dlib库 人脸

import cv2 as cv
import dlib

# 打开
filename = 'C:/Users/to_bo/Desktop/1.jpg'
img = cv.imread(filename)

# 返回检测器
detector = dlib.get_frontal_face_detector()
# 返回人脸框
faces = detector(img, 1)

# 假设有多个人脸框被返回，依次遍历
for i, d in enumerate(faces):
    # 画框框
    cv.rectangle(
        img,
        tuple([d.left(), d.top()]),
        tuple([d.right(), d.bottom()]),
        (0, 255, 255),
        2
    )
# 输出
cv.imshow('img', img)

# 结束
cv.waitKey(0)
cv.destroyAllWindows()

import cv2 as cv
import dlib

# 打开
path = 'C:/Users/to_bo/Desktop/1.jpg'
img = cv.imread(path)

# 返回检测器
detector = dlib.get_frontal_face_detector()
# 返回人脸框
faces = detector(img, 1)
# 返回定位关键点
predictor = dlib.shape_predictor(
    'C:/Users/to_bo/Desktop/1.dat'
)

# 假设有多个人脸框被返回，依次遍历
for face in faces:
    # 框框里面定位人脸关键点
    shape = predictor(img, face)
    # 遍历所有（68）点，打印出其坐标，并标记
    for p in shape.parts():
        cv.circle(img, (p.x, p.y), 1, (255, 0, 0), -1)
    # 输出
    cv.imshow("image", img)

# 结束
cv.waitKey(0)
cv.destroyAllWindows()

predictor = dlib.shape_predictor()关键点预测器用法

detector = dlib.get_frontal_face_detector()获取人脸框的用法

import cv2 as cv
import dlib

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(
    'C:/Users/to_bo/Desktop/1.dat'
)

cap = cv.VideoCapture(0, cv.CAP_DSHOW)
if not cap.isOpened():
    print("Can not open camera")
    exit(0)
while True:
    ret, frame = cap.read()
    if not ret:
        print("Can not receive frame")
        break
    faces = detector(frame, 1)
    for face in faces:
        shape = predictor(frame, face)
        for p in shape.parts():
            cv.circle(frame, (p.x, p.y), 2, (0, 255, 0), -1)
    cv.imshow('img', frame)
    if cv.waitKey(1) & 0xFF == 27:
        break

cap.release()
cv.destroyAllWindows()

detector.run()的识别（不太精确）

import cv2 as cv
import dlib

img = cv.imread('C:/Users/to_bo/Desktop/2.jpg')
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

detector = dlib.get_frontal_face_detector()
# predictor = dlib.shape_predictor('C:/Users/to_bo/Desktop/1.dat')

faces = detector(gray, 1)

# for face in faces:
#     points = predictor(gray, face)
#     for p in points.parts():
#         cv.circle(img, (p.x, p.y), 1, (0, 255, 0), -1)
# cv.namedWindow('img', cv.WINDOW_NORMAL)
# cv.imshow('img', img)
# cv.waitKey(0)
# cv.destroyWindow('img')

dets, scores, idx = detector.run(img, 1, -1)
for i, d in enumerate(dets):
    print("Detection {}, score: {}, face_type:{}".format(
        d, scores[i], idx[i]))
    if scores[i] > 0.8:
        cv.rectangle(img, (d.left(), d.top()), (d.right(), d.bottom()), (0, 255, 255), 2)
        cv.putText(
            img,
            str(scores[i]),
            (d.left(), d.bottom()),
            cv.FONT_HERSHEY_TRIPLEX,
            1,
            (0, 255, 0),
            2,
            cv.LINE_AA
        )
cv.namedWindow('img', cv.WINDOW_NORMAL)
cv.imshow('img', img)
cv.waitKey(0)
cv.destroyAllWindows()