import cv2
import numpy as np
import plyfile
import scipy.signal
import skimage.measure
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as T
from PIL import Image


def mse2psnr(x):
    return -10. * torch.log(x) / torch.log(torch.Tensor([10.]))


def visualize_depth_numpy(depth, minmax=None, cmap=cv2.COLORMAP_JET):
    """
    depth: (H, W)
    """

    x = np.nan_to_num(depth)  # change nan to 0
    if minmax is None:
        mi = np.min(x[x > 0])  # get minimum positive depth (ignore background)
        ma = np.max(x)
    else:
        mi, ma = minmax

    x = (x - mi) / (ma - mi + 1e-8)  # normalize to 0~1
    x = (255 * x).astype(np.uint8)
    x_ = cv2.applyColorMap(x, cmap)
    return x_, [mi, ma]


def init_log(log, keys):
    for key in keys:
        log[key] = torch.tensor([0.0], dtype=float)
    return log


def visualize_depth(depth, minmax=None, cmap=cv2.COLORMAP_JET):
    """
    depth: (H, W)
    """
    if type(depth) is not np.ndarray:
        depth = depth.cpu().numpy()

    x = np.nan_to_num(depth)  # change nan to 0
    if minmax is None:
        mi = np.min(x[x > 0])  # get minimum positive depth (ignore background)
        ma = np.max(x)
    else:
        mi, ma = minmax

    x = (x - mi) / (ma - mi + 1e-8)  # normalize to 0~1
    x = (255 * x).astype(np.uint8)
    x_ = Image.fromarray(cv2.applyColorMap(x, cmap))
    x_ = T.ToTensor()(x_)  # (3, H, W)
    return x_, [mi, ma]


def N_to_reso(n_voxels, bbox):
    xyz_min, xyz_max = bbox
    dim = len(xyz_min)
    voxel_size = ((xyz_max - xyz_min).prod() / n_voxels).pow(1 / dim)
    return ((xyz_max - xyz_min) / voxel_size).long().tolist()


def cal_n_samples(reso, step_ratio=0.5):
    return int(np.linalg.norm(reso) / step_ratio)


__LPIPS__ = {}


def init_lpips(net_name, device):
    assert net_name in ['alex', 'vgg']
    import lpips
    print(f'init_lpips: lpips_{net_name}')
    return lpips.LPIPS(net=net_name, version='0.1').eval().to(device)


def rgb_lpips(np_gt, np_im, net_name, device):
    if net_name not in __LPIPS__:
        __LPIPS__[net_name] = init_lpips(net_name, device)
    gt = torch.from_numpy(np_gt).permute([2, 0, 1]).contiguous().to(device)
    im = torch.from_numpy(np_im).permute([2, 0, 1]).contiguous().to(device)
    return __LPIPS__[net_name](gt, im, normalize=True).item()


def findItem(items, target):
    for one in items:
        if one[:len(target)] == target:
            return one
    return None


''' Evaluation metrics (ssim, lpips)
'''


def rgb_ssim(img0,
             img1,
             max_val,
             filter_size=11,
             filter_sigma=1.5,
             k1=0.01,
             k2=0.03,
             return_map=False):
    # Modified from https://github.com/google/mipnerf/blob/16e73dfdb52044dcceb47cda5243a686391a6e0f/internal/math.py#L58
    assert len(img0.shape) == 3
    assert img0.shape[-1] == 3
    assert img0.shape == img1.shape

    # Construct a 1D Gaussian blur filter.
    hw = filter_size // 2
    shift = (2 * hw - filter_size + 1) / 2
    f_i = ((np.arange(filter_size) - hw + shift) / filter_sigma)**2
    filt = np.exp(-0.5 * f_i)
    filt /= np.sum(filt)

    # Blur in x and y (faster than the 2D convolution).
    def convolve2d(z, f):
        return scipy.signal.convolve2d(z, f, mode='valid')

    def filt_fn(z):
        return np.stack([
            convolve2d(convolve2d(z[..., i], filt[:, None]), filt[None, :])
            for i in range(z.shape[-1])
        ], -1)

    mu0 = filt_fn(img0)
    mu1 = filt_fn(img1)
    mu00 = mu0 * mu0
    mu11 = mu1 * mu1
    mu01 = mu0 * mu1
    sigma00 = filt_fn(img0**2) - mu00
    sigma11 = filt_fn(img1**2) - mu11
    sigma01 = filt_fn(img0 * img1) - mu01

    # Clip the variances and covariances to valid values.
    # Variance must be non-negative:
    sigma00 = np.maximum(0., sigma00)
    sigma11 = np.maximum(0., sigma11)
    sigma01 = np.sign(sigma01) * np.minimum(
        np.sqrt(sigma00 * sigma11), np.abs(sigma01))
    c1 = (k1 * max_val)**2
    c2 = (k2 * max_val)**2
    numer = (2 * mu01 + c1) * (2 * sigma01 + c2)
    denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2)
    ssim_map = numer / denom
    ssim = np.mean(ssim_map)
    return ssim_map if return_map else ssim


class TVLoss(nn.Module):

    def __init__(self, TVLoss_weight=1):
        super(TVLoss, self).__init__()
        self.TVLoss_weight = TVLoss_weight

    def forward(self, x):
        batch_size = x.size()[0]
        h_x = x.size()[2]
        w_x = x.size()[3]
        count_h = self._tensor_size(x[:, :, 1:, :])
        count_w = self._tensor_size(x[:, :, :, 1:])
        h_tv = torch.pow((x[:, :, 1:, :] - x[:, :, :h_x - 1, :]), 2).sum()
        w_tv = torch.pow((x[:, :, :, 1:] - x[:, :, :, :w_x - 1]), 2).sum()
        return self.TVLoss_weight * 2 * (h_tv / count_h
                                         + w_tv / count_w) / batch_size

    def _tensor_size(self, t):
        return t.size()[1] * t.size()[2] * t.size()[3]


def convert_sdf_samples_to_ply(
    pytorch_3d_sdf_tensor,
    ply_filename_out,
    bbox,
    level=0.5,
    offset=None,
    scale=None,
):
    """
    Convert sdf samples to .ply

    :param pytorch_3d_sdf_tensor: a torch.FloatTensor of shape (n,n,n)
    :voxel_grid_origin: a list of three floats: the bottom, left, down origin of the voxel grid
    :voxel_size: float, the size of the voxels
    :ply_filename_out: string, path of the filename to save to

    This function adapted from: https://github.com/RobotLocomotion/spartan
    """

    numpy_3d_sdf_tensor = pytorch_3d_sdf_tensor.numpy()
    voxel_size = list(
        (bbox[1] - bbox[0]) / np.array(pytorch_3d_sdf_tensor.shape))

    verts, faces, normals, values = skimage.measure.marching_cubes(
        numpy_3d_sdf_tensor, level=level, spacing=voxel_size)
    faces = faces[..., ::-1]  # inverse face orientation

    # transform from voxel coordinates to camera coordinates
    # note x and y are flipped in the output of marching_cubes
    mesh_points = np.zeros_like(verts)
    mesh_points[:, 0] = bbox[0, 0] + verts[:, 0]
    mesh_points[:, 1] = bbox[0, 1] + verts[:, 1]
    mesh_points[:, 2] = bbox[0, 2] + verts[:, 2]

    # apply additional offset and scale
    if scale is not None:
        mesh_points = mesh_points / scale
    if offset is not None:
        mesh_points = mesh_points - offset

    # try writing to the ply file

    num_verts = verts.shape[0]
    num_faces = faces.shape[0]

    verts_tuple = np.zeros((num_verts, ),
                           dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4')])

    for i in range(0, num_verts):
        verts_tuple[i] = tuple(mesh_points[i, :])

    faces_building = []
    for i in range(0, num_faces):
        faces_building.append(((faces[i, :].tolist(), )))
    faces_tuple = np.array(
        faces_building, dtype=[('vertex_indices', 'i4', (3, ))])

    el_verts = plyfile.PlyElement.describe(verts_tuple, 'vertex')
    el_faces = plyfile.PlyElement.describe(faces_tuple, 'face')

    ply_data = plyfile.PlyData([el_verts, el_faces])
    print('saving mesh to %s' % (ply_filename_out))
    ply_data.write(ply_filename_out)


class Timing:
    """
    Timing environment
    usage:
    with Timing("message"):
        your commands here
    will print CUDA runtime in ms
    """

    def __init__(self, name, debug=False):
        self.name = name
        self.debug = debug

    def __enter__(self):
        if not self.debug:
            return

        self.start = torch.cuda.Event(enable_timing=True)
        self.end = torch.cuda.Event(enable_timing=True)
        self.start.record()

    def __exit__(self, type, value, traceback):
        if not self.debug:
            return

        self.end.record()
        torch.cuda.synchronize()
        print(self.name, 'elapsed', self.start.elapsed_time(self.end), 'ms')