Detect Face Then Autocrop Pictures

Detect face then autocrop pictures

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:

Cropping faces from an image using OpenCV in Python

The problem is in your code, face_crop is storing only the last face detected.

So make it as a list and append all faces to it. Then use a for loop to display all faces. Like this:

face_crop = []
for f in faces:
    x, y, w, h = [ v for v in f ]
    cv2.rectangle(image_copy, (x,y), (x+w, y+h), (255,0,0), 3)
    # Define the region of interest in the image  
    face_crop.append(gray_image[y:y+h, x:x+w])

for face in face_crop:
    cv2.imshow('face',face)
    cv2.waitKey(0)

I used cv2.imshow() to display the images. You can modify this to use plt.imshow()

Hope this helps!

C# - Detect face and crop image

You can use HaarCascade class in EmguCV (DotNet port of OpenCV) http://www.emgu.com/wiki/index.php/Face_detection

Notes in order to run this example:

• Create a Windows Form Application
• Add a PictureBox and a Timer (and Enable it) - Run it on a x86 system
• Be sure you have the OpenCV relevant dlls (included with the Emgu CV download) in the folder where you code executes.
• Adjust the path to find the Haarcascade xml (last line of the code)

using System;
using System.Windows.Forms;
using System.Drawing;
using Emgu.CV;
using Emgu.Util;
using Emgu.CV.Structure;
using Emgu.CV.CvEnum;
 
namespace opencvtut
{
    public partial class Form1 : Form
    {
                private Capture cap;
                private HaarCascade haar;
 
        public Form1()
        {
            InitializeComponent();
        }
 
        private void timer1_Tick(object sender, EventArgs e)
        {
                using (Image<Bgr, byte> nextFrame = cap.QueryFrame())
                {
                        if (nextFrame != null)
                        {
                                // there's only one channel (greyscale), hence the zero index
                                //var faces = nextFrame.DetectHaarCascade(haar)[0];
                                Image<Gray, byte> grayframe = nextFrame.Convert<Gray, byte>();
                                var faces =
                                        grayframe.DetectHaarCascade(
                                                haar, 1.4, 4,
                                                HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
                                                new Size(nextFrame.Width/8, nextFrame.Height/8)
                                                )[0];
 
                                foreach (var face in faces)
                                {
                                        nextFrame.Draw(face.rect, new Bgr(0,double.MaxValue,0), 3);
                                }
                                pictureBox1.Image = nextFrame.ToBitmap();
                        }
                }
        }
 
        private void Form1_Load(object sender, EventArgs e)
        {
            // passing 0 gets zeroth webcam
                        cap = new Capture(0);
            // adjust path to find your xml
                        haar = new HaarCascade(
                "..\\..\\..\\..\\lib\\haarcascade_frontalface_alt2.xml");
        }
    }
}

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