01_Prepare_Annotation_from_ImageJ_File.ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import sys\n",
    "import os\n",
    "import random\n",
    "from skimage import io\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.patches import Rectangle as BBox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing to do is prepare the images and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "anno_path = \"/home/simon/PycharmProjects/ObjectDetection/data/Rotated/train/annotations/\"\n",
    "image_path = \"/home/simon/PycharmProjects/ObjectDetection/data/Rotated/train/images/\""
   ]
  },
  {
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   "execution_count": 83,
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       "      <th>4</th>\n",
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       "      <td>BCC_13.png</td>\n",
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      "text/plain": [
       "           Label   BX   BY  Width  Height\n",
       "0  1   BCC_1.png   78   88      6       7\n",
       "1  2   BCC_1.png  194  100      8       7\n",
       "2  3  BCC_10.png   35  103      8       8\n",
       "3  4  BCC_10.png  225  100      6       8\n",
       "4  5  BCC_13.png   50   87      8       8"
      ]
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     "execution_count": 83,
     "metadata": {},
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   ],
   "source": [
    "data = pd.read_csv(os.path.join(anno_path, \"10x_raw_train.csv\"))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop first column\n",
    "data = data.drop(\" \", axis=1)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 85,
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      "text/plain": [
       "        Label   BX   BY  Width  Height\n",
       "0   BCC_1.png   78   88      6       7\n",
       "1   BCC_1.png  194  100      8       7\n",
       "2  BCC_10.png   35  103      8       8\n",
       "3  BCC_10.png  225  100      6       8\n",
       "4  BCC_13.png   50   87      8       8"
      ]
     },
     "execution_count": 85,
     "metadata": {},
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    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to make all of Width and Height = 10 so that they are standard size.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Width\"] = [10]*108\n",
    "data[\"Height\"] = [10]*108"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "        Label   BX   BY  Width  Height\n",
       "0   BCC_1.png   78   88     10      10\n",
       "1   BCC_1.png  194  100     10      10\n",
       "2  BCC_10.png   35  103     10      10\n",
       "3  BCC_10.png  225  100     10      10\n",
       "4  BCC_13.png   50   87     10      10"
      ]
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     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have to add the values of width and height to their respective x and y values, so we get the top left and bottom right corners of the bounding boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Width\"] = data[\"Width\"] + data[\"BX\"]\n",
    "data[\"Height\"] = data[\"Height\"] + data[\"BY\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "        Label   BX   BY  Width  Height\n",
       "0   BCC_1.png   78   88     88      98\n",
       "1   BCC_1.png  194  100    204     110\n",
       "2  BCC_10.png   35  103     45     113\n",
       "3  BCC_10.png  225  100    235     110\n",
       "4  BCC_13.png   50   87     60      97"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to standardise them to the image size, which in this case is 256px. This should then make them work for all sizes of the image that were re-orientated in the same way e.g. relative padding, squareness etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[\"BX\"] /= 256\n",
    "#data[\"BY\"] /= 256\n",
    "#data[\"Width\"] /= 256\n",
    "#data[\"Height\"] /= 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we want to Add the `ClassName` column on the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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       "      <td>BCC_10.png</td>\n",
       "      <td>225</td>\n",
       "      <td>100</td>\n",
       "      <td>235</td>\n",
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       "      <td>Margin</td>\n",
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       "      <td>Margin</td>\n",
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      ],
      "text/plain": [
       "        Label   BX   BY  Width  Height ClassName\n",
       "0   BCC_1.png   78   88     88      98    Margin\n",
       "1   BCC_1.png  194  100    204     110    Margin\n",
       "2  BCC_10.png   35  103     45     113    Margin\n",
       "3  BCC_10.png  225  100    235     110    Margin\n",
       "4  BCC_13.png   50   87     60      97    Margin"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ClassName\"] = [\"Margin\", \"Margin\"]*54 # Make them all the same \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must also rename the columns to `FileName\tXMin\tXMax\tYMin\tYMax\tClassName`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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       "      <td>BCC_13.png</td>\n",
       "      <td>50</td>\n",
       "      <td>87</td>\n",
       "      <td>60</td>\n",
       "      <td>97</td>\n",
       "      <td>Margin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FileName   X1   Y1   X2   Y2 ClassName\n",
       "0   BCC_1.png   78   88   88   98    Margin\n",
       "1   BCC_1.png  194  100  204  110    Margin\n",
       "2  BCC_10.png   35  103   45  113    Margin\n",
       "3  BCC_10.png  225  100  235  110    Margin\n",
       "4  BCC_13.png   50   87   60   97    Margin"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns = [\"FileName\", \"X1\", \"Y1\", \"X2\", \"Y2\", \"ClassName\"]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can prepend the directory for where the files live"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"FileName\"] = image_path  + data[\"FileName\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FileName</th>\n",
       "      <th>X1</th>\n",
       "      <th>Y1</th>\n",
       "      <th>X2</th>\n",
       "      <th>Y2</th>\n",
       "      <th>ClassName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/simon/PycharmProjects/ObjectDetection/da...</td>\n",
       "      <td>78</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>98</td>\n",
       "      <td>Margin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/simon/PycharmProjects/ObjectDetection/da...</td>\n",
       "      <td>194</td>\n",
       "      <td>100</td>\n",
       "      <td>204</td>\n",
       "      <td>110</td>\n",
       "      <td>Margin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/simon/PycharmProjects/ObjectDetection/da...</td>\n",
       "      <td>35</td>\n",
       "      <td>103</td>\n",
       "      <td>45</td>\n",
       "      <td>113</td>\n",
       "      <td>Margin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/simon/PycharmProjects/ObjectDetection/da...</td>\n",
       "      <td>225</td>\n",
       "      <td>100</td>\n",
       "      <td>235</td>\n",
       "      <td>110</td>\n",
       "      <td>Margin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/simon/PycharmProjects/ObjectDetection/da...</td>\n",
       "      <td>50</td>\n",
       "      <td>87</td>\n",
       "      <td>60</td>\n",
       "      <td>97</td>\n",
       "      <td>Margin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            FileName   X1   Y1   X2   Y2  \\\n",
       "0  /home/simon/PycharmProjects/ObjectDetection/da...   78   88   88   98   \n",
       "1  /home/simon/PycharmProjects/ObjectDetection/da...  194  100  204  110   \n",
       "2  /home/simon/PycharmProjects/ObjectDetection/da...   35  103   45  113   \n",
       "3  /home/simon/PycharmProjects/ObjectDetection/da...  225  100  235  110   \n",
       "4  /home/simon/PycharmProjects/ObjectDetection/da...   50   87   60   97   \n",
       "\n",
       "  ClassName  \n",
       "0    Margin  \n",
       "1    Margin  \n",
       "2    Margin  \n",
       "3    Margin  \n",
       "4    Margin  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, we want to save it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = os.path.join(anno_path, \"train.csv\")\n",
    "data.to_csv(fname, index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can show a bounding box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = io.imread(data[\"FileName\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# West\n",
    "x = data[\"X1\"][0] #* 256\n",
    "y = data[\"Y1\"][0] #* 256\n",
    "width = (data[\"X2\"][0] - data[\"X1\"][0]) #* 256\n",
    "height = (data[\"Y2\"][0] - data[\"Y1\"][0]) #* 256\n",
    "bbox1 = BBox((x, y), width, height, linewidth=1,edgecolor='green',facecolor='none', label=\"West\")\n",
    "\n",
    "# East\n",
    "x = data[\"X1\"][1] #* 256\n",
    "y = data[\"Y1\"][1] #* 256\n",
    "width = (data[\"X2\"][1] - data[\"X1\"][1]) #* 256\n",
    "height = (data[\"Y2\"][1] - data[\"Y1\"][1]) #* 256\n",
    "bbox2 = BBox((x, y), width, height, linewidth=1, edgecolor='red',facecolor='none', label=\"East\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,  figsize=(4,4))\n",
    "ax.imshow(img)\n",
    "ax.add_patch(bbox1)\n",
    "ax.add_patch(bbox2)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}