# Part of the implementation is borrowed and modified from pytorch3d,
# publicly available at https://github.com/facebookresearch/pytorch3d

import imageio
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from skimage.io import imread

from .. import utils
from ..utils import read_obj


def set_rasterizer():
    global Meshes, load_obj, rasterize_meshes
    from pytorch3d.structures import Meshes
    from pytorch3d.io import load_obj
    from pytorch3d.renderer.mesh import rasterize_meshes


class Pytorch3dRasterizer(nn.Module):
    # TODO: add support for rendering non-squared images, since pytorch3d supports this now
    """  Borrowed from https://github.com/facebookresearch/pytorch3d
    Notice:
        x,y,z are in image space, normalized
        can only render squared image now
    """

    def __init__(self, image_size=224):
        """
        use fixed raster_settings for rendering faces
        """
        super().__init__()
        raster_settings = {
            'image_size': image_size,
            'blur_radius': 0.0,
            'faces_per_pixel': 1,
            'bin_size': None,
            'max_faces_per_bin': None,
            'perspective_correct': False,
        }
        raster_settings = utils.dict2obj(raster_settings)
        self.raster_settings = raster_settings

    def forward(self, vertices, faces, attributes=None, h=None, w=None):
        fixed_vertices = vertices.clone()
        fixed_vertices[..., :2] = -fixed_vertices[..., :2]
        raster_settings = self.raster_settings
        if h is None and w is None:
            image_size = raster_settings.image_size
        else:
            image_size = [h, w]
            if h > w:
                fixed_vertices[..., 1] = fixed_vertices[..., 1] * h / w
            else:
                fixed_vertices[..., 0] = fixed_vertices[..., 0] * w / h

        meshes_screen = Meshes(
            verts=fixed_vertices.float(), faces=faces.long())
        pix_to_face, zbuf, bary_coords, dists = rasterize_meshes(
            meshes_screen,
            image_size=image_size,
            blur_radius=raster_settings.blur_radius,
            faces_per_pixel=raster_settings.faces_per_pixel,
            bin_size=raster_settings.bin_size,
            max_faces_per_bin=raster_settings.max_faces_per_bin,
            perspective_correct=raster_settings.perspective_correct,
        )
        vismask = (pix_to_face > -1).float()
        D = attributes.shape[-1]
        attributes = attributes.clone()
        attributes = attributes.view(attributes.shape[0] * attributes.shape[1],
                                     3, attributes.shape[-1])
        N, H, W, K, _ = bary_coords.shape
        mask = pix_to_face == -1
        pix_to_face = pix_to_face.clone()
        pix_to_face[mask] = 0
        idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D)
        pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D)
        pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2)
        pixel_vals[mask] = 0  # Replace masked values in output.
        pixel_vals = pixel_vals[:, :, :, 0].permute(0, 3, 1, 2)
        pixel_vals = torch.cat(
            [pixel_vals, vismask[:, :, :, 0][:, None, :, :]], dim=1)
        return pixel_vals


class SRenderY(nn.Module):

    def __init__(self, image_size, obj_filename, uvcoords_path, uv_size=256):
        super(SRenderY, self).__init__()
        self.image_size = image_size
        self.uv_size = uv_size

        self.rasterizer = Pytorch3dRasterizer(image_size)
        self.uv_rasterizer = Pytorch3dRasterizer(uv_size)

        mesh = read_obj(obj_filename)
        uvcoords = np.load(uvcoords_path)[None, ...]
        uvcoords = torch.from_numpy(uvcoords)
        verts = mesh['vertices']
        verts = torch.from_numpy(verts)
        uvfaces = mesh['faces'][None, ...] - 1
        uvfaces = torch.from_numpy(uvfaces)
        faces = mesh['faces'][None, ...] - 1
        faces = torch.from_numpy(faces)

        # faces
        dense_triangles = utils.generate_triangles(uv_size, uv_size)
        self.register_buffer(
            'dense_faces',
            torch.from_numpy(dense_triangles).long()[None, :, :])
        self.register_buffer('faces', faces)
        self.register_buffer('raw_uvcoords', uvcoords)

        # uv coords
        uvcoords = torch.cat([uvcoords, uvcoords[:, :, 0:1] * 0. + 1.],
                             -1)  # [bz, ntv, 3]
        uvcoords = uvcoords * 2 - 1
        uvcoords[..., 1] = -uvcoords[..., 1]
        face_uvcoords = utils.face_vertices(uvcoords, uvfaces)
        self.register_buffer('uvcoords', uvcoords)
        self.register_buffer('uvfaces', uvfaces)
        self.register_buffer('face_uvcoords', face_uvcoords)

        # shape colors, for rendering shape overlay
        colors = torch.tensor([180, 180, 180])[None, None, :].repeat(
            1,
            faces.max() + 1, 1).float() / 255.
        face_colors = utils.face_vertices(colors, faces)
        self.register_buffer('face_colors', face_colors)

        # SH factors for lighting
        pi = np.pi
        value_1 = 1 / np.sqrt(4 * pi)
        value_2 = ((2 * pi) / 3) * (np.sqrt(3 / (4 * pi)))
        value_3 = ((2 * pi) / 3) * (np.sqrt(3 / (4 * pi)))
        value_4 = ((2 * pi) / 3) * (np.sqrt(3 / (4 * pi)))
        value_5 = (pi / 4) * 3 * (np.sqrt(5 / (12 * pi)))
        value_6 = (pi / 4) * 3 * (np.sqrt(5 / (12 * pi)))
        value_7 = (pi / 4) * 3 * (np.sqrt(5 / (12 * pi)))
        value_8 = (pi / 4) * (3 / 2) * (np.sqrt(5 / (12 * pi)))
        value_9 = (pi / 4) * (1 / 2) * (np.sqrt(5 / (4 * pi)))
        constant_factor = torch.tensor([
            value_1, value_2, value_3, value_4, value_5, value_6, value_7,
            value_8, value_9
        ]).float()
        self.register_buffer('constant_factor', constant_factor)

    def forward(self,
                vertices,
                transformed_vertices,
                albedos,
                lights=None,
                light_type='point'):
        '''
            -- Texture Rendering
            vertices: [batch_size, V, 3], vertices in world space, for calculating normals, then shading
            transformed_vertices: [batch_size, V, 3], range:normalized to [-1,1], projected vertices in image space
                                    (that is aligned to the image pixel), for rasterization
            albedos: [batch_size, 3, h, w], uv map
            lights:
                spherical homarnic: [N, 9(shcoeff), 3(rgb)]
                points/directional lighting: [N, n_lights, 6(xyzrgb)]
            light_type:
                point or directional
        '''
        batch_size = vertices.shape[0]
        # rasterizer near 0 far 100. move mesh so minz larger than 0
        transformed_vertices[:, :, 2] = transformed_vertices[:, :, 2] + 10
        # attributes
        face_vertices = utils.face_vertices(
            vertices, self.faces.expand(batch_size, -1, -1))
        normals = utils.vertex_normals(vertices,
                                       self.faces.expand(batch_size, -1, -1))
        face_normals = utils.face_vertices(
            normals, self.faces.expand(batch_size, -1, -1))
        transformed_normals = utils.vertex_normals(
            transformed_vertices, self.faces.expand(batch_size, -1, -1))
        transformed_face_normals = utils.face_vertices(
            transformed_normals, self.faces.expand(batch_size, -1, -1))

        attributes = torch.cat([
            self.face_uvcoords.expand(batch_size, -1, -1, -1),
            transformed_face_normals.detach(),
            face_vertices.detach(), face_normals
        ], -1)
        # rasterize
        rendering = self.rasterizer(transformed_vertices,
                                    self.faces.expand(batch_size, -1, -1),
                                    attributes)

        ####
        # vis mask
        alpha_images = rendering[:, -1, :, :][:, None, :, :].detach()

        # albedo
        uvcoords_images = rendering[:, :3, :, :]
        grid = (uvcoords_images).permute(0, 2, 3, 1)[:, :, :, :2]
        albedo_images = F.grid_sample(albedos, grid, align_corners=False)

        # visible mask for pixels with positive normal direction
        transformed_normal_map = rendering[:, 3:6, :, :].detach()
        pos_mask = (transformed_normal_map[:, 2:, :, :] < -0.05).float()

        # shading
        normal_images = rendering[:, 9:12, :, :]
        if lights is not None:
            if lights.shape[1] == 9:
                shading_images = self.add_SHlight(normal_images, lights)
            else:
                if light_type == 'point':
                    vertice_images = rendering[:, 6:9, :, :].detach()
                    shading = self.add_pointlight(
                        vertice_images.permute(0, 2, 3,
                                               1).reshape([batch_size, -1, 3]),
                        normal_images.permute(0, 2, 3,
                                              1).reshape([batch_size, -1, 3]),
                        lights)
                    shading_images = shading.reshape([
                        batch_size, albedo_images.shape[2],
                        albedo_images.shape[3], 3
                    ]).permute(0, 3, 1, 2)
                else:
                    shading = self.add_directionlight(
                        normal_images.permute(0, 2, 3,
                                              1).reshape([batch_size, -1, 3]),
                        lights)
                    shading_images = shading.reshape([
                        batch_size, albedo_images.shape[2],
                        albedo_images.shape[3], 3
                    ]).permute(0, 3, 1, 2)
            images = albedo_images * shading_images
        else:
            images = albedo_images
            shading_images = images.detach() * 0.

        outputs = {
            'images': images * alpha_images,
            'albedo_images': albedo_images * alpha_images,
            'alpha_images': alpha_images,
            'pos_mask': pos_mask,
            'shading_images': shading_images,
            'grid': grid,
            'normals': normals,
            'normal_images': normal_images * alpha_images,
            'transformed_normals': transformed_normals,
        }

        return outputs

    def add_SHlight(self, normal_images, gamma, init_lit):
        '''
            sh_coeff: [bz, 9, 3]
        '''
        batch_size = gamma.shape[0]
        gamma = gamma.reshape([batch_size, 3, 9])
        gamma = gamma + init_lit
        sh_coeff = gamma.permute(0, 2, 1)

        N = normal_images
        tmp_value = 3 * (N[:, 2]**2) - 1
        sh = torch.stack([
            N[:, 0] * 0. + 1., N[:, 0], N[:, 1], N[:, 2], N[:, 0] * N[:, 1],
            N[:, 0] * N[:, 2], N[:, 1] * N[:, 2], N[:, 0]**2 - N[:, 1]**2,
            tmp_value
        ], 1)  # [bz, 9, h, w]
        sh = sh * self.constant_factor[None, :, None, None]
        shading = torch.sum(sh_coeff[:, :, :, None, None]
                            * sh[:, :, None, :, :], 1)  # [bz, 9, 3, h, w]
        return shading

    def add_pointlight(self, vertices, normals, lights):
        '''
            vertices: [bz, nv, 3]
            lights: [bz, nlight, 6]
        returns:
            shading: [bz, nv, 3]
        '''
        light_positions = lights[:, :, :3]
        light_intensities = lights[:, :, 3:]
        directions_to_lights = F.normalize(
            light_positions[:, :, None, :] - vertices[:, None, :, :], dim=3)
        # normals_dot_lights = torch.clamp((normals[:,None,:,:]*directions_to_lights).sum(dim=3), 0., 1.)
        normals_dot_lights = (normals[:, None, :, :]
                              * directions_to_lights).sum(dim=3)
        shading = normals_dot_lights[:, :, :,
                                     None] * light_intensities[:, :, None, :]
        return shading.mean(1)

    def add_directionlight(self, normals, lights):
        '''
            normals: [bz, nv, 3]
            lights: [bz, nlight, 6]
        returns:
            shading: [bz, nv, 3]
        '''
        light_direction = lights[:, :, :3]
        light_intensities = lights[:, :, 3:]
        directions_to_lights = F.normalize(
            light_direction[:, :, None, :].expand(-1, -1, normals.shape[1],
                                                  -1),
            dim=3)
        # normals_dot_lights = torch.clamp((normals[:,None,:,:]*directions_to_lights).sum(dim=3), 0., 1.)
        # normals_dot_lights = (normals[:,None,:,:]*directions_to_lights).sum(dim=3)
        normals_dot_lights = torch.clamp(
            (normals[:, None, :, :] * directions_to_lights).sum(dim=3), 0., 1.)
        shading = normals_dot_lights[:, :, :,
                                     None] * light_intensities[:, :, None, :]
        return shading.mean(1)

    def world2uv(self, vertices):
        '''
        warp vertices from world space to uv space
        vertices: [bz, V, 3]
        uv_vertices: [bz, 3, h, w]
        '''
        batch_size = vertices.shape[0]
        face_vertices = utils.face_vertices(
            vertices, self.faces.expand(batch_size, -1, -1))
        uv_vertices = self.uv_rasterizer(
            self.uvcoords.expand(batch_size, -1, -1),
            self.uvfaces.expand(batch_size, -1, -1), face_vertices)[:, :3]
        return uv_vertices