import cv2
import gradio as gr
import os
from PIL import Image, ImageFilter
import numpy as np
import torch
from torch.autograd import Variable
from torchvision import transforms
import torch.nn.functional as F
import gdown
import warnings

warnings.filterwarnings("ignore")

os.system("git clone https://github.com/xuebinqin/DIS")
os.system("mv DIS/IS-Net/* .")

# Project imports
from data_loader_cache import normalize, im_reader, im_preprocess 
from models import *

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

# Download official weights
if not os.path.exists("saved_models"):
    os.mkdir("saved_models")
    os.system("mv isnet.pth saved_models/")
    
class GOSNormalize(object):
    '''
    Normalize the Image using torch.transforms
    '''
    def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
        self.mean = mean
        self.std = std

    def __call__(self,image):
        image = normalize(image,self.mean,self.std)
        return image

transform = transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])

def load_image(im_path, hypar):
    im = im_reader(im_path)
    im, im_shp = im_preprocess(im, hypar["cache_size"])
    im = torch.divide(im,255.0)
    shape = torch.from_numpy(np.array(im_shp))
    return transform(im).unsqueeze(0), shape.unsqueeze(0)  # make a batch of image, shape

def build_model(hypar, device):
    net = hypar["model"]

    # Convert to half precision if specified
    if hypar["model_digit"] == "half":
        net.half()
        for layer in net.modules():
            if isinstance(layer, nn.BatchNorm2d):
                layer.float()

    net.to(device)

    if hypar["restore_model"] != "":
        net.load_state_dict(torch.load(hypar["model_path"] + "/" + hypar["restore_model"], map_location=device))
        net.to(device)
    net.eval()
    return net

def predict(net, inputs_val, shapes_val, hypar, device):
    '''
    Given an Image, predict the mask
    '''
    net.eval()

    if hypar["model_digit"] == "full":
        inputs_val = inputs_val.type(torch.FloatTensor)
    else:
        inputs_val = inputs_val.type(torch.HalfTensor)

    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device)  # wrap inputs in Variable
   
    ds_val = net(inputs_val_v)[0]  # list of 6 results

    pred_val = ds_val[0][0, :, :, :]  # B x 1 x H x W    # we want the first one which is the most accurate prediction

    ## recover the prediction spatial size to the original image size
    pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val, 0), (shapes_val[0][0], shapes_val[0][1]), mode='bilinear'))

    ma = torch.max(pred_val)
    mi = torch.min(pred_val)
    pred_val = (pred_val - mi) / (ma - mi)  # max = 1

    if device == 'cuda': torch.cuda.empty_cache()
    return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)  # it is the mask we need

# Set Parameters
hypar = {}  # parameters for inferencing

hypar["model_path"] = "./saved_models"  # load trained weights from this path
hypar["restore_model"] = "isnet.pth"  # name of the to-be-loaded weights
hypar["interm_sup"] = False  # indicate if activate intermediate feature supervision

##  choose floating point accuracy --
hypar["model_digit"] = "full"  # indicates "half" or "full" accuracy of float number
hypar["seed"] = 0

hypar["cache_size"] = [1024, 1024]  # cached input spatial resolution, can be configured into different size

## data augmentation parameters ---
hypar["input_size"] = [1024, 1024]  # model input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
hypar["crop_size"] = [1024, 1024]  # random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation

hypar["model"] = ISNetDIS()

# Build Model
net = build_model(hypar, device)

def refine_mask(mask):
    """
    Softly refine the mask using Gaussian Blur and feathering for smooth transitions.
    """
    # Apply Gaussian Blur to soften edges and make the mask more continuous
    refined_mask = cv2.GaussianBlur(mask, (5, 5), 0)
    
    # Feather the edges for a smoother transition between foreground and background
    feathered_mask = cv2.copyMakeBorder(refined_mask, 10, 10, 10, 10, cv2.BORDER_CONSTANT, value=[255])
    feathered_mask = cv2.GaussianBlur(feathered_mask, (21, 21), 0)
    refined_mask = feathered_mask[10:-10, 10:-10]  # Remove border

    return refined_mask

def inference(image):
    image_path = image

    image_tensor, orig_size = load_image(image_path, hypar) 
    mask = predict(net, image_tensor, orig_size, hypar, device)

    # Refine the mask using a softer approach
    refined_mask = refine_mask(mask)

    pil_mask = Image.fromarray(refined_mask).convert('L')

    im_rgb = Image.open(image).convert("RGB")
  
    im_rgba = im_rgb.copy()
    im_rgba.putalpha(pil_mask)

    return [im_rgba, pil_mask]

title = "Highly Accurate Dichotomous Image Segmentation"
description = "This is an unofficial demo for DIS, a model that can remove the background from a given image. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.<br>GitHub: https://github.com/xuebinqin/DIS<br>Telegram bot: https://t.me/restoration_photo_bot<br>[![](https://img.shields.io/twitter/follow/DoEvent?label=@DoEvent&style=social)](https://twitter.com/DoEvent)"
article = "<div><center><img src='https://visitor-badge.glitch.me/badge?page_id=max_skobeev_dis_cmp_public' alt='visitor badge'></center></div>"

interface = gr.Interface(
    fn=inference,
    inputs=gr.Image(type='filepath'),
    outputs=["image", "image"],
    examples=[['robot.png'], ['ship.png']],
    title=title,
    description=description,
    article=article,
    allow_flagging='never',
    cache_examples=False,
    delete_cache=(4000, 4000),
).queue().launch(show_error=True)