OpenCV--人脸关键点

detect_face_parts.py：

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

#https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/
#http://dlib.net/files/

# 参数
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
    help="path to facial landmark predictor")
ap.add_argument("-i", "--image", required=True,
    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)

效果：