需要一些帮助来减少使用OpenCV 3.4.1 DNN模块进行MobileNet SSD对象检测所检测到的对象类别

Question

我正在运行python脚本来检测和跟踪具有令人满意结果的人员。问题在于，我只想跟踪人物，而Caffe模型只跟踪约20个对象，为汽车和其他对象创建的矩形覆盖了整个图片。是否有机会减少检测到的物体数量？从脚本中删除它们失败，只有标签消失了。

#Import the neccesary libraries
import numpy as np
import argparse
import cv2 

# construct the argument parse 
parser = argparse.ArgumentParser(
    description='Script to run MobileNet-SSD object detection network ')
parser.add_argument("--video", help="path to video file. If empty, camera's stream will be used")
parser.add_argument("--prototxt", default="MobileNetSSD_deploy.prototxt",
help='Path to text network file: ''MobileNetSSD_deploy.prototxt for Caffe model or ')
parser.add_argument("--weights", default="MobileNetSSD_deploy.caffemodel",
help='Path to weights: ''MobileNetSSD_deploy.caffemodel for Caffe model or ')
parser.add_argument("--thr", default=0.2, type=float, help="confidence threshold to filter out weak detections")
args = parser.parse_args()

#Load the Caffe model 
net = cv2.dnn.readNetFromCaffe(args.prototxt, args.weights)

# Labels of Network.
classNames = { 0: 'background',
    1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat',
    5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair',
    10: 'cow', 11: 'diningtable', 12: 'dog', 13: 'horse',
    14: 'motorbike', 15: 'person', 16: 'pottedplant',
    17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor' }

# Open video file or capture device. 
if args.video:
    cap = cv2.VideoCapture(args.video)
else:
    cap = cv2.VideoCapture(0)
    # cap.open("http://192.168.0.17:8081")

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    frame_resized = cv2.resize(frame,(300, 300)) # resize frame for prediction

    # MobileNet requires fixed dimensions for input image so we have to resize to 300x300 pixels.
    blob = cv2.dnn.blobFromImage(frame_resized, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
    #Set to network the input blob 
    net.setInput(blob)
    #Prediction of network
    detections = net.forward()

    #Size of frame resize (300x300)
    cols = frame_resized.shape[1] 
    rows = frame_resized.shape[0]
    #For get the class and location of object detected, 
    # There is a fix index for class, location and confidence
    # value in @detections array .
    for i in range(detections.shape[2]):
        confidence = detections[0, 0, i, 2] #Confidence of prediction 
        if confidence > args.thr: # Filter prediction 
            class_id = int(detections[0, 0, i, 1]) # Class label

            # Object location 
            xLeftBottom = int(detections[0, 0, i, 3] * cols) 
            yLeftBottom = int(detections[0, 0, i, 4] * rows)
            xRightTop   = int(detections[0, 0, i, 5] * cols)
            yRightTop   = int(detections[0, 0, i, 6] * rows)

            # Factor for scale to original size of frame
            heightFactor = frame.shape[0]/300.0  
            widthFactor = frame.shape[1]/300.0 
            # Scale object detection to frame
            xLeftBottom = int(widthFactor * xLeftBottom) 
            yLeftBottom = int(heightFactor * yLeftBottom)
            xRightTop   = int(widthFactor * xRightTop)
            yRightTop   = int(heightFactor * yRightTop)
            # Draw location of object  
            cv2.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),
                          (0, 255, 0),2)

            # Draw label and confidence of prediction in frame resized
            if class_id in classNames:
                label = classNames[class_id] + ": " + str(confidence)
                labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)

                yLeftBottom = max(yLeftBottom, labelSize[1])
                cv2.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]),
                                     (xLeftBottom + labelSize[0], yLeftBottom + baseLine),
                                     (255, 255, 255), cv2.FILLED)
                cv2.putText(frame, label, (xLeftBottom, yLeftBottom),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))

                print(label) #print class and confidence

    cv2.namedWindow("frame", cv2.WINDOW_NORMAL)
    cv2.imshow("frame", frame)
    if cv2.waitKey(1) >= 0:  # Break with ESC 
        break

Answer 1

大家好，希望您一切都好置信度= detections [0，0，i，2]，我不理解这些参数[0,0，i，2]？有人可以解释吗