Question

我正在尝试找到一个可以检测我的照片中的脸部的应用程序，使检测到的脸部居中并裁剪出720 x 720像素的照片。这是非常耗时的。一丝不苟地编辑我打算做的数百张图片。

我尝试使用python opencv mentioned here执行此操作，但我认为它已过时。我也试过using this，但它也在我的系统中给了我一个错误。还尝试使用GIMP的面部检测插件，但它是为GIMP 2.6设计的，但我定期使用2.8。我也尝试过在ultrahigh博客上发布的内容，但它已经过时了（因为我使用的是Ubuntu的精确衍生版本，而博客帖子在它仍然是Hardy的时候就已经回归了）。也尝试使用Phatch，但没有面部检测，所以一些裁剪的图片的脸被切断。

我已经尝试了上述所有内容并浪费了半天的时间，试图让上述任何一项做我需要做的事情。

你们有没有建议我实现约800张照片的目标。

我的操作系统是Linux Mint 13 MATE。

注意：我打算再添加2个链接但是stackexchange阻止我发布两个链接，因为我还没有太多的声誉。

Answer 1

我已经设法从各种来源获取代码并将其拼接在一起。它仍在进行中。另外，你有任何示例图像吗？

'''
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的第一页上进行测试（Google搜索“面孔”）： enter image description here

的更新

此代码应该完全符合您的要求。如果您有疑问，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)

使用上面的图片，此代码从59个面中提取52个，生成裁剪文件，例如：

Answer 2

<强> facedetect

https://github.com/wavexx/facedetect是一个不错的Python OpenCV CLI包装器，我使用ImageMagick将这个示例作为自述文件的just added：

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Ubuntu 16.04上测试了数千条（未标记的）Facebook个人资料照片，请参阅：https://github.com/cirosantilli/art/tree/d4352a46064d156591817c4eae5199f5ac8f23be/facebook

Answer 3

另一个可用选项是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 4

对于更多（计算机）技术重点交流之一来说，这似乎是一个更好的问题。

那就是说，你看过something like this jquery人脸检测脚本吗？我不知道你有多精明，但它是一个独立于操作系统的选项。

This solution看起来也很有希望，但需要Windows。

Answer 5

以上代码有效，但这是最近使用OpenCV的实现我无法通过最新的方式运行上述内容并找到了有效的方法（来自不同的地方）

 app.get(prefix + '/GetAnalyticsByUserId', function (req, res, next) {
    var instaid = req.query.instaid;
    var lastdate = req.query.lastdate; // this is my date in the timestamp i want to using this equal or greater date and count 
    InstaAc.aggregate(
                { $match: { _id: ObjectId("595f6bcdeb3db12064f26336") } },
                { $unwind: '$History' },
                { $match: { 'History.Action': { $eq: "Comment" } } },
                { $match: { 'History.datetime': { $gte: "datetime" } } },
                { $group: { _id: '$_id', History: { $push: '$History' } } }).exec(function (err, data) {
                    if (err) {
                        console.log(err); res.send(err)
                    }
                    else {                            
                            console.log(data);
                            res.send(data);                           
                    }
                })
});

Answer 6

我使用了这个shell命令：

for f in *.jpg;do PYTHONPATH=/usr/local/lib/python2.7/site-packages python -c 'import cv2;import sys;rects=cv2.CascadeClassifier("/usr/local/opt/opencv/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml").detectMultiScale(cv2.cvtColor(cv2.imread(sys.argv[1]),cv2.COLOR_BGR2GRAY),1.3,5);print("\n".join([" ".join([str(item) for item in row])for row in rects]))' $f|while read x y w h;do convert $f -gravity NorthWest -crop ${w}x$h+$x+$y ${f%jpg}-$x-$y.png;done;done

您可以使用opencv在OS X上安装imagemagick和brew install opencv imagemagick。

Answer 7

Autocrop很适合我。它就像autocrop -i pics -o crop -w 400 -H 400一样简单。您可以在自述文件中获取用法。

usage: [-h] [-o OUTPUT] [-i INPUT] [-w WIDTH] [-H HEIGHT] [-v]

Automatically crops faces from batches of pictures

optional arguments:
  -h, --help            Show this help message and exit
  -o, --output, -p, --path
            Folder where cropped images will be placed.
            Default: current working directory
  -i, --input
            Folder where images to crop are located.
            Default: current working directory
  -w, --width
            Width of cropped files in px. Default=500
  -H, --height
            Height of cropped files in px. Default=500
  -v, --version         Show program's version number and exit

Answer 8

只需添加到@以色列阿贝贝的版本。如果在图像扩展之前添加计数器，该算法将给出检测到的所有面部。附加代码，与以色列安倍比的代码相同。只需添加一个计数器并接受级联文件作为参数即可。该算法效果很好！谢谢@以色列阿贝贝！

import cv2
import os
import sys

def facecrop(image):
facedata = sys.argv[1]
cascade = cv2.CascadeClassifier(facedata)

img = cv2.imread(image)

minisize = (img.shape[1],img.shape[0])
miniframe = cv2.resize(img, minisize)

faces = cascade.detectMultiScale(miniframe)
counter = 0
for f in faces:
    x, y, w, h = [ v for v in f ]
    cv2.rectangle(img, (x,y), (x+w,y+h), (255,255,255))

    sub_face = img[y:y+h, x:x+w]
    fname, ext = os.path.splitext(image)
    cv2.imwrite(fname+"_cropped_"+str(counter)+ext, sub_face)
    counter += 1
return

facecrop("Face_detect_1.jpg")

PS：添加为答案。由于积分问题，无法添加评论。

Answer 9

我已经使用python包'face_recognition'和'opencv-python'开发了一个“带有个人数据集的人脸识别”应用程序。 / p>

源代码和安装指南位于GitHub-Face-Recognition-with-Own-Data-Set

或运行源代码-

import face_recognition
import cv2
import numpy as np

import os
'''
    Get current working director and create a Data directory to store the faces
'''
currentDirectory = os.getcwd()
dirName = os.path.join(currentDirectory, 'Data')
print(dirName)
if not os.path.exists(dirName):
    try:
        os.makedirs(dirName)
    except:
        raise OSError("Can't create destination directory (%s)!" % (dirName))
'''
    For the given path, get the List of all files in the directory tree 
'''
def getListOfFiles(dirName):
    # create a list of file and sub directories
    # names in the given directory
    listOfFile = os.listdir(dirName)
    allFiles = list()
    # Iterate over all the entries
    for entry in listOfFile:
        # Create full path
        fullPath = os.path.join(dirName, entry)
        # If entry is a directory then get the list of files in this directory
        if os.path.isdir(fullPath):
            allFiles = allFiles + getListOfFiles(fullPath)
        else:
            allFiles.append(fullPath)

    return allFiles

def knownFaceEncoding(listOfFiles):
    known_face_encodings=list()
    known_face_names=list()
    for file_name in listOfFiles:
        # print(file_name)
        if(file_name.lower().endswith(('.png', '.jpg', '.jpeg'))):
            known_image = face_recognition.load_image_file(file_name)
            # known_face_locations = face_recognition.face_locations(known_image)
            # known_face_encoding = face_recognition.face_encodings(known_image,known_face_locations)
            face_encods = face_recognition.face_encodings(known_image)
            if face_encods:
                known_face_encoding = face_encods[0]
                known_face_encodings.append(known_face_encoding)
                known_face_names.append(os.path.basename(file_name[0:-4]))
    return known_face_encodings, known_face_names


# Get the list of all files in directory tree at given path
listOfFiles = getListOfFiles(dirName)
known_face_encodings, known_face_names = knownFaceEncoding(listOfFiles)

video_capture = cv2.VideoCapture(0)
cv2.namedWindow("Video", flags= cv2.WINDOW_NORMAL)
# cv2.namedWindow("Video")

cv2.resizeWindow('Video', 1024,640)
cv2.moveWindow('Video', 20,20)


# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True


while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()
    # print(ret)
    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]


    k = cv2.waitKey(1)
    # Hit 'c' on capture the image!
    # Hit 'q' on the keyboard to quit!
    if k == ord('q'):
        break
    elif k== ord('c'):
        face_loc = face_recognition.face_locations(rgb_small_frame)
        if face_loc:
            print("Enter Name -")
            name = input()
            img_name = "{}/{}.png".format(dirName,name)
            (top, right, bottom, left)= face_loc[0]
            top *= 4
            right *= 4
            bottom *= 4
            left *= 4
            cv2.imwrite(img_name, frame[top - 5 :bottom + 5,left -5 :right + 5])
            listOfFiles = getListOfFiles(dirName)
            known_face_encodings, known_face_names = knownFaceEncoding(listOfFiles)

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
        # print(face_locations)

        face_names = []

        for face_encoding,face_location in zip(face_encodings,face_locations):
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance= 0.55)
            name = "Unknown"
            distance = 0

            # use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            #print(face_distances)
            if len(face_distances) > 0:
                best_match_index = np.argmin(face_distances)
                if matches[best_match_index]:
                    name = known_face_names[best_match_index]
                    # distance = face_distances[best_match_index]
            #print(face_distances[best_match_index])
            # string_value = '{} {:.3f}'.format(name, distance)
            face_names.append(name)


    process_this_frame = not process_this_frame


    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom + 46), (right, bottom+11), (0, 0, 155), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom +40), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image
    cv2.imshow('Video', frame)

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

它将在当前位置创建一个“数据”目录，即使该目录不存在。

当用矩形标记人脸时，按'c'捕获图像，然后在命令提示符下询问人脸名称。输入图像名称并输入。您可以在“数据” 目录中找到此图片。

Answer 10

我认为最好的选择是Google Vision API。它已经更新，它使用机器学习，随着时间的推移而改进。

您可以查看文档中的示例： https://cloud.google.com/vision/docs/other-features

Answer 11

检测面部，然后进行裁剪并将裁剪后的图像保存到文件夹..

import numpy as np
import cv2 as cv
face_cascade = cv.CascadeClassifier('./haarcascade_frontalface_default.xml')
#eye_cascade = cv.CascadeClassifier('haarcascade_eye.xml')
img = cv.imread('./face/nancy-Copy1.jpg')
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
    cv.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:
     #   cv.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
    sub_face = img[y:y+h, x:x+w]
    face_file_name = "face/" + str(y) + ".jpg"
    plt.imsave(face_file_name, sub_face)
plt.imshow(sub_face)

检测面部然后自动拍摄照片

11 个答案:

的更新