IndexError：索引 14708 超出尺寸 295 的轴 0 的范围

我正在尝试用 yolo 制作对象检测软件，但出现了这个错误，我迷失了方向 谁能帮我 ！！ （代码不完整，如果这篇文章有任何错误，我很抱歉，因为我是新的 Stackoverflow）。该教程来自

Traceback (most recent call last):
  File "d:/opencv/objdetect_yolo.py", line 66, in <module>
    findobj(output,img)
  File "d:/opencv/objdetect_yolo.py", line 33, in findobj
    cofidence = scores[classId]
IndexError: index 14708 is out of bounds for axis 0 with size 295

IndexError：索引 14708 超出尺寸 295 的轴 0 的范围

import numpy as np 
import cv2

cap = cv2.VideoCapture(0)
whT = 320

classespath = 'coco.names.txt'
classes = []

with open(classespath,'rt')as f:
    classes = f.read().rstrip('\n').split('\n')
#print (classes)
#print(len(classes))

modelConfiguration = 'yolov3.cfg'
modelWeights = 'yolov3.weights'

net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

def findobj(outputs,img):
    hT, wT , cT = img.shape
    bbox = []
    classIds = []
    confs = []


    for output in outputs:
        for det in outputs:
            scores = det[5:]
            classId = np.argmax(scores)
            cofidence = scores[classId]
            if float(0.5) < cofidence:

            
                w,h = int(det[2]*wT),int(det[3]*hT)
                x,y = int((det[0]*wT) - w/2), int((det[1]*hT) - h/2)
                bbox.append([x,y,w,h])
                classIds.append(classId)
                confs.append(float(cofidence))
              




     
while True:
    succes, img = cap.read()

    blob = cv2.dnn.blobFromImage(img,1/255,(whT,whT),[0,0,0],1,crop=False)
    net.setInput(blob)

    layerNames = net.getLayerNames()
    #print(layerNames)
    outputNames = [layerNames[i[0]-1]for i in net.getUnconnectedOutLayers() ]
    #print(outputNames)
    #print(net.getUnconnectedOutLayers())
    output = net.forward(outputNames)


    findobj(output,img)


    cv2.imshow("objdetect",img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

您似乎遇到了问题，因为np.argmax将为您提供最大元素的原始数量而不是索引。因此，如果您有一个 3x3 矩阵，argmax 函数会将矩阵视为 9x1 线而不是 3x3 正方形。

# The matrix:
[[1, 2, 3],
 [4, 5, 6],
 [7, 8, 9]]

#will be treated as:
[1, 2, 3, 4, 5, 6, 7, 8, 9]

The 文档 https://numpy.org/doc/stable/reference/generated/numpy.argmax.html建议以下解决方案：

classId = np.unravel_index(np.argmax(scores, axis=None), scores.shape)