import argparse
import cv2
import glob
import numpy as np
from collections import OrderedDict
import os
import torch
import requests
import scipy
from scipy import ndimage, misc
import shutil
from os import listdir
from os.path import isfile, join
import random
import tqdm

from models.network_swinir import SwinIR as net
from utils import util_calculate_psnr_ssim as util


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default='color_dn', help='classical_sr, lightweight_sr, real_sr, '
                                                                     'gray_dn, color_dn, jpeg_car')
    parser.add_argument('--scale', type=int, default=1, help='scale factor: 1, 2, 3, 4, 8') # 1 for dn and jpeg car
    parser.add_argument('--noise', type=int, default=15, help='noise level: 15, 25, 50')
    parser.add_argument('--jpeg', type=int, default=40, help='scale factor: 10, 20, 30, 40')
    parser.add_argument('--training_patch_size', type=int, default=128, help='patch size used in training SwinIR. '
                                       'Just used to differentiate two different settings in Table 2 of the paper. '
                                       'Images are NOT tested patch by patch.')
    parser.add_argument('--large_model', action='store_true', help='use large model, only provided for real image sr')
    parser.add_argument('--model_path', type=str,
                        default='model_zoo/swinir/001_classicalSR_DIV2K_s48w8_SwinIR-M_x2.pth')
    parser.add_argument('--folder_lq', type=str, default=None, help='input low-quality test image folder')
    parser.add_argument('--folder_gt', type=str, default=None, help='input ground-truth test image folder')
    parser.add_argument('--tile', type=int, default=None, help='Tile size, None for no tile during testing (testing as a whole)')
    parser.add_argument('--tile_overlap', type=int, default=32, help='Overlapping of different tiles')
    args = parser.parse_args()

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    # set up model
    if os.path.exists(args.model_path):
        print(f'loading model from {args.model_path}')
    else:
        os.makedirs(os.path.dirname(args.model_path), exist_ok=True)
        url = 'https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/{}'.format(os.path.basename(args.model_path))
        r = requests.get(url, allow_redirects=True)
        print(f'downloading model {args.model_path}')
        open(args.model_path, 'wb').write(r.content)

    model = define_model(args)
    model.eval()
    model = model.to(device)

    # setup folder and path
    folder, save_dir, border, window_size = setup(args)
    os.makedirs(save_dir, exist_ok=True)
    test_results = OrderedDict()
    test_results['psnr'] = []
    test_results['ssim'] = []
    test_results['psnr_y'] = []
    test_results['ssim_y'] = []
    test_results['psnr_b'] = []
    psnr, ssim, psnr_y, ssim_y, psnr_b = 0, 0, 0, 0, 0

    files_list = glob.glob(os.path.join(folder, '*')) # files in original folder
          
    tvt_f = open("/content/drive/MyDrive/DEVKitArtDL.zip (Unzipped Files)/DEVKitArt/tvt.txt", "r")
    files_list = [line.replace("\n","")+".jpg" for line in tvt_f]
    
    done_images = [f.replace(".jpg","") for f in listdir(save_dir) if isfile(join(save_dir, f))]
    
    files_list = [os.path.join(folder, f) for f in files_list if f.replace(".jpg","").split("/")[-1] not in done_images]
    
    for idx, path in tqdm.tqdm(enumerate(random.sample(files_list, len(files_list)))):
        # see if image already considered
        #done_images = [f.replace(".jpg","") for f in listdir(save_dir) if isfile(join(save_dir, f))]

        # read image
        imgname, img_lq, img_gt = get_image_pair(args, path)  # image to HWC-BGR, float32
        
        if(imgname not in done_images):
            cv2_img = cv2.imread(os.path.join(folder, imgname + ".jpg"), 0)

            img_lq = np.transpose(img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]], (2, 0, 1))  # HCW-BGR to CHW-RGB
            img_lq = torch.from_numpy(img_lq).float().unsqueeze(0).to(device)  # CHW-RGB to NCHW-RGB

            blur_level_orig = cv2.Laplacian(cv2_img, cv2.CV_64F).var()  

            if(blur_level_orig < 400): # blurred image -> apply super-resolution
                #continue # there is no GPU, now!
                print("Blurred image: application of super-resolution")
                # inference
                with torch.no_grad():
                    # pad input image to be a multiple of window_size
                    _, _, h_old, w_old = img_lq.size()
                    h_pad = (h_old // window_size + 1) * window_size - h_old
                    w_pad = (w_old // window_size + 1) * window_size - w_old
                    img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]
                    img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad]
                    output = test(img_lq, model, args, window_size)
                    output = output[..., :h_old * args.scale, :w_old * args.scale]

                # save image
                output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
                if output.ndim == 3:
                    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))  # CHW-RGB to HCW-BGR
                output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8


                scale_percent = 25
                width = int(output.shape[1] * scale_percent / 100)
                height = int(output.shape[0] * scale_percent / 100)

                dim = (width, height)
                print(output.shape)
                print("\n")
                output = cv2.resize(output, dim, interpolation = cv2.INTER_AREA)

                cv2.imwrite(f'{save_dir}/{imgname}.jpg', output)
            else:
                continue
                print("Non-blurred image")
                shutil.copyfile(os.path.join(folder, imgname + ".jpg"), os.path.join(save_dir, imgname + ".jpg"))

        else:
            print("The image already exists!")


def define_model(args):
    # 001 classical image sr
    if args.task == 'classical_sr':
        model = net(upscale=args.scale, in_chans=3, img_size=args.training_patch_size, window_size=8,
                    img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
                    mlp_ratio=2, upsampler='pixelshuffle', resi_connection='1conv')
        param_key_g = 'params'

    # 002 lightweight image sr
    # use 'pixelshuffledirect' to save parameters
    elif args.task == 'lightweight_sr':
        model = net(upscale=args.scale, in_chans=3, img_size=64, window_size=8,
                    img_range=1., depths=[6, 6, 6, 6], embed_dim=60, num_heads=[6, 6, 6, 6],
                    mlp_ratio=2, upsampler='pixelshuffledirect', resi_connection='1conv')
        param_key_g = 'params'

    # 003 real-world image sr
    elif args.task == 'real_sr':
        if not args.large_model:
            # use 'nearest+conv' to avoid block artifacts
            model = net(upscale=4, in_chans=3, img_size=64, window_size=8,
                        img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
                        mlp_ratio=2, upsampler='nearest+conv', resi_connection='1conv')
        else:
            # larger model size; use '3conv' to save parameters and memory; use ema for GAN training
            model = net(upscale=4, in_chans=3, img_size=64, window_size=8,
                        img_range=1., depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240,
                        num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
                        mlp_ratio=2, upsampler='nearest+conv', resi_connection='3conv')
        param_key_g = 'params_ema'

    # 004 grayscale image denoising
    elif args.task == 'gray_dn':
        model = net(upscale=1, in_chans=1, img_size=128, window_size=8,
                    img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
                    mlp_ratio=2, upsampler='', resi_connection='1conv')
        param_key_g = 'params'

    # 005 color image denoising
    elif args.task == 'color_dn':
        model = net(upscale=1, in_chans=3, img_size=128, window_size=8,
                    img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
                    mlp_ratio=2, upsampler='', resi_connection='1conv')
        param_key_g = 'params'

    # 006 JPEG compression artifact reduction
    # use window_size=7 because JPEG encoding uses 8x8; use img_range=255 because it's sligtly better than 1
    elif args.task == 'jpeg_car':
        model = net(upscale=1, in_chans=1, img_size=126, window_size=7,
                    img_range=255., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
                    mlp_ratio=2, upsampler='', resi_connection='1conv')
        param_key_g = 'params'

    pretrained_model = torch.load(args.model_path)
    model.load_state_dict(pretrained_model[param_key_g] if param_key_g in pretrained_model.keys() else pretrained_model, strict=True)

    return model


def setup(args):
    # 001 classical image sr/ 002 lightweight image sr
    if args.task in ['classical_sr', 'lightweight_sr']:
        save_dir = f'results/swinir_{args.task}_x{args.scale}'
        folder = args.folder_gt
        border = args.scale
        window_size = 8

    # 003 real-world image sr
    elif args.task in ['real_sr']:
        save_dir = "/content/drive/MyDrive/ArtDL_SR"
        #save_dir = f'results/swinir_{args.task}_x{args.scale}'
        #if args.large_model:
        #    save_dir += '_large'
        folder = args.folder_lq
        border = 0
        window_size = 8

    # 004 grayscale image denoising/ 005 color image denoising
    elif args.task in ['gray_dn', 'color_dn']:
        save_dir = f'results/swinir_{args.task}_noise{args.noise}'
        folder = args.folder_gt
        border = 0
        window_size = 8

    # 006 JPEG compression artifact reduction
    elif args.task in ['jpeg_car']:
        save_dir = f'results/swinir_{args.task}_jpeg{args.jpeg}'
        folder = args.folder_gt
        border = 0
        window_size = 7

    return folder, save_dir, border, window_size


def get_image_pair(args, path):
    (imgname, imgext) = os.path.splitext(os.path.basename(path))

    # 001 classical image sr/ 002 lightweight image sr (load lq-gt image pairs)
    if args.task in ['classical_sr', 'lightweight_sr']:
        img_gt = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.
        img_lq = cv2.imread(f'{args.folder_lq}/{imgname}x{args.scale}{imgext}', cv2.IMREAD_COLOR).astype(
            np.float32) / 255.

    # 003 real-world image sr (load lq image only)
    elif args.task in ['real_sr']:
        img_gt = None
        img_lq = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.

    # 004 grayscale image denoising (load gt image and generate lq image on-the-fly)
    elif args.task in ['gray_dn']:
        img_gt = cv2.imread(path, cv2.IMREAD_GRAYSCALE).astype(np.float32) / 255.
        np.random.seed(seed=0)
        img_lq = img_gt + np.random.normal(0, args.noise / 255., img_gt.shape)
        img_gt = np.expand_dims(img_gt, axis=2)
        img_lq = np.expand_dims(img_lq, axis=2)

    # 005 color image denoising (load gt image and generate lq image on-the-fly)
    elif args.task in ['color_dn']:
        img_gt = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.
        np.random.seed(seed=0)
        img_lq = img_gt + np.random.normal(0, args.noise / 255., img_gt.shape)

    # 006 JPEG compression artifact reduction (load gt image and generate lq image on-the-fly)
    elif args.task in ['jpeg_car']:
        img_gt = cv2.imread(path, cv2.IMREAD_UNCHANGED)
        if img_gt.ndim != 2:
            img_gt = util.bgr2ycbcr(img_gt, y_only=True)
        result, encimg = cv2.imencode('.jpg', img_gt, [int(cv2.IMWRITE_JPEG_QUALITY), args.jpeg])
        img_lq = cv2.imdecode(encimg, 0)
        img_gt = np.expand_dims(img_gt, axis=2).astype(np.float32) / 255.
        img_lq = np.expand_dims(img_lq, axis=2).astype(np.float32) / 255.

    return imgname, img_lq, img_gt


def test(img_lq, model, args, window_size):
    if args.tile is None:
        # test the image as a whole
        output = model(img_lq)
    else:
        # test the image tile by tile
        b, c, h, w = img_lq.size()
        tile = min(args.tile, h, w)
        assert tile % window_size == 0, "tile size should be a multiple of window_size"
        tile_overlap = args.tile_overlap
        sf = args.scale

        stride = tile - tile_overlap
        h_idx_list = list(range(0, h-tile, stride)) + [h-tile]
        w_idx_list = list(range(0, w-tile, stride)) + [w-tile]
        E = torch.zeros(b, c, h*sf, w*sf).type_as(img_lq)
        W = torch.zeros_like(E)

        for h_idx in h_idx_list:
            for w_idx in w_idx_list:
                in_patch = img_lq[..., h_idx:h_idx+tile, w_idx:w_idx+tile]
                out_patch = model(in_patch)
                out_patch_mask = torch.ones_like(out_patch)

                E[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch)
                W[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch_mask)
        output = E.div_(W)

    return output

if __name__ == '__main__':
    main()
