notebook2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "08ea5b9f-9af6-46f6-93ce-1e4b46570b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging datasets\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "from osgeo import gdal\n",
    "from osgeo import osr\n",
    "\n",
    "def read_img(filename, with_data=True):\n",
    "    dataset = gdal.Open(filename)  # 打开文件\n",
    "    im_width = dataset.RasterXSize  # 栅格矩阵的列数\n",
    "    im_height = dataset.RasterYSize  # 栅格矩阵的行数\n",
    "    im_geotrans = dataset.GetGeoTransform()  # 仿射矩阵\n",
    "    im_proj = dataset.GetProjection()  # 地图投影信息\n",
    "    if not with_data:\n",
    "        return dataset, im_proj, im_geotrans, im_height, im_width\n",
    "    else:\n",
    "        im_data = dataset.ReadAsArray(0, 0, im_width, im_height)  # 将数据写成数组，对应栅格矩阵\n",
    "        return dataset, im_proj, im_geotrans, im_height, im_width, im_data\n",
    "\n",
    "def write_img(filename, im_proj, im_geotrans, im_data):\n",
    "    # gdal数据类型包括\n",
    "    # gdal.GDT_Byte,\n",
    "    # gdal .GDT_UInt16, gdal.GDT_Int16, gdal.GDT_UInt32, gdal.GDT_Int32,\n",
    "    # gdal.GDT_Float32, gdal.GDT_Float64\n",
    "    \n",
    "    # 判断栅格数据的数据类型\n",
    "    if 'int8' in im_data.dtype.name:\n",
    "        datatype = gdal.GDT_Byte\n",
    "    elif 'int16' in im_data.dtype.name:\n",
    "        datatype = gdal.GDT_UInt16\n",
    "    else:\n",
    "        datatype = gdal.GDT_Float32\n",
    "\n",
    "    # 判读数组维数\n",
    "    if len(im_data.shape) == 3:\n",
    "        im_bands, im_height, im_width = im_data.shape\n",
    "    else:\n",
    "        im_bands, (im_height, im_width) = 1, im_data.shape\n",
    "\n",
    "    # 创建文件\n",
    "    driver = gdal.GetDriverByName(\"GTiff\")  # 数据类型必须有，因为要计算需要多大内存空间\n",
    "    dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)\n",
    "\n",
    "    dataset.SetGeoTransform(im_geotrans)  # 写入仿射变换参数\n",
    "    dataset.SetProjection(im_proj)  # 写入投影\n",
    "\n",
    "    if im_bands == 1:\n",
    "        dataset.GetRasterBand(1).WriteArray(im_data)  # 写入数组数据\n",
    "    else:\n",
    "        for i in range(im_bands):\n",
    "            dataset.GetRasterBand(i + 1).WriteArray(im_data[i])\n",
    "    del dataset\n",
    "\n",
    "def make_merge(rgb_path, sar_path, merge_path):\n",
    "    ds_sar, im_proj_sar, im_geotrans_sar, height_sar, width_sar, data_sar = read_img(sar_path, True)\n",
    "    ds_opt, im_proj_opt, im_geotrans_opt, height_opt, width_opt, data_opt = read_img(rgb_path, True)\n",
    "    data_sar_cat = data_sar[np.newaxis, :, :]\n",
    "    data_merge = np.concatenate([data_opt, data_sar_cat], axis=0)\n",
    "    write_img(merge_path, im_proj_opt, im_geotrans_opt, data_merge)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "18aa8daa-87f7-4339-825a-7b52994dc192",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1774/1774 [01:51<00:00, 15.97it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 579/579 [00:27<00:00, 21.32it/s]\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 3] The system cannot find the path specified: 'C:\\\\Users\\\\saada\\\\Desktop\\\\Drive\\\\UBS\\\\Workshop_Contest\\\\data\\\\track2/test\\\\merge'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12352\\3242838022.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0mmerge_dir\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'merge'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmerge_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m             \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmerge_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrgb_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.tif'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: 'C:\\\\Users\\\\saada\\\\Desktop\\\\Drive\\\\UBS\\\\Workshop_Contest\\\\data\\\\track2/test\\\\merge'"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    root = sys.argv[1]\n",
    "    root = r'C:\\Users\\saada\\Desktop\\Drive\\UBS\\Workshop_Contest\\data\\track2/'\n",
    "    sets = ['train', 'val', 'test']\n",
    "    for set in sets:\n",
    "        set_dir = os.path.join(root, set)\n",
    "        rgb_dir = os.path.join(set_dir, 'rgb')\n",
    "        sar_dir = os.path.join(set_dir, 'sar')\n",
    "        merge_dir = os.path.join(set_dir, 'merge')\n",
    "        if not os.path.exists(merge_dir):\n",
    "            os.mkdir(merge_dir)\n",
    "        for f in tqdm(os.listdir(rgb_dir)):\n",
    "            if f.endswith('.tif'):\n",
    "                rgb_path = os.path.join(rgb_dir, f)\n",
    "                sar_path = os.path.join(sar_dir, f)\n",
    "                merge_path = os.path.join(merge_dir, f)\n",
    "                make_merge(rgb_path, sar_path, merge_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "029e5655",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 605/605 [00:26<00:00, 22.96it/s]\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    root = sys.argv[1]\n",
    "    root = r'C:\\Users\\saada\\Desktop\\Drive\\UBS\\Workshop_Contest\\track2\\HeightEstimation\\workspace\\dataset\\data/'\n",
    "    sets = [ 'track2_test_data']\n",
    "    for set in sets:\n",
    "        set_dir = os.path.join(root, set)\n",
    "        rgb_dir = os.path.join(set_dir, 'rgb')\n",
    "        sar_dir = os.path.join(set_dir, 'sar')\n",
    "        merge_dir = os.path.join(set_dir, 'merge')\n",
    "        if not os.path.exists(merge_dir):\n",
    "            os.mkdir(merge_dir)\n",
    "        for f in tqdm(os.listdir(rgb_dir)):\n",
    "            if f.endswith('.tif'):\n",
    "                rgb_path = os.path.join(rgb_dir, f)\n",
    "                sar_path = os.path.join(sar_dir, f)\n",
    "                merge_path = os.path.join(merge_dir, f)\n",
    "                make_merge(rgb_path, sar_path, merge_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fec8b5fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8f8ee28e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saada\\anaconda3\\envs\\pytorch\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from functools import partial\n",
    "from torch.nn import functional as F\n",
    "\n",
    "affine_par = True\n",
    "\n",
    "def conv3x3(in_planes,\n",
    "            out_planes,\n",
    "            stride=1):\n",
    "    \"3x3 convolution with padding\"\n",
    "    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n",
    "                     padding=1, bias=False)\n",
    "\n",
    "class PSPModule(nn.Module):\n",
    "    \"\"\"\n",
    "    Reference:s\n",
    "        Zhao, Hengshuang, et al. *\"Pyramid scene parsing network.\"*\n",
    "    \"\"\"\n",
    "    def __init__(self, features, out_features=512, sizes=(1, 2, 3, 6)):\n",
    "        super(PSPModule, self).__init__()\n",
    "\n",
    "        self.stages = []\n",
    "        self.stages = nn.ModuleList([self._make_stage(features, out_features, size) for size in sizes])\n",
    "        self.bottleneck = nn.Sequential(\n",
    "            nn.Conv2d(features+len(sizes)*out_features, out_features, kernel_size=3, padding=1, dilation=1, bias=False),\n",
    "            nn.BatchNorm2d(out_features),\n",
    "            nn.Dropout2d(0.1)\n",
    "            )\n",
    "\n",
    "    def _make_stage(self, features, out_features, size):\n",
    "        prior = nn.AdaptiveAvgPool2d(output_size=(size, size))\n",
    "        conv = nn.Conv2d(features, out_features, kernel_size=1, bias=False)\n",
    "        bn = nn.BatchNorm2d(out_features)\n",
    "        return nn.Sequential(prior, conv, bn)\n",
    "\n",
    "    def forward(self, feats):\n",
    "        h, w = feats.size(2), feats.size(3)\n",
    "        priors = [F.upsample(input=stage(feats), size=(h, w), mode='bilinear', align_corners=True) for stage in self.stages] + [feats]\n",
    "        bottle = self.bottleneck(torch.cat(priors, 1))\n",
    "        return bottle\n",
    "\n",
    "class Bottleneck(nn.Module):\n",
    "    expansion = 4\n",
    "    def __init__(self,\n",
    "                 inplanes,\n",
    "                 planes,\n",
    "                 stride=1,\n",
    "                 dilation=1,\n",
    "                 downsample=None,\n",
    "                 fist_dilation=1,\n",
    "                 multi_grid=1):\n",
    "        super(Bottleneck, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n",
    "                               padding=dilation*multi_grid, dilation=dilation*multi_grid, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
    "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
    "        self.relu = nn.ReLU(inplace=False)\n",
    "        self.relu_inplace = nn.ReLU(inplace=True)\n",
    "        self.downsample = downsample\n",
    "        self.dilation = dilation\n",
    "        self.stride = stride\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        out = out + residual\n",
    "        out = self.relu_inplace(out)\n",
    "        return out\n",
    "\n",
    "\n",
    "class ASPPModule(nn.Module):\n",
    "    \"\"\"\n",
    "    Reference: \n",
    "        Chen, Liang-Chieh, et al. *\"Rethinking Atrous Convolution for Semantic Image Segmentation.\"*\n",
    "    \"\"\"\n",
    "    def __init__(self,\n",
    "                 features,\n",
    "                 inner_features=256,\n",
    "                 out_features=512,\n",
    "                 dilations=(12, 24, 36)):\n",
    "        super(ASPPModule, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1,1)),\n",
    "                                   nn.Conv2d(features, inner_features, kernel_size=1, padding=0, dilation=1, bias=False),\n",
    "                                   nn.BatchNorm2d(inner_features))\n",
    "        self.conv2 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=1, padding=0, dilation=1, bias=False),\n",
    "                                   nn.BatchNorm2d(inner_features))\n",
    "        self.conv3 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[0], dilation=dilations[0], bias=False),\n",
    "                                   nn.BatchNorm2d(inner_features))\n",
    "        self.conv4 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[1], dilation=dilations[1], bias=False),\n",
    "                                   nn.BatchNorm2d(inner_features))\n",
    "        self.conv5 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[2], dilation=dilations[2], bias=False),\n",
    "                                   nn.BatchNorm2d(inner_features))\n",
    "\n",
    "        self.bottleneck = nn.Sequential(\n",
    "            nn.Conv2d(inner_features * 5, out_features, kernel_size=1, padding=0, dilation=1, bias=False),\n",
    "            nn.BatchNorm2d(out_features),\n",
    "            nn.Dropout2d(0.1)\n",
    "            )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        _, _, h, w = x.size()\n",
    "        feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)\n",
    "        feat2 = self.conv2(x)\n",
    "        feat3 = self.conv3(x)\n",
    "        feat4 = self.conv4(x)\n",
    "        feat5 = self.conv5(x)\n",
    "        out = torch.cat((feat1, feat2, feat3, feat4, feat5), 1)\n",
    "\n",
    "        bottle = self.bottleneck(out)\n",
    "        return bottle\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "    def __init__(self, block, layers, num_classes):\n",
    "        self.inplanes = 128\n",
    "        super(ResNet, self).__init__()\n",
    "        self.conv1 = conv3x3(3, 64, stride=2)\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.relu1 = nn.ReLU(inplace=False)\n",
    "        self.conv2 = conv3x3(64, 64)\n",
    "        self.bn2 = nn.BatchNorm2d(64)\n",
    "        self.relu2 = nn.ReLU(inplace=False)\n",
    "        self.conv3 = conv3x3(64, 128)\n",
    "        self.bn3 = nn.BatchNorm2d(128)\n",
    "        self.relu3 = nn.ReLU(inplace=False)\n",
    "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "\n",
    "        self.relu = nn.ReLU(inplace=False)\n",
    "\n",
    "        self.layer1 = self._make_layer(block, 64, layers[0])\n",
    "        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n",
    "        self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)\n",
    "        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, multi_grid=(1,2,4))\n",
    "        # self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, multi_grid=(1,1,1))\n",
    "\n",
    "        self.head = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),\n",
    "                                  nn.BatchNorm2d(256),\n",
    "                                  nn.Dropout2d(0.1),\n",
    "                                  nn.Conv2d(256, 1, kernel_size=1, stride=1, padding=0, bias=True),\n",
    "                                  nn.Sigmoid())\n",
    "\n",
    "        self.ppm = PSPModule(2048, 256)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.relu1(self.bn1(self.conv1(x))) # (64, 256, 256)\n",
    "        x = self.relu2(self.bn2(self.conv2(x))) # (64, 256, 256)\n",
    "        x = self.relu3(self.bn3(self.conv3(x))) # (128, 256, 256)\n",
    "        x0 = self.maxpool(x) # (128, 128, 128)\n",
    "\n",
    "        x1 = self.layer1(x0)  # (256, 128, 128)\n",
    "        x2 = self.layer2(x1) # (512, 64, 64)\n",
    "        x3 = self.layer3(x2) # (1024, 64, 64)\n",
    "        x4 = self.layer4(x3) # (2048, 64, 64)\n",
    "\n",
    "        conv_out = [x1, x2, x3, x4]\n",
    "        psp_out = self.ppm(x4)\n",
    "        f = psp_out\n",
    "        x = self.head(f)\n",
    "        return [x]\n",
    "\n",
    "    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(self.inplanes, planes * block.expansion,\n",
    "                          kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(planes * block.expansion,affine = affine_par))\n",
    "\n",
    "        layers = []\n",
    "        generate_multi_grid = lambda index, grids: grids[index%len(grids)] if isinstance(grids, tuple) else 1\n",
    "        layers.append(block(self.inplanes, planes, stride,dilation=dilation, downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid)))\n",
    "        self.inplanes = planes * block.expansion\n",
    "        for i in range(1, blocks):\n",
    "            layers.append(block(self.inplanes, planes, dilation=dilation, multi_grid=generate_multi_grid(i, multi_grid)))\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "\n",
    "# def Res_Deeplab(num_classes=6):\n",
    "#     model = ResNet(Bottleneck,[3, 4, 23, 3], num_classes)\n",
    "#     return model\n",
    "\n",
    "\n",
    "def Res_Deeplab(num_classes=6):\n",
    "    model = ResNet(Bottleneck,[3, 4, 6, 3], num_classes)\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9642fe74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6084e96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import argparse\n",
    "import os.path as osp\n",
    "import torch.optim as optim\n",
    "import torch.backends.cudnn as cudnn\n",
    "\n",
    "from tqdm import tqdm\n",
    "from torch.utils import data\n",
    "from torch.nn import functional as F\n",
    "#C:\\Users\\saada\\Desktop\\Drive\\UBS\\Workshop_Contest\\track2\\HeightEstimation\\utils\n",
    "from utils.eval_utils import *\n",
    "from networks.baseline import Res_Deeplab\n",
    "from dataset.dataset import ISPRSDataSet, ISPRSDataValSet\n",
    "\n",
    "from utils.criterion import CriterionDSN\n",
    "from utils.metrics import AverageMeter, Result\n",
    "from utils.encoding import DataParallelModel, DataParallelCriterion\n",
    "\n",
    "torch_ver = torch.__version__[:3]\n",
    "\n",
    "if torch_ver == '0.3':\n",
    "    from torch.autograd import Variable\n",
    "\n",
    "IGNORE_LABEL = 255\n",
    "IMG_MEAN = np.array((120.47595769, 81.79931481, 81.19268267), dtype=np.float32)\n",
    "\n",
    "\n",
    "DATA_DIRECTORY = 'workspace/dataset/data'\n",
    "DATA_LIST_PATH = 'workspace/dataset/train.txt'\n",
    "TEST_DATA_LIST_PATH = 'workspace/dataset/train.txt'\n",
    "'''\n",
    "DATA_DIRECTORY = '/workspace/dataset/data'\n",
    "DATA_LIST_PATH = '/workspace/dataset/train.txt'\n",
    "TEST_DATA_LIST_PATH = '/workspace/dataset/test.txt'\n",
    "'''\n",
    "\n",
    "BATCH_SIZE = 6\n",
    "LEARNING_RATE = 1e-2\n",
    "WEIGHT_DECAY = 0.0005\n",
    "\n",
    "GPU_NUM = '0'\n",
    "INPUT_SIZE = '512,512'\n",
    "\n",
    "POWER = 0.9\n",
    "MOMENTUM = 0.9\n",
    "NUM_CLASSES = 1\n",
    "NUM_STEPS = 80000\n",
    "RANDOM_SEED = 111\n",
    "\n",
    "SAVE_NUM_IMAGES = 2\n",
    "SAVE_PRED_EVERY = 5000\n",
    "\n",
    "RESTORE_FROM = '/workspace/pretrain/resnet50-imagenet.pth'\n",
    "SNAPSHOT_DIR = 'snapshots/baseline_results/'\n",
    "\n",
    "def str2bool(v):\n",
    "    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n",
    "        return True\n",
    "    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n",
    "        return False\n",
    "    else:\n",
    "        raise argparse.ArgumentTypeError('Boolean value expected.')\n",
    "\n",
    "\n",
    "def get_arguments():\n",
    "    \"\"\"Parse all the arguments provided from the CLI.\n",
    "\n",
    "    Returns:\n",
    "      A list of parsed arguments.\n",
    "    \"\"\"\n",
    "    parser = argparse.ArgumentParser(description=\"DeepLab-ResNet Network\")\n",
    "\n",
    "    parser.add_argument(\"--data-dir\", type=str, default=DATA_DIRECTORY, help=\"Path to the directory containing the PASCAL VOC dataset.\")\n",
    "    parser.add_argument(\"--data-list\", type=str, default=DATA_LIST_PATH, help=\"Path to the file listing the images in the dataset.\")\n",
    "    parser.add_argument(\"--ignore-label\", type=int, default=IGNORE_LABEL, help=\"The index of the label to ignore during the training.\")\n",
    "    parser.add_argument(\"--input-size\", type=str, default=INPUT_SIZE, help=\"Comma-separated string with height and width of images.\")\n",
    "\n",
    "    parser.add_argument(\"--is-training\", action=\"store_true\", help=\"Whether to updates the running means and variances during the training.\")\n",
    "    parser.add_argument(\"--batch-size\", type=int, default=BATCH_SIZE, help=\"Number of images sent to the network in one step.\")\n",
    "    parser.add_argument(\"--learning-rate\", type=float, default=LEARNING_RATE, help=\"Base learning rate for training with polynomial decay.\")\n",
    "    parser.add_argument(\"--momentum\", type=float, default=MOMENTUM, help=\"Momentum component of the optimiser.\")\n",
    "    parser.add_argument(\"--not-restore-last\", action=\"store_true\", help=\"Whether to not restore last (FC) layers.\")\n",
    "    parser.add_argument(\"--start-iters\", type=int, default=0, help=\"Number of classes to predict (including background).\")\n",
    "    parser.add_argument(\"--num-steps\", type=int, default=NUM_STEPS, help=\"Number of training steps.\")\n",
    "    parser.add_argument(\"--power\", type=float, default=POWER, help=\"Decay parameter to compute the learning rate.\")\n",
    "    parser.add_argument(\"--weight-decay\", type=float, default=WEIGHT_DECAY, help=\"Regularisation parameter for L2-loss.\")\n",
    "    parser.add_argument(\"--gpu\", type=str, default=GPU_NUM, help=\"choose gpu device.\")\n",
    "    parser.add_argument(\"--restore-from\", type=str, default=RESTORE_FROM, help=\"Where restore model parameters from.\")\n",
    "    parser.add_argument(\"--save-num-images\", type=int, default=SAVE_NUM_IMAGES, help=\"How many images to save.\")\n",
    "    parser.add_argument(\"--save-pred-every\", type=int, default=SAVE_PRED_EVERY, help=\"Save summaries and checkpoint every often.\")\n",
    "    parser.add_argument(\"--snapshot-dir\", type=str, default=SNAPSHOT_DIR, help=\"Where to save snapshots of the model.\")\n",
    "\n",
    "    parser.add_argument(\"--num-classes\", type=int, default=NUM_CLASSES, help=\"Number of classes to predict (including background).\")\n",
    "    parser.add_argument(\"--random-mirror\", action=\"store_true\", help=\"Whether to randomly mirror the inputs during the training.\")\n",
    "    parser.add_argument(\"--random-scale\", action=\"store_true\", help=\"Whether to randomly scale the inputs during the training.\")\n",
    "    parser.add_argument(\"--random-seed\", type=int, default=RANDOM_SEED, help=\"Random seed to have reproducible results.\")\n",
    "    parser.add_argument(\"--ft\", type=bool, default=False, help=\"fine-tune the model with large input size.\")\n",
    "    parser.add_argument(\"--ohem\", type=str2bool, default='False', help=\"use hard negative mining\")\n",
    "    parser.add_argument(\"--ohem-thres\", type=float, default=0.6, help=\"choose the samples with correct probability under the threshold.\")\n",
    "    parser.add_argument(\"--ohem-keep\", type=int, default=200000, help=\"choose the samples with correct probability under the threshold.\")\n",
    "    return parser.parse_args()\n",
    "\n",
    "# args = get_arguments()\n",
    "\n",
    "args = {'DATA_DIRECTORY':'/workspace/dataset/data',\n",
    "       \n",
    "        'BATCH_SIZE' : 6,\n",
    "        'LEARNING_RATE' : 1e-2,\n",
    "        'WEIGHT_DECAY' : 0.0005,\n",
    "\n",
    "        'GPU_NUM' : '0',\n",
    "        'INPUT_SIZE' : '512,512',\n",
    "\n",
    "        'POWER' : 0.9,\n",
    "        'MOMENTUM' : 0.9,\n",
    "        'NUM_CLASSES' : 1,\n",
    "        'NUM_STEPS' : 80000,\n",
    "        'RANDOM_SEED' : 111,\n",
    "\n",
    "        'SAVE_NUM_IMAGES' : 2,\n",
    "        'SAVE_PRED_EVERY' : 5000,\n",
    "\n",
    "        'RESTORE_FROM' : '/workspace/pretrain/resnet50-imagenet.pth',\n",
    "        'SNAPSHOT_DIR' : 'snapshots/baseline_results/'\n",
    "       }\n",
    "args = {'data-dir':'workspace/dataset/data',\n",
    "       \n",
    "        'batch-size' : 6,\n",
    "        'learning-rate' : 1e-2,\n",
    "        'weight-decay' : 0.0005,\n",
    "\n",
    "        'gpu' : '0',\n",
    "        'input-size' : '512,512',\n",
    "\n",
    "        'power' : 0.9,\n",
    "        'momentum' : 0.9,\n",
    "        'num-classes' : 1,\n",
    "        'num-steps' : 80000,\n",
    "        'random-seed' : 111,\n",
    "\n",
    "        'save-num-images' : 2,\n",
    "        'save-pred-every' : 5000,\n",
    "\n",
    "        'restore-from' : 'workspace/pretrain/resnet50-imagenet.pth',\n",
    "        'snapshot-dir' : 'snapshots/baseline_results/'\n",
    "       }\n",
    "print('here')\n",
    "\n",
    "def lr_poly(base_lr, iter, max_iter, power):\n",
    "    return base_lr * ((1 - float(iter) / max_iter) ** (power))\n",
    "\n",
    "\n",
    "def adjust_learning_rate(optimizer, i_iter):\n",
    "    \"\"\"Sets the learning rate to the initial LR divided by 5 at 60th, 120th and 160th epochs\"\"\"\n",
    "    lr = lr_poly(args.learning_rate, i_iter, args.num_steps, args.power)\n",
    "    optimizer.param_groups[0]['lr'] = lr\n",
    "    return lr\n",
    "\n",
    "\n",
    "def set_bn_eval(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('BatchNorm') != -1:\n",
    "        m.eval()\n",
    "\n",
    "\n",
    "def set_bn_momentum(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('BatchNorm') != -1 or classname.find('InPlaceABN') != -1:\n",
    "        m.momentum = 0.0003\n",
    "\n",
    "\n",
    "def eval(testloader, model, input_size):\n",
    "    average_meter = AverageMeter()\n",
    "    model.eval()\n",
    "\n",
    "    for index, batch in enumerate(tqdm(testloader)):\n",
    "        image, label, ndsm, size, name = batch\n",
    "\n",
    "        with torch.no_grad():\n",
    "            output = predict_sliding(model, image.numpy(), input_size, 1, True)\n",
    "        output = output * 183.17412\n",
    "        seg_pred = output\n",
    "        seg_pred = np.squeeze(seg_pred)\n",
    "\n",
    "        dsm = np.array(ndsm)\n",
    "        dsm = np.squeeze(dsm)\n",
    "        result = Result()\n",
    "        result.evaluate(seg_pred, dsm)\n",
    "        average_meter.update(result, image.size(0))\n",
    "\n",
    "    avg = average_meter.average()\n",
    "\n",
    "    print('----------------------------------------------')\n",
    "    print('[Eval]: DSM Estimate metrics:')\n",
    "    print('==============================================')\n",
    "    print('MAE={average.mae:.3f},    MSE={average.mse:.3f},    RMSE={average.rmse:.3f}\\n'\n",
    "          'Delta1={average.delta1:.3f}, Delta2={average.delta2:.3f}, Delta3={average.delta3:.3f}'.format(average=avg))\n",
    "    print('==============================================')\n",
    "    print('----------------------------------------------')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5160b44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data-dir': 'workspace/dataset/data',\n",
       " 'batch-size': 6,\n",
       " 'learning-rate': 0.01,\n",
       " 'weight-decay': 0.0005,\n",
       " 'gpu': '0',\n",
       " 'input-size': '512,512',\n",
       " 'power': 0.9,\n",
       " 'momentum': 0.9,\n",
       " 'num-classes': 1,\n",
       " 'num-steps': 80000,\n",
       " 'random-seed': 111,\n",
       " 'save-num-images': 2,\n",
       " 'save-pred-every': 5000,\n",
       " 'restore-from': 'workspace/pretrain/resnet50-imagenet.pth',\n",
       " 'snapshot-dir': 'snapshots/baseline_results/'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "108a7dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C:\\\\Users\\\\saada\\\\anaconda3\\\\envs\\\\pytorch\\\\lib\\\\site-packages\\\\ipykernel_launcher.py', '-f', 'C:\\\\Users\\\\saada\\\\AppData\\\\Roaming\\\\jupyter\\\\runtime\\\\kernel-27c89143-498e-457e-b581-4b7570b609ea.json']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.argv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b53c1305",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def main():\n",
    "    \"\"\"Create the model and start the training.\"\"\"\n",
    "    # writer = SummaryWriter(args.snapshot_dir)\n",
    "\n",
    "    if not args.gpu == 'None':\n",
    "        os.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu\n",
    "    h, w = map(int, args.input_size.split(','))\n",
    "    input_size = (h, w)\n",
    "    cudnn.enabled = True\n",
    "    # -----------------------------\n",
    "    # Create network.\n",
    "    # -----------------------------\n",
    "    net = Res_Deeplab(num_classes=args.num_classes)\n",
    "    saved_state_dict = torch.load(args.restore_from, map_location='cpu')\n",
    "    new_params = net.state_dict().copy()\n",
    "\n",
    "    for i in saved_state_dict:\n",
    "        i_parts = i.split('.')\n",
    "        if not i_parts[0] == 'fc':\n",
    "            new_params['.'.join(i_parts[0:])] = saved_state_dict[i]\n",
    "    net.load_state_dict(new_params)\n",
    "    model = DataParallelModel(net)\n",
    "    model.cuda()\n",
    "\n",
    "    if args.ohem:\n",
    "        criterion = CriterionOhemDSN(thresh=args.ohem_thres, min_kept=args.ohem_keep)\n",
    "    else:\n",
    "        criterion = CriterionDSN()\n",
    "\n",
    "    criterion = DataParallelCriterion(criterion)\n",
    "    cudnn.benchmark = True\n",
    "\n",
    "    if not os.path.exists(args.snapshot_dir):\n",
    "        os.makedirs(args.snapshot_dir)\n",
    "    traindataset = ISPRSDataSet(args.data_dir, args.data_list,\n",
    "                                max_iters=args.num_steps * args.batch_size,\n",
    "                         crop_size=input_size, scale=args.random_scale,\n",
    "                                mirror=args.random_mirror, mean=IMG_MEAN)\n",
    "    trainloader = data.DataLoader(traindataset, batch_size=args.batch_size, shuffle=True, num_workers=0, pin_memory=True)\n",
    "\n",
    "    testdataset = ISPRSDataValSet(args.data_dir, TEST_DATA_LIST_PATH, mean=IMG_MEAN, scale=False, mirror=False)\n",
    "    testloader = data.DataLoader(testdataset,batch_size=1, shuffle=False, pin_memory=True)\n",
    "\n",
    "    optimizer = optim.SGD([{'params': filter(lambda p: p.requires_grad, net.parameters()), 'lr': args.learning_rate}],\n",
    "                          lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay)\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    for i_iter, batch in enumerate(trainloader):\n",
    "        i_iter += args.start_iters\n",
    "\n",
    "        images, dsms, labels, size, _ = batch\n",
    "\n",
    "        images = images.cuda()\n",
    "        labels = labels.long().cuda()\n",
    "        dsms = dsms.cuda()\n",
    "\n",
    "        model.train()\n",
    "        model.float()\n",
    "\n",
    "        if torch_ver == \"0.3\":\n",
    "            images = Variable(images)\n",
    "            labels = Variable(labels)\n",
    "            dsms = Variable(dsms)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        lr = adjust_learning_rate(optimizer, i_iter)\n",
    "\n",
    "        preds = model(images)\n",
    "        pred_up = F.upsample(input=preds[0], size=(labels.size(1), labels.size(2)), mode='bilinear', align_corners=True)\n",
    "        loss = criterion(pred_up, dsms)\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if i_iter % 100 == 0:\n",
    "            print('======[Train]: iter:{}/{} | loss = {} | lr = {}======'.format(i_iter, args.num_steps, loss.data.cpu().numpy(), lr))\n",
    "\n",
    "        if i_iter >= args.num_steps - 1:\n",
    "            print('================Save model====================')\n",
    "            torch.save(net.state_dict(), osp.join(args.snapshot_dir, 'ISPRS_lr1e-2_' + str(args.num_steps) + '.pth'))\n",
    "            break\n",
    "\n",
    "        if i_iter!= 0 and i_iter % args.save_pred_every == 0:\n",
    "            print('----------------------------------------------')\n",
    "            print('===================Eval=======================')\n",
    "            h, w = map(int, args.input_size.split(','))\n",
    "            input_size = (h, w)\n",
    "            eval(testloader, model, input_size)\n",
    "            torch.save(net.state_dict(), osp.join(args.snapshot_dir, 'ISPRS_lr1e-2_' + str(i_iter) + '.pth'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "65ad07e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] [--data-dir DATA_DIR]\n",
      "                             [--data-list DATA_LIST]\n",
      "                             [--ignore-label IGNORE_LABEL]\n",
      "                             [--input-size INPUT_SIZE] [--is-training]\n",
      "                             [--batch-size BATCH_SIZE]\n",
      "                             [--learning-rate LEARNING_RATE]\n",
      "                             [--momentum MOMENTUM] [--not-restore-last]\n",
      "                             [--start-iters START_ITERS]\n",
      "                             [--num-steps NUM_STEPS] [--power POWER]\n",
      "                             [--weight-decay WEIGHT_DECAY] [--gpu GPU]\n",
      "                             [--restore-from RESTORE_FROM]\n",
      "                             [--save-num-images SAVE_NUM_IMAGES]\n",
      "                             [--save-pred-every SAVE_PRED_EVERY]\n",
      "                             [--snapshot-dir SNAPSHOT_DIR]\n",
      "                             [--num-classes NUM_CLASSES] [--random-mirror]\n",
      "                             [--random-scale] [--random-seed RANDOM_SEED]\n",
      "                             [--ft FT] [--ohem OHEM] [--ohem-thres OHEM_THRES]\n",
      "                             [--ohem-keep OHEM_KEEP]\n",
      "ipykernel_launcher.py: error: unrecognized arguments: -f C:\\Users\\saada\\AppData\\Roaming\\jupyter\\runtime\\kernel-27c89143-498e-457e-b581-4b7570b609ea.json\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
     ]
    }
   ],
   "source": [
    "args = get_arguments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ddbef4d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "python: can't open file 'get_arguments()': [Errno 2] No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!python "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63f0277f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1639eefb",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'gpu'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_11992\\2144625693.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'__main__'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_11992\\4151550716.py\u001b[0m in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;31m# writer = SummaryWriter(args.snapshot_dir)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgpu\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'None'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m         \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"CUDA_VISIBLE_DEVICES\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgpu\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_size\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'dict' object has no attribute 'gpu'"
     ]
    }
   ],
   "source": [
    "\n",
    "if __name__ == '__main__':\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a78d7e1c",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'torch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_29640\\2677521861.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'torch' is not defined"
     ]
    }
   ],
   "source": [
    "torch.__version__\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30debf8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saada\\anaconda3\\envs\\pytorch\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.7.15\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "!python --version\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e1294695",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "PackageNotInstalledError: Package is not installed in prefix.\n",
      "  prefix: C:\\Users\\saada\\anaconda3\\envs\\pytorch\n",
      "  package name: open-cv\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda upgrade -c conda-forge open-cv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e2d13fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.6.0\n"
     ]
    }
   ],
   "source": [
    "import cv2 \n",
    "print(cv2.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5cf7cfcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvcc: NVIDIA (R) Cuda compiler driver\n",
      "Copyright (c) 2005-2022 NVIDIA Corporation\n",
      "Built on Wed_Jun__8_16:59:34_Pacific_Daylight_Time_2022\n",
      "Cuda compilation tools, release 11.7, V11.7.99\n",
      "Build cuda_11.7.r11.7/compiler.31442593_0\n"
     ]
    }
   ],
   "source": [
    "!nvcc --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6869fe99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89226e3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "SV_Sydney_-33.8677_151.1908\n",
      "SV_Sydney_-33.8677_151.1925\n",
      "SV_Sydney_-33.8677_151.1941\n",
      "SV_Sydney_-33.8677_151.1954\n",
      "SV_Sydney_-33.8687_151.1874\n",
      "SV_Sydney_-33.8687_151.1891\n",
      "SV_Sydney_-33.8687_151.1908\n",
      "SV_Sydney_-33.8688_151.1924\n",
      "SV_Sydney_-33.8688_151.1941\n",
      "SV_Sydney_-33.8688_151.1954\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# Opening JSON file\n",
    "f = open(r'C:\\Users\\saada\\Desktop\\Drive\\UBS\\Workshop_Contest\\data\\track2\\buildings_only_train.json',)\n",
    "\n",
    "# returns JSON object as\n",
    "# a dictionary\n",
    "data = json.load(f)\n",
    "\n",
    "# Iterating through the json\n",
    "# list\n",
    "with open('train_imgs.txt', 'w') as f:\n",
    "    for i in data['images']:\n",
    "        print(i['file_name'].split('.ti')[0])\n",
    "        f.write(\"%s\\n\" % i['file_name'].split('.ti')[0])\n",
    "        \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f8e3f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SV_Sydney_-33.8688_151.1954'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1a9cb0be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data 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_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{'segmentation': [[171, 0, 172, 1, 173, 1, 174, 0, 179, 0, 179, 1, 180, 2, 180, 5, 181, 6, 181, 9, 182, 10, 182, 13, 183, 14, 183, 17, 184, 18, 184, 24, 182, 24, 181, 25, 178, 25, 177, 26, 174, 26, 173, 27, 170, 27, 169, 28, 165, 28, 164, 29, 161, 29, 160, 30, 157, 30, 156, 31, 153, 31, 152, 32, 146, 32, 146, 31, 145, 30, 145, 27, 144, 26, 144, 23, 142, 21, 141, 22, 138, 22, 137, 23, 133, 23, 132, 24, 129, 24, 128, 25, 125, 25, 124, 26, 120, 26, 119, 27, 116, 27, 115, 28, 111, 28, 110, 29, 107, 29, 106, 30, 103, 30, 102, 31, 100, 31, 100, 30, 99, 29, 99, 28, 98, 27, 98, 26, 97, 25, 97, 24, 96, 23, 96, 22, 95, 21, 95, 20, 94, 19, 94, 18, 93, 17, 93, 16, 92, 15, 92, 14, 91, 13, 91, 12, 90, 11, 90, 10, 89, 9, 89, 8, 88, 7, 88, 6, 87, 5, 87, 4, 86, 3, 86, 2, 85, 1, 85, 0]], 'iscrowd': 0, 'image_id': 1758, 'category_id': 1, 'bbox': [85, 0, 99, 32], 'area': 2586.0, 'id': 124582}\n",
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      "{'segmentation': {'size': [512, 512], 'counts': 'iXR52m?3N2M3M3N2M3N2M3M3N2EVO[Am0b>VO[Am0b>VO\\\\Al0b>;M3M3M3M3N2M3M3M3M3N2M3M3M3M3N2M3M3M2NN3K4M3L4L4L5K4M3L4LmBYNV<c1iCcNW<Y1gCmNX<o0gCWOX<h0dC^O[<c0_C@c<b0VC_Oo<a0kBAX=_14L4M3M4M2N2O1N2N3M2N2O1N2N2N3M2O1N2N2N3N1O1N2O1N3N1O1001O100O1O1O1O010O1O1O1O1_CD^9<\\\\F2[9O`F:[9FbFa0Z9@cFf0Y9ZOdFm0Y9SOdFR1Y9nNeFX1W9iNfF]1V9cNhFb1U9_NhFf1V9ZNgFk1V9UNhFP2U9QNiFS2T9mMjFX2S9iMjF\\\\2T9cMjFb2S9_MjFe2T9\\\\MiFi2T9WMjFn2S9SMiFR3V9mLgFY3V9hLhF\\\\3U9dLlF]3R9dLoF\\\\3n8eLSG]3j8dLVG]3h8WLlFXO=a4e8WLPGVO<e4a8TLVGVO9g4^8TL[GSO9i4Z8TL^GSO8j4X8SLbGQO7m4T8RLhGPO5n4P8SLmGmN4Q5l7SLRHkN2S5i7RLmHn3P7SLQIn3l6SLUIn3h6SLZIm3c6SL_In3]6TLeIl3W6VLjIk3S6VLnIk3n5VLUJj3g5XLZJj3b5XL^Ji3]5[LdJe3=fJ`2k1SM`38kJ]2l1ZM[34nJ[2l1bMW3MSKY2l1jMR3IWKU2j1UNQ3@[KV2c1_NS3VO_KY2Y1eN[3lNbKf1QOfMn1j1d3dNbKe1VOiM^1`0SNb0l5ZObKh1Y1WNcMd0h5mNjKV2h0SNPNh0f7T1RJiM`M6R1j0_7V1oIlMlNk0W7X1oIkMkNj0Y7Z1^KcNe4\\\\1\\\\KbNf4]1[K`Nh4`1XK^Nj4a1XK[Nk4d1VKZNl4f1TKWNo4h1RKUNQ5k1oJSNS5m1mJQNU5o1lJoMU5Q2]40000O1000000000000001O00001O001O1O001O001O1O1O1O1O'}, 'iscrowd': 0, 'image_id': 1758, 'category_id': 1, 'bbox': [324, 65, 187, 446], 'area': 33926.0, 'id': 124585}\n",
      "{'segmentation': [[490, 493, 491, 494, 492, 494, 493, 495, 494, 495, 496, 497, 497, 497, 498, 498, 499, 498, 501, 500, 502, 500, 504, 502, 505, 502, 506, 503, 507, 503, 509, 505, 510, 505, 511, 506, 511, 511, 443, 511, 443, 510, 444, 509, 444, 506, 446, 504, 447, 504, 452, 499, 453, 499, 456, 496, 461, 496, 462, 495, 473, 495, 474, 494, 484, 494, 485, 493]], 'iscrowd': 0, 'image_id': 1759, 'category_id': 1, 'bbox': [443, 493, 68, 18], 'area': 998.0, 'id': 124586}\n",
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      "{'segmentation': [[250, 493, 251, 494, 252, 494, 253, 495, 254, 495, 256, 497, 257, 497, 258, 498, 259, 498, 261, 500, 262, 500, 264, 502, 265, 502, 266, 503, 267, 503, 269, 505, 270, 505, 271, 506, 272, 506, 273, 507, 274, 507, 275, 508, 276, 508, 277, 509, 278, 509, 279, 510, 280, 510, 281, 511, 282, 511, 203, 511, 203, 510, 204, 509, 204, 506, 206, 504, 207, 504, 212, 499, 213, 499, 216, 496, 221, 496, 222, 495, 233, 495, 234, 494, 244, 494, 245, 493]], 'iscrowd': 0, 'image_id': 1760, 'category_id': 1, 'bbox': [203, 493, 79, 18], 'area': 1034.0, 'id': 124596}\n",
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      "{'segmentation': [[327, 406, 362, 401, 361, 427, 389, 440, 386, 449, 398, 474, 394, 479, 327, 449, 323, 444, 320, 434, 327, 406]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [320, 401, 78, 78], 'area': 3230.0, 'id': 124632}\n",
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      "{'segmentation': [[200, 352, 293, 398, 280, 424, 167, 369, 177, 348, 193, 356, 200, 352]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [167, 348, 126, 76], 'area': 3714.0, 'id': 124636}\n",
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      "{'segmentation': [[362, 401, 327, 406, 315, 318, 349, 313, 362, 401]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [315, 313, 47, 93], 'area': 3223.0, 'id': 124638}\n",
      "{'segmentation': [[349, 313, 315, 318, 311, 294, 346, 289, 349, 313]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [311, 289, 38, 29], 'area': 906.0, 'id': 124639}\n",
      "{'segmentation': [[235, 303, 228, 312, 214, 344, 200, 352, 193, 356, 177, 348, 180, 342, 192, 317, 195, 306, 195, 294, 199, 287, 209, 280, 212, 282, 235, 303]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [177, 280, 58, 76], 'area': 2320.0, 'id': 124640}\n",
      "{'segmentation': [[511, 281, 511, 283, 510, 284, 510, 286, 509, 287, 509, 289, 508, 290, 508, 291, 507, 292, 507, 294, 506, 295, 506, 297, 505, 298, 505, 300, 504, 301, 504, 303, 503, 304, 503, 306, 502, 307, 502, 309, 501, 310, 501, 311, 502, 311, 503, 312, 505, 312, 506, 313, 507, 313, 508, 314, 510, 314, 511, 315]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [501, 281, 10, 34], 'area': 203.0, 'id': 124641}\n",
      "{'segmentation': [[111, 336, 110, 331, 101, 322, 91, 301, 110, 295, 113, 285, 127, 282, 129, 276, 149, 270, 151, 265, 180, 276, 151, 352, 111, 336]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [91, 265, 89, 87], 'area': 4481.0, 'id': 124642}\n",
      "{'segmentation': [[235, 303, 212, 282, 209, 280, 227, 269, 242, 263, 255, 260, 272, 259, 277, 286, 248, 295, 241, 297, 235, 303]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [209, 259, 68, 44], 'area': 1842.0, 'id': 124643}\n",
      "{'segmentation': [[346, 289, 311, 294, 306, 255, 340, 250, 346, 289]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [306, 250, 40, 44], 'area': 1447.0, 'id': 124644}\n",
      "{'segmentation': [[493, 254, 473, 293, 446, 240, 493, 254]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [446, 240, 47, 53], 'area': 1127.0, 'id': 124645}\n",
      "{'segmentation': [[492, 169, 493, 170, 495, 170, 496, 171, 498, 171, 499, 172, 500, 172, 502, 174, 502, 175, 505, 178, 505, 179, 507, 181, 508, 180, 509, 180, 510, 179, 510, 180, 511, 181, 511, 262, 510, 263, 509, 263, 508, 264, 507, 264, 506, 265, 505, 265, 504, 266, 503, 266, 502, 267, 500, 267, 499, 266, 499, 265, 498, 264, 498, 263, 497, 262, 497, 261, 496, 260, 496, 259, 495, 258, 495, 257, 494, 256, 494, 255, 492, 253, 492, 252, 491, 251, 491, 250, 490, 249, 490, 248, 489, 247, 489, 246, 488, 245, 488, 244, 487, 243, 487, 242, 486, 241, 486, 240, 485, 239, 485, 238, 484, 237, 484, 236, 483, 235, 483, 234, 482, 233, 482, 232, 481, 231, 481, 230, 480, 229, 480, 228, 479, 227, 479, 226, 478, 225, 478, 224, 477, 223, 477, 222, 476, 221, 476, 220, 475, 219, 475, 218, 474, 217, 474, 216, 473, 215, 473, 214, 472, 213, 472, 212, 471, 211, 471, 210, 470, 209, 470, 208, 471, 207, 471, 205, 472, 204, 472, 202, 473, 201, 473, 199, 474, 198, 474, 196, 475, 195, 475, 192, 476, 191, 476, 189, 477, 188, 477, 186, 478, 185, 478, 183, 479, 182, 479, 180, 480, 179, 480, 178, 482, 176, 482, 175, 486, 171, 486, 170, 487, 169]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [470, 169, 41, 98], 'area': 2855.0, 'id': 124646}\n",
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      "{'segmentation': [[394, 167, 436, 146, 439, 144, 450, 166, 441, 171, 444, 178, 437, 182, 429, 206, 425, 207, 408, 174, 405, 169, 397, 173, 394, 167]], 'iscrowd': 0, 'image_id': 1762, 'category_id': 1, 'bbox': [394, 144, 56, 63], 'area': 1623.0, 'id': 124648}\n",
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      "{'segmentation': [[501, 330, 481, 340, 479, 341, 472, 327, 479, 323, 482, 331, 491, 327, 488, 320, 495, 317, 501, 330]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [472, 317, 29, 24], 'area': 355.0, 'id': 124676}\n",
      "{'segmentation': [[50, 401, 15, 406, 3, 318, 37, 313, 50, 401]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [3, 313, 47, 93], 'area': 3223.0, 'id': 124677}\n",
      "{'segmentation': [[275, 329, 259, 337, 230, 326, 240, 300, 264, 309, 275, 329]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [230, 300, 45, 37], 'area': 1058.0, 'id': 124678}\n",
      "{'segmentation': [[34, 289, 34, 293, 35, 294, 35, 301, 36, 302, 36, 313, 33, 313, 32, 314, 26, 314, 25, 315, 20, 315, 19, 316, 13, 316, 12, 317, 3, 317, 3, 315, 2, 314, 2, 309, 1, 308, 1, 303, 0, 302, 0, 294, 2, 294, 3, 293, 9, 293, 10, 292, 16, 292, 17, 291, 23, 291, 24, 290, 30, 290, 31, 289]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [0, 289, 36, 28], 'area': 887.0, 'id': 124679}\n",
      "{'segmentation': [[230, 283, 216, 321, 189, 311, 202, 273, 230, 283]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [189, 273, 41, 48], 'area': 1248.0, 'id': 124680}\n",
      "{'segmentation': [[305, 272, 329, 259, 341, 282, 317, 295, 305, 272]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [305, 259, 36, 36], 'area': 756.0, 'id': 124681}\n",
      "{'segmentation': [[28, 250, 28, 253, 29, 254, 29, 259, 30, 260, 30, 266, 31, 267, 31, 273, 32, 274, 32, 279, 33, 280, 33, 289, 30, 289, 29, 290, 23, 290, 22, 291, 17, 291, 16, 292, 10, 292, 9, 293, 0, 293, 0, 254, 4, 254, 5, 253, 11, 253, 12, 252, 17, 252, 18, 251, 24, 251, 25, 250]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [0, 250, 33, 43], 'area': 1295.0, 'id': 124682}\n",
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      "{'segmentation': [[329, 259, 305, 272, 292, 248, 295, 247, 316, 235, 329, 259]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [292, 235, 37, 37], 'area': 787.0, 'id': 124685}\n",
      "{'segmentation': [[329, 213, 334, 210, 351, 202, 367, 234, 376, 229, 387, 250, 394, 246, 405, 268, 410, 265, 422, 288, 424, 287, 434, 307, 413, 317, 403, 297, 400, 299, 389, 277, 384, 280, 374, 259, 367, 263, 356, 241, 346, 246, 329, 213]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [329, 202, 105, 115], 'area': 3347.0, 'id': 124686}\n",
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      "{'segmentation': [[82, 167, 124, 146, 127, 144, 138, 166, 129, 171, 132, 178, 125, 182, 117, 206, 113, 207, 96, 174, 93, 169, 85, 173, 82, 167]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [82, 144, 56, 63], 'area': 1623.0, 'id': 124693}\n",
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      "{'segmentation': [[388, 129, 410, 118, 440, 179, 418, 190, 388, 129]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [388, 118, 52, 72], 'area': 1756.0, 'id': 124696}\n",
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      "{'segmentation': [[511, 99, 509, 101, 508, 101, 507, 102, 506, 102, 505, 103, 504, 103, 503, 104, 502, 104, 501, 105, 500, 105, 499, 106, 498, 106, 497, 107, 496, 107, 495, 108, 494, 108, 493, 109, 492, 109, 491, 110, 490, 110, 489, 111, 488, 111, 487, 112, 486, 112, 485, 113, 484, 113, 483, 114, 482, 114, 481, 115, 480, 115, 479, 116, 478, 116, 477, 117, 476, 117, 475, 118, 474, 118, 473, 119, 472, 119, 471, 120, 470, 120, 469, 121, 468, 121, 467, 122, 466, 122, 465, 123, 466, 124, 466, 125, 467, 126, 467, 127, 468, 128, 468, 129, 469, 130, 469, 131, 470, 132, 470, 133, 471, 134, 471, 135, 472, 136, 472, 137, 473, 138, 473, 139, 474, 140, 474, 141, 475, 142, 475, 143, 476, 144, 476, 145, 477, 146, 477, 147, 478, 148, 478, 149, 479, 150, 479, 151, 480, 152, 480, 153, 481, 154, 481, 155, 482, 156, 482, 157, 484, 159, 484, 160, 485, 161, 485, 162, 486, 163, 486, 164, 487, 165, 487, 166, 488, 167, 488, 168, 489, 169, 489, 170, 490, 171, 490, 172, 491, 173, 491, 174, 492, 175, 492, 176, 493, 177, 493, 178, 494, 179, 494, 180, 495, 181, 495, 182, 496, 183, 496, 184, 497, 185, 497, 186, 498, 187, 498, 188, 499, 189, 499, 190, 500, 191, 500, 192, 502, 194, 502, 195, 503, 196, 503, 197, 504, 198, 504, 199, 505, 200, 505, 201, 506, 202, 506, 203, 507, 204, 507, 205, 508, 206, 508, 207, 509, 208, 509, 209, 510, 210, 510, 211, 511, 212, 511, 213]], 'iscrowd': 0, 'image_id': 1763, 'category_id': 1, 'bbox': [465, 99, 46, 114], 'area': 2722.0, 'id': 124703}\n",
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      "{'segmentation': [[263, 495, 263, 496, 264, 497, 264, 498, 265, 499, 265, 500, 266, 501, 266, 502, 267, 503, 267, 504, 268, 505, 268, 506, 269, 507, 269, 508, 270, 509, 270, 510, 271, 511, 237, 511, 237, 510, 236, 509, 236, 508, 237, 508, 238, 507, 239, 507, 240, 506, 241, 506, 242, 505, 243, 505, 244, 504, 245, 504, 246, 503, 247, 503, 248, 502, 249, 502, 250, 501, 251, 501, 252, 500, 253, 500, 254, 499, 255, 499, 256, 498, 257, 498, 258, 497, 259, 497, 260, 496, 261, 496, 262, 495]], 'iscrowd': 0, 'image_id': 1764, 'category_id': 1, 'bbox': [236, 495, 35, 16], 'area': 356.0, 'id': 124723}\n",
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      "{'segmentation': [[400, 304, 355, 328, 347, 312, 392, 289, 400, 304]], 'iscrowd': 0, 'image_id': 1764, 'category_id': 1, 'bbox': [347, 289, 53, 39], 'area': 951.0, 'id': 124781}\n",
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      "{'segmentation': [[88, 304, 43, 328, 35, 312, 80, 289, 88, 304]], 'iscrowd': 0, 'image_id': 1765, 'category_id': 1, 'bbox': [35, 289, 53, 39], 'area': 951.0, 'id': 124816}\n",
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      "{'segmentation': [[441, 0, 442, 1, 442, 2, 443, 3, 443, 4, 444, 5, 444, 6, 445, 7, 445, 8, 446, 9, 446, 10, 447, 11, 447, 12, 448, 13, 448, 14, 449, 15, 449, 17, 448, 18, 447, 18, 446, 19, 445, 19, 444, 20, 443, 20, 442, 21, 441, 21, 440, 22, 439, 22, 438, 21, 438, 20, 437, 19, 437, 18, 436, 17, 436, 16, 434, 14, 434, 13, 433, 12, 433, 11, 432, 10, 432, 9, 431, 8, 431, 7, 430, 6, 431, 5, 432, 5, 434, 3, 435, 3, 436, 2, 437, 2, 439, 0]], 'iscrowd': 0, 'image_id': 1769, 'category_id': 1, 'bbox': [430, 0, 19, 22], 'area': 264.0, 'id': 124925}\n",
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      "{'segmentation': [[326, 498, 327, 499, 327, 500, 328, 501, 328, 502, 329, 503, 329, 504, 330, 505, 330, 506, 331, 507, 331, 508, 332, 509, 332, 510, 333, 511, 298, 511, 299, 511, 300, 510, 301, 510, 302, 509, 303, 509, 304, 508, 305, 508, 306, 507, 307, 507, 308, 506, 309, 506, 310, 505, 311, 505, 312, 504, 313, 504, 314, 503, 315, 503, 316, 502, 317, 502, 318, 501, 319, 501, 320, 500, 321, 500, 322, 499, 323, 499, 324, 498, 325, 498, 326, 497]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [298, 497, 35, 14], 'area': 274.0, 'id': 124931}\n",
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      "{'segmentation': [[401, 490, 401, 491, 402, 492, 402, 493, 403, 494, 403, 495, 404, 496, 404, 497, 405, 498, 405, 499, 406, 500, 406, 501, 407, 502, 407, 503, 408, 504, 408, 505, 409, 506, 409, 507, 410, 508, 410, 509, 411, 510, 411, 511, 380, 511, 380, 510, 379, 509, 379, 508, 378, 507, 378, 506, 377, 505, 377, 504, 376, 503, 376, 502, 377, 502, 378, 501, 379, 501, 380, 500, 381, 500, 382, 499, 383, 499, 384, 498, 385, 498, 386, 497, 387, 497, 388, 496, 389, 496, 390, 495, 391, 495, 392, 494, 393, 494, 394, 493, 395, 493, 396, 492, 397, 492, 398, 491, 399, 491, 400, 490]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [376, 490, 35, 21], 'area': 506.0, 'id': 124933}\n",
      "{'segmentation': [[320, 487, 321, 488, 321, 489, 322, 490, 322, 491, 323, 492, 323, 493, 324, 494, 324, 495, 325, 496, 325, 497, 324, 498, 323, 498, 322, 499, 321, 499, 320, 500, 319, 500, 318, 501, 317, 501, 316, 502, 315, 502, 314, 503, 313, 503, 312, 504, 311, 504, 310, 505, 309, 505, 308, 506, 307, 506, 306, 507, 305, 507, 304, 508, 303, 508, 302, 509, 301, 509, 300, 510, 299, 510, 298, 511, 296, 511, 295, 510, 295, 509, 294, 508, 294, 507, 293, 506, 293, 505, 292, 504, 292, 503, 291, 502, 291, 501, 292, 500, 293, 500, 294, 499, 295, 499, 296, 498, 297, 498, 298, 497, 299, 497, 300, 496, 301, 496, 302, 495, 303, 495, 304, 494, 305, 494, 306, 493, 307, 493, 308, 492, 309, 492, 310, 491, 311, 491, 312, 490, 313, 490, 314, 489, 315, 489, 316, 488, 317, 488, 318, 487, 319, 487, 320, 486]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [291, 486, 34, 25], 'area': 434.0, 'id': 124934}\n",
      "{'segmentation': [[487, 481, 486, 482, 485, 482, 484, 483, 483, 483, 482, 484, 481, 484, 480, 485, 479, 485, 478, 486, 477, 486, 476, 487, 475, 487, 474, 488, 473, 488, 472, 489, 471, 489, 470, 490, 469, 490, 468, 491, 467, 491, 466, 492, 465, 492, 464, 493, 463, 493, 462, 494, 461, 494, 460, 495, 459, 495, 458, 496, 457, 496, 458, 497, 458, 498, 459, 499, 459, 500, 460, 501, 460, 502, 461, 503, 461, 504, 462, 505, 462, 506, 463, 507, 463, 508, 464, 509, 464, 510, 465, 511, 486, 511, 487, 510, 487, 509, 488, 508, 488, 507, 489, 506, 489, 504, 490, 503, 490, 502, 491, 501, 491, 499, 492, 498, 492, 497, 493, 496, 493, 495, 494, 494, 494, 487, 491, 484, 490, 484]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [457, 481, 37, 30], 'area': 778.0, 'id': 124935}\n",
      "{'segmentation': [[257, 494, 233, 506, 226, 493, 251, 481, 257, 494]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [226, 481, 31, 25], 'area': 441.0, 'id': 124936}\n",
      "{'segmentation': [[320, 486, 291, 501, 281, 482, 311, 467, 320, 486]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [281, 467, 39, 34], 'area': 745.0, 'id': 124937}\n",
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      "{'segmentation': [[487, 481, 452, 498, 440, 475, 495, 448, 507, 471, 487, 481]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [440, 448, 67, 50], 'area': 1678.0, 'id': 124939}\n",
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      "{'segmentation': [[511, 413, 511, 423, 511, 422, 510, 421, 510, 420, 509, 419, 509, 418, 508, 417, 508, 416, 507, 415, 508, 414, 509, 414, 510, 413]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [507, 413, 4, 10], 'area': 31.0, 'id': 124949}\n",
      "{'segmentation': [[280, 405, 251, 420, 237, 392, 266, 377, 280, 405]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [237, 377, 43, 43], 'area': 1080.0, 'id': 124950}\n",
      "{'segmentation': [[359, 402, 310, 425, 296, 395, 344, 372, 359, 402]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [296, 372, 63, 53], 'area': 1860.0, 'id': 124951}\n",
      "{'segmentation': [[466, 389, 411, 416, 400, 394, 454, 367, 455, 366, 466, 389]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [400, 366, 66, 50], 'area': 1597.0, 'id': 124952}\n",
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      "{'segmentation': [[491, 371, 484, 356, 499, 349, 506, 364, 491, 371]], 'iscrowd': 0, 'image_id': 1770, 'category_id': 1, 'bbox': [484, 349, 22, 22], 'area': 305.0, 'id': 124957}\n",
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      "{'segmentation': [[107, 294, 105, 322, 57, 317, 60, 289, 107, 294]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [57, 289, 50, 33], 'area': 1418.0, 'id': 125130}\n",
      "{'segmentation': [[60, 289, 57, 317, 57, 325, 37, 334, 10, 283, 60, 289]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [10, 283, 50, 51], 'area': 1608.0, 'id': 125131}\n",
      "{'segmentation': [[208, 305, 146, 344, 151, 282, 220, 290, 221, 291, 222, 293, 207, 302, 208, 305]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [146, 282, 76, 62], 'area': 2505.0, 'id': 125132}\n",
      "{'segmentation': [[402, 300, 395, 334, 375, 332, 330, 342, 328, 342, 326, 339, 332, 303, 336, 298, 343, 295, 351, 289, 359, 285, 368, 281, 377, 279, 386, 277, 396, 276, 407, 275, 402, 300]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [326, 275, 81, 67], 'area': 3766.0, 'id': 125133}\n",
      "{'segmentation': [[449, 308, 402, 300, 407, 275, 421, 277, 450, 282, 453, 286, 449, 308]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [402, 275, 51, 33], 'area': 1288.0, 'id': 125134}\n",
      "{'segmentation': [[108, 278, 107, 294, 60, 289, 62, 272, 108, 278]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [60, 272, 48, 22], 'area': 839.0, 'id': 125135}\n",
      "{'segmentation': [[62, 272, 60, 289, 10, 283, 1, 264, 62, 272]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [1, 264, 61, 25], 'area': 1049.0, 'id': 125136}\n",
      "{'segmentation': [[1, 246, 2, 247, 9, 247, 10, 248, 16, 248, 17, 249, 23, 249, 24, 250, 30, 250, 31, 251, 37, 251, 38, 252, 45, 252, 46, 253, 52, 253, 53, 254, 59, 254, 60, 255, 63, 255, 63, 266, 62, 267, 62, 271, 52, 271, 51, 270, 44, 270, 43, 269, 36, 269, 35, 268, 29, 268, 28, 267, 21, 267, 20, 266, 13, 266, 12, 265, 5, 265, 4, 264, 1, 264, 0, 263, 0, 246]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [0, 246, 63, 25], 'area': 1163.0, 'id': 125137}\n",
      "{'segmentation': [[294, 260, 241, 289, 233, 288, 236, 266, 216, 264, 215, 274, 152, 266, 156, 235, 295, 253, 294, 260]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [152, 235, 143, 54], 'area': 4119.0, 'id': 125138}\n",
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      "{'segmentation': [[12, 211, 13, 212, 24, 212, 25, 213, 37, 213, 38, 214, 49, 214, 50, 215, 61, 215, 62, 216, 68, 216, 68, 226, 67, 227, 67, 234, 66, 235, 66, 242, 65, 243, 65, 250, 64, 251, 64, 254, 54, 254, 53, 253, 47, 253, 46, 252, 39, 252, 38, 251, 32, 251, 31, 250, 25, 250, 24, 249, 17, 249, 16, 248, 10, 248, 9, 247, 3, 247, 2, 246, 0, 246, 0, 211]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [0, 211, 68, 43], 'area': 2588.0, 'id': 125140}\n",
      "{'segmentation': [[417, 224, 423, 182, 441, 185, 434, 227, 417, 224]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [417, 182, 24, 45], 'area': 813.0, 'id': 125141}\n",
      "{'segmentation': [[423, 182, 417, 224, 393, 220, 399, 179, 423, 182]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [393, 179, 30, 45], 'area': 1083.0, 'id': 125142}\n",
      "{'segmentation': [[399, 179, 393, 220, 333, 212, 339, 170, 399, 179]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [333, 170, 66, 50], 'area': 2645.0, 'id': 125143}\n",
      "{'segmentation': [[178, 170, 175, 191, 153, 188, 156, 167, 178, 170]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [153, 167, 25, 24], 'area': 515.0, 'id': 125144}\n",
      "{'segmentation': [[250, 173, 270, 176, 315, 183, 310, 211, 185, 193, 189, 164, 250, 173]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [185, 164, 130, 47], 'area': 3830.0, 'id': 125145}\n",
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      "{'segmentation': [[347, 114, 341, 152, 340, 164, 332, 163, 273, 154, 280, 104, 347, 114]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [273, 104, 74, 60], 'area': 3527.0, 'id': 125147}\n",
      "{'segmentation': [[280, 104, 273, 154, 272, 164, 270, 176, 250, 173, 252, 163, 261, 101, 280, 104]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [250, 101, 30, 75], 'area': 1511.0, 'id': 125148}\n",
      "{'segmentation': [[238, 68, 265, 72, 261, 101, 252, 163, 192, 154, 206, 63, 238, 68]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [192, 63, 73, 100], 'area': 5689.0, 'id': 125149}\n",
      "{'segmentation': [[450, 0, 452, 2, 452, 3, 454, 5, 454, 42, 455, 43, 455, 72, 456, 73, 459, 73, 459, 137, 456, 140, 456, 141, 451, 146, 451, 147, 447, 151, 447, 152, 442, 157, 442, 158, 438, 162, 438, 163, 434, 167, 432, 167, 431, 166, 430, 166, 429, 165, 428, 165, 427, 164, 423, 164, 422, 163, 416, 163, 415, 162, 409, 162, 408, 161, 402, 161, 401, 160, 395, 160, 394, 159, 388, 159, 387, 158, 380, 158, 379, 157, 373, 157, 372, 156, 366, 156, 365, 155, 359, 155, 358, 154, 352, 154, 351, 153, 345, 153, 344, 152, 341, 152, 341, 149, 342, 148, 342, 143, 343, 142, 343, 137, 344, 136, 344, 130, 345, 129, 345, 124, 346, 123, 346, 118, 347, 117, 347, 114, 350, 114, 351, 115, 357, 115, 358, 116, 363, 116, 364, 117, 370, 117, 371, 118, 377, 118, 378, 119, 383, 119, 384, 120, 390, 120, 391, 121, 397, 121, 398, 122, 403, 122, 404, 123, 410, 123, 411, 124, 413, 124, 415, 122, 415, 121, 416, 120, 416, 118, 417, 117, 417, 116, 418, 115, 418, 108, 419, 107, 419, 94, 420, 93, 420, 87, 425, 87, 426, 86, 426, 68, 425, 67, 418, 67, 418, 48, 422, 48, 423, 47, 423, 37, 422, 36, 418, 36, 417, 35, 407, 35, 406, 34, 395, 34, 394, 33, 384, 33, 383, 32, 378, 32, 378, 25, 377, 24, 372, 24, 371, 23, 364, 23, 364, 18, 365, 17, 365, 6, 366, 5, 366, 0]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [341, 0, 118, 167], 'area': 10700.0, 'id': 125150}\n",
      "{'segmentation': [[343, 0, 343, 5, 342, 6, 342, 12, 341, 13, 341, 19, 340, 20, 340, 25, 339, 26, 339, 32, 338, 33, 338, 39, 337, 40, 337, 46, 336, 47, 336, 52, 335, 53, 335, 59, 334, 60, 334, 66, 333, 67, 333, 72, 332, 73, 332, 79, 331, 80, 331, 83, 322, 83, 321, 82, 315, 82, 314, 81, 309, 81, 308, 80, 302, 80, 301, 79, 295, 79, 294, 78, 289, 78, 288, 77, 282, 77, 281, 76, 278, 76, 278, 73, 279, 72, 279, 66, 280, 65, 280, 60, 281, 59, 281, 57, 280, 56, 278, 56, 277, 55, 272, 55, 271, 54, 265, 54, 264, 53, 258, 53, 257, 52, 252, 52, 251, 51, 245, 51, 244, 50, 242, 50, 241, 51, 241, 52, 240, 53, 240, 58, 239, 59, 239, 64, 238, 65, 238, 68, 235, 68, 234, 67, 229, 67, 228, 66, 223, 66, 222, 65, 216, 65, 215, 64, 210, 64, 209, 63, 202, 63, 201, 62, 193, 62, 192, 61, 188, 61, 188, 58, 189, 57, 189, 51, 190, 50, 190, 45, 191, 44, 191, 38, 192, 37, 192, 31, 193, 30, 193, 25, 194, 24, 194, 18, 195, 17, 195, 11, 196, 10, 196, 5, 197, 4, 197, 0]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [188, 0, 155, 83], 'area': 10014.0, 'id': 125151}\n",
      "{'segmentation': [[160, 0, 161, 1, 161, 2, 162, 3, 162, 4, 163, 5, 163, 6, 164, 7, 164, 8, 165, 9, 164, 9, 163, 10, 162, 10, 161, 11, 160, 11, 159, 12, 158, 12, 157, 13, 156, 13, 155, 14, 154, 14, 153, 15, 152, 15, 151, 16, 150, 16, 149, 17, 148, 17, 147, 18, 147, 19, 146, 20, 146, 21, 145, 22, 145, 23, 144, 24, 144, 25, 142, 27, 142, 28, 141, 29, 141, 30, 140, 31, 140, 32, 139, 33, 139, 34, 138, 35, 138, 36, 139, 37, 139, 39, 140, 40, 140, 43, 139, 44, 139, 47, 138, 48, 138, 51, 137, 52, 137, 55, 136, 56, 136, 59, 135, 60, 135, 63, 134, 64, 134, 67, 133, 68, 133, 71, 132, 72, 132, 75, 131, 76, 131, 79, 130, 80, 130, 83, 129, 84, 129, 87, 128, 88, 128, 91, 127, 92, 127, 95, 126, 96, 126, 99, 125, 100, 125, 101, 127, 103, 128, 103, 129, 104, 130, 104, 131, 105, 133, 105, 134, 106, 135, 106, 136, 107, 137, 107, 138, 108, 139, 108, 140, 109, 141, 109, 143, 111, 144, 111, 151, 118, 151, 119, 156, 124, 156, 125, 157, 126, 157, 127, 158, 128, 158, 129, 159, 130, 159, 131, 160, 132, 160, 133, 161, 134, 161, 135, 157, 135, 156, 134, 148, 134, 147, 133, 142, 133, 141, 132, 136, 132, 135, 131, 130, 131, 129, 130, 127, 130, 126, 131, 126, 132, 125, 133, 125, 138, 124, 139, 124, 142, 123, 141, 122, 142, 122, 144, 121, 145, 121, 151, 120, 152, 120, 158, 119, 159, 119, 164, 118, 165, 118, 171, 117, 172, 117, 178, 116, 179, 116, 182, 106, 182, 105, 181, 99, 181, 98, 180, 92, 180, 91, 179, 85, 179, 84, 178, 78, 178, 77, 177, 71, 177, 70, 176, 64, 176, 63, 175, 57, 175, 56, 174, 50, 174, 49, 173, 43, 173, 42, 172, 35, 172, 34, 171, 28, 171, 27, 170, 21, 170, 20, 169, 14, 169, 13, 168, 7, 168, 6, 167, 0, 167, 0, 137, 1, 137, 2, 136, 2, 134, 3, 133, 3, 128, 4, 127, 4, 124, 6, 124, 7, 125, 11, 125, 12, 126, 13, 126, 15, 124, 15, 123, 16, 122, 16, 120, 17, 119, 17, 118, 18, 117, 18, 116, 17, 115, 16, 115, 15, 114, 14, 115, 14, 116, 2, 104, 4, 102, 2, 100, 4, 98, 5, 98, 9, 94, 10, 94, 14, 90, 15, 90, 20, 85, 21, 85, 25, 81, 26, 81, 27, 80, 25, 78, 22, 78, 21, 77, 17, 77, 16, 76, 12, 76, 11, 75, 8, 75, 7, 74, 3, 74, 2, 73, 0, 73, 0, 0]], 'iscrowd': 0, 'image_id': 1772, 'category_id': 1, 'bbox': [0, 0, 165, 182], 'area': 23490.0, 'id': 125152}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Iterating through the json\n",
    "# list\n",
    "for i in data['annotations']:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c41a2b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'supercategory': 'building', 'id': 1, 'name': 'building'}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Iterating through the json\n",
    "# list\n",
    "for i in data['categories']:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e284061e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !python main.py --model S2ENet --flip_augmentation --patch_size 7 --epoch 128 --lr 0.001 --batch_size 64 --seed 0 --dataset Houston2013 --folder '../' --train_set '../Houston2013/TRLabel.mat' --test_set '../Houston2013/TSLabel.mat' --cuda 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac14917b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f651f198",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "GF2_Brasilia_-15.8653_-47.9277\n",
      "GF2_Brasilia_-15.8653_-47.9279\n",
      "GF2_Brasilia_-15.8653_-47.9293\n",
      "GF2_Brasilia_-15.8653_-47.9308\n",
      "GF2_Brasilia_-15.8653_-47.9322\n",
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      "GF2_Brasilia_-15.8666_-47.9352\n",
      "GF2_Brasilia_-15.8666_-47.9366\n",
      "GF2_Brasilia_-15.8666_-47.9381\n",
      "GF2_Brasilia_-15.8666_-47.9395\n",
      "GF2_Brasilia_-15.8667_-47.9277\n",
      "GF2_Brasilia_-15.8667_-47.9279\n",
      "GF2_Brasilia_-15.8667_-47.9293\n",
      "GF2_Brasilia_-15.8667_-47.9308\n",
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      "SV_Sydney_-33.8688_151.1954\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# Opening JSON file\n",
    "f = open(r'C:\\Users\\saada\\Desktop\\Drive\\UBS\\Workshop_Contest\\data\\track2\\buildings_only_train.json',)\n",
    "\n",
    "# returns JSON object as\n",
    "# a dictionary\n",
    "data = json.load(f)\n",
    "\n",
    "# Iterating through the json\n",
    "# list\n",
    "with open('train_imgs.txt', 'w') as f:\n",
    "    for i in data['images']:\n",
    "        print(i['file_name'].split('.ti')[0])\n",
    "        #f.write(\"%s\\n\" % i['file_name'].split('.ti')[0])\n",
    "        \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2392d6b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e7230b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "path= r'C:\\Users\\saada\\Desktop\\Drive\\UBS\\Workshop_Contest\\track2\\HeightEstimation\\workspace\\dataset\\data\\val\\rgb/'\n",
    "\n",
    "glob.glob(path+\"*\")\n",
    "\n",
    "\n",
    "with open('val_imgs.txt', 'w') as f:\n",
    "    for i in glob.glob(path+\"*\"):\n",
    "        g = i.split('rgb\\P')[1].split('.')[0]\n",
    "        g='P'+g\n",
    "        print(g)\n",
    "\n",
    "        f.write(\"%s\\n\" % g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdf37a05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5028a5ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "path= r'C:\\Users\\saada\\Desktop\\Drive\\UBS\\Workshop_Contest\\track2\\HeightEstimation\\workspace\\dataset\\data\\track2_test_data\\rgb/'\n",
    "\n",
    "glob.glob(path+\"*\")\n",
    "\n",
    "\n",
    "with open('test.txt', 'w') as f:\n",
    "    for i in glob.glob(path+\"*\"):\n",
    "        g = i.split('rgb\\P')[1].split('.')[0]\n",
    "        g='P'+g\n",
    "        print(g)\n",
    "\n",
    "        f.write(\"%s\\n\" % g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0181539",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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