import cv2
from PIL import Image
import glob
import os
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url

from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact

def realEsrgan(model_name="RealESRGAN_x4plus_anime_6B",
               model_path = None,
               input_dir = 'inputs',
               output_dir = 'results',
               denoise_strength = 0.5, 
               outscale = 4,
               suffix = 'out',
               tile = 200,
               tile_pad = 10,
               pre_pad = 0,
               face_enhance = True,
               alpha_upsampler = 'realsrgan',
               out_ext = 'auto',
               fp32 = True,
               gpu_id = None,
                ):

    # determine models according to model names
    model_name = model_name.split('.')[0]
    if model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
    elif model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
    elif model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
    elif model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
        netscale = 2
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
    elif model_name == 'realesr-animevideov3':  # x4 VGG-style model (XS size)
        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
    elif model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
        netscale = 4
        file_url = [
            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
        ]

    # determine model paths
    if model_path is None:
        model_path = os.path.join('weights', model_name + '.pth')
        if not os.path.isfile(model_path):
            ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
            for url in file_url:
                # model_path will be updated
                model_path = load_file_from_url(
                    url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)

    # use dni to control the denoise strength
    dni_weight = None
    if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
        model_path = [model_path, wdn_model_path]
        dni_weight = [denoise_strength, 1 - denoise_strength]

    # restorer
    upsampler = RealESRGANer(
        scale=netscale,
        model_path=model_path,
        dni_weight=dni_weight,
        model=model,
        tile=tile,
        tile_pad=tile_pad,
        pre_pad=pre_pad,
        half=not fp32,
        gpu_id=gpu_id)

    if face_enhance:  # Use GFPGAN for face enhancement
        from gfpgan import GFPGANer
        face_enhancer = GFPGANer(
            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
            upscale=outscale,
            arch='clean',
            channel_multiplier=2,
            bg_upsampler=upsampler)
    os.makedirs(output_dir, exist_ok=True)

    if os.path.isfile(input_dir):
        paths = [input_dir]
    else:
        paths = sorted(glob.glob(os.path.join(input_dir, '*')))
    
    Imgs = []
    for idx, path in enumerate(paths):
        imgname, extension = os.path.splitext(os.path.basename(path))
        print(f'Scaling x{outscale}:', path)

        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
        if len(img.shape) == 3 and img.shape[2] == 4:
            img_mode = 'RGBA'
        else:
            img_mode = None

        try:
            if face_enhance:
                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
            else:
                output, _ = upsampler.enhance(img, outscale=outscale)
        except RuntimeError as error:
            print('Error', error)
            print('If you encounter CUDA or RAM out of memory, try to set --tile with a smaller number.')
        else:
            if out_ext == 'auto':
                extension = extension[1:]
            else:
                extension = out_ext
            if img_mode == 'RGBA':  # RGBA images should be saved in png format
                extension = 'png'
            if suffix == '':
                save_path = os.path.join(output_dir, f'{imgname}.{extension}')
            else:
                save_path = os.path.join(output_dir, f'{imgname}_{suffix}.{extension}')
            
            cv2.imwrite(save_path, output)
            
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            img = Image.fromarray(img)
            Imgs.append(img)

    return Imgs