import os
import numpy as np
import os.path as osp
import cv2
import argparse
import torch
#from torch.utils.data import DataLoader
import torchvision
from RCFPyTorch0.dataset import BSDS_Dataset
from RCFPyTorch0.models import RCF
import gradio as gr
from PIL import Image
import sys
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from MODNet.src.models.modnet import MODNet
# 网页制作
import cv2


def single_scale_test(image):
    ref_size = 512
    # define image to tensor transform
    im_transform = transforms.Compose(
        [
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
        ]
    )

    # create MODNet and load the pre-trained ckpt
    modnet = MODNet(backbone_pretrained=False)
    modnet = nn.DataParallel(modnet).cuda()
    modnet.load_state_dict(torch.load('MODNet/pretrained/modnet_photographic.ckpt'))
    modnet.eval()
# 注：程序中的数字仅表示某张输入图片尺寸，如1080x1440，此处只为记住其转换过程。
    # inference images
#    im_names = os.listdir(args.input_path)
#    for im_name in im_names:
#        print('Process image: {0}'.format(im_name))
        # read image

        # unify image channels to 3
    image = np.asarray(image)
    if len(image.shape) == 2:
        image = image[:, :, None]
    if image.shape[2] == 1:
        image = np.repeat(image, 3, axis=2)
    elif image.shape[2] == 4:
        image = image[:, :, 0:3]
    im_org = image                                # 保存numpy原始数组 (1080,1440,3)
    # convert image to PyTorch tensor
    image = Image.fromarray(image)
    image = im_transform(image)
    # add mini-batch dim
    image = image[None, :, :, :]
    # resize image for input
    im_b, im_c, im_h, im_w = image.shape
    if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
        if im_w >= im_h:
            im_rh = ref_size
            im_rw = int(im_w / im_h * ref_size)
        elif im_w < im_h:
            im_rw = ref_size
            im_rh = int(im_h / im_w * ref_size)
    else:
        im_rh = im_h
        im_rw = im_w
    im_rw = im_rw - im_rw % 32
    im_rh = im_rh - im_rh % 32
    image = F.interpolate(image, size=(im_rh, im_rw), mode='area')

        # inference
    _, _, matte = modnet(image.cuda(), True)    # 从模型获得的 matte ([1,1,512, 672])

        # resize and save matte，foreground picture
    matte = F.interpolate(matte, size=(im_h, im_w), mode='area')  #内插，扩展到([1,1,1080,1440])  范围[0,1]
    matte = matte[0][0].data.cpu().numpy()    # torch 张量转换成numpy (1080, 1440)
#        matte_name = im_name.split('.')[0] + '_matte.png'
#        Image.fromarray(((matte * 255).astype('uint8')), mode='L').save(os.path.join(args.output_path, matte_name))
    matte_org = np.repeat(np.asarray(matte)[:, :, None], 3, axis=2)   # 扩展到 (1080, 1440, 3) 以便和im_org计算
        
    foreground = im_org * matte_org + np.full(im_org.shape, 255) * (1 - matte_org)         # 计算前景，获得抠像
#        fg_name = im_name.split('.')[0] + '_fg.png'
    Image.fromarray(((foreground).astype('uint8')), mode='RGB').save(os.path.join('MODNet/output-img', 'fg_name.png'))
    output = Image.open(os.path.join('MODNet/output-img', 'fg_name.png'))
    image = np.array(output)

    model = RCF().cuda()
    checkpoint = torch.load("RCFPyTorch0/bsds500_pascal_model.pth")
    model.load_state_dict(checkpoint)
    model.eval()
#    if not osp.isdir(save_dir):
#        os.makedirs(save_dir)
#    for idx, image in enumerate(test_loader):
    image = torch.from_numpy(image).float().permute(2,0,1).unsqueeze(0)
    image = image.cuda()
    _, _, H, W = image.shape
    results = model(image)
    all_res = torch.zeros((len(results), 1, H, W))
    for i in range(len(results)):
        all_res[i, 0, :, :] = results[i]
    #filename = osp.splitext(test_list[idx])[0]
    torchvision.utils.save_image(1 - all_res, osp.join('RCFPyTorch0/results/RCF', 'result.jpg'))
    fuse_res = torch.squeeze(results[1].detach()).cpu().numpy()
    fuse_res = ((1 - fuse_res) * 255).astype(np.uint8)
    cv2.imwrite(osp.join("RCFPyTorch0/results/RCF", 'result_ss.png'), fuse_res)
    #print('\rRunning single-scale test [%d/%d]' % (idx + 1, len(test_loader)), end='')
    #print('Running single-scale test done')
    output = Image.open(os.path.join('RCFPyTorch0/results/RCF', 'result_ss.png'))
    return output

parser = argparse.ArgumentParser(description='PyTorch Testing')
parser.add_argument('--gpu', default='0', type=str, help='GPU ID')
#parser.add_argument('--checkpoint', default=None, type=str, help='path to latest checkpoint')
#parser.add_argument('--save-dir', help='output folder', default='results/RCF')
#parser.add_argument('--dataset', help='root folder of dataset', default='data/HED-BSDS')
args = parser.parse_args()

os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu

#if not osp.isdir(args.save_dir):
#    os.makedirs(args.save_dir)

#test_dataset  = BSDS_Dataset(root=args.dataset, split='test')
#test_loader   = DataLoader(test_dataset, batch_size=1, num_workers=1, drop_last=False, shuffle=False)
#test_list = [osp.split(i.rstrip())[1] for i in test_dataset.file_list]
#assert len(test_list) == len(test_loader)



#if osp.isfile(args.checkpoint):
#    print("=> loading checkpoint from '{}'".format(args.checkpoint))
#    checkpoint = torch.load(args.checkpoint)
#    model.load_state_dict(checkpoint)
#    print("=> checkpoint loaded")
#else:
#    print("=> no checkpoint found at '{}'".format(args.checkpoint))

#print('Performing the testing...')


interface = gr.Interface(fn=single_scale_test, inputs="image", outputs="image")
interface.launch(share=True)