我正在尝试找到一个可以检测图片中的人脸的应用程序，使检测到的人脸居中并裁剪图片的 720 x 720 像素。编辑我打算这样做的数百张图片相当耗时且细致。
我曾尝试使用此处提到的 python opencv执行此操作，但我认为它已经过时了。我也尝试过使用它，但它也在我的系统中给我一个错误。还尝试使用 GIMP 的人脸检测插件，但它是为 GIMP 2.6 设计的，但我经常使用 2.8。我也尝试过在超高博客上发布的内容，但它已经过时了（因为我使用的是 Ubuntu 的 Precise 衍生产品，而博文是在它仍然是 Hardy 的时候发布的）。也尝试使用 Phatch，但没有人脸检测，所以一些裁剪的图片的脸被剪掉了。
我已经尝试了以上所有方法，并浪费了半天时间试图使上述任何一种方法完成我需要做的事情。
你们有什么建议可以实现我拥有的大约 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，但由于我还没有太多声誉，stackexchange 阻止我再发布两个链接。
我已经设法从各种来源获取一些代码并将它们拼接在一起。它仍在进行中。另外，您有任何示例图像吗？
'''
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（用 Google 搜索的“面孔”）：
这段代码应该完全符合您的要求。如果您有任何问题，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)
使用上图，这段代码从 59 个人脸中提取 52 个人脸，生成裁剪文件，例如：
另一个可用选项是dlib，它基于机器学习方法。
import dlib
from PIL 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)
facedetect用 Python 编写的 OpenCV CLI 包装器
https://github.com/wavexx/facedetect是一个不错的 Python OpenCV CLI 包装器，我在他们的 README 中添加了以下示例。
安装：
sudo apt install python3-opencv opencv-data imagemagick
git clone https://gitlab.com/wavexx/facedetect
git -C facedetect checkout 5f9b9121001bce20f7d87537ff506fcc90df48ca
获取我的测试图像：
mkdir -p pictures
wget -O pictures/test.jpg https://raw.githubusercontent.com/cirosantilli/media/master/Ciro_Santilli_with_a_stone_carved_Budai_in_the_Feilai_Feng_caves_near_the_Lingyin_Temple_in_Hangzhou_in_2012.jpg
用法：
mkdir -p faces
for file in pictures/*.jpg; do
name=$(basename "$file")
i=0
facedetect/facedetect --data-dir /usr/share/opencv4 "$file" |
while read x y w h; do
convert "$file" -crop ${w}x${h}+${x}+${y} "faces/${name%.*}_${i}.${name##*.}"
i=$(($i+1))
done
done
如果您不通过--data-dir此系统，它将失败并显示：
facedetect: error: cannot load HAAR_FRONTALFACE_ALT2 from /usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml
并且它正在寻找的文件可能位于：/usr/share/opencv4/haarcascades系统上。
运行后，文件：
faces/test_0.jpg
包含：
从原始图像中提取pictures/test.jpg：
Budai 未被识别 :-( 如果有，它会出现在 下faces/test_1.jpg，但该文件不存在。
让我们尝试另一种面部部分转向的人https://raw.githubusercontent.com/cirosantilli/media/master/Ciro_Santilli_with_his_mother_in_law_during_his_wedding_in_2017.jpg
嗯，没有命中，软件的面孔不够清晰。
在 Ubuntu 20.10、OpenCV 4.2.0 上测试。
自动裁剪对我来说效果很好。它就像autocrop -i pics -o crop -w 400 -H 400. 您可以在他们的自述文件中获得用法。
usage: autocrop [-h] [-i INPUT] [-o OUTPUT] [-r REJECT] [-w WIDTH] [-H HEIGHT]
[-v] [--no-confirm] [--facePercent FACEPERCENT] [-e EXTENSION]
Automatically crops faces from batches of pictures
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
Folder where images to crop are located. Default:
current working directory
-o OUTPUT, --output OUTPUT, -p OUTPUT, --path OUTPUT
Folder where cropped images will be moved to. Default:
current working directory, meaning images are cropped
in place.
-r REJECT, --reject REJECT
Folder where images that could not be cropped will be
moved to. Default: current working directory, meaning
images that are not cropped will be left in place.
-w WIDTH, --width WIDTH
Width of cropped files in px. Default=500
-H HEIGHT, --height HEIGHT
Height of cropped files in px. Default=500
-v, --version show program's version number and exit
--no-confirm Bypass any confirmation prompts
--facePercent FACEPERCENT
Percentage of face to image height
-e EXTENSION, --extension EXTENSION
Enter the image extension which to save at output
上面的代码有效，但这是最近使用 OpenCV 的实现
import cv2
import os
def facecrop(image):
facedata = "haarcascade_frontalface_alt.xml"
cascade = cv2.CascadeClassifier(facedata)
img = cv2.imread(image)
minisize = (img.shape[1],img.shape[0])
miniframe = cv2.resize(img, minisize)
faces = cascade.detectMultiScale(miniframe)
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_"+ext, sub_face)
return
facecrop("1.jpg")
只是添加到@Israel Abebe 的版本。如果您在图像扩展之前添加一个计数器，该算法将给出检测到的所有面部。附上代码，与 Israel Abebe 的相同。只需添加一个计数器并接受级联文件作为参数。该算法运行良好！感谢@Israel Abebe ！
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。
检测人脸，然后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)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opencv使用.imagemagickbrew install opencv imagemagick
我认为最好的选择是 Google Vision API。它已经更新，它使用机器学习，并且随着时间的推移而改进。
您可以查看文档以获取示例： https ://cloud.google.com/vision/docs/other-features