Python中的Viola-Jones，带有openCV，检测嘴巴和鼻子

Question

我在Python 中有一个算法Viola-Jones。我正在使用haarcascade xml，它是从openCV 根文件加载的。但是openCV中没有任何嘴巴和鼻子的xml文件，所以我从EmguCV下载了这些文件。人脸检测结果还可以，但是眼睛检测不好，鼻子和嘴巴检测很差。我尝试更改face_cascade.detectMultiScale中的参数，但完全没有帮助。

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我的代码：

import cv2
import sys

def facedet(img):
    face_cascade = cv2.CascadeClassifier('/home/kattynka/opencv/data/haarcascades/haarcascade_frontalface_default.xml')
    eye_cascade = cv2.CascadeClassifier('/home/kattynka/opencv/data/haarcascades/haarcascade_eye.xml')
    mouth_cascade = cv2.CascadeClassifier('/home/kattynka/opencv/data/haarcascades/haarcascade_mcs_mouth.xml')
    nose_cascade = cv2.CascadeClassifier('/home/kattynka/opencv/data/haarcascades/haarcascade_mcs_nose.xml')

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

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

    for (x,y,w,h) in faces:
        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)
        nose =  nose_cascade.detectMultiScale(roi_gray)
        mouth = mouth_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)
        for (nx, ny, nw, nh) in nose:
            cv2.rectangle(roi_color, (nx, ny), (nx + nw, ny + nh), (0, 0, 255), 2)
        for (mx, my, mw, mh) in mouth:
            cv2.rectangle(roi_color, (mx, my), (mx + mw, my + mh), (0, 0, 0), 2)

    cv2.namedWindow('image', cv2.WINDOW_NORMAL)
    cv2.imshow('image',img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


if __name__ == '__main__':
    #img = sys.argv[1]
    facedet(img)

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我的问题

我做错了什么？有没有简单的解决方案，这会给我一个更好的结果？

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输出：

Answer 1

这对我来说非常有效。

我发现如果你把脸分成两部分，让眼睛在顶部寻找眼睛，在下部寻找嘴巴，效果非常好。

face
--------
| eyes |
|------|
|mouth |
--------

这是我对下面代码所做的粗略说明。

我知道我使用的级联是smile，但嘴巴似乎不起作用。

import cv2
import sys

mouthCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_smile.xml')
faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
eyeCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
video_capture = cv2.VideoCapture(0)

while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()

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

    mouth = mouthCascade.detectMultiScale(gray, 1.3, 5)
    faces = faceCascade.detectMultiScale(
                gray,
                scaleFactor=1.1,
                minNeighbors=5,
                minSize=(30, 30)
            )
            # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
            # Draw a rectangle around the faces
        roi_gray_mouth = gray[y+(int(h/2)):y+h, x:x+w]
        roi_color_mouth = frame[y+(int(h/2)):y+h, x:x+w]

        roi_gray_eye = gray[y-(int(h/2)):y+h, x:x+w]
        roi_color_eye = frame[y-(int(h/2)):y+h, x:x+w]

        mouth = mouthCascade.detectMultiScale(roi_gray_mouth)
        eyes = eyeCascade.detectMultiScale(roi_gray_eye)
        for (ex,ey,ew,eh) in mouth:
            cv2.rectangle(roi_color_mouth, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)

        for (eex,eey,eew,eeh) in eyes:
            d = int(eew / 2)
            cv2.circle(roi_color_eye, (int(eex + eew / 4) + int(d / 2), int(eey + eeh / 4) + int(d / 2)), int(d) ,(0,0,255),2)

    # Display the resulting frame
    cv2.imshow('Video', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()

Answer 2

导入 cv2 导入系统

face_cascade = cv2.CascadeClassifier('/home/kattynka/opencv/data/haarcascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('/home/kattynka/opencv/data/haarcascades/haarcascade_eye.xml')
mouth_cascade = cv2.CascadeClassifier('/home/kattynka/opencv/data/haarcascades/haarcascade_mcs_mouth.xml')
nose_cascade = cv2.CascadeClassifier('/home/kattynka/opencv/data/haarcascades/haarcascade_mcs_nose.xml')

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

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

for (x,y,w,h) in faces:
    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(gray, 1.3, 5)
    nose =  nose_cascade.detectMultiScale(gray, 1.3, 5)
    mouth = mouth_cascade.detectMultiScale(gray, 1.7, 11)

    for (ex,ey,ew,eh) in eyes:
        cv2.rectangle(img, (ex,ey), (ex+ew, ey+eh), (0,255,0), 2)
    for (nx, ny, nw, nh) in nose:
        cv2.rectangle(img, (nx, ny), (nx + nw, ny + nh), (0, 0, 255), 2)
    for (mx, my, mw, mh) in mouth:
        cv2.rectangle(img, (mx, my), (mx + mw, my + mh), (0, 0, 0), 2)

cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.imshow('image',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

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你可以试试这个代码。它对我有用。

Answer 3

Haar 级联对面部表现良好，但对较小的单个部分表现不佳。一个更好的解决方案是一起检测所有的面部标志。一个很好的算法是“One Millisecond Face Alignment with an Ensemble of Regression Trees by Vahid Kazemi 和 Josephine Sullivan，CVPR 2014”，在 Dlib (http://dlib.net/face_landmark_detection.py.html) 中实现。