Код для IVPort (интегрированной платы для нескольких камер) с Rasperry Pi для обнаружения объектов

Question

Я использую IVPORT (интегральная плата для нескольких камер с Raspberry Pi) для распознавания лиц.Я могу запустить код обнаружения лица для одной камеры Pi, но не для платы интеграции нескольких камер.Может ли любой орган рассказать, как изменить код для платы интеграции нескольких пи камеры.Вот код для одной пи камеры:

# USAGE
# python detect_faces_video.py --prototxt deploy.prototxt.txt --model 
res10_300x300_ssd_iter_140000.caffemodel

# import the necessary packages
from imutils.video import VideoStream
import numpy as np
import argparse
import imutils
import time
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# initialize the video stream and allow the cammera sensor to warmup
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)

# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=400)

# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,
    (300, 300), (104.0, 177.0, 123.0))

# pass the blob through the network and obtain the detections and
# predictions
net.setInput(blob)
detections = net.forward()

# loop over the detections
for i in range(0, detections.shape[2]):
    # extract the confidence (i.e., probability) associated with the
    # prediction
    confidence = detections[0, 0, i, 2]

    # filter out weak detections by ensuring the `confidence` is
    # greater than the minimum confidence
    if confidence < args["confidence"]:
        continue

    # compute the (x, y)-coordinates of the bounding box for the
    # object
    box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
    (startX, startY, endX, endY) = box.astype("int")

    # draw the bounding box of the face along with the associated
    # probability
    text = "{:.2f}%".format(confidence * 100)
    y = startY - 10 if startY - 10 > 10 else startY + 10
    cv2.rectangle(frame, (startX, startY), (endX, endY),
        (0, 0, 255), 2)
    cv2.putText(frame, text, (startX, y),
        cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF

# if the `q` key was pressed, break from the loop
if key == ord("q"):
    break

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()