import torch
import numpy as np
from tqdm import tqdm
import utils3d
from PIL import Image

from ..renderers import OctreeRenderer, MeshRenderer,GaussianRenderer
from  ..renderers.gaussian_render import GaussianRenderer
# from  ..renderers.mesh_renderer import MeshRenderer
# from ..renderers.octree_renderer import OctreeRenderer
from ..representations import Octree, Gaussian, MeshExtractResult
from ..modules import sparse as sp
from .random_utils import sphere_hammersley_sequence


def yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitchs, rs, fovs):
    is_list = isinstance(yaws, list)
    if not is_list:
        yaws = [yaws]
        pitchs = [pitchs]
    if not isinstance(rs, list):
        rs = [rs] * len(yaws)
    if not isinstance(fovs, list):
        fovs = [fovs] * len(yaws)
    extrinsics = []
    intrinsics = []
    for yaw, pitch, r, fov in zip(yaws, pitchs, rs, fovs):
        fov = torch.deg2rad(torch.tensor(float(fov))).cuda()
        yaw = torch.tensor(float(yaw)).cuda()
        pitch = torch.tensor(float(pitch)).cuda()
        orig = torch.tensor([
            torch.sin(yaw) * torch.cos(pitch),
            torch.cos(yaw) * torch.cos(pitch),
            torch.sin(pitch),
        ]).cuda() * r
        extr = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda())
        intr = utils3d.torch.intrinsics_from_fov_xy(fov, fov)
        extrinsics.append(extr)
        intrinsics.append(intr)
    if not is_list:
        extrinsics = extrinsics[0]
        intrinsics = intrinsics[0]
    return extrinsics, intrinsics


def render_frames(sample, extrinsics, intrinsics, options={}, colors_overwrite=None, verbose=True, **kwargs):
    if isinstance(sample, Octree):
        renderer = OctreeRenderer()
        renderer.rendering_options.resolution = options.get('resolution', 512)
        renderer.rendering_options.near = options.get('near', 0.8)
        renderer.rendering_options.far = options.get('far', 1.6)
        renderer.rendering_options.bg_color = options.get('bg_color', (0, 0, 0))
        renderer.rendering_options.ssaa = options.get('ssaa', 4)
        renderer.pipe.primitive = sample.primitive
    elif isinstance(sample, Gaussian):
        renderer = GaussianRenderer()
        renderer.rendering_options.resolution = options.get('resolution', 512)
        renderer.rendering_options.near = options.get('near', 0.8)
        renderer.rendering_options.far = options.get('far', 1.6)
        renderer.rendering_options.bg_color = options.get('bg_color', (0, 0, 0))
        renderer.rendering_options.ssaa = options.get('ssaa', 1)
        renderer.pipe.kernel_size = kwargs.get('kernel_size', 0.1)
        renderer.pipe.use_mip_gaussian = True
    elif isinstance(sample, MeshExtractResult):
        renderer = MeshRenderer()
        renderer.rendering_options.resolution = options.get('resolution', 512)
        renderer.rendering_options.near = options.get('near', 1)
        renderer.rendering_options.far = options.get('far', 100)
        renderer.rendering_options.ssaa = options.get('ssaa', 4)
    else:
        raise ValueError(f'Unsupported sample type: {type(sample)}')
    
    rets = {}
    for j, (extr, intr) in tqdm(enumerate(zip(extrinsics, intrinsics)), desc='Rendering', disable=not verbose):
        if not isinstance(sample, MeshExtractResult):
            res = renderer.render(sample, extr, intr, colors_overwrite=colors_overwrite)
            if 'color' not in rets: rets['color'] = []
            if 'depth' not in rets: rets['depth'] = []
            rets['color'].append(np.clip(res['color'].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255).astype(np.uint8))
            if 'percent_depth' in res:
                rets['depth'].append(res['percent_depth'].detach().cpu().numpy())
            elif 'depth' in res:
                rets['depth'].append(res['depth'].detach().cpu().numpy())
            else:
                rets['depth'].append(None)
        else:
            res = renderer.render(sample, extr, intr)
            if 'normal' not in rets: rets['normal'] = []
            rets['normal'].append(np.clip(res['normal'].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255).astype(np.uint8))
    return rets


def render_video(sample, resolution=512, bg_color=(0, 0, 0), num_frames=300, r=2, fov=40, **kwargs):
    yaws = torch.linspace(0, 2 * 3.1415, num_frames)
    pitch = 0.25 + 0.5 * torch.sin(torch.linspace(0, 2 * 3.1415, num_frames))
    yaws = yaws.tolist()
    pitch = pitch.tolist()
    extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitch, r, fov)
    
    return render_frames(sample, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': bg_color}, **kwargs)

def render_multiview(sample, resolution=512, nviews=30,**kwargs):
    r = 2
    fov = 40
    bg_color=(0, 0, 0)
    cams = [sphere_hammersley_sequence(i, nviews) for i in range(nviews)]
    yaws = [cam[0] for cam in cams]
    pitchs = [cam[1] for cam in cams]
    extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitchs, r, fov) #计算相机内外参
    res = render_frames(sample, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': bg_color}, **kwargs)
    return res['color'], extrinsics, intrinsics


def render_snapshot(samples, resolution=512, bg_color=(0, 0, 0), offset=(-16 / 180 * np.pi, 20 / 180 * np.pi), r=10, fov=8, **kwargs):
    yaw = [0, np.pi/2, np.pi, 3*np.pi/2]
    yaw_offset = offset[0]
    yaw = [y + yaw_offset for y in yaw]
    pitch = [offset[1] for _ in range(4)]
    extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaw, pitch, r, fov)
    return render_frames(samples, extrinsics, intrinsics, {'resolution': resolution, 'bg_color': bg_color}, **kwargs)