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：文章来源：https://www.toymoban.com/news/detail-743057.html
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：文章来源地址https://www.toymoban.com/news/detail-743057.html
# 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)
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