extracting_faces.py

#0916: Seongjin Shin
#sungjin712@gmail
#There are 2 requirements in this code
#1. Create folder called "sorted_set" and inside of it, emotion folders ("neutral", "anger" and so on) and images exists
#2. Create folder called "dataset" and insde of it, emotion folders("neutral", "anger" and so on) should exists
#It will extract face and save into "dataset folders"

import glob
import cv2
from shutil import copyfile

faceDet = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
faceDet2 = cv2.CascadeClassifier("haarcascade_frontalface_alt2.xml")
faceDet3 = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
faceDet4 = cv2.CascadeClassifier("haarcascade_frontalface_alt_tree.xml")

emotion = 'surprise'
emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"] #Define emotions

files = glob.glob("sorted_set/%s/*" %emotion)

def detect_faces(emotion):
    files = glob.glob("sorted_set/%s/*" %emotion) #Get list of all images with emotion
    filenumber = 0
    for f in files:
        frame = cv2.imread(f) #Open image
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #Convert image to grayscale

        #Detect face using 4 different classifiers
        face = faceDet.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
        face2 = faceDet2.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
        face3 = faceDet3.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
        face4 = faceDet4.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)

        #Go over detected faces, stop at first detected face, return empty if no face.
        if len(face) == 1:
            facefeatures = face
        elif len(face2) == 1:
            facefeatures == face2
        elif len(face3) == 1:
            facefeatures = face3
        elif len(face4) == 1:
            facefeatures = face4
        else:
            facefeatures = ""

        #Cut and save face
        for (x, y, w, h) in facefeatures: #get coordinates and size of rectangle containing face
            print "face found in file: %s" %f
            gray = gray[y:y+h, x:x+w] #Cut the frame to size

            try:
                out = cv2.resize(gray, (350, 350)) #Resize face so all images have same size
                cv2.imwrite("dataset/%s/%s.jpg" %(emotion, filenumber), out) #Write image
            except:
               pass #If error, pass file
        filenumber += 1 #Increment image number

for emotion in emotions:
    detect_faces(emotion) #Call functiona