detect_img.py

import os
import tensorflow as tf
import cv2
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

def prediction_img(img,detect_model,classNames):
    frame = cv2.imread(img)
    face_detect = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

    gray_img = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    faces = face_detect.detectMultiScale(gray_img,1.1,4)
    for x,y,w,h in faces:
        roi_gray_img = gray_img[y:y+h,x:x+w]
        roi_color = frame[y:y+h,x:x+w]
        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
        facess = face_detect.detectMultiScale(roi_gray_img)
        if len(facess) == 0:
            print("Face not detected")
        else:
            for (ex,ey,ew,eh) in facess:
                face_roi = roi_color[ey: ey+eh,ex:ex +ew]    

    final_img = cv2.resize(face_roi,(224,224))
    final_img = np.expand_dims(final_img,axis=0) # need 4th dimension
    final_img = final_img/255 # normalizing

    prediction = detect_model.predict(final_img)
    pred = np.argmax(prediction[0])
    return classNames[pred]

if __name__ =="__main__":
    classNames= []
    classFile = 'label.names'
    with open(classFile,'rt') as f:
        classNames = f.read().rstrip('\n').split('\n')    

    detect_model = tf.keras.models.load_model("face_emotion_rec_v2.h5")    
    img = "surprise_girl.jpg"
    print(prediction_img(img,detect_model,classNames))