import cv2 as cv
import numpy as np
import torch
from PIL import Image, ImageOps

from comic_style.face_detection import align

torch.set_grad_enabled(False)
model = torch.jit.load('comic_style/u2net_bce_itr_16000_train_3.835149_tar_0.542587-400x_360x.jit.pt')
model.eval()


# https://en.wikipedia.org/wiki/Unsharp_masking
# https://stackoverflow.com/a/55590133/1495606
def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=2.0, threshold=0):
    """Return a sharpened version of the image, using an unsharp mask."""
    blurred = cv.GaussianBlur(image, kernel_size, sigma)
    sharpened = float(amount + 1) * image - float(amount) * blurred
    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
    sharpened = sharpened.round().astype(np.uint8)
    if threshold > 0:
        low_contrast_mask = np.absolute(image - blurred) < threshold
        np.copyto(sharpened, image, where=low_contrast_mask)
    return sharpened


def normPRED(d):
    ma = np.max(d)
    mi = np.min(d)

    dn = (d - mi) / (ma - mi)

    return dn


def array_to_np(array_in):
    array_in = normPRED(array_in)
    array_in = np.squeeze(255.0 * (array_in))
    array_in = np.transpose(array_in, (1, 2, 0))
    return array_in


def array_to_image(array_in):
    array_in = normPRED(array_in)
    array_in = np.squeeze(255.0 * (array_in))
    array_in = np.transpose(array_in, (1, 2, 0))
    im = Image.fromarray(array_in.astype(np.uint8))
    return im


def image_as_array(image_in):
    image_in = np.array(image_in, np.float32)
    tmpImg = np.zeros((image_in.shape[0], image_in.shape[1], 3))
    image_in = image_in / np.max(image_in)
    if image_in.shape[2] == 1:
        tmpImg[:, :, 0] = (image_in[:, :, 0] - 0.485) / 0.229
        tmpImg[:, :, 1] = (image_in[:, :, 0] - 0.485) / 0.229
        tmpImg[:, :, 2] = (image_in[:, :, 0] - 0.485) / 0.229
    else:
        tmpImg[:, :, 0] = (image_in[:, :, 0] - 0.485) / 0.229
        tmpImg[:, :, 1] = (image_in[:, :, 1] - 0.456) / 0.224
        tmpImg[:, :, 2] = (image_in[:, :, 2] - 0.406) / 0.225

    tmpImg = tmpImg.transpose((2, 0, 1))
    image_out = np.expand_dims(tmpImg, 0)
    return image_out


def find_aligned_face(image_in, size=400):
    aligned_image, n_faces, quad = align(image_in, face_index=0, output_size=size)
    return aligned_image, n_faces, quad


def align_first_face(image_in, size=400):
    aligned_image, n_faces, quad = find_aligned_face(image_in, size=size)
    if n_faces == 0:
        try:
            image_in = ImageOps.exif_transpose(image_in)
        except:
            print("exif problem, not rotating")
        image_in = image_in.resize((size, size))
        im_array = image_as_array(image_in)
    else:
        im_array = image_as_array(aligned_image)

    return im_array


def img_concat_h(im1, im2):
    dst = Image.new('RGB', (im1.width + im2.width, im1.height))
    dst.paste(im1, (0, 0))
    dst.paste(im2, (im1.width, 0))
    return dst


def face2hero(
        img: Image.Image,
        size: int
) -> Image.Image:
    aligned_img = align_first_face(img)
    if aligned_img is None:
        output = None
    else:
        input = torch.Tensor(aligned_img)
        results = model(input)
        hero_np_image = array_to_np(results[1].detach().numpy())
        hero_image = unsharp_mask(hero_np_image)
        hero_image = Image.fromarray(hero_image)
        # hero_image = hero_image.resize((int(hero_image.width * 0.3), int(hero_image.height * 0.3)), Image.ANTIALIAS)
        # output = img_concat_h(array_to_image(aligned_img), hero_image)
        del results
    return hero_image


def inference(img):
    out = face2hero(img, 400)
    return out