Contents
OpenCVを使ったARマーカーの認識
1.目的
ARマーカーを使った画像認識をR-CPSで行うために、PythonならびにOpenCVを使った方法を確認する。ここでは、PDHでPyhon＋OpenCVで動作させます。
2.PythonのOpenCVのライブラリのインストール
以下のコマンドを使って、OpenCVのライブラリをインストールします。
$ python -m pip install opencv-python opencv-contrib-python
インストールされているライブラリの確認は、以下のコマンドで行います。
$ python –m pip list
結果は、下を参照
3.ARマーカーの生成
ARマーカーの生成は簡単です。以下の3行でファイルに書き出せます。1行目は、ARマーカーの辞書を指定します。2行目でARマーカーを生成します。その際に、辞書とID番号とイメージのサイズを指定します。3行目はファイルへの書出しです。その下に、サンプルプログラムを示します。生成したマーカーの表示も行います。
dictionary = aruco.getPredefinedDictionary(aruco.DICT_4X4_50)
generator = aruco.generateImageMarker(dictionary, ID, size)
cv2.imwrite(fileName, generator)
下に、生成したARマーカーを示します。
次に、ARマーカーを生成するサンプルプログラムを載せます。
# -*- coding: utf-8 -*-
import cv2
from cv2 import aruco
size = 200
ID = 0
fileName = "ar00.png"
dictionary = aruco.getPredefinedDictionary(aruco.DICT_4X4_50)
def arGenerator():
# generate ARmarker and save ARmarker
generator = aruco.generateImageMarker(dictionary, ID, size)
cv2.imwrite(fileName, generator)
# read ARmarker and display it, press any key for closing
img = cv2.imread(fileName)
cv2.imshow('ArMarker',img)
cv2.waitKey(0)
def main():
arGenerator()
if __name__ == "__main__":
main()
4.ARマーカーの認識
前ページを印刷し、カメラで撮った画像を、ARマーカーの認識プログラムで認識させたのが、下の写真です。緑の枠でARマーカーが囲われ、青い文字でID番号が正しく表示されているのが分かります。
プログラムを以下に示します。大きく4つの部分に分かれます。
1)辞書とパラメータの準備。辞書は生成した際に使用したものと同じものです。
2)認識するイメージをリードします。
3)aruco.detectMarkers()を使って、マーカーの認識を行います。
また、aruco.drawDetectedMarkers()で認識結果を合成します。
4)合成したイメージを保存します。
次に、ARマーカーを検出するプログラム例を示します。
# -*- coding: utf-8 -*-
import cv2
from cv2 import aruco
# get dicionary and get parameters
dictionary = aruco.getPredefinedDictionary(aruco.DICT_4X4_50)
parameters = aruco.DetectorParameters()
# read from image
input_file = "/home/pi/Pictures/ArMaker/Armaker.jpeg"
output_file = "IMG_7225-2.png"
input_img = cv2.imread(input_file)
# detect and draw marker's information
corners, ids, rejectedCandidates = aruco.detectMarkers(input_img, dictionary, parameters=parameters)
print(ids)
ar_image = aruco.drawDetectedMarkers(input_img, corners, ids)
cv2.imwrite(output_file, ar_image)5.Node-REDを使った画像の自動読み取りとARマーカーの認識
以下の様に、5秒おきにPDHに接続したUSBカメラで画像を取得し、指定したIDのARマーカーが存在しているかを検出します。
そして有れば、ダッシュボードのLEDを点灯するプログラムをNode-REDで作成します。検出部分は、Pythonで作成します。
動作のフローを以下に示します。
下に、Node-RED＋Pythonで実装した結果を示します。
上がARマーカーを検出した場合、下が検出できなかった場合です。
画像に左側がオリジナルで、右側が検出結果を重ねた画像です。右上の画像には、ID番号が見られます。
5-1.Pythonのプログラム
ARマーカーを検出して、結果のjsonをUDPで送信するプログラムを以下に示します。
# -*- coding: utf-8 -*-
import cv2
from cv2 import aruco
import numpy as np
import socket
import json
dictionary = aruco.getPredefinedDictionary(aruco.DICT_4X4_50)
parameters = aruco.DetectorParameters()
input_file = "/home/pi/Pictures/ArMaker/Armaker.jpeg"
output_file = "/home/pi/Pictures/ArMaker/Dtmaker.jpeg"
target_ids = 7
UDP_IP = "127.0.0.1"
UDP_PORT = 5005
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
def process_result(result):
if isinstance(result, np.ndarray):
if (np.any(result == target_ids)):
message = {"result":"Found target id"}
else:
message = {"result":"Not target id"}
elif isinstance(result, type(None)):
message = {"result":"No AR-Marker"}
else:
message = {"result":"Unknown type."}
print(message)
message = json.dumps(message)
sock.sendto(message.encode(), (UDP_IP, UDP_PORT))
def arDetector():
input_img = cv2.imread(input_file)
corners, ids, rejectedCandidates = aruco.detectMarkers(input_img, dictionary, parameters=parameters)
process_result(ids)
ar_image = aruco.drawDetectedMarkers(input_img, corners, ids)
cv2.imwrite(output_file, ar_image)
def main():
arDetector()
if __name__ == "__main__":
main()5-2.Pythonのプログラム
ARマーカーを検出して、結果のjsonをUDPで送信するプログラムを以下に示します。このフローのダッシュボードが面が、前に示した検出結果です。
以下に、Node-REDのフローを載せます。
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