使用DNN人脸检测器的Python中的人脸对齐

问题描述

我正在尝试在python中进行人脸对齐代码。我正在关注此article，但是在本文中，有关人脸检测使用了dlib。下面是原始代码：

from imutils.face_utils import FaceAligner
from imutils.face_utils import rect_to_bb
import argparse
import imutils
import dlib
import cv2

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
fa = FaceAligner(predictor,desiredFaceWidth=256)

image = cv2.imread('images\\1.jpg')

image = imutils.resize(image,width=300)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

rects = detector(gray,2)

for rect in rects:
    (x,y,w,h) = rect_to_bb(rect)
    faceOrig = imutils.resize(image[y:y + h,x:x + w],width=256)

    faceAligned = fa.align(image,gray,rect)  # Here we get the aligned face

我有一些人脸图像没有被dlib人脸检测器检测到。所以我正在修改上面的代码，并使用DNN人脸检测。下面是我的代码：

from imutils.face_utils import FaceAligner
from imutils.face_utils import rect_to_bb
import argparse
import imutils
import dlib
import cv2

protoPath = "deploy.prototxt"
modelPath = "res10_300x300_ssd_iter_140000.caffemodel"
detector = cv2.dnn.readNetFromCaffe(protoPath,modelPath)

fa = FaceAligner(predictor,desiredFaceWidth=256)

image = cv2.imread('images\\1.jpg')
image = imutils.resize(image,width=300)

(h,w) = image.shape[:2]
rgb = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
imageBlob = cv2.dnn.blobFromImage(cv2.resize(image,(300,300)),1.0,300),(104.0,177.0,123.0),swapRB=False,crop=False)

detector.setInput(imageBlob)
detections = detector.forward()

for i in range(0,detections.shape[2]):
    confidence = detections[0,i,2]

    if confidence > 0.5:
        box = detections[0,3:7] * np.array([w,h,h])
        (startX,startY,endX,endY) = box.astype("int")
    
        face = image[startY:endY,startX:endX]
        gray = cv2.cvtColor(face,cv2.COLOR_BGR2GRAY)
        r = dlib.rectangle(int(startX),int(startY),int(endX),int(endY))
        
        faceAligned = fa.align(face,r)

但是在上面的代码faceAligned中全为零，因此为空白图像。我不确定自己在做什么错。任何人都可以指出错误并帮助我解决问题。请帮忙。谢谢

解决方法

我建议您深入研究。它包装了opencv，ssd，dlib和mtcnn来检测和对齐人脸。

detectFace函数分别在后台应用检测和对齐。

#!pip install deepface
from deepface import DeepFace
backends = ['opencv','ssd','dlib','mtcnn']
DeepFace.detectFace("img.jpg",detector_backend = backends[2])

此外，您可以手动应用检测和对齐。

from deepface.commons import functions
img = functions.load_image("img.jpg")
backends = ['opencv','mtcnn']

detected_face = functions.detect_face(img = img,detector_backend = backends[2])
plt.imshow(detected_face)

aligned_face = functions.align_face(img = img,detector_backend = backends[2])
plt.imshow(aligned_face)

processed_img = functions.detect_face(img = aligned_face,detector_backend = backends[2])
plt.imshow(processed_img)

如果您打算应用人脸识别，它也会在后台处理这些预处理步骤。

from deepface import DeepFace
DeepFace.verify("img1.jpg","img2.jpg",detector_backend = 'dlib')

,

您将人脸和灰色裁剪图像传递给 fa.align(face,gray,r)，正如您在第一个代码中所示，此参数必须是完整图像和矩形。这是完整的示例：

import numpy as np
import imutils
import dlib
import cv2

from imutils.face_utils import FaceAligner

protoPath = "path/to/deploy.prototxt.txt"
modelPath = "path/to/res10_300x300_ssd_iter_140000.caffemodel"
detector = cv2.dnn.readNetFromCaffe(protoPath,modelPath)
predictor = dlib.shape_predictor("path/to/shape_predictor_68_face_landmarks.dat")

fa = FaceAligner(predictor,desiredFaceWidth=256)

image = cv2.imread('path/to/image.jpg')
image = imutils.resize(image,width=300)
cv2.imshow("image",image)
(h,w) = image.shape[:2]

rgb = image.copy()
grey = cv2.cvtColor(rgb,cv2.COLOR_BGR2GRAY)
imageBlob = cv2.dnn.blobFromImage(rgb,1.0,(300,300),(104.0,177.0,123.0),swapRB=False,crop=False)

detector.setInput(imageBlob)
detections = detector.forward()

for i in range(0,detections.shape[2]):
    confidence = detections[0,i,2]

    if confidence > 0.5:
        box = detections[0,3:7] * np.array([w,h,w,h])
        (startX,startY,endX,endY) = box.astype("int")

        face = image[startY:endY,startX:endX]

        r = dlib.rectangle(int(startX),int(startY),int(endX),int(endY))
        faceAligned = fa.align(rgb,grey,r)
        cv2.imshow("FACE ALIGNED {:d}".format(i),faceAligned)

k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()

祝你好运。