#!/usr/bin/env python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools

import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import numpy as np
import PIL.Image as Image
from PIL import ImageDraw
import tensorflow as tf
import tensorflow.contrib.slim as slim
from libs.logs.log import LOG
import libs.configs.config_v1 as cfg
import libs.nets.resnet_v1 as resnet_v1
import libs.datasets.dataset_factory as dataset_factory
import libs.datasets.coco as coco
import libs.preprocessings.coco_v1 as preprocess_coco
from libs.layers import ROIAlign

resnet50 = resnet_v1.resnet_v1_50
FLAGS = tf.app.flags.FLAGS

with tf.Graph().as_default():

  image, ih, iw, gt_boxes, gt_masks, num_instances, img_id = \
    coco.read('./data/coco/records/coco_trainval2014_00000-of-00048.tfrecord')
  
  image, gt_boxes, gt_masks = \
    preprocess_coco.preprocess_image(image, gt_boxes, gt_masks)

  

  sess = tf.Session()
  init_op = tf.group(tf.global_variables_initializer(),
                     tf.local_variables_initializer())
  # init_op = tf.initialize_all_variables()

  boxes = [[100, 100, 200, 200],
           [50, 50, 100, 100],
           [100, 100, 750, 750],
           [50, 50, 60, 60]]
  # boxes = np.zeros((0, 4))
  boxes = tf.constant(boxes, tf.float32)
  feat = ROIAlign(image, boxes, False, 16, 7, 7)
  sess.run(init_op)

  tf.train.start_queue_runners(sess=sess)
  with sess.as_default():
      for i in range(20000):
        image_np, ih_np, iw_np, gt_boxes_np, gt_masks_np, num_instances_np, img_id_np, \
        feat_np = \
            sess.run([image, ih, iw, gt_boxes, gt_masks, num_instances, img_id,
                feat])
        # print (image_np.shape, gt_boxes_np.shape, gt_masks_np.shape)
            
        if i % 100 == 0:
            print ('%d, image_id: %s, instances: %d'%  (i, str(img_id_np), num_instances_np))
            image_np = 256 * (image_np * 0.5 + 0.5)
            image_np = image_np.astype(np.uint8)
            image_np = np.squeeze(image_np)
            print (image_np.shape, ih_np, iw_np)
            print (feat_np.shape)
            im = Image.fromarray(image_np)
            imd = ImageDraw.Draw(im)
            for i in range(gt_boxes_np.shape[0]):
                imd.rectangle(gt_boxes_np[i, :])
            im.save(str(img_id_np) + '.png')
            # print (gt_boxes_np)
  sess.close()