# waifu2x.py by @marcan42 based on https://github.com/nagadomi/waifu2x
# MIT license, see https://github.com/nagadomi/waifu2x/blob/master/LICENSE
import json, sys, numpy as np
from scipy import misc, signal
from PIL import Image
# Model export script: https://mrcn.st/t/export_model.lua (needs a working waifu2x install)
infile, outfile, modelpath = sys.argv[1:]
model = json.load(open(modelpath))
im = Image.open(infile).convert("YCbCr")
im = misc.fromimage(im.resize((2*im.size[0], 2*im.size[1]), resample=Image.NEAREST)).astype("float32")
planes = [np.pad(im[:,:,0], len(model), "edge") / 255.0]
count = sum(step["nInputPlane"] * step["nOutputPlane"] for step in model)
progress = 0
for step in model:
    assert step["nInputPlane"] == len(planes)
    assert step["nOutputPlane"] == len(step["weight"]) == len(step["bias"])
    o_planes = []
    for bias, weights in zip(step["bias"], step["weight"]):
        partial = None
        for ip, kernel in zip(planes, weights):
            p = signal.convolve2d(ip, np.float32(kernel), "valid")
            if partial is None:
                partial = p
            else:
                partial += p
            progress += 1
            sys.stderr.write("\r%.1f%%..." % (100 * progress / float(count)))
        partial += np.float32(bias)
        o_planes.append(partial)
    planes = [np.maximum(p, 0) + 0.1 * np.minimum(p, 0) for p in o_planes]

assert len(planes) == 1
im[:,:,0] = np.clip(planes[0], 0, 1) * 255
misc.toimage(im, mode="YCbCr").convert("RGB").save(outfile)
sys.stderr.write("Done\n")