import argparse
import torch
import torch.backends.cudnn as cudnn
import numpy as np
import PIL.Image as pil_image

from model_SRCNN import SRCNN
from utils import convert_rgb_to_ycbcr, convert_ycbcr_to_rgb, calc_psnr

def super_resolution(weights,image,in_scale=2):
    weights_file = weights
    image_file = image
    scale = in_scale

    cudnn.benchmark = True
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    model = SRCNN().to(device)

    state_dict = model.state_dict()
    for n, p in torch.load(weights_file, map_location=lambda storage, loc: storage).items():
        if n in state_dict.keys():
            state_dict[n].copy_(p)
        else:
            raise KeyError(n)

    model.eval()

    image = pil_image.open(image_file).convert('RGB')

    image_width = (image.width // scale) * scale
    image_height = (image.height // scale) * scale
    image = image.resize((image_width, image_height), resample=pil_image.BICUBIC)
    image = image.resize((image.width * scale, image.height * scale), resample=pil_image.BICUBIC)

    image = np.array(image).astype(np.float32)
    ycbcr = convert_rgb_to_ycbcr(image)

    y = ycbcr[..., 0]
    y /= 255.
    y = torch.from_numpy(y).to(device)
    y = y.unsqueeze(0).unsqueeze(0)

    with torch.no_grad():
        preds = model(y).clamp(0.0, 1.0)

    psnr = calc_psnr(y, preds)
    print('PSNR: {:.2f}'.format(psnr))

    preds = preds.mul(255.0).cpu().numpy().squeeze(0).squeeze(0)

    output = np.array([preds, ycbcr[..., 1], ycbcr[..., 2]]).transpose([1, 2, 0])
    output = np.clip(convert_ycbcr_to_rgb(output), 0.0, 255.0).astype(np.uint8)
    output = pil_image.fromarray(output)
    # output.save(image_file.replace('.', '_srcnn_x{}.'.format(scale)))
    return output