input image pantone card with aruco markers in the corners
source image
aruco markers on card define region of interest
perspective transform source image
multiply transformed image with mask
multiply input image with inverted mask
add previous 2 images
butterfly appear on the card
#main.py
import numpy as np
import argparse
import imutils
import sys
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to input image containing ArUCo tag")
ap.add_argument("-s", "--source", required=True,
help="path to input source image that will be put on input")
args = vars(ap.parse_args())
# load the input image from disk, resize it, and grab its spatial
# dimensions
print("[INFO] loading input image and source image...")
image = cv2.imread(args["image"])
image = imutils.resize(image, width=600)
(imgH, imgW) = image.shape[:2]
# load the source image from disk
source = cv2.imread(args["source"])
# load the ArUCo dictionary, grab the ArUCo parameters, and detect
# the markers
print("[INFO] detecting markers...")
arucoDict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_ARUCO_ORIGINAL)
arucoParams = cv2.aruco.DetectorParameters_create()
(corners, ids, rejected) = cv2.aruco.detectMarkers(image, arucoDict,
parameters=arucoParams)
# if we have not found four markers in the input image then we cannot
# apply our augmented reality technique
if len(corners) != 4:
print("[INFO] could not find 4 corners...exiting")
sys.exit(0)
# otherwise, we've found the four ArUco markers, so we can continue
# by flattening the ArUco IDs list and initializing our list of
# reference points
print("[INFO] constructing augmented reality visualization...")
ids = ids.flatten()
refPts = []
# loop over the IDs of the ArUco markers in top-left, top-right,
# bottom-right, and bottom-left order
for i in (923, 1001, 241, 1007):
# grab the index of the corner with the current ID and append the
# corner (x, y)-coordinates to our list of reference points
j = np.squeeze(np.where(ids == i))
corner = np.squeeze(corners[j])
refPts.append(corner)
# unpack our ArUco reference points and use the reference points to
# define the *destination* transform matrix, making sure the points
# are specified in top-left, top-right, bottom-right, and bottom-left
# order
(refPtTL, refPtTR, refPtBR, refPtBL) = refPts
dstMat = [refPtTL[0], refPtTR[1], refPtBR[2], refPtBL[3]]
dstMat = np.array(dstMat)
# grab the spatial dimensions of the source image and define the
# transform matrix for the *source* image in top-left, top-right,
# bottom-right, and bottom-left order
(srcH, srcW) = source.shape[:2]
srcMat = np.array([[0, 0], [srcW, 0], [srcW, srcH], [0, srcH]])
# compute the homography matrix and then warp the source image to the
# destination based on the homography
(H, _) = cv2.findHomography(srcMat, dstMat)
warped = cv2.warpPerspective(source, H, (imgW, imgH))
# construct a mask for the source image now that the perspective warp
# has taken place (we'll need this mask to copy the source image into
# the destination)
mask = np.zeros((imgH, imgW), dtype="uint8")
cv2.fillConvexPoly(mask, dstMat.astype("int32"), (255, 255, 255),
cv2.LINE_AA)
# this step is optional, but to give the source image a black border
# surrounding it when applied to the source image, you can apply a
# dilation operation
rect = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
mask = cv2.dilate(mask, rect, iterations=2)
# create a three channel version of the mask by stacking it depth-wise,
# such that we can copy the warped source image into the input image
maskScaled = mask.copy() / 255.0
maskScaled = np.dstack([maskScaled] * 3)
# copy the warped source image into the input image by (1) multiplying
# the warped image and masked together, (2) multiplying the original
# input image with the mask (giving more weight to the input where
# there *ARE NOT* masked pixels), and (3) adding the resulting
# multiplications together
warpedMultiplied = cv2.multiply(warped.astype("float"), maskScaled)
imageMultiplied = cv2.multiply(image.astype(float), 1.0 - maskScaled)
output = cv2.add(warpedMultiplied, imageMultiplied)
output = output.astype("uint8")
# show the input image, source image, output of our augmented reality
cv2.imshow("Input", image)
cv2.imshow("Source", source)
cv2.imshow("OpenCV AR Output", output)
cv2.imshow("mask", mask)
cv2.imshow("warped source", warpedMultiplied.astype("uint8"))
cv2.imshow("masked Input", imageMultiplied.astype("uint8"))
cv2.waitKey(0)
------------------
#terminal
python main.py -i assets/pantone.jpg -s assets/butterfly.jpg
reference:
detect aruco marker
np.squeeze
np.dstack
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