one by one, then print confusion matrix and intersection-union score
class_docstr_tok: ["Streaming","interface","to","allow","for","any","source","of","predictions",".","Initialize","it",",","count","predictions","one","by","one",",","then","print","confusion","matrix","and","intersection-union","score"]
code: def count_gt(self, ground_truth):
      return self.confusion_matrix[ground_truth, :].sum()
  
file_path: torch-points3d/torch_points3d/metrics/confusion_matrix.py
imports: import numpy
  import torch
  import os
label_desc: Use image node_id 10 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.count_gt(ground_truth) and returns: self