本文使用Python库dlib和OpenCV来实现面部特征点的检测和标注。

下面是代码的主要步骤和相关的代码片段：

步骤一：导入必要的库和设置参数

首先，代码导入了必要的Python库，并通过argparse设置了输入图像和面部标记预测器的参数。

from collections import OrderedDict
import numpy as np
import argparse
import dlib
import cv2

步骤二：定义面部关键点索引

使用OrderedDict定义了两组面部关键点，一组包含68个点，另一组包含5个点，这些关键点用于后续的特征提取。

FACIAL_LANDMARKS_68_IDXS = OrderedDict([
    ("mouth", (48, 68)),
    ("right_eyebrow", (17, 22)),
    ("left_eyebrow", (22, 27)),
    ("right_eye", (36, 42)),
    ("left_eye", (42, 48)),
    ("nose", (27, 36)),
    ("jaw", (0, 17))
])

步骤三：人脸检测和关键点预测

使用dlib的面部检测器和预测器，对输入的图像进行人脸检测，并对每个检测到的人脸进行关键点定位。

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

步骤四：关键点转换和可视化

将dlib的关键点数据结构转换为NumPy数组，然后通过自定义的visualize_facial_landmarks函数在图像上绘制关键点和凸包。

def shape_to_np(shape, dtype="int"):
    coords = np.zeros((shape.num_parts, 2), dtype=dtype)
    for i in range(0, shape.num_parts):
        coords[i] = (shape.part(i).x, shape.part(i).y)
    return coords

def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
    # 创建overlay, 绘制关键点和凸包

步骤五：处理每一个检测到的人脸

对于图像中每一个检测到的人脸，提取关键点，可视化，并显示每个部分的区域图像。

for (i, rect) in enumerate(rects):
    shape = predictor(gray, rect)
    shape = shape_to_np(shape)
    output = visualize_facial_landmarks(image, shape)
    cv2.imshow("Image", output)
    cv2.waitKey(0)

本文使用dlib和OpenCV对人脸图像进行关键点检测，并将检测到的关键点用于图像处理和分析。通过不同的面部部分的关键点，可以在应用程序中实现多种面部识别和分析功能。

#导入工具包
from collections import OrderedDict
import numpy as np
import argparse
import dlib
import cv2


# 参数
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", default="shape_predictor_68_face_landmarks.dat",
	help="path to facial landmark predictor")
ap.add_argument("-i", "--image", default="images/liudehua2.jpg",
	help="path to input image")
args = vars(ap.parse_args())

FACIAL_LANDMARKS_68_IDXS = OrderedDict([
	("mouth", (48, 68)),
	("right_eyebrow", (17, 22)),
	("left_eyebrow", (22, 27)),
	("right_eye", (36, 42)),
	("left_eye", (42, 48)),
	("nose", (27, 36)),
	("jaw", (0, 17))
])


FACIAL_LANDMARKS_5_IDXS = OrderedDict([
	("right_eye", (2, 3)),
	("left_eye", (0, 1)),
	("nose", (4))
])

def shape_to_np(shape, dtype="int"):
	# 创建68*2
	coords = np.zeros((shape.num_parts, 2), dtype=dtype)
	# 遍历每一个关键点
	# 得到坐标
	for i in range(0, shape.num_parts):
		coords[i] = (shape.part(i).x, shape.part(i).y)
	return coords

def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
	# 创建两个copy
	# overlay and one for the final output image
	overlay = image.copy()
	output = image.copy()
	# 设置一些颜色区域
	if colors is None:
		colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
			(168, 100, 168), (158, 163, 32),
			(163, 38, 32), (180, 42, 220)]
	# 遍历每一个区域
	for (i, name) in enumerate(FACIAL_LANDMARKS_68_IDXS.keys()):
		# 得到每一个点的坐标
		(j, k) = FACIAL_LANDMARKS_68_IDXS[name]
		pts = shape[j:k]
		# 检查位置
		if name == "jaw":
			# 用线条连起来
			for l in range(1, len(pts)):
				ptA = tuple(pts[l - 1])
				ptB = tuple(pts[l])
				cv2.line(overlay, ptA, ptB, colors[i], 2)
		# 计算凸包
		else:
			hull = cv2.convexHull(pts)
			cv2.drawContours(overlay, [hull], -1, colors[i], -1)
	# 叠加在原图上，可以指定比例
	cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
	return output

# 加载人脸检测与关键点定位
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

# 读取输入数据，预处理
image = cv2.imread(args["image"])
(h, w) = image.shape[:2]
width=500
r = width / float(w)
dim = (width, int(h * r))
image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 人脸检测
rects = detector(gray, 1)

# 遍历检测到的框
for (i, rect) in enumerate(rects):
	# 对人脸框进行关键点定位
	# 转换成ndarray
	shape = predictor(gray, rect)
	shape = shape_to_np(shape)

	# 遍历每一个部分
	for (name, (i, j)) in FACIAL_LANDMARKS_68_IDXS.items():
		clone = image.copy()
		cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
			0.7, (0, 0, 255), 2)

		# 根据位置画点
		for (x, y) in shape[i:j]:
			cv2.circle(clone, (x, y), 3, (0, 0, 255), -1)

		# 提取ROI区域
		(x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))
		
		roi = image[y:y + h, x:x + w]
		(h, w) = roi.shape[:2]
		width=250
		r = width / float(w)
		dim = (width, int(h * r))
		roi = cv2.resize(roi, dim, interpolation=cv2.INTER_AREA)
		
		# 显示每一部分
		cv2.imshow("ROI", roi)
		cv2.imshow("Image", clone)
		cv2.waitKey(0)

	# 展示所有区域
	output = visualize_facial_landmarks(image, shape)
	cv2.imshow("Image", output)
	cv2.waitKey(0)