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...
在infer函数中通过屏蔽后处理部分的代码,来对比运行情况,如下: # 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, 6...
SOFT-NMS (二) (non maximum suppression,非极大值抑制) 2019-09-30 16:32 − import numpy as npboxes = np.array([[200, 200, 400, 400], [220, 220, 420, 420], [200, 240, 400, 440], [240, 200, 440, 400], [1, 1, 2, 2]], dtype=np.fl... tangjunjun 0 909 Unsuper...
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...
实现极大值抑制non max suppression,其中boxes是不同boxes的坐标,scores是不同boxes预测的分数,max_boxes是保留的最大box的个数。 iou_threshold是一个阈值,去掉大于这个阈值的所有boxes。 下面是和python对比的效果: #!/usr/bin/env python# _*_ coding:utf-8_*_importnumpyasnpimporttensorflowastf ...
importnumpyasnpfromlsnmsimportnms,wbc# Create boxes: approx 30 pixels wide / high in Pascal VOC format:# bbox = (x0, y0, x1, y1) with x1 > x0, y1 > y0image_size=10_000n_predictions=10_000topleft=np.random.uniform(0.0,high=image_size,size=(n_predictions,2))wh=np.random.unifo...
- image_size is a numpy.ndarray containing the image’s original size [image_height, image_width] Returns a tuple of (boxes, box_confidences, box_class_probs): - boxes: a list of numpy.ndarrays of shape (grid_height, grid_width, anchor_boxes, 4) containing the processed boundary bo...
@image is a numpy array '''clone = image.copy() (rects, weights) = HOGCV.detectMultiScale(image, winStride=(4,4), padding=(8,8), scale=1.05) print(rects) print(weights)# draw the original bounding boxesfor(x, y, w, h)inrects:print("inside rects:",x,y,w,h) ...
import onnxruntime as rt import numpy as np import cv2 import os import onnx import torch import torch import sys path_roiheads = os.path.join(path,'detectron2_roiheads.onnx') # put your frozen onnx model here sess_roiheads = rt.InferenceSession(path_roiheads) input_roiheads_n = [...