import cv2
import dlib
import numpy as np
import tensorflow as tf

import config
from predictors.predict_gender import detect_genders
from predictors.predict_bmi import predict_bmi
from predictors.predict_age import predict_age
#from predictors.predict_beauty import predict_beauty

detector = dlib.get_frontal_face_detector()


def extract_metadata(image_file_path):
    img = cv2.imread(image_file_path)
    # img = cv2.resize(img, (640, 480))
    input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img_h, img_w, _ = np.shape(input_img)

    detected, scores, idx = detector.run(input_img, 1)
    faces = np.empty((len(detected), config.RESNET50_DEFAULT_IMG_WIDTH,
                      config.RESNET50_DEFAULT_IMG_WIDTH, 3))
    filteredFaces = []

    for i, d in enumerate(detected):
        x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, \
                               d.bottom() + 1, d.width(), d.height()
        faceSize = (d.right() - d.left()) * (d.bottom() - d.top())
        faceSizePercentage = (faceSize / (img_w * img_h)) * 100
        xw1 = max(int(x1 - config.MARGIN * w), 0)
        yw1 = max(int(y1 - config.MARGIN * h), 0)
        xw2 = min(int(x2 + config.MARGIN * w), img_w - 1)
        yw2 = min(int(y2 + config.MARGIN * h), img_h - 1)
        # cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
        cv2.imwrite(''.join(image_file_path.split('.')[:-1]) + '-cropped-' + str(i) + '.jpg',
                    img[yw1:yw2, xw1:xw2])
        faces[i, :, :, :] = cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1, :], (
            config.RESNET50_DEFAULT_IMG_WIDTH,
            config.RESNET50_DEFAULT_IMG_WIDTH)) / 255.00

        face_boundary = (d.top(), d.right(), d.bottom(), d.left())
        gender = detect_genders(face_boundary, input_img)

        # FILTERING
        female = gender["woman"] >= 0.5
        # faceSizePercentage > config.MIN_FACE_SIZE_PERCENTAGE:
        if female and faceSize > config.MIN_FACE_SIZE_PIXELS and idx[i] == 0:
            filteredFaces.append((d, faceSize, faceSizePercentage, gender["woman"]))
    results = []
    for i, d in enumerate(filteredFaces):
        bmiResult = predict_bmi([d[0]], img)
        if len(bmiResult) > 0:
            bmiResult = float(str(bmiResult[0][0]))
        else:
            bmiResult = None
        ageResult = predict_age([d[0]], img)
        if len(ageResult) > 0:
            ageResult = float(str(ageResult[0]))
        else:
            ageResult = None

        # beautyResult = predict_beauty([d[0]], img)
        results.append({
            "face": {
                "left": d[0].left(),
                "top": d[0].top(),
                "right": d[0].right(),
                "bottom": d[0].bottom(),
                "size": d[1],
                "sizePercentage": d[2]
            },
            "gender_woman": d[3],
            "bmi": bmiResult,
            "age": ageResult
        })
    return results