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我使用 dlib 和 numpy 来提取面部关键点列表。

代码：

def get_landmarks(im):
    rects = detector(im, 1)

    if len(rects) > 1:
        raise TooManyFaces
    if len(rects) == 0:
        raise NoFaces

    return numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])


for f in glob.glob(os.path.join(faces_folder_path, "*")):
    print("Processing file: {}".format(f))
    img = io.imread(f)

    win.clear_overlay()
    win.set_image(img)

    dets = detector(img, 1)
    print("Number of faces detected: {}".format(len(dets)))
    for k, d in enumerate(dets):
        # Get the landmarks/parts for the face in box d.
        shape = predictor(img, d)
        lms = get_landmarks(img)
        print ("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(k, d.left(), d.top(), d.right(), d.bottom()))
        print ("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
        newSection()
        print ("Keypoints:" + (str(lms)))
        # Draw the face landmarks on the screen.
        win.add_overlay(shape)




    win.add_overlay(dets)
    dlib.hit_enter_to_continue()

结果： 如您所见，它运行良好。但是，我需要的不是蓝色对齐线，而是关键点和图像之间有编号的相关性，就像这样。如何用图像点覆盖 numpy 矩阵：

numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])

在opencv图像上？我自己尝试过：

for idx, point in enumerate(lms):
     pos = (point[0, 0], point[0, 1])
     cv2.putText(imB, str(idx), pos,
                fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
                fontScale=0.4,
                color=(0, 0, 255))
     cv2.circle(im, pos, 3, color=(0, 255, 255))

     WIDTH = 1000
     HEIGHT = 1000

     cv2.namedWindow('image', cv2.WINDOW_NORMAL)
     cv2.imshow('image', imB)
     cv2.resizeWindow('image', WIDTH, HEIGHT)

但它只是给了我一个空白的盒子，里面有一个红色的盒子。

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