基于标记的 AR：这种类型的 AR 使用预定义的标记或图像来触发 3D 模型或视频的显示。
你可以使用OpenCV 中的
cv2.aruco模块来检测视频流中的标记，并将 3D 模型叠加在上面。
import cv2
# Load the camera
cap = cv2.VideoCapture(0)
# Set the dictionary to use
dictionary = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
# Detect markers
corners, ids, _ = cv2.aruco.detectMarkers(frame, dictionary)
# Draw markers
frame = cv2.aruco.drawDetectedMarkers(frame, corners, ids)
# Display the resulting frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()基于图像的 AR：这种类型的 AR 使用图像的特征将虚拟内容与现实世界对齐。
你可以使用 OpenCV 中的
cv2.sift或cv2.surf模块来检测相机源中的关键点，并将虚拟内容覆盖在上面。
import cv2
import numpy as np
# Load the reference image
img1 = cv2.imread("reference.jpg")
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
# Load the camera
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
gray2 = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect keypoints and compute descriptors
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(gray1, None)
kp2, des2 = sift.detectAndCompute(gray2, None)
# Match the descriptors
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
# Filter the matches using the ratio test
good = []
for m,n in matches:
if m.distance < 0.75*n.distance:
good.append([m])
# Draw the matches
img3 = cv2.drawMatchesKnn(img1, kp1, frame, kp2, good, None, flags=2)
# Display the resulting frame
cv2.imshow('frame', img3)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()基于对象检测的 AR：这种类型的 AR 使用对象检测算法来识别和跟踪相机馈送中的特定对象。
你可以使用OpenCV 中的
cv2.dnn模块运行 YOLO 或 SSD 等对象检测模型，并将虚拟内容覆盖在检测到的对象之上。
import cv2
# Load the model
net = cv2.dnn.readNetFromCaffe("MobileNetSSD_deploy.prototxt.txt", "MobileNetSSD_deploy.caffemodel")
# Set the input image size
inpWidth = 300
inpHeight = 300
# Load the camera
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
# Resize the frame
frame = cv2.resize(frame, (inpWidth, inpHeight))
# Convert the frame to a blob
blob = cv2.dnn.blobFromImage(frame, 0.007843, (inpWidth, inpHeight), (127.5, 127.5, 127.5), False)
# Pass the blob through the model
net.setInput(blob)
detections = net.forward()
# Draw bounding boxes around the detected objects
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > 0.5:
x1 = int(detections[0, 0, i, 3] * frame.shape[1])
y1 = int(detections[0, 0, i, 4] * frame.shape[0])
x2 = int(detections[0, 0, i, 5] * frame.shape[1])
y2 = int(detections[0, 0, i, 6] * frame.shape[0])
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
# Display the resulting frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break基于人脸检测的增强现实：这种类型的增强现实使用人脸检测算法来识别和跟踪摄像头画面中的人脸，并在其上叠加虚拟内容。
你可以使用 OpenCV 中的
cv2.CascadeClassifier或cv2.dnn模块来运行人脸检测模型，并将虚拟内容覆盖在检测到的人脸之上。
import cv2
# Load the cascade
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
# Load the camera
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = faceCascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)
# Draw rectangles around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
# Display the resulting frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()请注意，这些只是示例，构建完整的 AR 应用程序可能会涉及额外的步骤和技术，具体取决于项目的具体要求。
☆ END ☆
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