import argparse
import cv2
import glob
import numpy as np
from collections import OrderedDict
from skimage import img_as_ubyte
import os
import torch
import requests
from PIL import Image
import torchvision.transforms.functional as TF
import torch.nn.functional as F
from natsort import natsorted
from model.CMFNet import CMFNet

def main():
    parser = argparse.ArgumentParser(description='Demo Image Dehaze')
    parser.add_argument('--input_dir', default='test/', type=str, help='Input images')
    parser.add_argument('--result_dir', default='results/', type=str, help='Directory for results')
    parser.add_argument('--weights',
                        default='experiments/pretrained_models/dehaze_model.pth', type=str,
                        help='Path to weights')

    args = parser.parse_args()

    inp_dir = args.input_dir
    out_dir = args.result_dir

    os.makedirs(out_dir, exist_ok=True)

    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))

    if len(files) == 0:
        raise Exception(f"No files found at {inp_dir}")

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # Load corresponding models architecture and weights
    model = CMFNet()
    model = model.to(device)
    model.eval()
    load_checkpoint(model, args.weights)
    

    mul = 8
    for file_ in files:
        img = Image.open(file_).convert('RGB')
        input_ = TF.to_tensor(img).unsqueeze(0).to(device)

        # Pad the input if not_multiple_of 8
        h, w = input_.shape[2], input_.shape[3]
        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
        padh = H - h if h % mul != 0 else 0
        padw = W - w if w % mul != 0 else 0
        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
        
        with torch.no_grad():
            restored = model(input_)
            
        restored = torch.clamp(restored, 0, 1)
        restored = restored[:, :, :h, :w]
        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
        restored = img_as_ubyte(restored[0])

        f = os.path.splitext(os.path.split(file_)[-1])[0]
        save_img((os.path.join(out_dir, f + '.png')), restored)



def save_img(filepath, img):
    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))


def load_checkpoint(model, weights):
    checkpoint = torch.load(weights, map_location=torch.device('cpu'))
    try:
        model.load_state_dict(checkpoint["state_dict"])
    except:
        state_dict = checkpoint["state_dict"]
        new_state_dict = OrderedDict()
        for k, v in state_dict.items():
            name = k[7:]  # remove `module.`
            new_state_dict[name] = v
        model.load_state_dict(new_state_dict)


if __name__ == '__main__':
    main()