检测人脸然后自动复制图片

Question

我试图找到一个应用程序，可以检测我的照片中的脸，使检测到的脸居中，并裁剪720x 720像素的图片。编辑成百上千张我打算这样做的图片是非常耗时和细致的。

我试过用python opencv mentioned here来做这个，但我认为它已经过时了。我也试过using this，但这也给我的系统带来了一个错误。也尝试过为GIMP使用人脸检测插件，但它是为GIMP 2.6设计的，但我经常使用2.8。我也试过做在超高博客上发布的东西，但它已经过时了（因为我使用的是Ubuntu的一个精确的派生版本，而blogpost是在它还很难发布的时候发布的）。也尝试了使用Phatch，但没有人脸检测，所以一些裁剪的图片有他们的脸被切掉了。

我已经尝试了以上所有的方法，浪费了半天的时间试图让上面的任何一种方法做我需要做的事情。

你们有什么建议可以达到我有800张照片的目标吗。

我的操作系统是LinuxMint13Mate。

注意：我本来打算再添加2个链接，但stackexchange阻止我再发布2个链接，因为我还没有太多的声誉。

Answer 1

面部检测

https://github.com/wavexx/facedetect是一个很好的Python OpenCV CLI包装器，我使用ImageMagick在他们的自述文件中有just added这个示例：

for file in path/to/pictures/*.jpg; do
  name=$(basename "$file")
  i=0
  facedetect "$file" | while read x y w h; do
    convert "$file" -crop ${w}x${h}+${x}+${y} "path/to/faces/${name%.*}_${i}.${name##*.}"
    i=$(($i+1))
  done
done

在Ubuntu16.04上测试了数千张（未标记的）Facebook个人资料图片，请参见：https://github.com/cirosantilli/art/tree/d4352a46064d156591817c4eae5199f5ac8f23be/facebook

Answer 2

另一个可用的选项是dlib，它基于机器学习方法。

import dlib
import Image
from skimage import io
import matplotlib.pyplot as plt


def detect_faces(image):

    # Create a face detector
    face_detector = dlib.get_frontal_face_detector()

    # Run detector and get bounding boxes of the faces on image.
    detected_faces = face_detector(image, 1)
    face_frames = [(x.left(), x.top(),
                    x.right(), x.bottom()) for x in detected_faces]

    return face_frames

# Load image
img_path = 'test.jpg'
image = io.imread(img_path)

# Detect faces
detected_faces = detect_faces(image)

# Crop faces and plot
for n, face_rect in enumerate(detected_faces):
    face = Image.fromarray(image).crop(face_rect)
    plt.subplot(1, len(detected_faces), n+1)
    plt.axis('off')
    plt.imshow(face)

Answer 3

我设法从不同的源代码中获取一些代码，并将其缝合在一起。这仍在进行中。还有，你有没有图片的例子？

'''
Sources:
http://pythonpath.wordpress.com/2012/05/08/pil-to-opencv-image/
http://www.lucaamore.com/?p=638
'''

#Python 2.7.2
#Opencv 2.4.2
#PIL 1.1.7

import cv
import Image

def DetectFace(image, faceCascade):
    #modified from: http://www.lucaamore.com/?p=638

    min_size = (20,20)
    image_scale = 1
    haar_scale = 1.1
    min_neighbors = 3
    haar_flags = 0

    # Allocate the temporary images
    smallImage = cv.CreateImage(
            (
                cv.Round(image.width / image_scale),
                cv.Round(image.height / image_scale)
            ), 8 ,1)

    # Scale input image for faster processing
    cv.Resize(image, smallImage, cv.CV_INTER_LINEAR)

    # Equalize the histogram
    cv.EqualizeHist(smallImage, smallImage)

    # Detect the faces
    faces = cv.HaarDetectObjects(
            smallImage, faceCascade, cv.CreateMemStorage(0),
            haar_scale, min_neighbors, haar_flags, min_size
        )

    # If faces are found
    if faces:
        for ((x, y, w, h), n) in faces:
            # the input to cv.HaarDetectObjects was resized, so scale the
            # bounding box of each face and convert it to two CvPoints
            pt1 = (int(x * image_scale), int(y * image_scale))
            pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
            cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 5, 8, 0)

    return image

def pil2cvGrey(pil_im):
    #from: http://pythonpath.wordpress.com/2012/05/08/pil-to-opencv-image/
    pil_im = pil_im.convert('L')
    cv_im = cv.CreateImageHeader(pil_im.size, cv.IPL_DEPTH_8U, 1)
    cv.SetData(cv_im, pil_im.tostring(), pil_im.size[0]  )
    return cv_im

def cv2pil(cv_im):
    return Image.fromstring("L", cv.GetSize(cv_im), cv_im.tostring())


pil_im=Image.open('testPics/faces.jpg')
cv_im=pil2cv(pil_im)
#the haarcascade files tells opencv what to look for.
faceCascade = cv.Load('C:/Python27/Lib/site-packages/opencv/haarcascade_frontalface_default.xml')
face=DetectFace(cv_im,faceCascade)
img=cv2pil(face)
img.show()

在Google的第一页进行测试（Googled“faces”）：

---

更新

这段代码应该完全符合您的要求。如果你有问题请告诉我。我试图在代码中包含很多注释：

'''
Sources:
http://opencv.willowgarage.com/documentation/python/cookbook.html
http://www.lucaamore.com/?p=638
'''

#Python 2.7.2
#Opencv 2.4.2
#PIL 1.1.7

import cv #Opencv
import Image #Image from PIL
import glob
import os

def DetectFace(image, faceCascade, returnImage=False):
    # This function takes a grey scale cv image and finds
    # the patterns defined in the haarcascade function
    # modified from: http://www.lucaamore.com/?p=638

    #variables    
    min_size = (20,20)
    haar_scale = 1.1
    min_neighbors = 3
    haar_flags = 0

    # Equalize the histogram
    cv.EqualizeHist(image, image)

    # Detect the faces
    faces = cv.HaarDetectObjects(
            image, faceCascade, cv.CreateMemStorage(0),
            haar_scale, min_neighbors, haar_flags, min_size
        )

    # If faces are found
    if faces and returnImage:
        for ((x, y, w, h), n) in faces:
            # Convert bounding box to two CvPoints
            pt1 = (int(x), int(y))
            pt2 = (int(x + w), int(y + h))
            cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 5, 8, 0)

    if returnImage:
        return image
    else:
        return faces

def pil2cvGrey(pil_im):
    # Convert a PIL image to a greyscale cv image
    # from: http://pythonpath.wordpress.com/2012/05/08/pil-to-opencv-image/
    pil_im = pil_im.convert('L')
    cv_im = cv.CreateImageHeader(pil_im.size, cv.IPL_DEPTH_8U, 1)
    cv.SetData(cv_im, pil_im.tostring(), pil_im.size[0]  )
    return cv_im

def cv2pil(cv_im):
    # Convert the cv image to a PIL image
    return Image.fromstring("L", cv.GetSize(cv_im), cv_im.tostring())

def imgCrop(image, cropBox, boxScale=1):
    # Crop a PIL image with the provided box [x(left), y(upper), w(width), h(height)]

    # Calculate scale factors
    xDelta=max(cropBox[2]*(boxScale-1),0)
    yDelta=max(cropBox[3]*(boxScale-1),0)

    # Convert cv box to PIL box [left, upper, right, lower]
    PIL_box=[cropBox[0]-xDelta, cropBox[1]-yDelta, cropBox[0]+cropBox[2]+xDelta, cropBox[1]+cropBox[3]+yDelta]

    return image.crop(PIL_box)

def faceCrop(imagePattern,boxScale=1):
    # Select one of the haarcascade files:
    #   haarcascade_frontalface_alt.xml  <-- Best one?
    #   haarcascade_frontalface_alt2.xml
    #   haarcascade_frontalface_alt_tree.xml
    #   haarcascade_frontalface_default.xml
    #   haarcascade_profileface.xml
    faceCascade = cv.Load('haarcascade_frontalface_alt.xml')

    imgList=glob.glob(imagePattern)
    if len(imgList)<=0:
        print 'No Images Found'
        return

    for img in imgList:
        pil_im=Image.open(img)
        cv_im=pil2cvGrey(pil_im)
        faces=DetectFace(cv_im,faceCascade)
        if faces:
            n=1
            for face in faces:
                croppedImage=imgCrop(pil_im, face[0],boxScale=boxScale)
                fname,ext=os.path.splitext(img)
                croppedImage.save(fname+'_crop'+str(n)+ext)
                n+=1
        else:
            print 'No faces found:', img

def test(imageFilePath):
    pil_im=Image.open(imageFilePath)
    cv_im=pil2cvGrey(pil_im)
    # Select one of the haarcascade files:
    #   haarcascade_frontalface_alt.xml  <-- Best one?
    #   haarcascade_frontalface_alt2.xml
    #   haarcascade_frontalface_alt_tree.xml
    #   haarcascade_frontalface_default.xml
    #   haarcascade_profileface.xml
    faceCascade = cv.Load('haarcascade_frontalface_alt.xml')
    face_im=DetectFace(cv_im,faceCascade, returnImage=True)
    img=cv2pil(face_im)
    img.show()
    img.save('test.png')


# Test the algorithm on an image
#test('testPics/faces.jpg')

# Crop all jpegs in a folder. Note: the code uses glob which follows unix shell rules.
# Use the boxScale to scale the cropping area. 1=opencv box, 2=2x the width and height
faceCrop('testPics/*.jpg',boxScale=1)

使用上面的图像，此代码从59个面中提取52个面，生成裁剪文件，例如：