TestModel.py

import cv2
import numpy as np
from keras.models import model_from_json


emotion_dict = {
    0: "Red",
    1: "Yellow",
    2: "Red",
    3: "Green",
    4: "Blue",
    5: "Blue",
    6: "Yellow",
}


# load json and create model
json_file = open("model/emotion_model.json", "r")
loaded_model_json = json_file.read()
json_file.close()
emotion_model = model_from_json(loaded_model_json)

print("==================================================")
print(loaded_model_json)
print("==================================================")


# load weights into new model
emotion_model.load_weights("model/emotion_model.h5")
print("Loaded model from disk")

# start the webcam feed
# cap = cv2.VideoCapture(0)

# pass here your video path
cap = cv2.VideoCapture(0)

while True:
    # Find haar cascade to draw bounding box around face
    ret, frame = cap.read()
    frame = cv2.resize(frame, (1080, 760))
    if not ret:
        break
    face_detector = cv2.CascadeClassifier(
        "haarcascades/haarcascade_frontalface_default.xml"
    )
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # detect faces available on camera
    num_faces = face_detector.detectMultiScale(
        gray_frame, scaleFactor=1.3, minNeighbors=5
    )

    # take each face available on the camera and Preprocess it
    for x, y, w, h in num_faces:
        cv2.rectangle(frame, (x, y - 50), (x + w, y + h + 10), (0, 255, 0), 4)
        roi_gray_frame = gray_frame[y : y + h, x : x + w]
        cropped_img = np.expand_dims(
            np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0
        )

        # predict the emotions
        emotion_prediction = emotion_model.predict(cropped_img)
        print(emotion_prediction)
        maxindex = int(np.argmax(emotion_prediction))
        print(maxindex)

        try:
            cv2.putText(
                frame,
                emotion_dict[maxindex],
                (x + 5, y - 20),
                cv2.FONT_HERSHEY_SIMPLEX,
                1,
                (255, 0, 0),
                2,
                cv2.LINE_AA,
            )
        except:
            print("invalid index")

    cv2.imshow("Emotion Detection", frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()