# Detecte Emotion By photo
import cv2
import os
from keras.models import model_from_json
import numpy as np
import matplotlib.pyplot as plt
import threading


def display_image(image_array):
    cv2.imshow('My Image', image_array)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


# os.chdir('models')
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful",
                3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}

# load json and create model
# put your model path there
json_file = open(
    "emotion_recognition/models/emotion_model(MyNet0.82).json", 'r')
loaded_model_json = json_file.read()
json_file.close()
emotion_model = model_from_json(loaded_model_json)

# load weights into new model
# put your weight path there
emotion_model.load_weights(
    "emotion_recognition/models/emotion_model(MyNet0.82).h5")
print("Loaded model from disk")


def detectFace(net, frame, confidence_threshold=0.7):
    frameOpencvDNN = frame.copy()
    print(frameOpencvDNN.shape)
    frameHeight = frameOpencvDNN.shape[0]
    frameWidth = frameOpencvDNN.shape[1]
    blob = cv2.dnn.blobFromImage(frameOpencvDNN, 1.0, (227, 227), [
                                 124.96, 115.97, 106.13], swapRB=True, crop=False)
    net.setInput(blob)
    detections = net.forward()
    faceBoxes = []
    for i in range(detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence > confidence_threshold:
            x1 = int(detections[0, 0, i, 3]*frameWidth)
            y1 = int(detections[0, 0, i, 4]*frameHeight)
            x2 = int(detections[0, 0, i, 5]*frameWidth)
            y2 = int(detections[0, 0, i, 6]*frameHeight)
            print("x1=", x1, " x2=", x2, " y1=", y1, " y2=", y2)
            faceBoxes.append([x1, y1, x2, y2])
            cv2.rectangle(frameOpencvDNN, (x1, y1), (x2, y2),
                          (0, 255, 0), int(round(frameHeight/150)), 8)
    return frameOpencvDNN, faceBoxes


faceProto = 'emotion_recognition/models/opencv_face_detector.pbtxt'
faceModel = 'emotion_recognition/models/opencv_face_detector_uint8.pb'
# Loding detecting face model
faceNet = cv2.dnn.readNet(faceModel, faceProto)

# Get a Test image and process it to send it to model


def ai(path):
    f = cv2.imread(path, cv2.IMREAD_COLOR)
    # cv2.imshow("fla",f)
    gray_frame = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
    resultImg, faceBoxes = detectFace(faceNet, f)
    print('faceBoxes', faceBoxes)

    # Get the cordnate of face
    x1, y1, x2, y2 = faceBoxes[0][0], faceBoxes[0][1], faceBoxes[0][2], faceBoxes[0][3]
    print("x , y , w , h", x1, y1, x2, y2)

    roi_gray_frame = gray_frame[y1-20:y2+10, x1-20:x2+10]
    cropped_img = np.expand_dims(np.expand_dims(
        cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
    img_resized = cv2.resize(resultImg, (0, 0), fx=0.5,
                             fy=0.5, interpolation=cv2.INTER_AREA)
    # send photo to model
    emotion_prediction = emotion_model.predict(cropped_img)

    # Get the result
    maxindex = int(np.argmax(emotion_prediction))
    cv2.putText(img_resized, emotion_dict[maxindex], (x1+5, y1-20),
                cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)

    # cv2.imshow("crop",resultImg)
    # cv2.resizeWindow("crop",720,460)
    # cv2.imshow("crop2",roi_gray_frame)
    # cv2.resizeWindow("crop2",720,460)
    print("emotion_prediction=", emotion_dict[maxindex])

    display_thread = threading.Thread(
        target=display_image, args=(img_resized,))
    display_thread.start()

    return emotion_dict[maxindex]