Detect face then autocrop pictures

Question

I am trying to find an app that can detect faces in my pictures, make the detected face centered and crop 720 x 720 pixels of the picture. It is rather very time consuming & meticulous to edit around hundreds of pictures I plan to do that.

I have tried doing this using python opencv mentioned here but I think it is outdated. I've also tried using this but it's also giving me an error in my system. Also tried using face detection plugin for GIMP but it is designed for GIMP 2.6 but I am using 2.8 on a regular basis. I also tried doing what was posted at ultrahigh blog but it is very outdated (since I'm using a Precise derivative of Ubuntu, while the blogpost was made way back when it was still Hardy). Also tried using Phatch but there is no face detection so some cropped pictures have their face cut right off.

I have tried all of the above and wasted half a day trying to make any of the above do what I needed to do.

Do you guys have suggestion to achieve a goal to around 800 pictures I have.

My operating system is Linux Mint 13 MATE.

Note: I was going to add 2 more links but stackexchange prevented me to post two more links as I don't have much reputation yet.

Answer 1

I have managed to grab bits of code from various sources and stitch this together. It is still a work in progress. Also, do you have any example images?

''' 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() 

Testing on the first page of Google (Googled "faces"):

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Update

This code should do exactly what you want. Let me know if you have questions. I tried to include lots of comments in the code:

''' 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) 

Using the image above, this code extracts 52 out of the 59 faces, producing cropped files such as:

Answer 2

Another available option is dlib, which is based on machine learning approaches.

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)