import os
import sys
import time
import cv2
import random
import numpy as np
import tensorflow as tf
import cmapy as cm
import plotly.graph_objects as go
import plotly.express as ex

from dataset import DatasetFactory, StaticDataset
from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces
from custom_utils.smpl import SMPL

if __name__ == '__main__':
    os.environ["CUDA_VISIBLE_DEVICES"] = "5"

    uv2mesh = UV2Mesh()
    smpl = SMPL()

    F = np.load("custom_utils/smpl_faces.npy")

    factory = DatasetFactory()
    signature_dynamic = { 'id': (),
                          'pose_shape': (72 + 11,),
                        #   'uv_posed': (3, 512, 256,),
                        #   'uv_body_posed': (3, 512, 256,),
                          # 'mesh_body_posed': (14475, 3,),
                        #   'mesh_posed_body_no_orient': (14475, 3,),
                          'mesh_posed': (14475, 3,),
                          'mesh_unposed': (14475, 3,),
                          # 'uv_unposed': (3, 512, 256,),
                        #   'uv_curvature': (512, 256),
                        #   'mesh_curvature':  (14475)
                        }
    signature_static = { #'uv_static': (3, 512, 256,),
                        #  'mesh_static': (14475, 3,),
                          'mesh_mask': (14475,),
                        #  'uv_mask': (512, 256,),
                        #  'uv_shaped_body': (3, 512, 256,),
                          }

    dataset = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train',
                                  batch_size=1,
                                  signature=signature_dynamic)
    static_dataset = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train',
                                   signature=signature_static)


    uv_res = 256



    for i, batch in enumerate(dataset):
        print(i)
        batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic))

        mesh_unposed = np.squeeze(batch['mesh_unposed'].numpy())
        mask = np.squeeze(batch['mesh_mask'].numpy()).astype(bool)
        pose, shape = batch['pose_shape'][:, :72].numpy(), batch['pose_shape'][:, 72:].numpy()
        # mesh_posed = np.squeeze(batch['mesh_posed'].numpy())
        gender = int(shape[0, -1] > 0)
        print(gender)

        shape_offsets = tf.tensordot(shape[:,:10], smpl.shapedirs[gender], axes=[[1], [2]]).numpy()
        

        mesh_unposed[mask, :] = mesh_unposed[mask, :] - np.squeeze(shape_offsets)[smpl.to_keep, :][mask, :]

        np.savez_compressed(os.path.join(folder, id, 'mesh_posed_body_no_orient', id + '_' + str(i) + '.npz'), mesh_posed_body_no_orient = T[i, :, :])

        
        # _, faces = _maskFaces(np.zeros((14475, 3), dtype=np.float32), F, mask)

        # x = mesh_unposed[mask, :]
        # y = mesh_unposed[mask, :] - np.squeeze(shape_offsets)[smpl.to_keep, :][mask, :]

        # # x = smpl.v_template.numpy()[0, smpl.to_keep, :]
        # # R = smpl.run_rodrigues(np.zeros((1, 72), dtype=np.float32))
        # # x = smpl.run(R, shape)[0][0]
        # # y = smpl.v_template.numpy()[gender, smpl.to_keep, :] + np.squeeze(shape_offsets)[smpl.to_keep, :]
        # # faces = F

        # fig = go.Figure(data=[
        #     go.Mesh3d(
        #         # 8 vertices of a cube
        #         x=x[:,0],
        #         y=x[:,1],
        #         z=x[:,2],
        #         # i, j and k give the vertices of triangles
        #         i = faces[:,0],
        #         j = faces[:,1],
        #         k = faces[:,2]
        #     ),
        #     go.Mesh3d(
        #         # 8 vertices of a cube
        #         x=y[:,0],
        #         y=y[:,1],
        #         z=y[:,2],
        #         # i, j and k give the vertices of triangles
        #         i = faces[:,0],
        #         j = faces[:,1],
        #         k = faces[:,2]
        #     )])

        # fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
        # fig.update_layout(
        #     scene = dict(
        #         xaxis = dict(nticks=4, range=[-1,1],),
        #                     yaxis = dict(nticks=4, range=[-1,1],),
        #                     zaxis = dict(nticks=4, range=[-1,1],),)
        #                     )

        # fig.show()

        print()