import functools
import cv2
import gradio as gr
import torch
import torch.nn as nn
import numpy as np
from PIL import Image
from einops import rearrange
from sketch_models import SimpleGenerator, UnetGenerator
from utils import common_input_validate, resize_image_with_pad, HWC3

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

def get_sketch(input_image, mode='anime', detect_resolution=512, output_type="pil", upscale_method="INTER_LANCZOS4", **kwargs):
        
        input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
        detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)

        H, W, C = input_image.shape
        Hn = 256 * int(np.ceil(float(H) / 256.0))
        Wn = 256 * int(np.ceil(float(W) / 256.0))

        assert detected_map.ndim == 3

        if mode == 'realistic':
            model = SimpleGenerator(3,1,3).to(device)
            model.load_state_dict(torch.load("models/sk_model.pth", map_location=device))
            model.eval()

            with torch.no_grad():
                image = torch.from_numpy(detected_map).float().to(device)
                image = image / 255.0
                image = rearrange(image, 'h w c -> 1 c h w')

                line = model(image)[0][0]
                line = line.cpu().numpy()
                line = (line * 255.0).clip(0, 255).astype(np.uint8)

            detected_map = HWC3(line)

            detected_map = remove_pad(detected_map)
            detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LANCZOS4)

        elif mode == 'anime':
            norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
            model = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False).to(device)
            ckpt = torch.load("models/netG.pth", map_location=device)
            for key in list(ckpt.keys()):
                if 'module.' in key:
                    ckpt[key.replace('module.', '')] = ckpt[key]
                    del ckpt[key]
            model.load_state_dict(ckpt)
            model.eval()

            input_image = cv2.resize(input_image, (Wn, Hn), interpolation=cv2.INTER_LANCZOS4)

            with torch.no_grad():
                image_feed = torch.from_numpy(input_image).float().to(device)
                image_feed = image_feed / 127.5 - 1.0
                image_feed = rearrange(image_feed, 'h w c -> 1 c h w')

                line = model(image_feed)[0, 0] * 127.5 + 127.5
                line = line.cpu().numpy()
                line = line.clip(0, 255).astype(np.uint8)
            
            #A1111 uses INTER AREA for downscaling so ig that is the best choice
            detected_map = remove_pad(255 - detected_map)
            detected_map = cv2.resize(HWC3(line), (W, H), interpolation=cv2.INTER_LANCZOS4)
        
        else: # standard
            guassian_sigma=6.0
            intensity_threshold=8
            x = detected_map.astype(np.float32)
            g = cv2.GaussianBlur(x, (0, 0), guassian_sigma)
            intensity = np.min(g - x, axis=2).clip(0, 255)
            intensity /= max(16, np.median(intensity[intensity > intensity_threshold]))
            intensity *= 127
            detected_map = intensity.clip(0, 255).astype(np.uint8)
            
            detected_map = remove_pad(255 - detected_map)
            detected_map = cv2.resize(HWC3(detected_map), (W, H), interpolation=cv2.INTER_LANCZOS4)

        if output_type == "pil":
            detected_map = Image.fromarray(detected_map)
        
        return detected_map

iface = gr.Interface(
    fn=get_sketch,
    inputs=[
         gr.Image(type="numpy", label="Upload Image"),
         gr.Radio(["anime", "realistic", "standard"], label="Mode", info="Process methods"),
    ],
    outputs=gr.Image(type="numpy", label="Sketch Output"),
    title="Get a Sketch",
    description="Upload an image and get a simplified sketch"
)

if __name__ == "__main__":
    iface.launch()