data/compute_mean_stddev.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "# Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all' # default is ‘last_expr’\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from PIL import Image\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "import torchvision.transforms.functional as TF\n",
    "from skimage import transform\n",
    "from torchvision import transforms\n",
    "import random\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AverageMeter(object):\n",
    "    \"\"\"Computes and stores the average and current value\n",
    "    https://github.com/pytorch/examples/blob/master/imagenet/main.py\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.val = 0\n",
    "        self.avg = 0\n",
    "        self.sum = 0\n",
    "        self.count = 0\n",
    "\n",
    "    def update(self, val, n=1):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            val: mini-batch loss or accuracy value\n",
    "            n: mini-batch size\n",
    "        \"\"\"\n",
    "        self.val = val\n",
    "        self.sum += val \n",
    "        self.count += n\n",
    "        self.avg = self.sum / self.count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = './xBD_all/' \n",
    "\n",
    "all_disaster_splits_json_filename = './all_disaster_splits.json'\n",
    "label_map_json_file = './nlrc.building-damage-assessment/constants/class_lists/xBD_label_map.json'\n",
    "\n",
    "def load_json_files(json_filename):\n",
    "    with open(json_filename) as f:\n",
    "        file_content = json.load(f)\n",
    "    return file_content\n",
    "\n",
    "splits = load_json_files(all_disaster_splits_json_filename)\n",
    "label_map = load_json_files(label_map_json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['guatemala-volcano', 'hurricane-florence', 'hurricane-harvey', 'mexico-earthquake', 'midwest-flooding', 'palu-tsunami', 'santa-rosa-wildfire', 'socal-fire', 'lower-puna-volcano', 'nepal-flooding', 'pinery-bushfire', 'portugal-wildfire', 'sunda-tsunami', 'woolsey-fire', 'hurricane-matthew', 'hurricane-michael', 'joplin-tornado', 'moore-tornado', 'tuscaloosa-tornado'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11034"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['raw/hold/labels/guatemala-volcano_00000012',\n",
       " 'raw/hold/labels/guatemala-volcano_00000014',\n",
       " 'raw/hold/labels/guatemala-volcano_00000020',\n",
       " 'raw/hold/labels/guatemala-volcano_00000022',\n",
       " 'raw/test/labels/guatemala-volcano_00000003',\n",
       " 'raw/test/labels/guatemala-volcano_00000005',\n",
       " 'raw/test/labels/guatemala-volcano_00000009',\n",
       " 'raw/test/labels/guatemala-volcano_00000011',\n",
       " 'raw/test/labels/guatemala-volcano_00000021',\n",
       " 'raw/train/labels/guatemala-volcano_00000000',\n",
       " 'raw/train/labels/guatemala-volcano_00000001',\n",
       " 'raw/train/labels/guatemala-volcano_00000006',\n",
       " 'raw/train/labels/guatemala-volcano_00000008',\n",
       " 'raw/train/labels/guatemala-volcano_00000010',\n",
       " 'raw/train/labels/guatemala-volcano_00000013',\n",
       " 'raw/train/labels/guatemala-volcano_00000015',\n",
       " 'raw/train/labels/guatemala-volcano_00000018',\n",
       " 'raw/train/labels/guatemala-volcano_00000023',\n",
       " 'raw/train/labels/guatemala-volcano_00000024']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get list of train, val, test images\n",
    "splits.keys()\n",
    "all_images_ls = [] \n",
    "for item, val in splits.items():\n",
    "    all_images_ls += val['train'] \n",
    "    all_images_ls += val['test'] \n",
    "    all_images_ls += val['val'] \n",
    "\n",
    "\n",
    "len(all_images_ls)\n",
    "all_images_ls[1:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DisasterDataset_img(Dataset):\n",
    "    def __init__(self, data_dir, data_dir_ls, transform:bool, scale=1, mask_suffix=''):\n",
    "        \n",
    "        self.data_dir = data_dir\n",
    "        self.dataset_sub_dir = data_dir_ls\n",
    "        self.scale = scale\n",
    "        self.transform = transform\n",
    "        \n",
    "        assert 0 < scale <= 1, 'Scale must be between 0 and 1'\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.dataset_sub_dir)\n",
    "        \n",
    "        return img_trans\n",
    "    \n",
    "    \n",
    "    def __getitem__(self, i):\n",
    "        \n",
    "        imgs_dir = self.data_dir + self.dataset_sub_dir[i].replace('labels', 'images')\n",
    "\n",
    "        idx = imgs_dir\n",
    "        \n",
    "        pre_img_file_name = imgs_dir + '_pre_disaster'\n",
    "        pre_img_file = glob(pre_img_file_name + '.*')\n",
    "\n",
    "        post_img_file_name = pre_img_file_name.replace('pre', 'post')\n",
    "        post_img_file = glob(post_img_file_name + '.*')\n",
    "\n",
    "        assert len(pre_img_file) == 1, \\\n",
    "            f'Either no image or multiple images found for the ID {idx}: {pre_img_file}'\n",
    "        assert len(post_img_file) == 1, \\\n",
    "            f'Either no post disaster image or multiple images found for the ID {idx}: {post_img_file}'\n",
    "\n",
    "        pre_img = np.array(Image.open(pre_img_file[0]))\n",
    "        post_img = np.array(Image.open(post_img_file[0]))\n",
    "\n",
    "        assert pre_img.size == post_img.size, \\\n",
    "            f'Pre_ & _post disaster Images {idx} should be the same size, but are {pre_img.size} and {post_img.size}'\n",
    "\n",
    "        return {'pre_image': pre_img, 'post_image': post_img}, {'pre_img_file_name': pre_img_file_name, 'post_img_file_name':post_img_file_name}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xBD_disaster_dataset length: 11034\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 31,
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     "output_type": "execute_result"
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      "text/plain": [
       "2"
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      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
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     "data": {
      "text/plain": [
       "10919"
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     },
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    {
     "data": {
      "text/plain": [
       "10920"
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    {
     "data": {
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    },
    {
     "data": {
      "text/plain": [
       "10922"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10923"
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     },
     "execution_count": 31,
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    },
    {
     "data": {
      "text/plain": [
       "10924"
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     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10925"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10926"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10927"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10928"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10929"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10930"
      ]
     },
     "execution_count": 31,
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     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10931"
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     "execution_count": 31,
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    },
    {
     "data": {
      "text/plain": [
       "10932"
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     },
     "execution_count": 31,
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     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10933"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10934"
      ]
     },
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    },
    {
     "data": {
      "text/plain": [
       "10935"
      ]
     },
     "execution_count": 31,
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     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10936"
      ]
     },
     "execution_count": 31,
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    },
    {
     "data": {
      "text/plain": [
       "10937"
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     },
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     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10938"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10939"
      ]
     },
     "execution_count": 31,
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     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10940"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10941"
      ]
     },
     "execution_count": 31,
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    },
    {
     "data": {
      "text/plain": [
       "10942"
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     },
     "execution_count": 31,
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    },
    {
     "data": {
      "text/plain": [
       "10943"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10944"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10945"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10946"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10947"
      ]
     },
     "execution_count": 31,
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    },
    {
     "data": {
      "text/plain": [
       "10948"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10949"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10950"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10951"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10952"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10953"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10954"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10955"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10956"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10957"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10958"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10959"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10960"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10961"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10962"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10963"
      ]
     },
     "execution_count": 31,
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    },
    {
     "data": {
      "text/plain": [
       "10964"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10965"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10966"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10967"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10968"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10969"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10970"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10971"
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     },
     "execution_count": 31,
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     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10972"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10973"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "10974"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10975"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10976"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10977"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10978"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10979"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10980"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10981"
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     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "10982"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10983"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10984"
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     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "10985"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10986"
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     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "10988"
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     },
     "execution_count": 31,
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    },
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 31,
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     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10990"
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     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "10991"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10992"
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     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "10993"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10994"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10995"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10996"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10997"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10998"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10999"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11001"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11002"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11003"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "11004"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "11005"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11006"
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     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "11007"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11008"
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     },
     "execution_count": 31,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "11009"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11010"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11011"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11012"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11013"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11014"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11015"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11016"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11017"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11018"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11019"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11020"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11021"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11022"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11023"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11024"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11025"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11026"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11027"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11028"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11029"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11030"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11031"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11032"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11033"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "84.05686894512401"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "88.20147053573537"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "66.52192156743114"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute mean\n",
    "\n",
    "mean_c1 = AverageMeter()\n",
    "mean_c2 = AverageMeter()\n",
    "mean_c3 = AverageMeter()\n",
    "\n",
    "xBD_all = DisasterDataset_img(data_dir, all_images_ls, transform=True)\n",
    "print('xBD_disaster_dataset length: {}'.format(len(xBD_all)))\n",
    "\n",
    "for batch_idx, data in enumerate(xBD_all):\n",
    "\n",
    "    data = data[0]\n",
    "    x_pre = data['pre_image']\n",
    "    x_post = data['post_image']\n",
    "    \n",
    "    mean_c1.update(x_pre[:,:,0].sum(), x_pre[:,:,0].size)\n",
    "    mean_c1.update(x_post[:,:,0].sum(), x_post[:,:,0].size)\n",
    "\n",
    "    mean_c2.update(x_pre[:,:,1].sum(), x_pre[:,:,1].size)\n",
    "    mean_c2.update(x_post[:,:,1].sum(), x_post[:,:,1].size)\n",
    "\n",
    "    mean_c3.update(x_pre[:,:,2].sum(), x_pre[:,:,2].size)\n",
    "    mean_c3.update(x_post[:,:,2].sum(), x_post[:,:,2].size)\n",
    "\n",
    "\n",
    "mean_c1.avg\n",
    "mean_c2.avg\n",
    "mean_c3.avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.85410106262986"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "36.61292422955513"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "34.146194424515556"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute stddev\n",
    "\n",
    "stddev_c1 = AverageMeter()\n",
    "stddev_c2 = AverageMeter()\n",
    "stddev_c3 = AverageMeter()\n",
    "\n",
    "\n",
    "for batch_idx, data in enumerate(xBD_all):\n",
    "    data = data[0]\n",
    "\n",
    "    x_pre = data['pre_image']\n",
    "    x_post = data['post_image']\n",
    "    \n",
    "    diff_pre = np.power(x_pre[:,:,0] - mean_c1.avg, 2.0) \n",
    "    diff_post = np.power(x_post[:,:,0] - mean_c1.avg, 2.0) \n",
    "    stddev_c1.update(diff_pre.sum(), diff_pre.size)\n",
    "    stddev_c1.update(diff_post.sum(), diff_post.size)\n",
    "\n",
    "    diff_pre = np.power(x_pre[:,:,1] - mean_c2.avg, 2.0) \n",
    "    diff_post = np.power(x_post[:,:,1] - mean_c2.avg, 2.0) \n",
    "    stddev_c2.update(diff_pre.sum(), diff_pre.size)\n",
    "    stddev_c2.update(diff_post.sum(), diff_post.size)\n",
    "\n",
    "    diff_pre = np.power(x_pre[:,:,2] - mean_c3.avg, 2.0) \n",
    "    diff_post = np.power(x_post[:,:,2] - mean_c3.avg, 2.0)\n",
    "    stddev_c3.update(diff_pre.sum(), diff_pre.size)\n",
    "    stddev_c3.update(diff_post.sum(), diff_post.size)\n",
    "\n",
    "\n",
    "stddev_c1.avg ** 0.5\n",
    "stddev_c2.avg ** 0.5\n",
    "stddev_c3.avg ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': {'c1': 84.05686894512401,\n",
       "  'c2': 88.20147053573537,\n",
       "  'c3': 66.52192156743114},\n",
       " 'std_dev': {'c1': 43.85410106262986,\n",
       "  'c2': 36.61292422955513,\n",
       "  'c3': 34.146194424515556}}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save as json\n",
    "\n",
    "mean_stdev_dict = {'mean':{'c1': mean_c1.avg, 'c2': mean_c2.avg, 'c3': mean_c3.avg},\n",
    "              'std_dev':{'c1': stddev_c1.avg** 0.5, 'c2': stddev_c2.avg** 0.5, 'c3': stddev_c3.avg** 0.5}}\n",
    "mean_stdev_dict\n",
    "\n",
    "with open('./nlrc.building-damage-assessment/constants/splits/all_disaster_mean_stddev.json', 'w') as f:\n",
    "    json.dump(mean_stdev_dict, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(1024, 1024, 3)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1048576"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(1024, 1024)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "95049061"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 42.91700547,  40.91700547,  39.91700547, ..., -27.08299453,\n",
       "        -27.08299453, -25.08299453],\n",
       "       [ 38.91700547,  38.91700547,  36.91700547, ..., -25.08299453,\n",
       "        -24.08299453, -23.08299453],\n",
       "       [ 39.91700547,  40.91700547,  40.91700547, ..., -24.08299453,\n",
       "        -23.08299453, -23.08299453],\n",
       "       ...,\n",
       "       [-25.08299453, -22.08299453, -26.08299453, ..., -10.08299453,\n",
       "        -16.08299453, -17.08299453],\n",
       "       [-25.08299453, -22.08299453, -25.08299453, ..., -13.08299453,\n",
       "        -16.08299453, -23.08299453],\n",
       "       [-24.08299453, -28.08299453, -29.08299453, ..., -14.08299453,\n",
       "        -18.08299453, -29.08299453]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1246103109.5355492"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1048576"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test a few things\n",
    "\n",
    "type(x_post)\n",
    "x_pre.shape\n",
    "\n",
    "x_pre[:,:,0].size\n",
    "x_pre[:,:,0].shape\n",
    "x_pre[:,:,0].sum()\n",
    "\n",
    "x_pre[:,:,0] - mean_c1.avg\n",
    "out = np.power(x_pre[:,:,0] - mean_c1.avg, 2.0) \n",
    "out.sum()\n",
    "out.size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot histogram of the channels for one image #entire dataset\n",
    "from skimage import io\n",
    "import matplotlib.pyplot as plt\n",
    "image = x_pre\n",
    "_ = plt.hist(image[:, :, 0].ravel(), bins = 256, color = 'red', alpha = 0.5)\n",
    "_ = plt.hist(image[:, :, 1].ravel(), bins = 256, color = 'Green', alpha = 0.5)\n",
    "_ = plt.hist(image[:, :, 2].ravel(), bins = 256, color = 'Blue', alpha = 0.5)\n",
    "_ = plt.xlabel('Intensity Value')\n",
    "_ = plt.ylabel('Count')\n",
    "_ = plt.legend(['Red_Channel', 'Green_Channel', 'Blue_Channel'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute mean & stddev for each tile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xBD_disaster_dataset length: 11034\n"
     ]
    }
   ],
   "source": [
    "# compute mean actual values\n",
    "\n",
    "mean_std_tile = {}\n",
    "\n",
    "xBD_all = DisasterDataset_img(data_dir, all_images_ls, transform=True)\n",
    "print('xBD_disaster_dataset length: {}'.format(len(xBD_all)))\n",
    "\n",
    "for batch_idx, data in enumerate(xBD_all):\n",
    "    \n",
    "    x_pre = data[0]['pre_image']\n",
    "    x_post = data[0]['post_image']\n",
    "    x_pre_filename = data[1]['pre_img_file_name'].replace('xBD', 'xBD_sliced')\n",
    "    x_post_filename = data[1]['post_img_file_name'].replace('xBD', 'xBD_sliced')\n",
    "    \n",
    "    mean_std_tile[x_pre_filename]=[(x_pre[:,:,0].mean(), x_pre[:,:,1].mean(), x_pre[:,:,2].mean()), (x_pre[:,:,0].std(), x_pre[:,:,1].std(), x_pre[:,:,2].std())]\n",
    "    mean_std_tile[x_post_filename]= [(x_post[:,:,0].mean(), x_post[:,:,1].mean(), x_post[:,:,2].mean()), (x_post[:,:,0].std(), x_post[:,:,1].std(), x_post[:,:,2].std())]\n",
    "\n",
    "with open('./nlrc.building-damage-assessment/constants/splits/all_disaster_mean_stddev_tiles.json', 'w') as f:\n",
    "    json.dump(mean_std_tile, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xBD_disaster_dataset length: 11034\n"
     ]
    }
   ],
   "source": [
    "# compute mean [0,1]\n",
    "\n",
    "mean_std_tile = {}\n",
    "eps = np.finfo(float).eps\n",
    "xBD_all = DisasterDataset_img(data_dir, all_images_ls, transform=True)\n",
    "print('xBD_disaster_dataset length: {}'.format(len(xBD_all)))\n",
    "\n",
    "for batch_idx, data in enumerate(xBD_all):\n",
    "    \n",
    "    x_pre = data[0]['pre_image']/255.0\n",
    "    x_post = data[0]['post_image']/255.0\n",
    "    x_pre_filename = data[1]['pre_img_file_name'].replace('xBD', 'xBD_sliced')\n",
    "    x_post_filename = data[1]['post_img_file_name'].replace('xBD', 'xBD_sliced')\n",
    "    \n",
    "    mean_std_tile[x_pre_filename]=[(x_pre[:,:,0].mean(), x_pre[:,:,1].mean(), x_pre[:,:,2].mean()), (max(eps, x_pre[:,:,0].std()), max(eps, x_pre[:,:,1].std()), max(eps, x_pre[:,:,2].std()))]\n",
    "    mean_std_tile[x_post_filename]= [(x_post[:,:,0].mean(), x_post[:,:,1].mean(), x_post[:,:,2].mean()), (x_post[:,:,0].std(), x_post[:,:,1].std(), x_post[:,:,2].std())]\n",
    "\n",
    "with open('./nlrc.building-damage-assessment/constants/splits/all_disaster_mean_stddev_tiles_0_1.json', 'w') as f:\n",
    "    json.dump(mean_std_tile, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps = np.finfo(float).eps\n",
    "0/eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xBD_disaster_dataset length: 1674\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(92.90903091430664, 103.32747745513916, 72.64891052246094)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(39.426787318121015, 33.477596964451315, 35.165765158137496)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[[114, 109,  74],\n",
       "        [111, 106,  72],\n",
       "        [108, 105,  70],\n",
       "        ...,\n",
       "        [125, 121,  99],\n",
       "        [125, 122,  99],\n",
       "        [127, 124, 101]],\n",
       "\n",
       "       [[118, 111,  75],\n",
       "        [120, 113,  77],\n",
       "        [114, 109,  74],\n",
       "        ...,\n",
       "        [123, 120,  97],\n",
       "        [127, 124, 101],\n",
       "        [128, 125, 102]],\n",
       "\n",
       "       [[125, 116,  79],\n",
       "        [118, 111,  76],\n",
       "        [119, 113,  77],\n",
       "        ...,\n",
       "        [121, 118,  95],\n",
       "        [127, 124, 100],\n",
       "        [127, 124, 100]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 28,  43,  25],\n",
       "        [ 37,  54,  32],\n",
       "        [ 40,  58,  35],\n",
       "        ...,\n",
       "        [109, 122,  79],\n",
       "        [100, 114,  72],\n",
       "        [104, 118,  74]],\n",
       "\n",
       "       [[ 29,  43,  25],\n",
       "        [ 39,  57,  34],\n",
       "        [ 42,  61,  36],\n",
       "        ...,\n",
       "        [112, 126,  80],\n",
       "        [104, 119,  74],\n",
       "        [ 98, 113,  69]],\n",
       "\n",
       "       [[ 36,  53,  31],\n",
       "        [ 36,  52,  30],\n",
       "        [ 37,  53,  31],\n",
       "        ...,\n",
       "        [112, 126,  80],\n",
       "        [104, 119,  74],\n",
       "        [ 98, 113,  69]]], dtype=uint8)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[[-2.3452, -2.3455, -2.3458,  ..., -2.3441, -2.3441, -2.3439],\n",
       "         [-2.3448, -2.3446, -2.3452,  ..., -2.3443, -2.3439, -2.3438],\n",
       "         [-2.3441, -2.3448, -2.3447,  ..., -2.3445, -2.3439, -2.3439],\n",
       "         ...,\n",
       "         [-2.3537, -2.3528, -2.3525,  ..., -2.3457, -2.3465, -2.3462],\n",
       "         [-2.3536, -2.3526, -2.3523,  ..., -2.3454, -2.3462, -2.3467],\n",
       "         [-2.3529, -2.3529, -2.3528,  ..., -2.3454, -2.3462, -2.3467]],\n",
       "\n",
       "        [[-3.0737, -3.0740, -3.0742,  ..., -3.0723, -3.0722, -3.0719],\n",
       "         [-3.0735, -3.0732, -3.0737,  ..., -3.0724, -3.0719, -3.0718],\n",
       "         [-3.0729, -3.0735, -3.0732,  ..., -3.0726, -3.0719, -3.0719],\n",
       "         ...,\n",
       "         [-3.0814, -3.0801, -3.0797,  ..., -3.0722, -3.0731, -3.0726],\n",
       "         [-3.0814, -3.0798, -3.0793,  ..., -3.0717, -3.0725, -3.0732],\n",
       "         [-3.0803, -3.0804, -3.0803,  ..., -3.0717, -3.0725, -3.0732]],\n",
       "\n",
       "        [[-2.0576, -2.0579, -2.0581,  ..., -2.0549, -2.0549, -2.0546],\n",
       "         [-2.0575, -2.0573, -2.0576,  ..., -2.0551, -2.0546, -2.0545],\n",
       "         [-2.0571, -2.0574, -2.0573,  ..., -2.0553, -2.0547, -2.0547],\n",
       "         ...,\n",
       "         [-2.0631, -2.0623, -2.0620,  ..., -2.0571, -2.0579, -2.0576],\n",
       "         [-2.0631, -2.0621, -2.0619,  ..., -2.0570, -2.0576, -2.0582],\n",
       "         [-2.0624, -2.0626, -2.0624,  ..., -2.0570, -2.0576, -2.0582]]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test a few things\n",
    "from torchvision import transforms\n",
    "\n",
    "mean_std_tile = {}\n",
    "\n",
    "xBD_all = DisasterDataset_img(data_dir, all_images_ls, transform=True)\n",
    "print('xBD_disaster_dataset length: {}'.format(len(xBD_all)))\n",
    "\n",
    "for batch_idx, data in enumerate(xBD_all):\n",
    "    \n",
    "    x_pre = data[0]['pre_image']\n",
    "    x_post = data[0]['post_image']\n",
    "    x_pre_filename = data[1]['pre_img_file_name'].replace('xBD', 'xBD_sliced')\n",
    "    x_post_filename = data[1]['post_img_file_name'].replace('xBD', 'xBD_sliced')\n",
    "    \n",
    "    mean_std_tile[x_pre_filename]=[(x_pre[:,:,0].mean(), x_pre[:,:,1].mean(), x_pre[:,:,2].mean()), (x_pre[:,:,0].std(), x_pre[:,:,1].std(), x_pre[:,:,2].std())]\n",
    "    mean_std_tile[x_post_filename]= [(x_post[:,:,0].mean(), x_post[:,:,1].mean(), x_post[:,:,2].mean()), (x_post[:,:,0].std(), x_post[:,:,1].std(), x_post[:,:,2].std())]\n",
    "    \n",
    "    mean_pre = mean_std_tile[x_pre_filename][0]\n",
    "    stddev_pre = mean_std_tile[x_pre_filename][1]\n",
    "    mean_pre\n",
    "    stddev_pre\n",
    "    norm_pre = transforms.Compose([\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize(mean=mean_pre, std=stddev_pre),\n",
    "    ])\n",
    "    make_img_pre = transforms.Compose([\n",
    "                transforms.ToTensor(),\n",
    "                transforms.ToPILImage()])\n",
    "    x_pre\n",
    "    pre_img = norm_pre(x_pre)\n",
    "    pre_img\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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