问idxs =cv2.dnn.NMSBoxes(方框、信任、MIN_CORP、NMS_THRESH) TypeError:无法解析‘分数’。输入参数不提供序列协议

EN

C:\Users\User\Downloads\Social_Distancing_Detection_Real_Time>python Run.py
[INFO] loading YOLO from disk...
[INFO] setting preferable backend and target to CUDA...
[INFO] accessing video stream...
[ WARN:0] global D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\dnn.cpp (1447) cv::dnn::dnn4_v20211004::Net::Impl::setUpNet DNN module was not built with CUDA backend; switching to CPU
Traceback (most recent call last):
  File "C:\Users\User\Downloads\Social_Distancing_Detection_Real_Time\Run.py", line 77, in <module>
    results = detect_people(frame, net, ln,
  File "C:\Users\User\Downloads\Social_Distancing_Detection_Real_Time\mylib\detection.py", line 58, in detect_people
    idxs = cv2.dnn.NMSBoxes(boxes, confidence, MIN_CORP, NMS_THRESH)
TypeError: Can't parse 'scores'. Input argument doesn't provide sequence protocol
[ WARN:1] global D:\a\opencv-python\opencv-python\opencv\modules\videoio\src\cap_msmf.cpp (438) `anonymous-namespace'::SourceReaderCB::~SourceReaderCB terminating async callback

#import the necessary packages
from .config import NMS_THRESH, MIN_CORP, People_Counter
import numpy as np
import cv2

def detect_people(frame, net, In, personIdx = 0):
    #grab the dimensions of the frame and initialize the list of results
    (H, W) = frame.shape[:2]
    results = []
    
    #construct a blob from the input frame and then perform a forward
    #pass of the YOLO object detector, giving us our boarding boxes
    #add associated probabilities
    blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
        swapRB=True, crop=False)
    net.setInput(blob)
    layerOutputs = net.forward(In)
        
    #initialize out lists of detected bounding boxes, centroids and 
    #confidence, respectively
    boxes = []
    centroids = []
    confidences = []
    
    #loop over each of the layer outputs
    for output in layerOutputs:
        #for detection in output;
        for detection in output:
            #extract the class ID and confidence[i.e., probability)
            #of the current object detection
            scores = detection[5:]
            classID = np.argmax(scores)
            confidence = scores[classID]
            
            #filter detections by (1) ensuring that the object
            #detected was a person and (2) that the minimum
            #confidence is met
            if classID == personIdx and confidence > MIN_CORP:
                #scale the bounding box coordinates back relative to 
                #the size of the image, keeping in mind that YOLO
                #actually returns the center (x,y) -coordinates of 
                #the bounding box followed by the boxes' width and height
                box = detection[0:4] * np.array([W, H, W, H])
                (centerX, centerY, width, height) = box.astype("int")
                
                #use the center (x,y) -coordinates to derive the top
                #and left corner of the bounding box
                x = int(centerX - (width / 2))
                y = int(centerY - (height / 2))
                    
                #update our list of bounding box coordinates,
                #centroids and confidences
                boxes.append([x, y, int(width), int(height)])
                centroids.append((centerX, centerY))
                confidences.append(float(confidence))                  
 
    #apply non-maxim suppression to suppress weak, overlapping bounding boxes
    idxs = cv2.dnn.NMSBoxes(boxes, confidence, MIN_CORP, NMS_THRESH)
    #print('Total people count:', len(idxs))
    #compute the total people counter
    #if People_Counter:
        #human_count = "Human count: {}".format(len(idxs))
        #cv2.putText(frame, human_count, (470, frame.shape[0] - 75), cv2.FONT_HERSHEY_SIMPLEX, 0.70, (0, 0, 0), 2)
    
    #ensure at least one detection exists
    if len(idxs) > 0:
    #loop over the indexes we are keeping
        for i in idxs.flatten():
            #extract the bounding box coordinates
            (x, y) = (boxes[i][0], boxes[i][1])
            (w, h) = (boxes[i][2], boxes[i][3])
        
            #update our results list to consist of the person
            #prediction probability, bounding box coordinates,
            #and the centroids
            r = (confidences[i], (x, y, x + w, y + h), centroids[i])
            results.append(r)
        
    #return the list of the results
    return results