from __future__ import print_function
import os
from PIL import Image
import numpy as np
import torch

print('?')

# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array


def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
    if isinstance(image_tensor, list):
        image_numpy = []
        for i in range(len(image_tensor)):
            image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
        return image_numpy
    image_numpy = image_tensor.cpu().float().numpy()
    # if normalize:
    #    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
    # else:
    #    image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
    image_numpy = (image_numpy + 1) / 2.0
    image_numpy = np.clip(image_numpy, 0, 1)
    if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:
        image_numpy = image_numpy[:, :, 0]

    return image_numpy

# Converts a one-hot tensor into a colorful label map


def tensor2label(label_tensor, n_label, imtype=np.uint8):
    if n_label == 0:
        return tensor2im(label_tensor, imtype)
    label_tensor = label_tensor.cpu().float()
    if label_tensor.size()[0] > 1:
        label_tensor = label_tensor.max(0, keepdim=True)[1]
    label_tensor = Colorize(n_label)(label_tensor)
    #label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
    label_numpy = label_tensor.numpy()
    label_numpy = label_numpy / 255.0

    return label_numpy


def save_image(image_numpy, image_path, grayscale=False):
    image_pil = Image.fromarray(image_numpy)
    image_pil.save(image_path)


def save_tensor_as_image(image_tensor, image_path, grayscale=False):
    image_numpy = tensor_to_image(image_tensor, grayscale)
    save_image(image_numpy, image_path, grayscale)


def tensor_to_image(img_tensor, grayscale=False):
    if grayscale:
        tensor = img_tensor.cpu().clamp(0, 255)
    else:
        tensor = (img_tensor.clone() + 1) * 0.5 * 255
        tensor = tensor.cpu().clamp(0, 255)

    try:
        array = tensor.numpy().astype('uint8')
    except:
        array = tensor.detach().numpy().astype('uint8')

    if array.shape[0] == 1:
        array = array.squeeze(0)
    elif array.shape[0] == 3:
        array = array.swapaxes(0, 1).swapaxes(1, 2)

    return array


def mkdirs(paths):
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)

###############################################################################
# Code from
# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
# Modified so it complies with the Citscape label map colors
###############################################################################


def uint82bin(n, count=8):
    """returns the binary of integer n, count refers to amount of bits"""
    return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])


def labelcolormap(N):
    if N == 35:  # cityscape
        cmap = np.array([(0,  0,  0), (0,  0,  0), (0,  0,  0), (0,  0,  0), (0,  0,  0), (111, 74,  0), (81,  0, 81),
                         (128, 64, 128), (244, 35, 232), (250, 170, 160), (230,
                                                                           150, 140), (70, 70, 70), (102, 102, 156), (190, 153, 153),
                         (180, 165, 180), (150, 100, 100), (150, 120, 90), (153,
                                                                            153, 153), (153, 153, 153), (250, 170, 30), (220, 220,  0),
                         (107, 142, 35), (152, 251, 152), (70, 130, 180), (220,
                                                                           20, 60), (255,  0,  0), (0,  0, 142), (0,  0, 70),
                         (0, 60, 100), (0,  0, 90), (0,  0, 110), (0, 80, 100), (0,  0, 230), (119, 11, 32), (0,  0, 142)],
                        dtype=np.uint8)
    else:
        cmap = np.zeros((N, 3), dtype=np.uint8)
        for i in range(N):
            r, g, b = 0, 0, 0
            id = i
            for j in range(7):
                str_id = uint82bin(id)
                r = r ^ (np.uint8(str_id[-1]) << (7-j))
                g = g ^ (np.uint8(str_id[-2]) << (7-j))
                b = b ^ (np.uint8(str_id[-3]) << (7-j))
                id = id >> 3
            cmap[i, 0] = r
            cmap[i, 1] = g
            cmap[i, 2] = b
    return cmap


class Colorize(object):
    def __init__(self, n=35):
        self.cmap = labelcolormap(n)
        self.cmap = torch.from_numpy(self.cmap[:n])

    def __call__(self, gray_image):
        size = gray_image.size()
        color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)

        for label in range(0, len(self.cmap)):
            mask = (label == gray_image[0]).cpu()
            color_image[0][mask] = self.cmap[label][0]
            color_image[1][mask] = self.cmap[label][1]
            color_image[2][mask] = self.cmap[label][2]

        return color_image