face_cam.py

from keras.models import load_model
from keras.preprocessing.image import img_to_array
import numpy as np
import cv2


face_cascade = cv2.CascadeClassifier('models/haarcascade-frontalface-default.xml')
emotion_model = load_model('models/_mini_XCEPTION.102-0.66.hdf5', compile=False)
gender_model = load_model('models/gender_mini_XCEPTION.21-0.95.hdf5', compile=False)
gender=["kadin","erkek"]
emo = ["kizgin" ,"igrenme","korku", "mutlu", "uzgun", "sasirma","notr"]

def cam_detect(frame):
    
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)
    
    frameClone = frame.copy()

    for (x, y, w, h) in faces:

        roi = gray[y:y + h, x:x + w]
        roi = cv2.resize(roi, (64, 64))
        roi = roi.astype("float") / 255.0
        roi = img_to_array(roi)
        roi = np.expand_dims(roi, axis=0)

        preds = emotion_model.predict(roi)[0]
        emotion_probability = np.max(preds)

        preds1=gender_model.predict(roi)[0]
        gender_probability=np.max(preds1)

        label =[" Duygu durumu: "+str(emo[preds.argmax()])+" (%"+str(round(emotion_probability*100,2))+") ",
                " Cinsiyet: "+str(gender[preds1.argmax()])+" (%"+str(round(gender_probability*100,2))+") "]

        eksirota=[62,30]

        cv2.rectangle(frameClone, (x, y-90), (x + w, y -15),(255,0,0),5)
        cv2.rectangle(frameClone, (x, y-90), (x + w, y -15),(255,0,0),-1)

        for i in range(0,2):
            cv2.putText(frameClone, label[i], (x,y-eksirota[i]),cv2.FONT_HERSHEY_SIMPLEX, float((w)/600), (255, 255, 255), 2)

        cv2.rectangle(frameClone, (x, y), (x + w, y + h),(255,0,0),5)
    
    return frameClone