#File to process images import cv2 import numpy as np import faceAnalysis as fa def imageProcess(image): #Objectives : detect faces, identify emotion associated on it, modify the image by framing faces and writing their emotions associated #Import faces and eyes detectors from cv2 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml') eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_eye.xml') #CV2 detection is made on gray pictures gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) #This return a list of tuple locating faces on image #For each face, detect eyes call imageProcess to process the face and modify the image for face in faces: x,y,w,h = face #Create blue rectangle around face of thickness 2 cv2.rectangle(image, (x,y), (x+w,y+h), (255,0,0), 2 ) #Select face image face_gray = gray[y:y+h, x:x+w] face_color = image[y:y+h, x:x+w] #Detect eyes on the face, create green rectangle eyes = eye_cascade.detectMultiScale(face_gray) for (ex,ey,ew,eh) in eyes: cv2.rectangle(face_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),1) #Write emotion on the image emotion = fa.detectEmotion(face_color) cv2.putText(image, emotion, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0), 2) def selectFace(image): #Return a face identified on an colored image #Import cv2 face detector face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml') #Face detection is made on gray images gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) #This return a list of tuple locating faces on image #The face returned is the first face detected on the image (if exists) if faces != []: x,y,w,h = faces[0] face = image[y:y+h, x:x+w] return face # image = cv2.imread("cagnol.jpg", 1) #Load Cagnol colored image # imageProcess(image) # cv2.imshow("Cagnol", image) # cv2.waitKey(0) # cv2.destroyAllWindows()