from rabbit import builder
import logging
import numpy as np
from keras.models import load_model
from utils.datasets import get_labels
from utils.inference import detect_faces
from utils.inference import draw_text
from utils.inference import draw_bounding_box
from utils.inference import apply_offsets
from utils.inference import load_detection_model
from utils.inference import load_image
from utils.preprocessor import preprocess_input
import cv2
import json
import base64

detection_model_path = 'trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = 'trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')
font = cv2.FONT_HERSHEY_SIMPLEX

# hyper-parameters for bounding boxes shape
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]


def format_response(response):
    decoded_json = json.loads(response)
    return "Hello {}".format(decoded_json['name'])


def on_data(data):
    f = open('current.jpg', 'wb')
    f.write(base64.decodebytes(data))
    f.close()
    image_path = "current.jpg"

    out = []
    # loading images
    rgb_image = load_image(image_path, grayscale=False)
    gray_image = load_image(image_path, grayscale=True)
    gray_image = np.squeeze(gray_image)
    gray_image = gray_image.astype('uint8')

    faces = detect_faces(face_detection, gray_image)
    for face_coordinates in faces:
        x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
        gray_face = gray_image[y1:y2, x1:x2]

        try:
            gray_face = cv2.resize(gray_face, (emotion_target_size))
        except:
            continue

        gray_face = preprocess_input(gray_face, True)
        gray_face = np.expand_dims(gray_face, 0)
        gray_face = np.expand_dims(gray_face, -1)
        emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
        emotion_text = emotion_labels[emotion_label_arg]
        color = (0, 0, 255)

        draw_bounding_box(face_coordinates, rgb_image, color)
        draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)
        bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)

        cv2.imwrite('predicted.png', bgr_image)
        data = open('predicted.png', 'rb').read()
        encoded = base64.encodebytes(data).decode('utf-8')
        out.append({
            'image': encoded,
            'emotion': emotion_text,
        })

    return out

logging.basicConfig(level=logging.WARN)
rpc = builder.rpc("image.check", {'host': 'localhost', 'port': 5672})
rpc.server(on_data)
