run.py

from collections import abc
import cv2
import numpy as np
from keras import models
from tensorflow.keras.preprocessing.image import img_to_array

# Load haraarcascade to make box around face
detector = cv2.CascadeClassifier('utils/haarcascade_frontalface_default.xml')

# Load the saved model
model = models.load_model('utils/model_weights.h5')

# Capture video using cv2
video = cv2.VideoCapture(0)

# Prevents openCL usage and unnecessary logging messages
cv2.ocl.setUseOpenCL(False)

# Dictionary which assigns each label an emotion (alphabetical order)
emotion_dict = {0: "Marah", 1: "Bahagia", 2: "Netral", 3: "Sedih"}

# Keep looping
while True:
    # Retrive video frame to draw bounding box around face
    retrive, frame = video.read()
    if not retrive:
        break

        # Convert frame to BGR2GRAY
    gray = cv2.cvtColor(
        frame,
        cv2.COLOR_BGR2GRAY
    )

    # Detect face
    faces = detector.detectMultiScale(
        gray,
        scaleFactor=1.3,
        minNeighbors=5
    )

    # Loop each attribute in faces
    for (x, y, w, h) in faces:
        # Draw rectangle for face detection overlay
        cv2.rectangle(
            frame,
            (x, y - 50),
            (x + w, y + h + 10),
            (255, 0, 0),
            2
        )

        # Conver color to gray
        roi_gray = gray[
            y:y + h,
            x:x + w
        ]

        # crop image(chage size) to 64x64
        cropped_img = np.expand_dims(
            np.expand_dims(
                cv2.resize(
                    roi_gray,
                    (48, 48)
                ), -1
            ), 0
        )
            
        # Prediction
        prediction = model.predict(cropped_img)

        # Get index value(prediction array value from model_weight.h5)
        maxindex = int(np.argmax(prediction)) # <- not used, bcos now we compare by akurasi yg paling tinggi, if we use this nnti bakal di bulaitn ke 0/1 so we can't used this.

        print(maxindex)
        # Add text to rectangle overlay
        cv2.putText(
            frame,
            emotion_dict[maxindex],
            (x+20, y-60),
            cv2.FONT_HERSHEY_SIMPLEX, 1,
            (255, 255, 255), 2,
            cv2.LINE_AA
        )

    # Show image output
    cv2.imshow(
        'Video',
        cv2.resize(
            frame,
            (1600, 960),
            interpolation=cv2.INTER_CUBIC
        )
    )

    # Make q hotkey for close camera
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Run video
video.release()

# Cloase all related window(video)
cv2.destroyAllWindows()