opencv+python实时人脸检测、磨皮

import numpy as np

import cv2

cap = cv2.VideoCapture(0)

face_cascade = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml")

eye_cascade = cv2.CascadeClassifier("data/haarcascade_eye.xml")

smile_cascade = cv2.CascadeClassifier("data/haarcascade_smile.xml")

# img = cv2.imread("img/test1.jpg")

while True:

    ret, img = cap.read()

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    for (x, y, w, h) in faces:

        img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)

        roi_gray = gray[y : y + h, x : x + w]

        roi_color = img[y : y + h, x : x + w]

        eyes = eye_cascade.detectMultiScale(roi_gray)

        for (ex, ey, ew, eh) in eyes:

            cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)

        # smile = smile_cascade.detectMultiScale(

        #     roi_gray,

        #     scaleFactor=1.16,

        #     minNeighbors=35,

        #     minSize=(25, 25),

        #     flags=cv2.CASCADE_SCALE_IMAGE,

        # )

        # for (x2, y2, w2, h2) in smile:

        #     cv2.rectangle(roi_color, (x2, y2), (x2 + w2, y2 + h2), (255, 0, 0), 2)

        #     cv2.putText(img, "Smile", (x, y - 7), 3, 1.2, (0, 255, 0), 2, cv2.LINE_AA)

    cv2.imshow("img", img)

    if cv2.waitKey(1) & 0xFF == ord("q"):

        break

　　加点代码实现实时磨皮效果，sigmaSpace值取的越大，循环次数越多运行越卡，可以只对脸部区域磨皮、但是一旦失去脸部焦点，瞬间被打回原形。

import numpy as np

import cv2

cap = cv2.VideoCapture(0)

face_cascade = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml")

eye_cascade = cv2.CascadeClassifier("data/haarcascade_eye.xml")

smile_cascade = cv2.CascadeClassifier("data/haarcascade_smile.xml")

# img = cv2.imread("img/test1.jpg")

while True:

    ret, img = cap.read()

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    for (x, y, w, h) in faces:

        img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)

        img = cv2.bilateralFilter(src=img, d=0, sigmaColor=50, sigmaSpace=5)

        roi_gray = gray[y : y + h, x : x + w]

        roi_color = img[y : y + h, x : x + w]

        eyes = eye_cascade.detectMultiScale(roi_gray)

        for (ex, ey, ew, eh) in eyes:

            cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)

        # smile = smile_cascade.detectMultiScale(

        #     roi_gray,

        #     scaleFactor=1.16,

        #     minNeighbors=35,

        #     minSize=(25, 25),

        #     flags=cv2.CASCADE_SCALE_IMAGE,

        # )

        # for (x2, y2, w2, h2) in smile:

        #     cv2.rectangle(roi_color, (x2, y2), (x2 + w2, y2 + h2), (255, 0, 0), 2)

        #     cv2.putText(img, "Smile", (x, y - 7), 3, 1.2, (0, 255, 0), 2, cv2.LINE_AA)

    cv2.imshow("img", img)

    if cv2.waitKey(1) & 0xFF == ord("q"):

        break