#!/usr/bin/env python

from __future__ import annotations
import argparse
import functools
import os
import pathlib
import sys
from typing import Callable
import uuid
import cv2

sys.path.insert(0, 'APDrawingGAN2')

import gradio as gr
import huggingface_hub
import numpy as np
import PIL.Image

from io import BytesIO
import shutil

from options.test_options import TestOptions
from data import CreateDataLoader
from models import create_model
import dlib
from preprocess.get_partmask import get_68lm, get_partmask
from util import html

import ntpath
from util import util
from modnet import ModNet

ORIGINAL_REPO_URL = 'https://github.com/yiranran/APDrawingGAN2'
TITLE = 'yiranran/APDrawingGAN2'
DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}.

"""
ARTICLE = """

"""

MODEL_REPO = 'hylee/apdrawing_model'


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--theme', type=str)
    parser.add_argument('--live', action='store_true')
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    parser.add_argument('--allow-flagging', type=str, default='never')
    parser.add_argument('--allow-screenshot', action='store_true')
    return parser.parse_args()


def load_checkpoint():
    dir = 'checkpoint'
    checkpoint_path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                                      'checkpoints.zip',
                                                      force_filename='checkpoints.zip')
    print(checkpoint_path)
    shutil.unpack_archive(checkpoint_path, extract_dir=dir)
    print(os.listdir(dir + '/checkpoints'))
    return dir + '/checkpoints'

def load_modnet_model():
    modnet_path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                                      'modnet.onnx',
                                                      force_filename='modnet.onnx')
    return modnet_path


# save image to the disk
def save_images2(image_dir, visuals, image_path, aspect_ratio=1.0, width=256):
    short_path = ntpath.basename(image_path[0])
    name = os.path.splitext(short_path)[0]

    imgs = []

    for label, im_data in visuals.items():
        im = util.tensor2im(im_data)  # tensor to numpy array [-1,1]->[0,1]->[0,255]
        image_name = '%s_%s.png' % (name, label)
        save_path = os.path.join(image_dir, image_name)
        h, w, _ = im.shape
        if aspect_ratio > 1.0:
            im = np.array(PIL.Image.fromarray(arr).resize(im, (h, int(w * aspect_ratio))))
        if aspect_ratio < 1.0:
            im = np.array(PIL.Image.fromarray(arr).resize(im, (int(h / aspect_ratio), w)))
        util.save_image(im, save_path)
        imgs.append(save_path)

    return imgs


SAFEHASH = [x for x in "0123456789-abcdefghijklmnopqrstuvwxyz_ABCDEFGHIJKLMNOPQRSTUVWXYZ"]


def compress_UUID():
    '''
    根据http://www.ietf.org/rfc/rfc1738.txt,由uuid编码扩bai大字符域生成du串
    包括:[0-9a-zA-Z\-_]共64个
    长度:(32-2)/3*2=20
    备注:可在地球上人zhi人都用,使用100年不重复(2^120)
    :return:String
    '''
    row = str(uuid.uuid4()).replace('-', '')
    safe_code = ''
    for i in range(10):
        enbin = "%012d" % int(bin(int(row[i * 3] + row[i * 3 + 1] + row[i * 3 + 2], 16))[2:], 10)
        safe_code += (SAFEHASH[int(enbin[0:6], 2)] + SAFEHASH[int(enbin[6:12], 2)])
    safe_code = safe_code.replace('-', '')
    return safe_code

def run(
        image,
        landmarks_str : str,
        model,
        opt,
        detector,
        predictor,
        modnet : ModNet,
) -> tuple[PIL.Image.Image]:

    dataroot = 'images/' + compress_UUID()
    opt.dataroot = os.path.join(dataroot, 'src/')
    os.makedirs(opt.dataroot, exist_ok=True)
    opt.results_dir = os.path.join(dataroot, 'results/')
    os.makedirs(opt.results_dir, exist_ok=True)

    opt.lm_dir = os.path.join(dataroot, 'landmark/')
    opt.cmask_dir = os.path.join(dataroot, 'mask/')
    opt.bg_dir = os.path.join(dataroot, 'mask/bg')
    os.makedirs(opt.lm_dir, exist_ok=True)
    os.makedirs(opt.cmask_dir, exist_ok=True)
    os.makedirs(opt.bg_dir, exist_ok=True)

    shutil.copy(image.name, opt.dataroot)

    fullname = os.path.basename(image.name)
    name = fullname.split(".")[0]

    #bg = cv2.cvtColor(cv2.imread(mask.name), cv2.COLOR_BGR2GRAY)
    #cv2.imwrite(os.path.join(opt.bg_dir, name+'.png'), bg)
    modnet.segment(image.name, os.path.join(opt.bg_dir, name+'.png'))

    imgfile = os.path.join(opt.dataroot, fullname)
    lmfile5 = os.path.join(opt.lm_dir, name+'.txt')
    lmfile68 = os.path.join(opt.lm_dir, name + '_68.txt')

    # 预处理数据
    # get_68lm(imgfile, lmfile5, lmfile68, detector, predictor)
    f = open(lmfile68, 'w')
    print(landmarks_str, file=f)
    f.close()
    landmarks = np.loadtxt(lmfile68, dtype=np.int64)

    ff = open(lmfile5, 'w')
    lm = (landmarks[36] + landmarks[39]) / 2
    print(int(lm[0]), int(lm[1]), file=ff)
    lm = (landmarks[45] + landmarks[42]) / 2
    print(int(lm[0]), int(lm[1]), file=ff)
    lm = landmarks[30]
    print(lm[0], lm[1], file=ff)
    lm = landmarks[48]
    print(lm[0], lm[1], file=ff)
    lm = landmarks[54]
    print(lm[0], lm[1], file=ff)
    ff.close()

    imgs = []
    for part in ['eyel', 'eyer', 'nose', 'mouth']:
        savepath = os.path.join(opt.cmask_dir + part, name+'.png')
        get_partmask(imgfile, part, lmfile68, savepath)
        #imgs.append(savepath)

    data_loader = CreateDataLoader(opt)
    dataset = data_loader.load_data()

    # test
    # model.eval()
    for i, data in enumerate(dataset):
        if i >= opt.how_many:  # test code only supports batch_size = 1, how_many means how many test images to run
            break
        model.set_input(data)
        model.test()
        visuals = model.get_current_visuals()  # in test the loadSize is set to the same as fineSize
        img_path = model.get_image_paths()
        # if i % 5 == 0:
        #    print('processing (%04d)-th image... %s' % (i, img_path))
        imgs = save_images2(opt.results_dir, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)

    print(imgs)

    return PIL.Image.open(imgs[1])


def main():
    gr.close_all()

    args = parse_args()

    checkpoint_dir = load_checkpoint()

    opt = TestOptions().parse()
    opt.num_threads = 1  # test code only supports num_threads = 1
    opt.batch_size = 1  # test code only supports batch_size = 1
    opt.serial_batches = True  # no shuffle
    opt.no_flip = True  # no flip
    opt.display_id = -1  # no visdom display


    opt.checkpoints_dir = checkpoint_dir

    model = create_model(opt)
    model.setup(opt)

    modnet_path = load_modnet_model();
    modnet = ModNet(modnet_path)


    '''
    预处理数据
    '''
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(checkpoint_dir + '/shape_predictor_68_face_landmarks.dat')

    func = functools.partial(run, model=model, opt=opt, detector=detector, predictor=predictor, modnet=modnet)
    func = functools.update_wrapper(func, run)

    gr.Interface(
        func,
        [
            gr.inputs.Image(type='file', label='Input Image'),
            gr.inputs.Textbox(lines=1, label="Landmarks"),
        ],
        [
            gr.outputs.Image(
                type='pil',
                label='Result'),
        ],
        # examples=examples,
        theme=args.theme,
        title=TITLE,
        description=DESCRIPTION,
        article=ARTICLE,
        allow_screenshot=args.allow_screenshot,
        allow_flagging=args.allow_flagging,
        live=args.live,
    ).launch(
        enable_queue=args.enable_queue,
        server_port=args.port,
        share=args.share,
    )


if __name__ == '__main__':
    main()