delaunay和voronoi图 人脸三角剖分

先获取人脸68个特征点坐标，其中使用了官方的预训练模型shape_predictor_68_face_landmarks.dat：

import dlib
import cv2

predictor_path = "shape_predictor_68_face_landmarks.dat"
png_path = "face.jpg"

txt_path = "points.txt"
f = open(txt_path, 'w+')

# 与人脸检测相同，使用dlib自带的frontal_face_detector作为人脸检测器
detector = dlib.get_frontal_face_detector()
# 相撞
# 使用官方提供的模型构建特征提取器
predicator = dlib.shape_predictor(predictor_path)
win = dlib.image_window()
img1 = cv2.imread(png_path)

dets = detector(img1, 1)
print("Number of faces detected : {}".format(len(dets)))
for k, d in enumerate(dets):
    print("Detection {}  left:{}  Top: {} Right {}  Bottom {}".format(
        k, d.left(), d.top(), d.right(), d.bottom()
    ))
    lanmarks = [[p.x, p.y] for p in predicator(img1, d).parts()]
    for idx, point in enumerate(lanmarks):
        f.write(str(point[0]))
        f.write("\t")
        f.write(str(point[1]))
        f.write('\n')

实现人脸三角剖分：

# 日期:  2023/11/2 23:04
import cv2
import numpy as np
import random


# 检查点是否在矩形框内
def rect_contains(rect, point):
    if point[0] < rect[0]:
        return False
    elif point[1] < rect[1]:
        return False
    elif point[0] > rect[2]:
        return False
    elif point[1] > rect[3]:
        return False
    return True


# 画点
def draw_point(img, p, color):
    cv2.circle(img, p, 2, color)


# 绘制德劳内三角形
def draw_delaunay(img, subdiv, delaunay_color):
    trangleList = subdiv.getTriangleList()  # 获取Delaunay三角形的列表
    size = img.shape
    r = (0, 0, size[1], size[0])
    for t in trangleList:
        pt1 = (int(t[0]), int(t[1]))
        pt2 = (int(t[2]), int(t[3]))
        pt3 = (int(t[4]), int(t[5]))
        if rect_contains(r, pt1) and rect_contains(r, pt2) and rect_contains(r, pt3):
            cv2.line(img, pt1, pt2, delaunay_color, 1)  # 源图像，线段的两个端点，颜色，线宽
            cv2.line(img, pt2, pt3, delaunay_color, 1)
            cv2.line(img, pt3, pt1, delaunay_color, 1)


# Draw voronoi diagram
def draw_voronoi(img: object, subdiv: object) -> object:
    (facets, centers) = subdiv.getVoronoiFacetList([])  # 获取Voronoi构面的列表

    # 对于每个voronoi多边形
    for i in range(0, len(facets)):
        ifacet_arr = []
        # 得到每个多边形的顶点
        for f in facets[i]:
            ifacet_arr.append(f)

        ifacet = np.array(ifacet_arr, dtype=np.int32)
        # 随机颜色
        color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
        # 填充颜色
        cv2.fillConvexPoly(img, ifacet, color)  # 图像、多边形顶点、颜色
        vertex = np.array([ifacet])
        cv2.polylines(img, vertex, True, (0, 0, 0), 1)     # 绘制多边形，参数包括图像、多边形的点、线条是否闭合、颜色和线条宽度
        cv2.circle(img, (centers[i][0], centers[i][1]), 3, (0, 0, 0))   # 绘制圆，参数包括图像、中心点、半径、颜色


if __name__ == '__main__':
    # 定义窗口名称
    win_delaunary = "Delaunay Triangulation"
    win_voronoi = "Voronoi Diagram"

    # 在画三角形的时候开启动画
    animate = True

    # 定义画的颜色
    delaunary_color = (255, 255, 255)
    points_color = (0, 0, 255)

    # 读入图片
    img_path = "face.jpg"

    img = cv2.imread(img_path)

    # 复制
    img_orig = img.copy()

    # 矩形框用于Subdiv2D
    size = img.shape    # h, w, channel
    # x,y,w,h
    rect = (0, 0, size[1], size[0])

    # 创建一个Subdiv2D的实例
    subdiv = cv2.Subdiv2D(rect)

    # 创建点的列表
    points = []
    # 从文档中读取点的坐标
    with open("points.txt") as file:
        for line in file:
            x, y = line.split()
            points.append((int(x), int(y)))
    # 向subdiv中插入点
    for p in points:
        subdiv.insert(p)
        # 展示动画效果
        if animate:
            img_copy = img_orig.copy()
            # 绘制德劳内三角形
            draw_delaunay(img_copy, subdiv, (255, 255, 255))
            cv2.imshow(win_delaunary, img_copy)
            cv2.waitKey(100)

    # 绘制德劳内三角形
    draw_delaunay(img, subdiv, (255, 255, 255))

    # 绘制点
    for p in points:
        draw_point(img, p, (0, 0, 255))

    # 为沃罗诺伊图分配空间
    img_voronoi = np.zeros(img.shape, dtype=img.dtype)

    # 绘制沃罗诺伊图
    draw_voronoi(img_voronoi, subdiv)

    # 展示结果
    cv2.imshow(win_delaunary, img)
    cv2.imshow(win_voronoi, img_voronoi)
    cv2.waitKey(0)