# 后处理 # boxout = nms(torch.tensor(output), conf_thres=0.4, iou_thres=0.5) # 利用非极大值抑制处理模型输出,conf_thres 为置信度阈值,iou_thres 为iou阈值 # pred_all = boxout[0].numpy() # 转换为numpy数组 # scale_coords([640, 640], pred_all[:, :4], img_bgr.shape, ratio_pad...
附完整实验代码 importnumpyasnp# import cv2importmatplotlib.pyplotaspltdet_result=np.array([[20,5,200,100,0.353],[50,50,200,200,0.624],[20,120,150,150,0.667],[250,250,400,350,0.5],[90,10,300,300,0.3],[40,220,280,380,0.46]])colors=['red','blue','green','yellow','pink','g...
probs按照概率从小到大排序【argsort函数返回的是数组值从小到大的索引值numpy中argsort函数用法】 list = [1, 2, 3, 44, 5, 6, 7,33, 9, 11] idxs = np.argsort(list) print(idxs) 输出: [0 1 2 4 5 6 8 9 7 3] ---②--- 接下来就是按照概率从大到小取出框,且框的重合度不可以高于...
import numpy as np from lsnms import nms, wbc # Create boxes: approx 30 pixels wide / high in Pascal VOC format: # bbox = (x0, y0, x1, y1) with x1 > x0, y1 > y0 image_size = 10_000 n_predictions = 10_000 topleft = np.random.uniform(0.0, high=image_size, size=(n_pr...
I suspect that it is convesion issue in torch.onnx. Could you help me with the issue? Thanks in advance~ Code to reproduce, [download the model]: import onnxruntime as rt import numpy as np import cv2 import os import onnx