import os
import torch
import cv2
import numpy as np

from PIL import Image
from gfpgan.utils import GFPGANer
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from realesrgan.utils import RealESRGANer


def runcmd(cmd, verbose = False, *args, **kwargs):

    process = subprocess.Popen(
        cmd,
        stdout = subprocess.PIPE,
        stderr = subprocess.PIPE,
        text = True,
        shell = True
    )
    std_out, std_err = process.communicate()
    if verbose:
        print(std_out.strip(), std_err)
    pass

if not os.path.exists('GFPGANv1.4.pth'):
    runcmd("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
if not os.path.exists('realesr-general-x4v3.pth'):
    runcmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")

os.makedirs('output', exist_ok=True)

model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
model_path = 'realesr-general-x4v3.pth'
half = True if torch.cuda.is_available() else False
upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)

face_enhancer = GFPGANer(model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2)


def enhance_image(
    input_image: Image,
    scale: int,
    enhance_mode: int,
    keep_size: bool = False,
):
    only_face = enhance_mode == 1
    enhance_face = enhance_mode != 2

    if enhance_mode == 1:
        face_enhancer.upscale = scale
        face_enhancer.bg_upsampler = None
    elif enhance_mode == 2:
        pass
    else:
        face_enhancer.upscale = scale
        face_enhancer.bg_upsampler = upsampler
    
    img = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)

    h, w = img.shape[0:2]
    if h < 300:
        img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
    
    max_size = 3480 / scale
    if h > max_size:
        w = int(w * max_size / h)
        h = max_size
    
    if w > max_size:
        h = int(h * max_size / w)
        w = max_size
    
    if h != img.shape[0] or w != img.shape[1]:
        img = cv2.resize(img, (w, h), interpolation=cv2.INTER_LANCZOS4)
    
    if enhance_face:
        _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=only_face, paste_back=True)
    else:
        output, _ = upsampler.enhance(img, outscale=scale)
    
    h, w = img.shape[0:2]
    interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
    if keep_size:
        output = cv2.resize(output, (w, h), interpolation=interpolation)
    elif scale != 2:
        output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
    
    pil_output = Image.fromarray(cv2.cvtColor(output, cv2.COLOR_BGR2RGB))

    return pil_output