import os
import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np
import cv2
from PIL import Image
import json
import openmesh as om
import pdb
from utils import *

class BiCarDataset(Dataset):
    def __init__(self, dataset_folder,input_size=512):
        self.dataset_folder = dataset_folder
        self.data_index_list = os.listdir(dataset_folder)
        self.input_size = input_size
        
    def __getitem__(self, index):
        instance_index = self.data_index_list[index]
        instance_folder = os.path.join(self.dataset_folder,instance_index)
        input_kps= np.zeros(1)
        # image/mask/annotation
        #processed images and mask
        #input_image = cv2.imread(os.path.join(instance_folder,'image','image_reshape512.jpeg'))
        #input_mask = cv2.imread(os.path.join(instance_folder,'image','mask512.png'))
        #processed image in dataloader
        image = Image.open(os.path.join(instance_folder,'image','raw_image.jpeg')).convert('RGB')
        polygon,kps,bbox = readjson(os.path.join(instance_folder,'image','annotation.json'))
        mask = polygon2seg(image,polygon)
        input_image,input_mask,input_kps = reshape_image_and_anno(image,mask,kps,bbox,self.input_size)
	
        # this two function can be used to visualize        
        #utils.show_seg(nimage,nmask)
	#utils.show_kps(nimage,nkps)

        #params: shape and pose
        beta = np.load(os.path.join(instance_folder,'params','beta.npy'))[:100]
        theta = np.load(os.path.join(instance_folder,'params','pose.npy')).reshape(3,24)
        
        #mesh: Here we only read points and uvmap of body only.
        #Tbody: T-pose body; Pbody: Posed body.
        tmesh = om.read_polymesh(os.path.join(instance_folder,'tpose','m.obj'))
        tbody_points =  tmesh.points() 
        tbody_uv = cv2.imread(os.path.join(instance_folder,'tpose','m.BMP'))
        
        pmesh = om.read_polymesh(os.path.join(instance_folder,'pose','m.obj'))
        pbody_points =  pmesh.points() 
        pbody_uv = cv2.imread(os.path.join(instance_folder,'pose','m.BMP'))
        
        
        return {'input_image':input_image,
                'input_mask':input_mask,
                'input_kps':input_kps,
                #'json_annotation':annotation,
                'beta':beta,
                'theta':theta,
                'Tbody_points':tbody_points,
                'Tbody_uv':tbody_uv,
                'Pbody_points':pbody_points,
                'Pbody_uv':pbody_uv
                } 
        
    def __len__(self):
        return len(self.data_index_list)

dataset = BiCarDataset('./3DBiCar')
batch_size = 2
dataset.__getitem__(1)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

for batch in dataloader:
    for item in batch:
        print(item,batch[item].shape)
    break