to complete it i have consult some tutorial Augmented reality with Python and OpenCV and programming computer vision with python thanks Jan Erik Solem because of this book. in this project i have read many times of this to understand idea and math to make it.

So get start

opencv have a small code to do it opencv-doc the result of

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)

save mtx for late use

next i use OBR of opencv to get feature detect

orb = cv2.ORB_create()
model = cv2.imread('model.jpg', 0)
model = cv2.resize(model, (int(width/1.2), int(height/1.2)))
kp1, des1 = sift.detectAndCompute(model, None)

Do same with image in video input i have 2 key point and 2 descriptors

now we can match it with

bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)

matches is array of index of keypoint matched, we use it to find best key point to find homography

src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)

to here we can see how to findout mode image in video

cause im not study well in computer graphic that i use already function of OpenGl mentioned in programming computer vision with python

def draw_3D():
    glEnable(GL_LIGHTING)
    glEnable(GL_LIGHT0)
    glEnable(GL_DEPTH_TEST)
    glClear(GL_DEPTH_BUFFER_BIT)
    glMaterialfv(GL_FRONT, GL_AMBIENT, [0, 0, 0.1, 0])
    glMaterialfv(GL_FRONT, GL_DIFFUSE, [0.5, 0, 0, 0])
    glMaterialfv(GL_FRONT, GL_SHININESS, 0.25 * 128)
    glutSolidTeapot(0.1)

ketqua = cv2.addWeighted(frame_of_video, 1, image_of_model, 1, 1)

all done

pip3 install -r requirement.txt

python3 3dpygl.py