Warning: file_get_contents(/data/phpspider/zhask/data//catemap/8/python-3.x/15.json): failed to open stream: No such file or directory in /data/phpspider/zhask/libs/function.php on line 167

Warning: Invalid argument supplied for foreach() in /data/phpspider/zhask/libs/tag.function.php on line 1116

Notice: Undefined index: in /data/phpspider/zhask/libs/function.php on line 180

Warning: array_chunk() expects parameter 1 to be array, null given in /data/phpspider/zhask/libs/function.php on line 181
C++ 在opencv detectMultiScale3中,levelWeights是什么意思?_C++_Python 3.x_Opencv_Object Detection_Haar Classifier - Fatal编程技术网

C++ 在opencv detectMultiScale3中,levelWeights是什么意思?

C++ 在opencv detectMultiScale3中,levelWeights是什么意思?,c++,python-3.x,opencv,object-detection,haar-classifier,C++,Python 3.x,Opencv,Object Detection,Haar Classifier,这里是我的HaarCascade实现,在这里我训练了自己的分类器 import cv2 import numpy as np body_classifier = cv2.CascadeClassifier('C:\\Users\\Nemi\\MasteringComputerVision_V1.00\\Haarcascades\\trainedHuman.xml') image = cv2.imread("twn2.jpg") #####HEREEEEEEEEEEEEEEEEEEEEEEEEE

这里是我的HaarCascade实现,在这里我训练了自己的分类器

import cv2
import numpy as np

body_classifier = cv2.CascadeClassifier('C:\\Users\\Nemi\\MasteringComputerVision_V1.00\\Haarcascades\\trainedHuman.xml')
image = cv2.imread("twn2.jpg")
#####HEREEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE
bodies,rejectLevels, levelWeights = body_classifier.detectMultiScale3(
    image,
    scaleFactor=1.1,
    minNeighbors=20,
    minSize=(24, 24),
    maxSize=(96,96),
    flags = cv2.CASCADE_SCALE_IMAGE,
    outputRejectLevels = True
    )
print(rejectLevels)
print(levelWeights)
i = 0
font = cv2.FONT_ITALIC
for (x,y,w,h) in bodies:
    cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,255),2)
    font = cv2.FONT_HERSHEY_SIMPLEX
    #cv2.putText(image,str(i)+str(":")+str(np.log(levelWeights[i][0])),(x,y), font,0.5,(255,255,255),2,cv2.LINE_AA)
    cv2.putText(image,str(levelWeights[i][0]),(x,y), font,0.5,(255,255,255),2,cv2.LINE_AA)
    i = i+1

cv2.imshow("Detection",image)
cv2.waitKey(0)
cv2.destroyAllWindows()
结果输出如下:


我想知道这个levelWeights是什么意思,为什么值这么小?如果此levelWeights可以在检测窗口的置信度形式中使用,我应该做什么?

levelWights
返回特定阶段的置信度。在您的情况下,
i
是检测到的对象,
0
表示第一阶段。它非常小,可能是因为对象太远,并且分类器最初经过训练以捕获较大尺寸的对象