Код: Выделить всё
$ opencv_version -v | grep OpenCV
General configuration for OpenCV 4.1.0 =====================================
Код: Выделить всё
$ opencv_version -v | grep aruco
To be built: aruco bgsegm bioinspired calib3d ccalib core datasets dpm face features2d flann fuzzy gapi hfs highgui img_hash imgcodecs imgproc line_descriptor ml objdetect optflow phase_unwrapping photo plot python3 quality reg rgbd saliency shape stereo stitching structured_light superres surface_matching tracking ts video videoio videostab xfeatures2d ximgproc xobjdetect xphoto
Код: Выделить всё
$ python3 detect_aruco_cam.py --type DICT_4X4_100
Код: Выделить всё
#!/usr/bin/env python
# python detect_aruco_image.py --image images/example_01.png --type DICT_4X4_100
# import the necessary packages
import argparse
import imutils
import cv2
import sys
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--type", type=str,
default="DICT_4X4_100",
help="type of ArUCo tag to detect")
args = vars(ap.parse_args())
# open camera
videoCapture = cv2.VideoCapture(0)
if not videoCapture.isOpened():
print("[ERROR]Cannot open camera")
exit()
# define names of each possible ArUco tag OpenCV supports
ARUCO_DICT = {
"DICT_4X4_100": cv2.aruco.DICT_4X4_100,
"DICT_5X5_100": cv2.aruco.DICT_5X5_100,
"DICT_5X5_1000": cv2.aruco.DICT_5X5_1000
}
# capture input image from camera and resize it
print("[INFO] Capturing image...")
success, image = videoCapture.read()
if not success:
print("[ERROR]Cannot capture image from camera")
exit()
success = cv2.imwrite("filename-raw.jpg",image)
image = imutils.resize(image, width=600)
# verify that the supplied ArUCo tag exists and is supported by
# OpenCV
if ARUCO_DICT.get(args["type"], None) is None:
print("[INFO] ArUCo tag of '{}' is not supported".format(args["type"]))
sys.exit(0)
# load the ArUCo dictionary, grab the ArUCo parameters, and detect
# the markers
print("[INFO] detecting '{}' tags...".format(args["type"]))
arucoDict = cv2.aruco.Dictionary_get(ARUCO_DICT[args["type"]])
arucoParams = cv2.aruco.DetectorParameters_create()
(corners, ids, rejected) = cv2.aruco.detectMarkers(image, arucoDict,
parameters=arucoParams)
# verify *at least* one ArUco marker was detected
if len(corners) > 0:
# flatten the ArUco IDs list
ids = ids.flatten()
# loop over the detected ArUCo corners
for (markerCorner, markerID) in zip(corners, ids):
# extract the marker corners (which are always returned in
# top-left, top-right, bottom-right, and bottom-left order)
corners = markerCorner.reshape((4, 2))
(topLeft, topRight, bottomRight, bottomLeft) = corners
# convert each of the (x, y)-coordinate pairs to integers
topRight = (int(topRight[0]), int(topRight[1]))
bottomRight = (int(bottomRight[0]), int(bottomRight[1]))
bottomLeft = (int(bottomLeft[0]), int(bottomLeft[1]))
topLeft = (int(topLeft[0]), int(topLeft[1]))
# draw the bounding box of the ArUCo detection
cv2.line(image, topLeft, topRight, (0, 255, 0), 2)
cv2.line(image, topRight, bottomRight, (0, 255, 0), 2)
cv2.line(image, bottomRight, bottomLeft, (0, 255, 0), 2)
cv2.line(image, bottomLeft, topLeft, (0, 255, 0), 2)
# compute and draw the center (x, y)-coordinates of the ArUco
# marker
cX = int((topLeft[0] + bottomRight[0]) / 2.0)
cY = int((topLeft[1] + bottomRight[1]) / 2.0)
cv2.circle(image, (cX, cY), 4, (0, 0, 255), -1)
# draw the ArUco marker ID on the image
cv2.putText(image, str(markerID),
(topLeft[0], topLeft[1] - 15), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0), 2)
print("[INFO] ArUco marker ID: {}".format(markerID))
# show the output image
# cv2.imshow("Image", image)
# write output image
print("[INFO] write result image...")
success = cv2.imwrite("filename-result.jpg",image)
videoCapture.release()