test_HMS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pickle\n",
    "import copy\n",
    "import edt\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import cv2\n",
    "import pandas as pd\n",
    "from sklearn.metrics.cluster import adjusted_rand_score\n",
    "from skimage.metrics import adapted_rand_error\n",
    "\n",
    "import torch\n",
    "from torch import from_numpy as from_numpy\n",
    "from torchsummary import summary\n",
    "\n",
    "from func.run_pipeline_super_vox import segment_super_vox_3_channel, semantic_segment_crop_and_cat_3_channel_output, img_3d_erosion_or_expansion, \\\n",
    "generate_super_vox_by_watershed, get_outlayer_of_a_3d_shape, get_crop_by_pixel_val, Cluster_Super_Vox, assign_boudary_voxels_to_cells_with_watershed, \\\n",
    "delete_too_small_cluster, reassign\n",
    "from func.run_pipeline import segment, assign_boudary_voxels_to_cells, dbscan_of_seg, semantic_segment_crop_and_cat\n",
    "from func.cal_accuracy import IOU_and_Dice_Accuracy, VOI\n",
    "from func.network import VoxResNet, CellSegNet_basic_lite\n",
    "from func.unet_3d_basic import UNet3D_basic\n",
    "from func.ultis import save_obj, load_obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### init model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CellSegNet_basic_lite(\n",
       "  (conv1): Conv3d(1, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "  (conv2): Conv3d(16, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  (bnorm1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv3): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (resmodule1): ResModule(\n",
       "    (batchnorm_module): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv_module): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  )\n",
       "  (conv4): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (resmodule2): ResModule(\n",
       "    (batchnorm_module): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv_module): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  )\n",
       "  (conv5): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (resmodule3): ResModule(\n",
       "    (batchnorm_module): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv_module): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  )\n",
       "  (deconv1): ConvTranspose3d(64, 64, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (bnorm2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (deconv2): ConvTranspose3d(64, 64, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (bnorm3): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (deconv3): ConvTranspose3d(64, 32, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
       "  (bnorm4): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv6): Conv3d(32, 3, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model=UNet3D_basic(in_channels = 1, out_channels = 3)\n",
    "# load_path=''\n",
    "# model=VoxResNet(input_channel=1, n_classes=3, output_func = \"softmax\")\n",
    "# load_path=\"\"\n",
    "model=CellSegNet_basic_lite(input_channel=1, n_classes=3, output_func = \"softmax\")\n",
    "load_path='output/model_HMS_delete_fake_cells.pkl'\n",
    "checkpoint = torch.load(load_path)\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "\n",
    "model.eval()\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)\n",
    "\n",
    "#summary(model, (1, 64, 64, 64))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dataset info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "HMS_data_dict = load_obj(\"dataset_info/HMS_dataset_info\")\n",
    "HMS_data_dict_test = HMS_data_dict[\"test\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seg one img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "parameter setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we do not input the whole raw image to the model one time but input raw image crops\n",
    "crop_cube_size=64\n",
    "stride=32\n",
    "\n",
    "# hyperparameter for TASCAN, min touching area of two super pixels if they belong to the same cell\n",
    "min_touching_area=30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "choose a test image and load it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test cases: dict_keys(['90', '120', '135', '65'])\n",
      "for test case 135 : {'raw': '/data/CellSeg_dataset/HMS_processed/raw/135.npy', 'background': '/data/CellSeg_dataset/HMS_processed/segmentation/135/135_background_3d_mask.npy', 'boundary': '/data/CellSeg_dataset/HMS_processed/segmentation/135/135_boundary_3d_mask.npy', 'foreground': '/data/CellSeg_dataset/HMS_processed/segmentation/135/135_foreground_3d_mask.npy', 'ins': '/data/CellSeg_dataset/HMS_processed/segmentation/135/135_ins.npy'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Test cases: \"+str(HMS_data_dict_test.keys()))\n",
    "case = \"135\"\n",
    "print(\"for test case \"+str(case)+\" : \"+str(HMS_data_dict_test[case]))\n",
    "\n",
    "# you may load the image using another path\n",
    "raw_img=np.load(HMS_data_dict_test[case][\"raw\"]).astype(float)\n",
    "hand_seg=np.load(HMS_data_dict_test[case][\"ins\"]).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "feed raw image crops to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feed raw img to model\n",
      "1: Transpose the image to be: [0, 1, 2]\n",
      "Time elapsed:  11.854235410690308\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "# feed the raw img to the model\n",
    "print('Feed raw img to model')\n",
    "raw_img_size=raw_img.shape\n",
    "    \n",
    "seg_background_comp = np.zeros(raw_img_size)\n",
    "seg_boundary_comp = np.zeros(raw_img_size)\n",
    "\n",
    "transposes = [[0,1,2]]#,[2,0,1],[0,2,1]]\n",
    "reverse_transposes = [[0,1,2]]#,[1,2,0],[0,2,1]]\n",
    "\n",
    "for idx, transpose in enumerate(transposes):\n",
    "    print(str(idx+1)+\": Transpose the image to be: \"+str(transpose))\n",
    "    with torch.no_grad():\n",
    "        seg_img=\\\n",
    "        semantic_segment_crop_and_cat_3_channel_output(raw_img.transpose(transpose), model, device, crop_cube_size=crop_cube_size, stride=stride)\n",
    "    seg_img_background=seg_img['background']\n",
    "    seg_img_boundary=seg_img['boundary']\n",
    "    seg_img_foreground=seg_img['foreground']\n",
    "    torch.cuda.empty_cache()\n",
    "    \n",
    "    # argmax\n",
    "    print('argmax', end='\\r')\n",
    "    seg=[]\n",
    "    seg.append(seg_img_background)\n",
    "    seg.append(seg_img_boundary)\n",
    "    seg.append(seg_img_foreground)\n",
    "    seg=np.array(seg)\n",
    "    seg_argmax=np.argmax(seg, axis=0)\n",
    "    # probability map to 0 1 segment\n",
    "    seg_background=np.zeros(seg_img_background.shape)\n",
    "    seg_background[np.where(seg_argmax==0)]=1\n",
    "    seg_foreground=np.zeros(seg_img_foreground.shape)\n",
    "    seg_foreground[np.where(seg_argmax==2)]=1\n",
    "    seg_boundary=np.zeros(seg_img_boundary.shape)\n",
    "    seg_boundary[np.where(seg_argmax==1)]=1\n",
    "        \n",
    "    seg_background=seg_background.transpose(reverse_transposes[idx])\n",
    "    seg_foreground=seg_foreground.transpose(reverse_transposes[idx])\n",
    "    seg_boundary=seg_boundary.transpose(reverse_transposes[idx])\n",
    "        \n",
    "    seg_background_comp+=seg_background\n",
    "    seg_boundary_comp+=seg_boundary\n",
    "#print(\"Get model semantic seg by combination\")\n",
    "seg_background_comp = np.array(seg_background_comp>0, dtype=float)\n",
    "seg_boundary_comp = np.array(seg_boundary_comp>0, dtype=float)\n",
    "seg_foreground_comp = np.array(1 - seg_background_comp - seg_boundary_comp>0, dtype=float)\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "print(\"Time elapsed: \", end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# show current result\n",
    "\n",
    "N=100\n",
    "\n",
    "plt.figure()\n",
    "plt.title(\"raw_img\")\n",
    "plt.imshow(raw_img[N,:,:])\n",
    "plt.figure()\n",
    "plt.title(\"hand_seg\")\n",
    "plt.imshow(reassign(hand_seg[N,:,:]))\n",
    "plt.figure()\n",
    "plt.title(\"model_seg_foreground\")\n",
    "plt.imshow(seg_foreground_comp[N,:,:])\n",
    "plt.figure()\n",
    "plt.title(\"model_seg_boundary\")\n",
    "plt.imshow(seg_boundary_comp[N,:,:])\n",
    "plt.figure()\n",
    "plt.title(\"model_seg_background\")\n",
    "plt.imshow(seg_background_comp[N,:,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TASCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "generate super vox by watershed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "how_close_are_the_super_vox_to_boundary=2\n",
    "min_touching_percentage=0.51\n",
    "\n",
    "seg_foreground_erosion=1-img_3d_erosion_or_expansion(1-seg_foreground_comp, kernel_size=how_close_are_the_super_vox_to_boundary+1, device=device)\n",
    "seg_foreground_super_voxel_by_ws = generate_super_vox_by_watershed(seg_foreground_erosion, connectivity=min_touching_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1973 super voxels\n"
     ]
    }
   ],
   "source": [
    "print(\"There are \"+str(len(np.unique(seg_foreground_super_voxel_by_ws)))+\" super voxels\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "super voxel clustearing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of valid_neighbor_vals: 0ixel val: 7840[]503]7]02]1882]\r"
     ]
    }
   ],
   "source": [
    "cluster_super_vox=Cluster_Super_Vox(min_touching_area=min_touching_area, min_touching_percentage=min_touching_percentage)\n",
    "cluster_super_vox.fit(seg_foreground_super_voxel_by_ws)\n",
    "seg_foreground_single_cell_with_boundary = cluster_super_vox.output_3d_img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "delete too small cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_cell_size_threshold=10\n",
    "seg_foreground_single_cell_with_boundary_delete_too_small = delete_too_small_cluster(seg_foreground_single_cell_with_boundary, threshold=min_cell_size_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "assign boudary voxels to their nearest cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_final=assign_boudary_voxels_to_cells_with_watershed(seg_foreground_single_cell_with_boundary_delete_too_small, seg_boundary_comp, seg_background_comp, compactness=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "see the final result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colorful_seg(seg):\n",
    "    unique_vals, val_counts = np.unique(seg, return_counts=True)\n",
    "    \n",
    "    background_val = unique_vals[np.argsort(val_counts)[::-1][0]]\n",
    "    \n",
    "    seg_RGB = []\n",
    "    for i in range(seg.shape[0]):\n",
    "        mask_gray = cv2.normalize(src=seg[i,:,:], dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)\n",
    "        seg_slice_RGB = cv2.cvtColor(mask_gray, cv2.COLOR_GRAY2RGB)\n",
    "        seg_RGB.append(seg_slice_RGB)\n",
    "    seg_RGB = np.array(seg_RGB)\n",
    "    \n",
    "    for idx, unique_val in enumerate(unique_vals):\n",
    "        print(str(idx/len(unique_vals)), end=\"\\r\")\n",
    "        if unique_val == background_val:\n",
    "            COLOR = np.array([0,0,0], dtype=int)\n",
    "        else:\n",
    "            COLOR = np.array(np.random.choice(np.arange(256), size=3, replace=False), dtype=int)\n",
    "        \n",
    "        locs = np.where(seg==unique_val)\n",
    "        \n",
    "        for i in range(3):\n",
    "            seg_RGB[locs[0], locs[1], locs[2], i] = COLOR[i]\n",
    "        \n",
    "    return seg_RGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9971264367816092746\r"
     ]
    }
   ],
   "source": [
    "seg_RGB = colorful_seg(seg_final)\n",
    "hand_seg_RGB = colorful_seg(hand_seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd44840f070>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBJA7t5qShTsQvsSDFgFoXdH+OxSIXbIH22MzB92Go5i5HrLpedmCIT73+2omv7kPzRiavptDi/+7fA7cvw06sjO7fSAY9aKUi8aMNpO2dj9mcfbDCgChqkz4yrU0/exRxu2x0Wxvp8Xu61PmjLKxLC0ajP+RgOTOrn1Qgdtao8SE4DWPSixgI7qvZ7q99vOrMIPH9djSMHElQxMKrSWSFy6IIIcpkLIQEqFkYUURQDbHpaDuF98ELbMXjLOjk9U/+zkzXnszq3MGFYN2LflsnKrA1fPg8d0Q0kenOI1qUVKAEuy4X95G6NTZBC47adB1Ck1l7tVX8dWPF/Fu3m425vYdZshm2N7uZuXJoLmzW6dmtDoxS4MojvT8Wb7UHOHpZxcQOJxD+//NTll23x6ThUuUzKfdbTXUVLawf1oSQdIk0ROasb/b34kxXVRXBM/khqyPl44YZrkPpW5wLx+AzlkTCUX8mB0qjvz0XAIcfj+rfvVrFj38SNbnPewMsS6vI2UZVYFrlqoUzCviXy/62blzdI3nRrUoqQgmMPghxfF0FZmsuSTEisdmckJnguHCYWho60B1xbDlRyk+rTGj+vVaLzImcC1qQajpvf2bfryEqD7wz7l+rcGCxUrGHgUh1w52zjuSvIDdJHzR4UGJkpYTpGDhIBbtumOY01uPiZLncxMJ/LY6o+mrYEUJzSvm4ps5gWiwE0I+YsHjJh+EgntMRR9hF4bB+Xd8hznPPJt9+9NEUQWrbpjEtGVFzFzp5xvfqGLf/tHjRjCqRWl+r+BFeX96gej8SvSpyVeyp0vYK9m3JMqKx5ILnn9XPgDCFu8tZSpMsUYPmC3xMdr7TNixn4Bn3bCeQ2gxxl22ZtD12Jbnk/fJeMA7bYYH2/L8fqIkgwadt2zvd2wk30vVdecSKcnvVVgS6WjrVzYW7umdxF5uRDQEmD1IQTIKBe4fFnOlLS6qz961n66W/o6qF902g/Gz42XmzfPyi19M4/obdtHSMjqcMEetKCmAt9flabVtKB2B+BTUYB0PTVDT/P2lrtL62hja3i6l4uqDuMYFEJpM2gTTkJimyTP/tx3/b/188uGxKKpAtQ3NVLeU8NC/Ylx5bXqzgxKJoXRhqL6BCw+CqZ99Cnt+auP+QGj5+RRedg6KvcdVwja3v7+UlJK8P8yl89b4GjXTFJiqyq4vfRTDnTqJw1GMUK8h02I3sf+rT144DUwnNN3nQc1TGEO8/R+9Yy6JQj05PGqfXtrkyS6eeHweZ6zeTCj0wV/CMmpFaRn5/ZLalX7yNzT+/TaicycMSphKDqt87Dvp2y2koSBDCof/OhUUyaT/3IPiMLDl9VU2PWqw6bUaNr/RE7b2lyuPMPkUF+fdUYgrb2i6TdFo+uOZmNZMa+Hf0yorbSZmfgSlI/0ZPYCicg9aojC/GaIVFPQRpGQIIbAty6P47ZOIRm1s2DoFX5cr63tCOFQqzvHA85kfaxQIzFxB6y/dyOMWHzvc6T+e+fk2nnt2AVdfs4P6+gSzkx8gRnUyyv6LQaHshl/hXLN7yOtO9yhMhYO/nUXtvyYRaYq/laWUVO1oZutbR/oI0lGq3gjxyk87CLS+T/nT0rxUsyJI6MpDaZX15jtYvHoCi1dP4NbfnsnMSSuzbx/gmjqVwgsuSLu8EAIhBHurJtPhyxl07/k/v/PDjO4I0wOhVRodX3bS/DcvZtHgH8WSEju//90MZkwfejvqe8mo7CmVYcee5BYRukH+Tx6jYeWsrOpWYnDqvwcfOSBc56HhyfH4J6wnQBtb3jhMqtRnO58KIA1J3jiNlbfk9Zs9+/SlW/jNv5ekde5oBPbsMpgxK3XPS2LQ5X0jrToBcjph5c4QNnePsIalxvpQjx3vjI/OxJPvoLDMw/ILJvdsL78Nh83LtkNPpH2+3uSvWpVV4LyoXkQ4UobLeRghshv6LH/5NRyh9OfmpQa+zzgJXpy9R34yZsxw861vTeJbdxxiz54P5qzcqBSlEuzYUnQCtfp2Sj73R/yXLCd0VoIsICm49ju5LHw5s+FJMkLVXnYf6qKV9JKm7Xo2CAL8zQbn/k9Rn307qtL3jdJ1OLDXHFCUwMTveTvtenO6BKfujYCnZ+2aLhUm2HzsihQx8WOnc84Nc3C4+tuzVEVj9oRzsxKl3JUrEVl40B9ro16ElBpu14GsOkxvvjuTNcrX+AXfGrC39NCSq1l5xXOYK4ev17twYQ7fu3Myt966l9q6D95QblSK0kAowQinbKrl+vAO5NNVGIrgqh9ezIJ9TfzPH1M/hIc2X4cxhOurpnEGfpqJ0JXeARJ2PBng8PowC6/IYenH40OPt7ZWDFmbjp0qk/hNkoRT7zZhMsXRwazLF1N8w1wcruS3XIF3HCtn38xbO/+U9mlzTzwR7/z5iGy96LuJxTJfQCwlHHzSy8HqsZganOe9L+GdMUdr4Xu58R7nwpu2Y8yVWfXqMmnX5LE3c9ePZnPVpy4mFP5guQuMOlGaiIsSUneLZ3u8fLlyCmrYgLAfCbxx0z8RUqIdn9DxOMrG/ol1tZ9CN4cg+BtgFx7s0pO+KAHSgM5ag9d/3cEbv+3gqj+UDctS8rox30+7rCMC//nL5L0Vd649pSABqIqNHFcpmuogZgwcs0loGmpODkLL7jaOGSpGLLtjTQOOvObmwOM5cQ98AbWiv7tJnojwxYI3QcnBdOl4c30YwxSsT0qBNHPR/acT7TqT0mKFR+97iatuOp/Oro5hOedwMOoM3YLkRuhJLhcLvbl8e/J01N6Ob4DNMAcUJABN0Vk09m+4bdmHVz2ehVyFyOKnkCaYMfjnTY0U+9OLYHAURQGHZuDQDERSRcts4ZZqJi8ca2zD6Bw4PvaUsSuZOzG1wVpIlZLYfMqmr8Y9M/u1bjVHpnDwcE/KdMPwEot5Mc3ULzVpQN1bbvb8I3/AJUGd0sFnOs6kJpZD4KPbMCYOLvFDIozoOGLhGcRCS/HX/pJI50c4+mjn5xbwx5/9nQnjKof8vMPFqOspJWOeN4fPjJtIqX3w9iCn1sXM4qfZ23o2/ujQZJMdwxzq2ZbVsQI4s/lf3Df+62kfY9dMxuTFlyd3hjSMBIKS9ghDwtK1qYdP/pfexb1iAZ6T5g9Y3djCOeyvex1/qJVp0Yv7vWRU6WRB5EbqI4fYT38nyOxQCATjAuVw1OF0NCBl3N5kt7f2KVnzsoe996fvEtIpHewzChiqKF5SKkT9q4991rtWYcb65747SuX4yfzvF7/P93/1LfYf3DNErRg+PjSidHJ+4ZAI0lG89mYKnIeGRJSEEEyUy7MWJQCbGWFR56tsyjt9wLIup+TkE3tmmvJciWMnTImdy17bw2n1ls58duhczyePOQl9jwNfWzuT9LOS9nzzqovJqy6kc2J/j+t0KClqoLS4jqaWvg+0rud395YEup6PlCoORzyY0YHHcjj4tJdMupAVShenLNyAf3rrwIUHINJ5EWZsDHpgRUZtmDtrIV+99Vvc8eOvcriueuAD3kdGlSjloVFOf+E5MS+fZbl5Q36+8pwtdEbG44sMvZE5UzQMVsZ2cFFZfEHun9sKqNX723iEkNx4nUlFGqttSoy5cVEagKv+rmEbwkme0G5BScNSimOpHzp3qxdXqzdrUYq92Ybnzmcpj/T362m96lQi0+K/azgS/7KOPBvi0HMeZJLswsnYaxTyl4LxXD7mrazaCaAHlxDxXYCpl4NMz+v8eObNWshPvvVbPvWFa+nyD6+H/mAYVaKkIXAmWCyWp9nI04Y+fKxD86MpQRgwSpNgoEVsNtzM4WJ28HjW7XGpCpMccc/ob5Y1YwANusaPmooxpMBAYLPB2LLU9fS0WmNh5Ba2OP6EFMmnsEsbBEqa4VbSwQyCHECQjiIMJR4zKgOTnJQS/7sGB24JQSiEg/4P6JhfPoLsntGTCPbbp3CQuRkLEkBQqjSZLoyYQFEkIs22SgnSKMBf/11AAzn4nn7lhCn8++6nOfeakwdd13AxakRJAM4Ed6ZdKBQMgyABRKVA5r2O4n1ngILjMf0L+26TGpheAOpzXOiqgp8SAuECPKHECR9ToWlOZs276NhnZ3dsocl2nbvG1fN8l4dXRS433ChR0nwoBAKvLGdO9OPssf8bXfQ44xkxk2g43j2S5vt3G015bg7+sR10jesYsKyIepBaiMAmnV0Xp54mV6Ixjr5sGtVStihzs/L6FpiUF+1jcvE6nn1wHtPmNlJUOrDB3+PxYLPZCDR8Cxja+9fryWV8RSWHaw8Nab1DxagRJQcKM/D22z7Z5eLKssFHBjieiKlQpefgM+0D36uOw6iOvg6SUi+kWj2bGu847j1hGh2u+FtwwpFxzDzwMhNqN+GMDnzz9iaR78vRTefkBlh6lp1ITmZvW4GgwJzKmNgyDtteo7O5C8MwCfvDNByKz0C+5S7nMlGGNgS9JcMPhi+zLChqVwVa+8Axj2zNc4jlHWL35QO8RBKeKPNrK82rxmkPML/yNfZuj9seq3aXpnXsaatOoXLSxIzPmQ42m427fnwfX7/zv9iyY+OwnGMwjBpRSoQNG8u0swmETDyuFLGAMkSXgio9hzYz++60sLWxdmqYB6b1jZFdM24JNeOWMHP/S+T4mzlh8z/6mTMNp07HiX3jDtnxwDAujSswp7Kn9i0O7TuCEeu7HOOeGXX4bQaf3DN421qsWaDXpi8AHWMciM4TcEbSM7QrgVIw36VPnOAhpijnCCV5h5lQsgu7NrC/1fGMGVNGQUH+0DesF8WFJXzztjv54W/uYP3mLER6GBmVorTaeSaTtEmoQmWaNp3WjgBCGLid2YeX+DtBXiM+XMnFzkfM7IOZpcPuqasRpkHImcvp79wFQOtp+4iU+ZCaSXhi3yGeYqpsqP8LSw7emLTOnI1d6MU2TGfmPlF5ZiWRBq2fIAEg4MFJjYRVk8/sHN9//9gpcNIldETG44gINMfQOQ/6ShzozhEQdKqbfE8j8ypfx+vM3h+pPLeQcumG9r6REyJulZhj6FwLJ4yr5Cuf/1++9aOvsGPP1iGrd7CMGlE6+m492XEKq11nYhc9DnCG4aG5bSWK0CkvewZFRJPHM0IecyXsQnI58ZmdDiTBbk/cMRI+MgRtvvDQc+wpmMqW4sQZRaSisnPa2SimwVzb1+g8oRqpJX7Dm4pBS84eJGZSR0x7awxiw+NNbCrw9IQWNFNw8+6K+DS+qsEnvw82B7hziZpQt85EdUjKlwb7/QZGJwQ2pN9Laqtw0l6R3UxUuoSFg7cdK9IoaeKy+1k27WkctuwDZ48rKmVZ5QxsCcLLqHqsnxdA2KMSdfX6vTMcZk6oqKSoYDAx5YeeUSFKAlhOPrNss7jYfQlKgukN03Rg4qCm7nJsmo8xJS8D4FNC6L1mlv5FiD/Ry6A7jIloPbEQReE2FNPAVBK/7U1VY+usC9BzX6dS+3XK+lpy9rB+8t0sq/p04rocYlh9+GOK5JFJTXhjKlfWTcV23Z3gyevzoBhRBSMqqd/oZsyCIEqvO1CaICPpfeGmgKhTxdQyvyB7oYdwfXo9GYkgoqQWPpsW4rQ5D2C3hVCyXELitjvIcXn4yPJTE96/0J0X4bjqXV0Grq74/esrth0rIBWSCpSUklivUeVnzv8ptdXXc6B+ZPSWRoUowdEXiEj6g/agoMfyOVx/GXVaBz8seop99sE7tWXLZ7fdw86C6TR4UjhhCgWZjqOcgICjmaC9FXe0qN/u9lPzMd3ZDXU6ww2E9IHX50kB902vZ+5Z32W+Jz9pQyOdKs07nRTNiKA5JKY02W3sxZ+vUBwqpSDSv/0954jhG1dD/ax0ejDHnVkIlv3mWt64/PdplJbkaZ2Ui1rqZGJ7mcfRweKpz+O0Zx8mpCQ3n0tPOBW3I/NeX++7Iq9XONywWyFmV5ACDHv8mYgETPQwYELNRr1PLd+4+l5+9ODN7D6yPruLGEJGhygJQfji1ehtZbAjvUOOaG38puBl9tnf/7SjZxx5k39OvwyZrgNLCmqL1lPXtoGpjZmnm07FjsaXafDvHdI6gy02hAL7Kt6gU23lodCjMB9mty5gcscMBIKTa1f38egOlT6L4WjCP+lZ7LE7iWqDz1CTiDxbBw41wjh3LWNlA2+b8fNsNBYTwItdC1FRtJcxBQfJc2f3Uivw5FBZOpZ5E6ZkJUipcAZNCJrx1ONOlZZ2ia/RJNSRJJ+eENxy/g/4rz9mnhF4qBkVoiQVhdDNVxLZYsKO1LMdpjDZW76LRs3HeAfURZy0mJnZADpEjJe0VlbHkr/NM+Gyqid5YNqlxIZAlAAOlL1EWed8csJDsy4vU1blns94x6S0yr4ZfpOXO+8npPa4P+ws2sLOoi0godZbzVj/eE6rPZvA2EfwV96N1AJoEvJCd9DpuoOotnxI259vb2e8+wj27kDsxaKVi5QnAZgq9hPEjekyCE3I3iva63Sxev5SKgqHd8LENCW1O2J0pBGEwuXwcsGyT/LUunuGtU0DMSpEKRO2Vm6i090BAuZSQIXqJoJJwNR5MHgorTrCwmSvEmA1QyNKAN9Y/zPuOOHLQ1JXS+5eIpqPHMbQ5jJ5YEHcUdAgjGxSuLRkPEoGBtGajq3saHwx7fLTXXMp0NIznh5x7usjSH0QsLHsHRxFm9FLX2CecxdS60kuoJmHyA9+lTbPXcTU9LNsSkWjtWIOLl9TPF2SL55pxq5EmJJThU3RsSmJvfRnqfFQyn7Fzkayc4H4zJm5OGw5mMrwCpKUkh37wZ/myNKuObnwhJswzBjPbrhvWNuWilElSqYCuga27vspnokjbgSsGrOfpryG+Odez2OBGvc1koqT/8qd06e+iDS4u6v/kEUoEofbQNF0zIgKxuB6OAKY17qT2zf8lJ+c9IWEZRx2BzabB10Pkk7wpBfmfZca54+JCTc96eBM0OHfTdV8pGQCqgA1jd5ZJBYgqHekdS0qKppI77YyiGGmWL5y7PxamA5vHZL+4qXKeor819Cc8zSmGDPg7FMsavLID/YRzhtLMDe+3qatfDaKjHF+869RBwiJq9oUVv/3XFSHyuWK4LltIdbuT9zT/uxZeRR5+3+/DptAN1Ua0g+hlfx6pIEhe77DYNkW6k69/dhnGfTAT+8DU4PYwOF3nXYPl6/8PO3+ZtbueWbwDcyCUSVK2xdoPHmpyUcejHe7Y6rOmpndMaYH6BgIIXActz7NIVRuO06ohAJjlx9h+e2vAVD/oyUEN8a9dM2gRvRI5hEMAdRpFSyb7eXx/0mWZujLwJd58LErqDny2oD1HXJ/gYiaOBBdZ0znr/UHmO7O4aS8EhxCwdk942igoYvsnELtwsEVRTdwYcHVSctIKTns34tEsiNnDZvtr6IO8jZUCFHadQ7N3kcx1MlJy/nbojx/1yHCfiMuXuLo2rb4OjMlhSDZPRrOXBtLr52MK6/n4b54sZuLFyf+noczuqQuYzTrHbzt38GGYE84kjKtnjNsPV0jJS+I947ziO08meiLN2C2joNo6sQCmmqjNH8cNs2BHsvc+XOwjCpRAjgyTqG1SFDU2t2bGOR9cfyNNe7Ugyz98lvHXsjlX9lwbF/4QB7N9/Skze58buKAvSh18VSUMQW4vvVxRMq89fETXn7xAzzx7I3sr3o6acmQMgVT9F9yczx7g13McBjMctkZx34UJH5yaZM9K3ZjkSjs2U1Ol6ArJ3kPTUXjsqLruK70synPuanlFX619TbM7rd72enjyJs1+GGwIEpR4Eba3T9F15b229/REGbN/bV0tWQezsDh1ZhzwXjGL+7fzmyFRxEx7GoHUSM/42O3BatoN3w82/luWuWFANucN7HNeZPIS9dhNk0ktmU1qR6Oy1Z8Fiklz6z/C4b53mbRGXWitHmpBgJuNV+l5qGCIa170oV7mHfTxqQjBOeUTsbfGY/xLSW4Zreh13lo/Ufi6Ija8pk4v3YV6oT01kMBKIrKuat/w6uOb7J91z/67Mvze1iyezpBdR5Pzc+lLY1OWzENTKDnbejFh7fXqvmurnbE888xN19l2+wY/iRad0Pp57myOLk3+fbWNWxvW8OrtQ8fEySArgOduMfnYPMOPrNHpKuJXa98j4i26tg2iYuwcSEtNUGaq5MvwpUo7PcsZ1pgbZ/tmkNh3sUTqFgw8Nq6TNCUCDmOI7QG89M+ZnvwIIejjbzt30EsyzVFjtX3YZoqbxcL8ptKmLYtedC9y1d+Dpfdy7/f/EVW58qWUSdKAJuXaCizNjP7JJ2xNW7W3ZfeTFAqJl+8m1kf34LqSO9mEAKKP7YHw6+Rc3Idrf+eRtcrfZdgqAsnZyRIR3E4cjllxf8ipST//m2Mb4z3bJxRG+WtxYDJW1MjtOUMXf6vwg4Fd0jg9/bvLX1uzDc5r+DylMfv69zC09V/7bc9eLiL+ueqGXfxZBRb9stFjBg895CHxto6oCd5pikL8MVOHPB4KRRqxi5l7uRaIs/3rJM84bqplExNP8pkOoR1k0///jCGWY9u7E9a7r8vW01enoN/tL4AQFOsA5+ROotwe1Mhr/x7NbOXb6dsQuJU8aYw2X7iOzgDHqrm7mDR66dSWpvYaL/6xGvYdMnr7P/v927h7qgUpaOMnddJ6UwfsajChn9NSBm/OzESoUjGnXaIOddvRksSoTEVqjeG96QGXHNaMb+8gQMfP4domxP7SbNxfGzVwBUkwe0q4sz6c7Fvk9iM/j/j/3tmM1+/ZgUB59DlFvvcFTdSdlL/Wa4CrQjlcQYOK5WEcFMwYXrqo0gJJgKjOwqBQv+05w/ek0N7S7Kh8sABl1TN4LzrtuO0V+A4sQyJZFEsRn7u0IQNMaXENOHTdx1m15EwjR0xIAAkD1Nzy+8eYNl1DvwifcfMaNhBQ/VYJs090Ge7lBxzwM39+j+4zm7j/267m7rKg7SOaUCNaTiDbi6657jerk2Su7Qo/vW9RxnBR6UoiXAQzPg3qNokjUUNvFDUyqKuyeTE+hol7aaGmuSGtXmjnP/PBxEi/cBcyVBzddRcnSlPPcb/fG0VN/zPdxHewfRkojh1HTWBIAHkhqL85G9v8J83nTGo7K9CVdAKctFUQUFhAcX5CYYxUiLdUYQv++uJBXQUu9LHRiMlhE0HbdEC7vStOvY6+XPh/dhEDJeIkaNECAYEga74K6df++nErd5D0PhUyvNf8R/rcHgjhO1RyIEdMzYz5cBk1JCL3Ej2jo2GKWnsiPHwOx384qnmo7dlWrT7g/iMGIqW+vezBSS6C1Di5TS7jqbF3xDSlFAP+/QxvBWJxyC/uWUs44Nevvb57/D6hS/y5vmvAhDM6eK+r/6wX/0ekc+sP53E3lvXYQSyfPNkwKgTJdXXRu5rj6JObgc3BIJQ2xBPgbMpt6pf+WmBcnJjiWdPNEVn9043+QUxyscNzSyEUCXtLXU88Muvcsl/3E5B2bikZTe8+hpGLH4TjJ8+jbETJgASTWxDEwewTXyCaJEXWhMrpmqYTG3oYP/Y7GxrAYdGrKKE6T++jXFjPZSP9SS5KAhduB3n8zNRW7Kbfax5aB9TP9V3YXKbXsC69kUcFZujA8cb2z4KwHJ7NZe4trHrgSDRSOKnXZI3oCABNNbmop26iR2zNh/b9oeiZly6jWu2xhOWlvm9GQnUuv1Bmjp1Pn93bdrHZMOsR3Qa56lEPaAURpl8zRbGTesegnYBi2AaDUyjATV3Dq7FoHt0Dk+ppqO4V09NJM71JxCUXDIe37st1P5h37BeC4wSUSo+76xjvQHngW3YG+PphqJReOAJjRdeT36Z+3plc03E2h9PpHJykMXLepxKPF6Dlad1ZNTG3Tvd1Bx0HXtT1h7YwTP3/pRzr/8ihWXjCAWCPPKnu/sc8+fv3kk0EhfD//r+tVx76/mAxKX+K36554HxogPjxcRT+DZTcv1rO7n9mpUJ95d6JLZ8jVYbFPojx/oZAYdG2KbSkutCKmn2sjSTyOn7sK+ZjFaXn94xKagPl7LDN4tUM0RroxNZG53I5Mq9zGvajGpkP754V7aSO3Nzv7OFbDp/WRJfDza/fgwX7pmFN5raZWJjVZD1B4L84flW2v2ZG6TzZAEF3Y65js0aMidCbEb/+9TVYlK8x8TZLpn9SNwNRp3iJ++8DiDufsG9fY9xTv8CwpZDzB7g4Kz9tBdlHuV0uBkVojTm6ssSTs1GoqQUpHQ5VOXmUFVPb8rpMtizM/75rPNbGT8xeS+qpdnG4w+WUH3IRWN935v54I51NB+piouS38/vb/+fpPXYlE24tf4r27VrQxibNWhJbCQuDERYva2Gl+ZN6LO9yCVZVWlg99hpljbCdg0h42/JkF1DP87o3NYRoSDPjsed3MYi3TpGZWtCUVpasppNza9wqGtn4mMNk5a1DWiLZ1ITGkdrtICYTO+3q5oznTv2nk1OtP93ILFzMJrPk/6OpMd7P/oC3mteHnCUu3VsA2Ethqs7IUNlRwHLj/R8ry2+GHc+1Mie2jC7azPvWduknVnMw0suuXQnungHTGeUWHXcQz5ywn5kftzG5G2UTH2h73DKOJBD+Nmx2E9pgTuB3yY+l9vvYfb6eRyYk/56xtIrJ9L+aiPBPcObdGBUiBLSJP+5vxOYvxLXznV87DKdkkJJeJj8vsIhlXffzgfgwD43TlfyN7QeFTQ1Jn6zntoc4sTbv43D7aXtl0nungFQl+g47+4kfGUB6P2fKrvSwcro72lru5BNBStAgEuD86cZ5B0diQhBlzu1QTwYihHVTZIM4I4RG9+OOqkF7WDPMhOJZFzONAqdY5KKEhKaq2I0TZlL2MzQhiNhkeN0CpKEGJlnN9Gl5LnA8aIeF2HH4r0Ie3q2kr0lLb3+bmbt+J4wx52HFR77SoZZc7pHS0tZgRMXXvoPf5WwHfve+OyY1piP/6o1kKS9tmWteD63L95LeoGU0Uh9hZ00VySeoUtEzsJC7CVOS5TSwaHr2I/sx153kItWhTn3dIOoDqHwMAZD6qa1JbvZLUVKCqMmOY1HkEDJNVez6JRZbFpzAIzMHPxEpYHziTbCHymEUK/YRbYwjQtfwKYaXFz7Ny6o+xdtl3wFI38M2cy+m6ZESpnaYdBhYHqiSK3naTBKfEQWVXPDuR/jE+bV3H7Hl4gZMVaceCoXXdDjShCrbua5P7zAPectIJbEkVRKiQxFABlP1+1w8Jd3zydPTz6kcikK1+QWc0VOj/OjWdJK6KJneXncLtZPqMkq63nIFiNk6xnWd3k0IE1RkqChMYUZjGcSKmpaM8NKp4ecv6zCYfqZ0fZ6v/3CG0MpDMHngN39jw9s/Aw5pzyNYk9uZ5SGCd1DYUU6AIHEQAo9LnbDzKgQpcsfvI/XnRJkDE2D1nbBd39pp6Vt+EUpW+Z1RjmvMe7MJ4ELTvgdumLDYX8efUvcz8Zs3U/KufJuhAAx1oR7u6j6Zo/h3Fb0OKJ77Z8mDTRpMObR79B0468YKOVTIvYd9LFwThFOR+pj9YWH0afV9fNmt3X/9+Pv9+8VStOEeh8nbz+C32XnrbnjOFRRgdFLnGTtEfD5aL/9VxAzcCyfj/j6HVx8Lbxwf5RTjtiTPtiaEGi9g80pIB0m5zfPQPcG2Fp8GDPLAG3Z4CWXkzkjq2NFTCNKPvsL5lGqvdpnn80bIPpHAa9DoSqxxeLrQduKjl57FL9nP0LLpY0azJb+C/CM/U0YW+M9wLLILQjshJRddNpewazvyKrNmTAqROkbz67j7EvnHLshN21XCGQfc+s94ejtv7ZwLvXOEsxutwTbtLOxTYvHQgq//iOIxRd77vFX0NLZRXFe4hX1UanwUOVM7v9Lj4fulQ/t58S1r/d9TCU4Dm0mMnnJUF9SDxEl40XK4Re34vtpPOfdueuqOHddFZ/4yQ/pKOh5o8u3/4787d1w1vmgqkQA0dCFGAfnX9HMP54o4uIDqd0SJBJj6kHMvJ4hyCUHF2MzVbpsYXYXZh/HPV2EFJQyuLAyhj3I/pPeYt38N/rvjDrgBjj5dZ2cLklnnsKak3s/6v+v588Bco02Ou7q8zlvmh1/jY4cRs+AUSFKx3PO6QbPv6a+J8O3bBHAm8WL+O7sT3PYnfgGdZ7aE8rk5WYwH9zF2MJObr30xT5GWSnhz9ElPKX3Xc7y0KUfI+hys/rVZ9ltLKVKxgVLvmzHGXQzd+4wKHdMQCwzQQo89Db+e14esJy46mPg9sC5F8aHbr3QVfiPc9rpfNXkEzuTW77uc+3kktM2YNP69orOr15ASI0ysauIffmNVOU1p9X2qM+GHoy3xYgqzPhUmNZNGi0b+j9a3vESR6GkZNs8JhpT06o/EVKJUX/qP+ictSZluTdPHfp8h+WneWhcE0LvGj5PylEpSsdTOM3O2BP6G0EbN4Vp2TmE+aYzYFP+TB4ddxO17jTT1QKvbp0FSNq6PNxx3WPHtv8ichIvx6b0K2+qKi+svpADLKDrucl00L2kxQDHOyZCSObMSZ2U8SiR/LeJ5K1jEzYcei7L679A7oP9b/rwNAicktkwKPzKdoiluXznwkuT7mtzmXz91E7+PjvId97MY1lDX3vfPa5t/NW9g/u++lumVxTyk9uu6bPfZdg5sXEq0zvG0uEI8MjkDfjtiWdLjKhCoN6NGVExe4nwxEt0SpbH2P4zJx27NBSbpOyk+PdhzwXNBbacRngjO1GqP/XvhItqCVYkMBiNEkaFKCkofMYWdwtwvgmPFW9nwseaqegWc9Um0BKkFfKWa1Se1fcBatgQpnZNioWbQ/CCkECbIz8jQepB8MLGOWzYXwmA95JWgjNk0hjeeoebmtdP6JfhJBJRWN+xi31G3x7KBDGNhWJlfCgsTEx7C77KXyGVMKiR+KIIU7C545ectfsr/c5neEzSifeUDj//7ve5/sc/QKab0hdo8pg0eSJc9OsbcbxzdHGtoHj+6bSGmmg6uBG9qw1/V/JEj4URD4URD5/afgatpo0vGSXMW/YXhIhH7u88kIs0BDLJENU9RrLo9hA1j7uI+RW040aUoZLMw+dKZNxJtayaUFl/J+D3kiEKkJqUUSFKAF7hiicNiEIoYkPzDBylR3MoaMdN2kw4zc2EUxN7eANs+XMHsXD8oYuFJUYkswdQAj7bdHbn3pLRcb0xpUJLZ3zqOKKHcZBiGGYKRCjBXaTqGA4fIfou8NwjN7NHbuEM+5nYp/4FqQahnwFYonO8gVRCQRt47MDQLAQu8GU/9dwaaYVQPQiFvMkLKFywhGKHh+JTL2f33+/A5kk9S9Zm2Ph0w0oaDScSeOmxXzCu9FlK8teiCIOBYuLEUDAUhZgiOD4kkWIomDmQiXhHppkElpt4xRfxyhit4nZMOtI+fiiZ/8Ui1v9PesPbbBg1ojRUCCFS3m8Lb+4xvDZtCdO8s3/3PthoEPWbKFIyy9eTNWJr/jQ6bHnsyvvcoNajDQW2sYdwLUoWLE7iH/9nCrTkPcawx0dH2WEKhAJ5PrBFsX3iLxhMQ5VXYoghCPUhJfP27GXrrMShX/oVDwZgU3d8q/a4p7LNk0fRvFNpWv8c5SdfDkIw6xN3UOqVVHWaTE6gTYdjLr7TNpcGo7e4Co40nU+O61DKpKahLoW2wza2Puul+WBidxFVVSj5hJ5VLKZ4j9dOgfwKXfwNAJ1qpMgsxfugGOZbd1SKUtG+MjontBFz6QMXHgSlC5yULuhvq6p6vom2PQE0UzK31zTgS95L2e9KnHjyg4ZefpjAFX+jWNVQKnrWdrnYRxGP0CovwxCDi2elSsltf7mXG3/0/fQOqK9D3npzn01GNESw4SDecX2jGxz2C364XuGLi02m92pmje7mJx0z2akn7km1dC5inKOpu7fUl0hQsOHRHKreTd7THipUCsmXtwIQYg0GDQR5PkGv9oPHqBSl0p3laGEbuz6yEam+tz+S74gPPdCCsyAuiM/2ClbYFpbvWfgHAGJgf3R4HpBcaWNSkng9bnYh+BdN8gakGHgo5/3kGXR86wGI9H+JVPgn84mn9/B/56efGKA3ZjRMy9ZX8VZMQ7E78ZZPxdAjHH7xXmJTpvBU8XI2d0cpjapO3vLOZls0P2l9zR0nYRhuKsc+2Kezaxrw5n351O4Y2BvdNE12765i1qz+kxPZ4GIFUko0JhCjlqBIHpX0g8CoFCWAgqrieGbEITK6pkOwOUDdO4eJhYc/vMNRAk8XoJVHUAsTzF6ZoO5OMS189KtJ0h2Xkn6uBwAuFFZFU2fi+G2ghh3GHwGFGdokrnZf0K/Mu9GtPBt+HabAjWqM3uY9D3NRyUONeLnxqT2c+85hfnXVPNbO6Z5BTNSwJMSCPjr2bSBQX4VqdyGlSaS9gfICQUd0MR3dE7ABFbba8lKPrKVkxd58cg5e1Gfz79ZsoK4mvTeOlNDc0s6stEqnh0DgZCmmnIdTLsUvHibC9uTlBzEEE4pg+g157P1relmGM2XUiFJAj5Bj7/tWXvqnU9hw05uY9uGPMSylJBaOvaeCBGB2aphBFaXA6Puchk1cd+YjiSckFAgECg6pIIAp1Qu59pf/4vkz/8C+qe+CkEQc8aGmGlV4w7cEGVM5L+etY3WuC86mMVZEoTD4qFaXdKgQkVBnCOqM+DqxBqOZtyL9IxcamBjdi7PyxFm4+tyO6jGbizsSY0qdj08+9RQXvBMm5HDy86s+dqzk9c88wcOnncHnX36enWecxQOvvUzM6P+b6/52dNrJyc/jpju/jaL2nQDQm2pofOxrjPn0DxJel2qanNdwhOJoFBHpOzT97PRT+EZN/2Uf7zUKDhQqqNlWSX1j4gmQWSsOoyV4JgSg2pILq5A2QKM8+GWK3CH28qUhavXx50nxlhHigzNAHeMp4J8X9Z+i9pf42HXZRiJ5mSWczAQpJf66LqpfOZiy3NvhH9JmzhuOFpB3SwO28fFXvtFqEPhjB7mNOQghiMoohUYe5bKMT4YnkJNg9X3EHuSfV8ajFMx+pZC1H2mnfkby72yxCPFTW0O/7Z0m/C5o41U9s/fdjz51Lu5game/HZNbaS4c2K9qzY5tPLvuHcJ6Xx+03MJCXB43V3zmP9Bsic/VZi9mR8Eyomr/YecpzQ1MDfRflmELhyndvYevNrRSnaa/VU6Oh1NP6Z/gYKjYvPVJ6ht2ZXSMajOYtrRviBR3bgQhQJEeCiNX4InFVwL42xt55vf/L1E1aSGlTNpXGzU9pWR4m3OZ9sw8Dpy9g1Dh8K09qX41tSABlKrr6DCnY5I6Hk/mCHz3lOA9Jx7vOfpuGLPBoNJWiSo02sw2lofdXKwnTz/kiLq54e8/AaDT/ypreSrlGVukyk7TwWyl7+zjWl3NWJDiV5AavytK2JFeL3TFnHkYpkF9Wyvr9/Y4Ga4472xmLFqY8thJbfXMr3qDFmd/L/tclw1s/a+tqLoGu2Hw5YIcPtvckVYbh5OurmaCoczbYegqu9/uG0d+/KxmnA47lflnHhMkAJvDRWnlPJoObRtsc/sxakTJFwnyxP61XDS1fwrn/Joipjw/h33nbRuWHlPT1sa0TFcKUYbFxiUlwneE0D/7vsU365uP/b2coZ31q8HOz40ivkgLM0SUu0MaOoIaY3jmi1vzwnR50p9NPWXeQqSUFObk8fyGtQMf0E15W4SrX9+ccF9tWQlvLFtC0N3Ti3K3t2PrDsSXowjOdDl4MfTe50o7ij/QxvZdL9DZOTRr+A7vKsGuucibXEBOnxwXAlUb+mUsMIpEKRiLsKZuV0JRgrgwzXpkMduuXYsxSBuTLSwZt7OnjjEtbsIF43iz/UiKo6DBWIlJ9vGeUyFiLSn3v2SrYrZRwlRz6FIF7ZcOaqSNjc+EeOQEJ8YAsaRTsUt7mUWcnXCVv88Tob4kdRaPRAghOH3BImyayr5IgPHTUi/tyA3orNqS3Nu6orEZZyRyTJRcnZ0UHT6CpsfF0qMoLHXaeTEUYeVlu6iY3saDPz6JwrF+zvh43x6Fpink5a6l/cAKug4vyvjakhEO++joGNrwu9FYiG0H30BRVMYVTwNAjwSp3z88GU5GjSgBbGjYxz93vspHZ5+ecL+3KZdF95zMhptfT89VQMp+ttyp78bQdNB6vbS9mgupOrm0bDoHgu1s6xo+b9dEbVRDuxmoB9asBAmI9Nf5KYZAOZr+XIBMFK3ElDz4kol7u2TCoQAHr/EcC16fLsKQnPozH4GwznbtWebEzkagHBMniSSqmYTTTG11PJqqsnLOfE6tXEqgLAdfV3X3N9XfoGszJKWdqXtj5772FlG7jYfPPRMtqh8TpKNUzm3ilstfx+HRUTWTT//sBRTVxOVNVG8zjoJayhb2rGMMNk+hbu1He5VJ9uXLhMFnhmsFSEQPsXHfizhtHorzyofpLHFGlSjppkEwlvrBc/icLPrLSrZ99F10T/+yii5Rux9GNQZT1/W3YyR67IQQuFSNOd5ioqbBvkB797zXMCJjKOEqMP1D7mR72Z3jjl1o9fwgr97Q1HNaReLPi1K6th3H5i5MwOaTVD4Y5PBFLgxX+o/G0nv9jNmhI4BO0cAa232MM+czxohn3gh77Wyfmvlasd5oqorT4aaoPB6/yDSj7Dv0KIapYxjdw3kpyU0jU4czGsURjXL9g4/R7HGzu6QIo9csnqqZePJ7hm+evNRDOdUWAVtPmZzxm5kxfsuxz2bUxaEX/wtTd+CMuXF2vyVLbAafKem/DGejPcCXcuIZQxsCQfRM0qcMQMzQCUZ8mHJwYVcGYlSJEkBtVwutIR9FrsQJBAUCd5uXaY/P4OBJG/rtz++A4kEEhxNCsDhvDCaS/YH24ZMlU0eJHkYxhtZXxDC6MExfvKfS3fjKLR5uuK0noWfYY/Dy1ftwbW47tk0AjjaTknciNKxKb+2bp9nA02r2CGr3H0fUrRxRtyIBs2IxiMGl9RZ2N2pOyTEXA1V1MHPK1XQFjtDStgMAzTC57qVky26Oq6/7/6WBIDFF4UBRPjE1+0SafeoW0LvXqzqCTLnge/jrZ3L6wXM5UUn93S4dU8arV18JwLfWvMOhTh9v1A7dcG793ucBKLQNbfbp3ow6UXrl8FZOmzCP08Ynn3o3RBizuYrSZ/oahoVN4CpzgWfwX8uS3DEcDHYS6w4rMEaswC3G0jbAcWkhY3FBig2uB5GIiF5DVK9OWUYLSSa9FKFhEFrhbjVY8rd4LykZAlCO7MSYdUr2JwK0vLE4J/S32+R4xpHjiUfqFIbBwRMiTH73nYzqLu/yI5AEbTZq8oc2k25vvGN34w1VQlP69qdvrTiRSCzGbzdvZWdrK68cTm3zTJcN+17krNlXDEldiRh1ogTwwO43mFM8keIEvSWJQavzLSJafx8bxaagDYEgHWVZ3lje7qilVJzAEvUblDnL6DANfhcahJhIiRKuyqqHZLO/gD1JyMBo+BPEDD/B8MBTvIYao6Eo8Q3urjXwVMcITEz+PdqCJsv/5KdsdxqzabEooukgsnTwqddTIVWV/SetzFiUAMZ2BZACYicpFOc6iNSVc7A8dequTBnfUcn4zsqMj3NoGl9YupjDXV1c3tLCz9dv4kDn4HrX0jTY9PxfB1VHKkalKO1srSGoR5DOniD3srtL3OR+nqiSuL9ihAwibRHsBfHV3dms4j6KEIKJzjLytd9jJweXKGGWFne09AiFHwfTNIYf59z61w2PcvOMCmTabZMsoZ0bqKJEjaAlMPBKCXZnI831YzDM/s6BmWDzS+ydJoHj16j0QtFJT5AAIU1EoB3J8IoSQKCggC0XXMT8p57IMLl7/FpziiMgNE7cMY+F+2bwwrJ38LtCg15VXxQo5cwDF+HRvVnXMT4nh/E5OcwvLuFznu/TpaQ3m2lGVarvm9Nri0BKScOBzVm3ZSBGjUf38dgVjScu/1/sqg2JQbvzXQJaNWAMfJMIcI93o9jjBkwhBCLDWSVpuqHt1yD7p8yRUrIxFuJ3wVaiCaxO7lgUuxmfbZoSaOeeDT2zM6o0OW3pIjqTeCQfdyZm0sV32YqWwrolJVQfqcQ0+9tFTAmRPsHMJOuWPIOhJp8NkwLazrbTWmnvJ562gMnFX2zHFk7/1jK9hZiVC0HJzm5jK5lC7orr0jyZyZwXnmPGG8ntSxGl76/2avk6Zq48FbvT2UeIDcUgbI/y2MmvAqCrBqaaoeFZwoTOSVy8+5qBy6ZJQIS4quC2tMRSSpCxeMGWt8bRuaWE6OtbIDy4iK0fSo/uqBmLG0rR8Tm2EbBlEK1PQrCmx/tb82rYC+2oDnVAcZJGPpgl4PtcQkGCuMgtsbm5SbazprO/EfI/q9azquVQ0nM8sHUH5yxZOOBlzMLH99ia1os62T3SGdH4x66xvUsyqbIIV2lTwvIQXxL3g3v28edrx9Hp1uhyqRwudZFTH+P0H/syEiQAxd8GjVWYY6dldFxWKAqBwkKiThf2cOIlLQ9OfgFd7TsMnqHSr2eomiqesItrXzwPgC1T9nK4tJGW/I6E6bET4Yy5hlSQAGxpJvmE7kw5tnhbS08/TO7kGg69bWIM36qt0StKAC9Wb2TlTDtd9szWAB1PzB8j5o/hnuBGcyX/yqRRAIEbIHLSgHXaA+1cv/15/qt+cG3rPjOKrW/65WUxndvk/kGNHEwJBzr6hj7JnXwA36HJKUXpKD+6J553/s3Z+fxo1SROuKcLT9t7GbsljhnyobcfwVYwbuDCwKFly1EMgznPP4ct2ndKv9pbhymyu4YFB6az4MB03pm9jaAjTPXYAbyuJUxvnZ3VuYaD4C5J7S9MjMGN8AdkVIvSXVseZ/6C9+ZHlaYb/DdDNL1Flu6OOgqyFSQRQ3X27WEptrZjL+qT9SjXxUI4B+mQsKY2ny3NPXaMgpk7KF32Dqp9YHvQC/mFrIvFexP1MQfTHw9SUDP80RoSYfibCWx5Au+Ci9EKKtI6purEFehOJ0se+jdKt4ljf24N60p2YCiDE9YTd84jbIsytrUni/DGGbuJ2vp/ryuqVw3qXIlQUbk2dCH/cD+Z0XGdb0iiQzOBl5JRLUrBqMG9b9Zy/cnp3YgDEW4M4xnvQah9+x/SdxsYxRBLb3hhC3YwftszWbXBAL62UEO1J3cumB2LkZ9lJtNDnU42NsZnLRsCdnobHrzja9ISJIDNnl5DVwPGNL23IV2Ox+hsoGvjQ+Se+HFUT3pLbQ4vWMiShx88NtlQ724movW3pcyoXIJNyyxTslO3M6umx3hf3JkPEYOCV/uOi9RzlGzyhqZEReGK0NloqNznfmzgA4CuDRLfG++NiXlUi5Ipobo1jGFK1AwN1Qnri5j4D/nxTi6A6GLo6g69KnNI28FfSjQ9jKtr4OHP8egCrl3hZL93aP23DSm4b0c5uqEQMwW6meBahPk+h1qV/aPOZYjpb6XztT+Sf+b/Q7EP7OApgXvHv4E044Iq7MFjfo1CQFnRBGZNWoam2lEyyLiSiJLOAlS/SdnuHlumBMyG7fivmxdfvjME9/BRHNgZbwzsmR3zSfbfYiKjYKaXjWvQDHOylPef/U1B/vjqYRo6I0Rjg7dnCHM8suVv4PsiyLz4vwy+RmdXM3Of/3nG5223wadOcMQFKcWD6ZYSdwa9JF/Yzk9eWoYvohGKqYkFCShZtB7vuJqM2z1UqM0NaK1dqBE7ajT71elSD9Hxws8xgh1plQ+pOuHufyFDIxR1E4q6MU2BIjTsNuegBSkZAlA6wuT+ah2uZw6gtIdgCO7hdIk2SfbdaGJ0vneCBKO8p3SUtw908vaBTq4+YQzl+Q4WTsjJygdJ1ebiyvl2vxxqmTDr1bsQGdp6GpyCH8yysbVg4H78Sj3Kilh6Q6xmv4v7N86gOZA6jrfmDmDP63jfErAI3HjUk3E0z4JmMBUDX7dzou4IY6Y5pDyKjEXwrbmXnKVXoeWPTVouWrv2uER/PV9AWHcTjWU2ZMuGo2e072nFvqeV0CnjMQucSLuKMSF1mqiBKDTzqDDKqFX7x1oP7Zcc/r6JOYyzbMn4UIjSUe5/twFVgY+fVM7q2ZmtkVBty3C6P48Q2QfiLz60HmWABcPH02qHn8y08VrZ0P9U2+qLOdiWP2A5V2kjeZPfnwSILuUEVFGAQ+lJs6SYKvlH4sHIfGPqCNk7Mq7XDLQRPrgW76JLE+4PHXgO31s/SJl91Bfw0eZrozB3aNJJeXYNLK6uNw4DYLo0ogt7kplGFo8BR2b3yMzYZE6JLOFf7p5EA9FGScdLkq41Er3/oof3hA+VKAEYZlycth/xs3J6PksrB37bqNoinO7PoqilA5ZNRvHBdxm/9WlUI31R+vp8Oy0Owbqi9Cyd5YbBWdH0AozVdnjZUDOwTUFzByietzmtOocaj3omDjF7UJ71qdCbq4g27sNe1neCIrT/WbrW/hwZTT333envZPOezSydtZRc7+DWveWtieDel/5kgBKK4Xy7ZwZWrfODTcEodtO6dCaqMHDZUo+5dmsHedMRX5QupeTID0xibRDcmd01DBUfOlECCOsmG6p97G0MkOPUmF4W7/0kuvkVdRJO75dRlOxXRUsk7s56tOjA4XiPDuy+tNDOK2VqBstJwCsllQOEqpASuiJ2/vzOXDrDAwecU2x6Wj5JQ41HPQuHmDlsggRghjrxvfkjpNl3LaIZ7kRG0lsf5gv4eHvb25y+5HQc9izCHEtJ/lsRXAdig/Ips1XH2xsO2mmaFX/ZJMpNd+y0gBRjyXlsF/vefASA6NDGhsuaD6UoHaUrbPCDp6pQFcFPrp6BXVNw2+O9EgmEbU40NQdPloIkkRiKjs/ZzJ6z81ill1LS0I4jrCe8AXUBf6vUuGuqDV0hI0FKh5CuohsK33thObqRTu9LotoHH9pVKgLDZUP3KGhBPcUkXjxRkEs5AYeYlZYgCVOhO11LVpihNszw4Az4oUiI59c+z4UnX5ixiOZsjOLaPzhB6o2UCka3x/bR/8eMGP5QTwbdWEzn+u9eE78/TYMhDLk0JHyoRQniwznDlNz6991MLnFx7YljGZvnwOvS+OvqmykNFnND8vRZSZFIAvYOaoq2dm8RvHTxMoRhcu7DPSvR89v8OCI6uoCHxmv8asbwGE/bgg7+b90catrTH2YI1aDyokeyPqcEzJJczJJcqj86iWpg+l+24egIo4ZieOp7FoVqYgwqBXi1szM6R07TGHRXCN39Hk4PJcA0TTq6OijIzewFJsyhzYLtD/nZXrW1z7aaxmp+fv+PhvAsw8uHXpR6U9Uc4rtPVHHqjAImFbswzpYEbBGqc9uY6MvMmOlzNlOXv7vfdqkqPHPlimOfZ2yrpqDZx9pAPb+YMfSB2INRjc21pWyrL85IkADypu4d1LmNyWVET5zWx4Vh743xOFfO5iDT/raD3KpO7GIqXvUchPjg3o5SSt7e9jZLZi6hrKhs4AMA1WeiDXLZzfpAE1t7hcLZe6SDP7yaucvJSOKDexcMI6/vaef1Pe0UO/+Gs7wM7TyV8w7OYkKawtTuqqcptyqtRZd75k1kTUsV69uGXpAe2zaFjpCDrXWZG+iL5m2mZPG6rN0A9JkV6AsmJvWpCpe4OXD1LGY/AAXV894XQTL1Nkx96ALlRfUoB+sOpi1K9hYDZ31mS29+0biFQ9GeMLgbgy1sG0x8rhGIJUopaHnpDRSng/Z3NzF1wReYMOnSAY/pdDbRlHMQQ0l/JmVvV9Ogw+aqmp3CFRfxb8Ngyc61rH9SZV3NWMzkESJSkjPhEIqW3Vo1fUY5+rwJoKW2WwXLvehjylFqsnezAMhpGEv7xEPITMOCxAJg+AculwGtna0cqjtEZXnloOuSUnLRgaeJmj2/w85wGwHz/V2yM9xYojQAZjhCYM8B/lT139yrfhObZud3n34LgUBVNVSl5ysM2Duoy9uDzGDB5nP1O/Hpg7OHCEVl6elXYnO6aQCeW3EhR57diymzrFcYWS8pkQJkjgvs792tZYs4GVrLTGIMU2IO4C2vGxE6A37CenLhuPj79xEIR8EEoaeur914/3LIvV9YopQmET1IRI9P6V/zs3j+sAuXfopzFn6CMfkTMew6uhrOSJAAoqaRdS9Jszmw2Z3MOeFcbI6etVwxmx1TZO91XrxgE86i1HnkEiEVQWxmBbHpyb2khws1aiPm7BvAr9XXiZFiasmMdGEG0vcGf25HG7sa0hH6GuDFtOu16IslSoPgyfV38+T6u/n02d/DX94UtyFVw7gJU8gvKB64gkFgc7iYPPtESitSJ1jMhpbNSxGqSdG8zShpDomkEMRmlKMvSh22VkpJ3asHORrx1LavnaYWz6DbTKuks6Kvo83Db75KIPw+rJOwGBSjNhzu+8msuUsoGzu+3/alJ66ioKikz7YnardRFUi/VzJuynxUzYbLnUdJxZR++9v27KB+7Rv4X2km1pbZmrDjKZq3BUVNz34hVYXY7DSCqEnYc+9GpGndWh9mUoXDtUTpPWRC5TTcnr4hchvDPgIZLD0pKK5AUZN3cAON9fgO7c+6jRYW7wWWKFlYWIwoUonSqI+nZGFh8cHCEiULC4sRhSVKFhYWIwpLlCwsLEYUlihZWFiMKCxRsrCwGFFYomRhYTGisETJwsJiRGGJkoWFxYjCEiULC4sRhSVKFhYWIwpLlCwsLEYUlihZWFiMKCxRsrCwGFFYomRhYTGisETJwsJiRGGJkoWFxYjCEiULC4sRhSVKFhYWIwpLlCwsLEYUlihZWFiMKCxRsrCwGFFYomRhYTGisETJwsJiRGGJkoWFxYjCEiULC4sRhSVKFhYWIwpLlCwsLEYUlihZWFiMKCxRsrCwGFFYomRhYTGisETJwsJiRGGJkoWFxYhCSCnf7zZYWFhYHMPqKVlYWIwoLFGysLAYUViiZGFhMaKwRMnCwmJEYYmShYXFiMISJQsLixHF/wcJ0MiKebZggQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N=100\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "plt.title(\"model seg\")\n",
    "plt.axis('off')\n",
    "plt.imshow(seg_RGB[:,:,N,:])#, cmap=\"gray\")\n",
    "#plt.savefig('_RGB_'+str(N)+'.png',bbox_inches='tight',dpi=fig.dpi,pad_inches=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd448432040>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "plt.title(\"hand seg\")\n",
    "plt.axis('off')\n",
    "plt.imshow(hand_seg_RGB[:,:,N,:])#, cmap=\"gray\")\n",
    "#plt.savefig('_RGB_'+str(N)+'.png',bbox_inches='tight',dpi=fig.dpi,pad_inches=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"There are \"+str(len(np.unique(seg_final)))+\" cells in model prediction\")\n",
    "# print(\"There are \"+str(len(np.unique(hand_seg)))+\" cells in hand seg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/v-andongwang/anaconda3/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:389: RuntimeWarning: overflow encountered in long_scalars\n",
      "  return 2. * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) +\n",
      "/home/v-andongwang/anaconda3/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:390: RuntimeWarning: overflow encountered in long_scalars\n",
      "  (tp + fp) * (fp + tn))\n"
     ]
    }
   ],
   "source": [
    "ARI = adjusted_rand_score(hand_seg.flatten(), seg_final.flatten())\n",
    "ARE = adapted_rand_error(hand_seg.astype(int).flatten(), seg_final.astype(int).flatten())\n",
    "VOI_val = VOI(seg_final.astype(int),hand_seg.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARI: -2.0024416381517023\n",
      "ARE: (0.3395005969048239, 0.6550275313051844, 0.666063465232361)\n",
      "VOI: (1.1497308202445824, 0.9805741732155469)\n"
     ]
    }
   ],
   "source": [
    "print(\"ARI: \"+str(ARI))\n",
    "print(\"ARE: \"+str(ARE))\n",
    "print(\"VOI: \"+str(VOI_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(181, 331, 160) --> (90, 165, 80)\n"
     ]
    }
   ],
   "source": [
    "def img_3d_interpolate(img_3d, output_size, device = torch.device('cpu'), mode='nearest'):\n",
    "    img_3d = img_3d.reshape(1,1,img_3d.shape[0],img_3d.shape[1],img_3d.shape[2])\n",
    "    img_3d=torch.from_numpy(img_3d).float().to(device)\n",
    "    img_3d=torch.nn.functional.interpolate(img_3d, size=output_size, mode='nearest')\n",
    "    img_3d=img_3d.detach().cpu().numpy()\n",
    "    img_3d=img_3d.reshape(img_3d.shape[2],img_3d.shape[3],img_3d.shape[4])\n",
    "    \n",
    "    return img_3d\n",
    "\n",
    "scale_factor = 0.5\n",
    "org_shape = seg_final.shape\n",
    "output_size = (int(org_shape[0]*scale_factor), int(org_shape[1]*scale_factor), int(org_shape[2]*scale_factor))\n",
    "print(str(org_shape)+\" --> \"+str(output_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell count accuracy iou >0.7: 0.5331412103746398\n",
      "cell count accuracy dice >0.7: 0.7146974063400576\n",
      "cell count accuracy iou >0.5: 0.7377521613832853\n",
      "cell count accuracy dice >0.5: 0.8904899135446686\n",
      "avg iou: 0.6391003464888083\n",
      "avg dice: 0.7563323639655319\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21916/148196946.py:4: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  iou=np.array(accuracy_record[:,1]>0.7, dtype=np.float)\n",
      "/tmp/ipykernel_21916/148196946.py:7: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dice=np.array(accuracy_record[:,2]>0.7, dtype=np.float)\n",
      "/tmp/ipykernel_21916/148196946.py:10: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  iou=np.array(accuracy_record[:,1]>0.5, dtype=np.float)\n",
      "/tmp/ipykernel_21916/148196946.py:13: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dice=np.array(accuracy_record[:,2]>0.5, dtype=np.float)\n"
     ]
    }
   ],
   "source": [
    "accuracy=IOU_and_Dice_Accuracy(img_3d_interpolate(hand_seg, output_size = output_size), img_3d_interpolate(seg_final, output_size = output_size))\n",
    "accuracy_record=accuracy.cal_accuracy_II()\n",
    "\n",
    "iou=np.array(accuracy_record[:,1]>0.7, dtype=np.float)\n",
    "print('cell count accuracy iou >0.7: '+str(sum(iou)/len(iou)))\n",
    "\n",
    "dice=np.array(accuracy_record[:,2]>0.7, dtype=np.float)\n",
    "print('cell count accuracy dice >0.7: '+str(sum(dice)/len(dice)))\n",
    "    \n",
    "iou=np.array(accuracy_record[:,1]>0.5, dtype=np.float)\n",
    "print('cell count accuracy iou >0.5: '+str(sum(iou)/len(iou)))\n",
    "\n",
    "dice=np.array(accuracy_record[:,2]>0.5, dtype=np.float)\n",
    "print('cell count accuracy dice >0.5: '+str(sum(dice)/len(dice)))\n",
    "\n",
    "print('avg iou: '+str(np.mean(accuracy_record[:,1])))\n",
    "print('avg dice: '+str(np.mean(accuracy_record[:,2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seg all imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img_3d_interpolate(img_3d, output_size, device = torch.device('cpu'), mode='nearest'):\n",
    "    img_3d = img_3d.reshape(1,1,img_3d.shape[0],img_3d.shape[1],img_3d.shape[2])\n",
    "    img_3d=torch.from_numpy(img_3d).float().to(device)\n",
    "    img_3d=torch.nn.functional.interpolate(img_3d, size=output_size, mode='nearest')\n",
    "    img_3d=img_3d.detach().cpu().numpy()\n",
    "    img_3d=img_3d.reshape(img_3d.shape[2],img_3d.shape[3],img_3d.shape[4])\n",
    "    \n",
    "    return img_3d\n",
    "\n",
    "def pipeline(raw_img, hand_seg, model, device,\n",
    "             crop_cube_size, stride,\n",
    "             how_close_are_the_super_vox_to_boundary=2,\n",
    "             min_touching_area=30,\n",
    "             min_touching_percentage=0.51,\n",
    "             min_cell_size_threshold=1,\n",
    "             transposes = [[0,1,2]], reverse_transposes = [[0,1,2]]):\n",
    "    \n",
    "    seg_final=segment_super_vox_3_channel(raw_img, model, device,\n",
    "                                          crop_cube_size=crop_cube_size, stride=stride,\n",
    "                                          how_close_are_the_super_vox_to_boundary=how_close_are_the_super_vox_to_boundary,\n",
    "                                          min_touching_area=min_touching_area,\n",
    "                                          min_touching_percentage=min_touching_percentage,\n",
    "                                          min_cell_size_threshold=min_cell_size_threshold,\n",
    "                                          transposes = transposes, reverse_transposes = reverse_transposes)\n",
    "    \n",
    "    ari = adjusted_rand_score(hand_seg.flatten(), seg_final.flatten())\n",
    "    voi = VOI(seg_final.astype(np.int),hand_seg.astype(np.int))\n",
    "    \n",
    "    scale_factor = 0.5\n",
    "    org_shape = seg_final.shape\n",
    "    output_size = (int(org_shape[0]*scale_factor), int(org_shape[1]*scale_factor), int(org_shape[2]*scale_factor))\n",
    "    print(str(org_shape)+\" --> \"+str(output_size))\n",
    "    \n",
    "    accuracy=IOU_and_Dice_Accuracy(img_3d_interpolate(hand_seg, output_size = output_size),\n",
    "                                   img_3d_interpolate(seg_final, output_size = output_size))\n",
    "    accuracy_record=accuracy.cal_accuracy_II()\n",
    "    hand_seg_after_accuracy=accuracy.gt\n",
    "    seg_final_after_accuracy=accuracy.pred\n",
    "    \n",
    "    return accuracy_record, hand_seg_after_accuracy, seg_final_after_accuracy, ari, voi, seg_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mass process\n",
    "seg_final_dict={}\n",
    "accuracy_record_dict = {}\n",
    "ari_dict = {}\n",
    "voi_dict = {}\n",
    "for test_file in HMS_data_dict_test.keys():\n",
    "    print(test_file)\n",
    "    raw_img=np.load(HMS_data_dict_test[test_file][\"raw\"])\n",
    "    hand_seg=np.load(HMS_data_dict_test[test_file][\"ins\"])\n",
    "    accuracy_record, hand_seg_after_accuracy, seg_final_after_accuracy, ari, voi, seg_final=\\\n",
    "    pipeline(raw_img, hand_seg, model, device,\n",
    "             crop_cube_size=64,\n",
    "             stride=32)\n",
    "    \n",
    "    seg_final_dict[test_file] = seg_final\n",
    "    accuracy_record_dict[test_file] = accuracy_record\n",
    "    ari_dict[test_file] = ari\n",
    "    voi_dict[test_file] = voi\n",
    "    \n",
    "    iou=np.array(accuracy_record[:,1]>0.7, dtype=np.float)\n",
    "    print('cell count accuracy iou >0.7: '+str(sum(iou)/len(iou)))\n",
    "\n",
    "    dice=np.array(accuracy_record[:,2]>0.7, dtype=np.float)\n",
    "    print('cell count accuracy dice >0.7: '+str(sum(dice)/len(dice)))\n",
    "    \n",
    "    iou=np.array(accuracy_record[:,1]>0.5, dtype=np.float)\n",
    "    print('cell count accuracy iou >0.5: '+str(sum(iou)/len(iou)))\n",
    "\n",
    "    dice=np.array(accuracy_record[:,2]>0.5, dtype=np.float)\n",
    "    print('cell count accuracy dice >0.5: '+str(sum(dice)/len(dice)))\n",
    "\n",
    "    print('avg iou: '+str(np.mean(accuracy_record[:,1])))\n",
    "    print('avg dice: '+str(np.mean(accuracy_record[:,2])))\n",
    "    print(\"ari: \"+str(ari))\n",
    "    print(\"voi: \"+str(voi))\n",
    "    print(\"----------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in seg_final_dict.keys():\n",
    "    print(item)\n",
    "    accuracy_record = accuracy_record_dict[item]\n",
    "    ari = ari_dict[item]\n",
    "    voi = voi_dict[item]\n",
    "    iou=np.array(accuracy_record[:,1]>0.7, dtype=np.float)\n",
    "    print('cell count accuracy iou >0.7: '+str(sum(iou)/len(iou)))\n",
    "\n",
    "    dice=np.array(accuracy_record[:,2]>0.7, dtype=np.float)\n",
    "    print('cell count accuracy dice >0.7: '+str(sum(dice)/len(dice)))\n",
    "    \n",
    "    iou=np.array(accuracy_record[:,1]>0.5, dtype=np.float)\n",
    "    print('cell count accuracy iou >0.5: '+str(sum(iou)/len(iou)))\n",
    "\n",
    "    dice=np.array(accuracy_record[:,2]>0.5, dtype=np.float)\n",
    "    print('cell count accuracy dice >0.5: '+str(sum(dice)/len(dice)))\n",
    "\n",
    "    print('avg iou: '+str(np.mean(accuracy_record[:,1])))\n",
    "    print('avg dice: '+str(np.mean(accuracy_record[:,2])))\n",
    "    print(\"ari: \"+str(ari))\n",
    "    print(\"voi: \"+str(voi))\n",
    "    print(\"----------\")"
   ]
  }
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