import os
from PIL import Image
import numpy as np
from collections import OrderedDict

import torch
import torch.nn.functional as F
import torchvision.transforms as transforms

from networks.u2net import U2NET
device = 'cuda'

image_dir = '/content/inputs/test/cloth'
result_dir = '/content/inputs/test/cloth-mask'
checkpoint_path = 'cloth_segm_u2net_latest.pth'

def load_checkpoint_mgpu(model, checkpoint_path):
    if not os.path.exists(checkpoint_path):
        print("----No checkpoints at given path----")
        return
    model_state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))
    new_state_dict = OrderedDict()
    for k, v in model_state_dict.items():
        name = k[7:]  # remove `module.`
        new_state_dict[name] = v

    model.load_state_dict(new_state_dict)
    print("----checkpoints loaded from path: {}----".format(checkpoint_path))
    return model

class Normalize_image(object):
    """Normalize given tensor into given mean and standard dev

    Args:
        mean (float): Desired mean to substract from tensors
        std (float): Desired std to divide from tensors
    """

    def __init__(self, mean, std):
        assert isinstance(mean, (float))
        if isinstance(mean, float):
            self.mean = mean

        if isinstance(std, float):
            self.std = std

        self.normalize_1 = transforms.Normalize(self.mean, self.std)
        self.normalize_3 = transforms.Normalize([self.mean] * 3, [self.std] * 3)
        self.normalize_18 = transforms.Normalize([self.mean] * 18, [self.std] * 18)

    def __call__(self, image_tensor):
        if image_tensor.shape[0] == 1:
            return self.normalize_1(image_tensor)

        elif image_tensor.shape[0] == 3:
            return self.normalize_3(image_tensor)

        elif image_tensor.shape[0] == 18:
            return self.normalize_18(image_tensor)

        else:
            assert "Please set proper channels! Normlization implemented only for 1, 3 and 18"


def get_palette(num_cls):
    """ Returns the color map for visualizing the segmentation mask.
    Args:
        num_cls: Number of classes
    Returns:
        The color map
    """
    n = num_cls
    palette = [0] * (n * 3)
    for j in range(0, n):
        lab = j
        palette[j * 3 + 0] = 0
        palette[j * 3 + 1] = 0
        palette[j * 3 + 2] = 0
        i = 0
        while lab:
            palette[j * 3 + 0] = 255
            palette[j * 3 + 1] = 255
            palette[j * 3 + 2] = 255
            # palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
            # palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
            # palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
            i += 1
            lab >>= 3
    return palette


transforms_list = []
transforms_list += [transforms.ToTensor()]
transforms_list += [Normalize_image(0.5, 0.5)]
transform_rgb = transforms.Compose(transforms_list)

net = U2NET(in_ch=3, out_ch=4)
net = load_checkpoint_mgpu(net, checkpoint_path)
net = net.to(device)
net = net.eval()

palette = get_palette(4)

images_list = sorted(os.listdir(image_dir))
for image_name in images_list:
    img = Image.open(os.path.join(image_dir, image_name)).convert('RGB')
    img_size = img.size
    img = img.resize((768, 768), Image.BICUBIC)
    image_tensor = transform_rgb(img)
    image_tensor = torch.unsqueeze(image_tensor, 0)
    
    output_tensor = net(image_tensor.to(device))
    output_tensor = F.log_softmax(output_tensor[0], dim=1)
    output_tensor = torch.max(output_tensor, dim=1, keepdim=True)[1]
    output_tensor = torch.squeeze(output_tensor, dim=0)
    output_tensor = torch.squeeze(output_tensor, dim=0)
    output_arr = output_tensor.cpu().numpy()

    output_img = Image.fromarray(output_arr.astype('uint8'), mode='L')
    output_img = output_img.resize(img_size, Image.BICUBIC)
    
    output_img.putpalette(palette)
    output_img = output_img.convert('L')
    output_img.save(os.path.join(result_dir, image_name[:-4]+'.jpg'))