"""This script is the differentiable renderer for Deep3DFaceRecon_pytorch
    Attention, antialiasing step is missing in current version.
"""

import torch
import torch.nn.functional as F
import kornia
from kornia.geometry.camera import pixel2cam
import numpy as np
from typing import List
import nvdiffrast.torch as dr
from scipy.io import loadmat
from torch import nn

def ndc_projection(x=0.1, n=1.0, f=50.0):
    return np.array([[n/x,    0,            0,              0],
                     [  0, n/-x,            0,              0],
                     [  0,    0, -(f+n)/(f-n), -(2*f*n)/(f-n)],
                     [  0,    0,           -1,              0]]).astype(np.float32)

class MeshRenderer(nn.Module):
    def __init__(self,
                rasterize_fov,
                znear=0.1,
                zfar=10, 
                rasterize_size=224):
        super(MeshRenderer, self).__init__()

        x = np.tan(np.deg2rad(rasterize_fov * 0.5)) * znear
        self.ndc_proj = torch.tensor(ndc_projection(x=x, n=znear, f=zfar)).matmul(
                torch.diag(torch.tensor([1., -1, -1, 1])))
        self.rasterize_size = rasterize_size
        self.glctx = None
    
    def forward(self, vertex, tri, feat=None):
        """
        Return:
            mask               -- torch.tensor, size (B, 1, H, W)
            depth              -- torch.tensor, size (B, 1, H, W)
            features(optional) -- torch.tensor, size (B, C, H, W) if feat is not None

        Parameters:
            vertex          -- torch.tensor, size (B, N, 3)
            tri             -- torch.tensor, size (B, M, 3) or (M, 3), triangles
            feat(optional)  -- torch.tensor, size (B, C), features
        """
        device = vertex.device
        rsize = int(self.rasterize_size)
        ndc_proj = self.ndc_proj.to(device)
        # trans to homogeneous coordinates of 3d vertices, the direction of y is the same as v
        if vertex.shape[-1] == 3:
            vertex = torch.cat([vertex, torch.ones([*vertex.shape[:2], 1]).to(device)], dim=-1)
            vertex[..., 1] = -vertex[..., 1] 


        vertex_ndc = vertex @ ndc_proj.t()
        if self.glctx is None:
            self.glctx = dr.RasterizeGLContext(device=device)
            print("create glctx on device cuda:%d"%device.index)
        
        ranges = None
        if isinstance(tri, List) or len(tri.shape) == 3:
            vum = vertex_ndc.shape[1]
            fnum = torch.tensor([f.shape[0] for f in tri]).unsqueeze(1).to(device) 
            fstartidx = torch.cumsum(fnum, dim=0) - fnum 
            ranges = torch.cat([fstartidx, fnum], axis=1).type(torch.int32).cpu()
            for i in range(tri.shape[0]):
                tri[i] = tri[i] + i*vum
            vertex_ndc = torch.cat(vertex_ndc, dim=0)
            tri = torch.cat(tri, dim=0)

        # for range_mode vetex: [B*N, 4], tri: [B*M, 3], for instance_mode vetex: [B, N, 4], tri: [M, 3]
        tri = tri.type(torch.int32).contiguous()
        rast_out, _ = dr.rasterize(self.glctx, vertex_ndc.contiguous(), tri, resolution=[rsize, rsize], ranges=ranges)

        depth, _ = dr.interpolate(vertex.reshape([-1,4])[...,2].unsqueeze(1).contiguous(), rast_out, tri) 
        depth = depth.permute(0, 3, 1, 2)
        mask =  (rast_out[..., 3] > 0).float().unsqueeze(1)
        depth = mask * depth
        

        image = None
        if feat is not None:
            image, _ = dr.interpolate(feat, rast_out, tri)
            image = image.permute(0, 3, 1, 2)
            image = mask * image
        
        return mask, depth, image

