import torch
from torch.utils.data import Dataset
from tqdm import tqdm
import os
from PIL import Image
from torchvision import transforms as T

from .ray_utils import *


def circle(radius=3.5, h=0.0, axis='z', t0=0, r=1):
    if axis == 'z':
        return lambda t: [radius * np.cos(r * t + t0), radius * np.sin(r * t + t0), h]
    elif axis == 'y':
        return lambda t: [radius * np.cos(r * t + t0), h, radius * np.sin(r * t + t0)]
    else:
        return lambda t: [h, radius * np.cos(r * t + t0), radius * np.sin(r * t + t0)]


def cross(x, y, axis=0):
    T = torch if isinstance(x, torch.Tensor) else np
    return T.cross(x, y, axis)


def normalize(x, axis=-1, order=2):
    if isinstance(x, torch.Tensor):
        l2 = x.norm(p=order, dim=axis, keepdim=True)
        return x / (l2 + 1e-8), l2

    else:
        l2 = np.linalg.norm(x, order, axis)
        l2 = np.expand_dims(l2, axis)
        l2[l2 == 0] = 1
        return x / l2,


def cat(x, axis=1):
    if isinstance(x[0], torch.Tensor):
        return torch.cat(x, dim=axis)
    return np.concatenate(x, axis=axis)


def look_at_rotation(camera_position, at=None, up=None, inverse=False, cv=False):
    """
    This function takes a vector 'camera_position' which specifies the location
    of the camera in world coordinates and two vectors `at` and `up` which
    indicate the position of the object and the up directions of the world
    coordinate system respectively. The object is assumed to be centered at
    the origin.
    The output is a rotation matrix representing the transformation
    from world coordinates -> view coordinates.
    Input:
        camera_position: 3
        at: 1 x 3 or N x 3  (0, 0, 0) in default
        up: 1 x 3 or N x 3  (0, 1, 0) in default
    """

    if at is None:
        at = torch.zeros_like(camera_position)
    else:
        at = torch.tensor(at).type_as(camera_position)
    if up is None:
        up = torch.zeros_like(camera_position)
        up[2] = -1
    else:
        up = torch.tensor(up).type_as(camera_position)

    z_axis = normalize(at - camera_position)[0]
    x_axis = normalize(cross(up, z_axis))[0]
    y_axis = normalize(cross(z_axis, x_axis))[0]

    R = cat([x_axis[:, None], y_axis[:, None], z_axis[:, None]], axis=1)
    return R


def gen_path(pos_gen, at=(0, 0, 0), up=(0, -1, 0), frames=180):
    c2ws = []
    for t in range(frames):
        c2w = torch.eye(4)
        cam_pos = torch.tensor(pos_gen(t * (360.0 / frames) / 180 * np.pi))
        cam_rot = look_at_rotation(cam_pos, at=at, up=up, inverse=False, cv=True)
        c2w[:3, 3], c2w[:3, :3] = cam_pos, cam_rot
        c2ws.append(c2w)
    return torch.stack(c2ws)


class TanksTempleDataset(Dataset):
    """NSVF Generic Dataset."""

    def __init__(self, datadir, split='train', downsample=1.0, wh=[1920, 1080], is_stack=False):
        self.root_dir = datadir
        self.split = split
        self.is_stack = is_stack
        self.downsample = downsample
        self.img_wh = (int(wh[0] / downsample), int(wh[1] / downsample))
        self.define_transforms()

        self.white_bg = True
        self.near_far = [0.01, 6.0]
        self.scene_bbox = torch.from_numpy(np.loadtxt(f'{self.root_dir}/bbox.txt')).float()[:6].view(2, 3) * 1.2

        self.blender2opencv = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
        self.read_meta()
        self.define_proj_mat()

        self.center = torch.mean(self.scene_bbox, axis=0).float().view(1, 1, 3)
        self.radius = (self.scene_bbox[1] - self.center).float().view(1, 1, 3)

    def bbox2corners(self):
        corners = self.scene_bbox.unsqueeze(0).repeat(4, 1, 1)
        for i in range(3):
            corners[i, [0, 1], i] = corners[i, [1, 0], i]
        return corners.view(-1, 3)

    def read_meta(self):

        self.intrinsics = np.loadtxt(os.path.join(self.root_dir, "intrinsics.txt"))
        self.intrinsics[:2] *= (np.array(self.img_wh) / np.array([1920, 1080])).reshape(2, 1)
        pose_files = sorted(os.listdir(os.path.join(self.root_dir, 'pose')))
        img_files = sorted(os.listdir(os.path.join(self.root_dir, 'rgb')))

        if self.split == 'train':
            pose_files = [x for x in pose_files if x.startswith('0_')]
            img_files = [x for x in img_files if x.startswith('0_')]
        elif self.split == 'val':
            pose_files = [x for x in pose_files if x.startswith('1_')]
            img_files = [x for x in img_files if x.startswith('1_')]
        elif self.split == 'test':
            test_pose_files = [x for x in pose_files if x.startswith('2_')]
            test_img_files = [x for x in img_files if x.startswith('2_')]
            if len(test_pose_files) == 0:
                test_pose_files = [x for x in pose_files if x.startswith('1_')]
                test_img_files = [x for x in img_files if x.startswith('1_')]
            pose_files = test_pose_files
            img_files = test_img_files

        # ray directions for all pixels, same for all images (same H, W, focal)
        self.directions = get_ray_directions(self.img_wh[1], self.img_wh[0], [self.intrinsics[0, 0], self.intrinsics[1, 1]], center=self.intrinsics[:2, 2])  # (h, w, 3)
        self.directions = self.directions / torch.norm(self.directions, dim=-1, keepdim=True)

        self.poses = []
        self.all_rays = []
        self.all_rgbs = []

        assert len(img_files) == len(pose_files)
        for img_fname, pose_fname in tqdm(zip(img_files, pose_files), desc=f'Loading data {self.split} ({len(img_files)})'):
            image_path = os.path.join(self.root_dir, 'rgb', img_fname)
            img = Image.open(image_path)
            if self.downsample != 1.0:
                img = img.resize(self.img_wh, Image.LANCZOS)
            img = self.transform(img)  # (4, h, w)
            img = img.view(img.shape[0], -1).permute(1, 0)  # (h*w, 4) RGBA
            if img.shape[-1] == 4:
                img = img[:, :3] * img[:, -1:] + (1 - img[:, -1:])  # blend A to RGB
            self.all_rgbs.append(img)

            c2w = np.loadtxt(os.path.join(self.root_dir, 'pose', pose_fname))  # @ cam_trans
            c2w = torch.FloatTensor(c2w)
            self.poses.append(c2w)  # C2W
            rays_o, rays_d = get_rays(self.directions, c2w)  # both (h*w, 3)
            self.all_rays += [torch.cat([rays_o, rays_d], 1)]  # (h*w, 8)

        self.poses = torch.stack(self.poses)

        center = torch.mean(self.scene_bbox, dim=0)
        radius = torch.norm(self.scene_bbox[1] - center) * 1.2
        up = torch.mean(self.poses[:, :3, 1], dim=0).tolist()
        pos_gen = circle(radius=radius, h=-0.2 * up[1], axis='y')
        self.render_path = gen_path(pos_gen, up=up, frames=200)
        self.render_path[:, :3, 3] += center

        if 'train' == self.split:
            if self.is_stack:
                self.all_rays = torch.stack(self.all_rays, 0).reshape(-1, *self.img_wh[::-1], 6)  # (len(self.meta['frames])*h*w, 3)
                self.all_rgbs = torch.stack(self.all_rgbs, 0).reshape(-1, *self.img_wh[::-1], 3)  # (len(self.meta['frames])*h*w, 3)
            else:
                self.all_rays = torch.cat(self.all_rays, 0)  # (len(self.meta['frames])*h*w, 3)
                self.all_rgbs = torch.cat(self.all_rgbs, 0)  # (len(self.meta['frames])*h*w, 3)
        else:
            self.all_rays = torch.stack(self.all_rays, 0)  # (len(self.meta['frames]),h*w, 3)
            self.all_rgbs = torch.stack(self.all_rgbs, 0).reshape(-1, *self.img_wh[::-1], 3)  # (len(self.meta['frames]),h,w,3)

    def define_transforms(self):
        self.transform = T.ToTensor()

    def define_proj_mat(self):
        self.proj_mat = torch.from_numpy(self.intrinsics[:3, :3]).unsqueeze(0).float() @ torch.inverse(self.poses)[:, :3]

    def world2ndc(self, points):
        device = points.device
        return (points - self.center.to(device)) / self.radius.to(device)

    def __len__(self):
        if self.split == 'train':
            return len(self.all_rays)
        return len(self.all_rgbs)

    def __getitem__(self, idx):

        if self.split == 'train':  # use data in the buffers
            sample = {'rays': self.all_rays[idx],
                      'rgbs': self.all_rgbs[idx]}

        else:  # create data for each image separately

            img = self.all_rgbs[idx]
            rays = self.all_rays[idx]

            sample = {'rays': rays,
                      'rgbs': img}
        return sample
