【Mind+Python】人脸检测

python的“face_recognition”包进行人脸检测、用OpenCV在原图像中绘制人脸box框和landmark关键点。
【安装】


【人脸检测】face_recognition可以输出一幅图像中人脸框的左上和右下点的坐标

import cv2
import face_recognition

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()

    frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5)

    # Find all the faces in the video
    face_locations = face_recognition.face_locations(frame)

    number_of_faces = len(face_locations)
    print("I found {} face(s) in this video.".format(number_of_faces))

    for face_location in face_locations:
        # Print the location of each face in this image. Each face is a list of co-ordinates in (top, right, bottom, left) order.
        top, right, bottom, left = face_location
        print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom,
                                                                                                    right))
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 3)

    cv2.imshow("Frame", frame)

    ch = cv2.waitKey(1)
    if ch & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()
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【landmark关键点】face_recognition可输出如下图所示人脸五官的landmark坐标（共68个坐标点）。

import numpy as np
import cv2
import face_recognition

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()

    frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5)

    # Find all facial features in all the faces in the video
    face_landmarks_list = face_recognition.face_landmarks(frame)

    for face_landmarks in face_landmarks_list:
        # Loop over each facial feature (eye, nose, mouth, lips, etc)
        for name, list_of_points in face_landmarks.items():

            hull = np.array(face_landmarks[name])
            hull_landmark = cv2.convexHull(hull)
            cv2.drawContours(frame, hull_landmark, -1, (0, 255, 0), 3)


    cv2.imshow("Frame", frame)

    ch = cv2.waitKey(1)
    if ch & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()
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