import os

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # Currently, memory growth needs to be the same across GPUs
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)


import sys
import time
import cv2
import random
import numpy as np
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__':

    uv2mesh = UV2Mesh(is_smpl_orig=True)
    smpl = SMPL(orig_smpl=True)

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

    factory = DatasetFactory()
    signature_dynamic = { 'id': (), 'frame_nr': (),
                          'pose_shape': (72 + 11,),
                        #   'uv_posed': (3, 512, 256,),
                        #   'uv_body_posed': (3, 512, 256,),
                          'mesh_body_posed': (3638, 3,),
                          'mesh_posed': (3638, 3,),
                          'mesh_unposed': (3638, 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': (3638,),
                        #  'uv_mask': (512, 256,),
                        #  'uv_shaped_body': (3, 512, 256,),
                          }

    dataset = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/train/female',
                                  batch_size=1,
                                  signature=signature_dynamic,
                                  resolutions=[512],
                                  is_cape=True)
    static_dataset = StaticDataset(folder='/home/workspace/local-data/cape_processed/train/female',
                                   signature=signature_static,
                                   resolutions=[512],
                                   is_cape=True)



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

        J0_old = np.load('custom_data/smpl_root_joint.npy')

        id = batch['id'][0].numpy().decode("utf-8")
        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()
        gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
        shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]).numpy()
        mesh_unposed_shaped = batch['mesh_unposed'] + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
        J0_new = smpl.run(smpl.run_rodrigues(np.zeros((1, 72), np.float32)), shape)[1][0,0,:].numpy()

        min_body = np.load(os.path.join('/home/workspace/local-data/temp/cape_release', 'minimal_body_shape', id.split('_')[0], id.split('_')[0] + '_minimal.npy'))[np.newaxis, :, :].astype(np.float32)
        J0_2 = smpl.get_root_joint(shape, v_shaped=tf.stack([min_body, min_body], axis=-1))[0].numpy()
        # min_body -=  J0_2[np.newaxis,np.newaxis,:]

        mesh_unposed_shaped = mesh_unposed_shaped + J0_old[tf.newaxis,tf.newaxis,:]
        mesh_reposed = smpl.run_garment(pose, shape, mesh_unposed_shaped, v_shaped=tf.stack([min_body, min_body], axis=-1)).numpy()[0]
        mesh_posed = np.squeeze(batch['mesh_posed'].numpy()) + J0_old[np.newaxis,:]
        mesh_body_posed_no_orient = np.squeeze(batch['mesh_body_posed'].numpy())
        mesh_body_posed = smpl.run(smpl.run_rodrigues(pose), shape)[0][0].numpy()

        # x = smpl.run(smpl.run_rodrigues(pose), shape)[0][0]
        x = mesh_reposed[mask, :]
        y = mesh_posed[mask, :]
        _, faces = _maskFaces(np.zeros((3638, 3), dtype=np.float32), F, mask)
        fig = go.Figure(data=[
                go.Mesh3d(
                    x=x[:,0],
                    y=x[:,1],
                    z=x[:,2],
                    i = faces[:,0],
                    j = faces[:,1],
                    k = faces[:,2]
                ),
                go.Mesh3d(
                    x=y[:,0],
                    y=y[:,1],
                    z=y[:,2],
                    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=[-1.5,1.5],),
                yaxis = dict(nticks=4, range=[-1.5,1.5],),
                zaxis = dict(nticks=4, range=[-1.5,1.5],),)
                )

        fig.show()


        x = mesh_unposed_shaped.numpy()[0, mask, :]
        # y = smpl.run(smpl.run_rodrigues(np.zeros((1, 72), np.float32)), shape)[0][0]
        y = min_body[0, smpl.to_keep]
        _, faces = _maskFaces(np.zeros((3638, 3), dtype=np.float32), F, mask)
        fig = go.Figure(data=[
                go.Mesh3d(
                    x=x[:,0],
                    y=x[:,1],
                    z=x[:,2],
                    i = faces[:,0],
                    j = faces[:,1],
                    k = faces[:,2]
                ),
                go.Mesh3d(
                    x=y[:,0],
                    y=y[:,1],
                    z=y[:,2],
                    i = F[:,0],
                    j = F[:,1],
                    k = F[:,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=[-1.5,1.5],),
                yaxis = dict(nticks=4, range=[-1.5,1.5],),
                zaxis = dict(nticks=4, range=[-1.5,1.5],),)
                )

        fig.show()

        print()