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coord_to_intersections.py

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  • imageProcess.py 2.36 KiB
    #File to process images
    import cv2
    import numpy as np
    import faceAnalysis as fa
    import timeit as ti
    
    def imageProcess(image, writeEmotion=True, writeRectangle=True, returnEmotion=False):
        #Objectives : detect faces, identify emotion associated on it, modify the image by framing faces and writing their emotions associated
        
        facesList = []
        emotionsList = []
    
        #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
            if writeRectangle:
                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]
            facesList.append(face_color)
    
            #Write emotion on the image
            if writeEmotion:
                emotion = fa.detectEmotion(face_color)
                cv2.putText(image, emotion, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0), 2)
                emotionsList.append(emotion)
    
        if returnEmotion: return emotionsList
        return facesList
    
    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, 1.03, 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
    
    #Some tests here.
    # image = cv2.imread("cagnol.jpg", 1)  #Load Cagnol colored image
    # imageProcess(image)
    # cv2.imshow("Cagnol", image)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()