preprocess_yolo_labels_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time output center point of ROI instead of upper left corner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import pandas as pd\n",
    "import csv\n",
    "import pickle\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "GRID_SIZE = 4\n",
    "location_data = np.zeros((GRID_SIZE,GRID_SIZE,5))\n",
    "\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        location_data[i,j,:] = [0, 390, 365, 225, 335]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coords = np.arange(0, 1024, 128)\n",
    "\n",
    "fig,ax = plt.subplots(1, figsize=(10,10))\n",
    "\n",
    "rect_1 = patches.Rectangle((195,511),218,271,linewidth=1,edgecolor='r',facecolor='none')\n",
    "plt.plot(304, 646.5, marker=\"o\", color=\"r\")\n",
    "rect_2 = patches.Rectangle((612,214),109,89,linewidth=1,edgecolor='b',facecolor='none')\n",
    "plt.plot(666.5, 258.5, marker=\"o\", color=\"b\")\n",
    "rect_3 = patches.Rectangle((610,599),265,245,linewidth=1,edgecolor='g',facecolor='none')\n",
    "plt.plot(742.5, 721.5, marker=\"o\", color=\"g\")\n",
    "ax.add_patch(rect_1)\n",
    "ax.add_patch(rect_2)\n",
    "ax.add_patch(rect_3)\n",
    "plt.xticks(coords)\n",
    "plt.yticks(coords)\n",
    "for item in coords:\n",
    "    plt.axvline(item, linewidth=0.5)\n",
    "    plt.axhline(item, linewidth=0.5)\n",
    "plt.xlim(0,1024)\n",
    "plt.ylim(0,1024)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(4.0, 10.0), (10.0, 4.0), (11.0, 11.0)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxes = [\n",
    "    [195,511,218,271],\n",
    "    [612,214,109,89],\n",
    "    [610,599,265,245]\n",
    "]\n",
    "\n",
    "centers = []\n",
    "\n",
    "for box in boxes:\n",
    "    x,y,w,h = box\n",
    "    center_x = x + (w / 2)\n",
    "    center_y = y + (h / 2)\n",
    "    centers.append((center_x,center_y))\n",
    "\n",
    "pixel_locs = []    \n",
    "for center in centers:\n",
    "    x_pixel = center[0] // 64\n",
    "    y_pixel = center[1] // 64\n",
    "    \n",
    "    pixel_locs.append((x_pixel,y_pixel))\n",
    "    \n",
    "pixel_locs    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dir: ./stage_1_train_images\n"
     ]
    }
   ],
   "source": [
    "ROOT_DIR = \"./\"\n",
    "\n",
    "train_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_train_images')\n",
    "test_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_test_images')\n",
    "print(\"Train dir:\", train_dicom_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "GRID_SIZE = 4\n",
    "\n",
    "# empty dictionary\n",
    "pneumonia_locations = {}\n",
    "used_filenames = []\n",
    "pixel_means = []\n",
    "\n",
    "# load table\n",
    "with open(os.path.join('./stage_1_train_labels.csv'), mode='r') as infile:\n",
    "    # open reader\n",
    "    reader = csv.reader(infile)\n",
    "    # skip header\n",
    "    next(reader, None)\n",
    "    # loop through rows\n",
    "    for rows in reader:\n",
    "        # retrieve information\n",
    "        filename = rows[0]\n",
    "        location = rows[1:5]\n",
    "        pneumonia = rows[5]\n",
    "        \n",
    "        # initialize our data\n",
    "        if filename not in used_filenames:\n",
    "            location_data = np.zeros((GRID_SIZE,GRID_SIZE,5))\n",
    "            \n",
    "            # default label to mean values\n",
    "            for i in range(4):\n",
    "                for j in range(4):\n",
    "                    location_data[i,j,:] = [0, 390, 365, 225, 335]\n",
    "            \n",
    "            used_filenames.append(filename)\n",
    "            locs = np.zeros((GRID_SIZE, GRID_SIZE))\n",
    "        else:\n",
    "            location_data = pneumonia_locations[filename]\n",
    "            \n",
    "        # if row contains pneumonia add label to dictionary\n",
    "        # the dictionary will contain the presence, x, y, h, w of the pneumonia for each pixel in a 16x16 grid\n",
    "        if pneumonia == '1':\n",
    "            # extract the location and cast them to floats\n",
    "            x,y,w,h = location\n",
    "            x = float(x)\n",
    "            y = float(y)\n",
    "            w = float(w)\n",
    "            h = float(h)\n",
    "            \n",
    "            # figure out the center of the ROI\n",
    "            center_x = x + (w / 2)\n",
    "            center_y = y + (h / 2)\n",
    "            \n",
    "            # figure out which pixel is in the center\n",
    "            x_pixel = int(center_x // (1024 // GRID_SIZE))\n",
    "            y_pixel = int(center_y // (1024 // GRID_SIZE))\n",
    "            locs[y_pixel, x_pixel] = 1\n",
    "            # write the data to our list\n",
    "            location_data[y_pixel,x_pixel,0] = 1\n",
    "            location_data[y_pixel,x_pixel,1] = center_x\n",
    "            location_data[y_pixel,x_pixel,2] = center_y\n",
    "            location_data[y_pixel,x_pixel,3] = w\n",
    "            location_data[y_pixel,x_pixel,4] = h\n",
    "        else:\n",
    "            pass\n",
    "        \n",
    "        pixel_means.append(np.mean(locs))\n",
    "        pneumonia_locations[filename] = location_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save our dict to disk\n",
    "with open('yolo_labels_center_4x4.p', 'wb') as handle:\n",
    "    pickle.dump(pneumonia_locations, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('yolo_labels_center_4x4.p', 'rb') as handle:\n",
    "    b = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ]],\n",
       "\n",
       "       [[  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ]],\n",
       "\n",
       "       [[  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  1. , 289.5, 549.5, 225. , 529. ],\n",
       "        [  1. , 741.5, 627.5, 201. , 309. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ]],\n",
       "\n",
       "       [[  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ],\n",
       "        [  0. , 390. , 365. , 225. , 335. ]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pneumonia_locations[\"fffb2395-8edd-4954-8a89-ffe2fd329be3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.441394898288664\n"
     ]
    }
   ],
   "source": [
    "print(1 / np.mean(pixel_means))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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