from statistics import mode

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

from utils.datasets import get_labels
from utils.inference import load_detection_model
from utils.preprocessor import preprocess_input

# parameters for loading data and images
detection_model_path = './data/data_opencv/res10_300x300_ssd_iter_140000.caffemodel'
emotion_model_path = 'data/trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')

# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)

# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)

# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]

# starting lists for calculating modes
emotion_window = []


def smile_detect(bgr_image):
    gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
    x1 = y1 = 0
    (x2, y2, channel) = bgr_image.shape

    gray_face = gray_image[y1:y2, x1:x2]

    gray_face = cv2.resize(gray_face, (emotion_target_size))

    gray_face = preprocess_input(gray_face, True)
    gray_face = np.expand_dims(gray_face, 0)
    gray_face = np.expand_dims(gray_face, -1)
    emotion_prediction = emotion_classifier.predict(gray_face)
    emotion_label_arg = np.argmax(emotion_prediction)
    emotion_text = emotion_labels[emotion_label_arg]

    return emotion_text