brainstem_StARQ_datagen.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyM2VjPqWN35vopz7woCwcVU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/itsasimiqbal/StARQ/blob/main/brainstem_StARQ_datagen.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Colab to generate training/testing date for SeBRe 2.0`"
      ],
      "metadata": {
        "id": "lMztlbnBB2-e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`import libraries`"
      ],
      "metadata": {
        "id": "MCp8BhWuB_1Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import pickle\n",
        "import glob\n",
        "import cv2\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "parent_fold = '/content/drive/My Drive/1 Asim Iqbal/Cornell/Datasets/cord_dataset/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYFMk21cCBkN",
        "outputId": "4d45dd05-fa47-46a2-a434-38525f0a03ec"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`loading dicts`"
      ],
      "metadata": {
        "id": "o_ZKpLVMCx3g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/1 Asim Iqbal/Cornell/Code/train_sects.pkl', 'rb') as f:\n",
        "    train_sects = pickle.load(f)\n",
        "with open('/content/drive/MyDrive/1 Asim Iqbal/Cornell/Code/test_sects.pkl', 'rb') as f:\n",
        "    test_sects = pickle.load(f)\n",
        "with open('/content/drive/MyDrive/1 Asim Iqbal/Cornell/Code/train_masks.pkl', 'rb') as f:\n",
        "    train_masks = pickle.load(f)\n",
        "with open('/content/drive/MyDrive/1 Asim Iqbal/Cornell/Code/test_masks.pkl', 'rb') as f:\n",
        "    test_masks = pickle.load(f)"
      ],
      "metadata": {
        "id": "4bdc8ZNvCVMq"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,5,1); plt.imshow(test_sects['A1_S13.png'],cmap='inferno')\n",
        "for x in range(4):\n",
        "  plt.subplot(1,5,x+2); plt.imshow(test_masks['A1_S13.png'][x])\n",
        "plt.figure()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "id": "iRVpzQnCCtKn",
        "outputId": "d39db797-5140-4389-e763-d67c5fdd004f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# `Data gen`"
      ],
      "metadata": {
        "id": "jtZTf8JbC03d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels = {0:'Midbrain',\n",
        "          1:'Pons',\n",
        "          2:'thalamus',\n",
        "          3:'Medulla'}"
      ],
      "metadata": {
        "id": "L9zRUcxOJmSI"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`train`"
      ],
      "metadata": {
        "id": "v7sHhIqzLWE7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "count = 0\n",
        "for key in train_sects:\n",
        "  # brain sect\n",
        "  temp_sect = np.zeros((256,256,3))\n",
        "  for x in range(3):\n",
        "    temp_sect[:,:,x] = (train_sects[key]/np.max(train_sects[key]))*255\n",
        "  cv2.imwrite(parent_fold+'train/Gray/'+str(count)+'.png', temp_sect)\n",
        "  # mask sects\n",
        "  temp_masks = train_masks[key]\n",
        "  cv2.imwrite(parent_fold+'train/masks2train/0_'+str(count)+'.png', temp_masks[0])\n",
        "  cv2.imwrite(parent_fold+'train/masks2train/1_'+str(count)+'.png', temp_masks[1])\n",
        "  cv2.imwrite(parent_fold+'train/masks2train/2_'+str(count)+'.png', temp_masks[2])\n",
        "  cv2.imwrite(parent_fold+'train/masks2train/3_'+str(count)+'.png', temp_masks[3])\n",
        "  count+=1\n",
        "  print(count,'/',len(train_sects),' --- processed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTGBJb-3DYms",
        "outputId": "0a26e493-05f7-47e8-88b3-1bac22fe8da9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 / 44  --- processed\n",
            "2 / 44  --- processed\n",
            "3 / 44  --- processed\n",
            "4 / 44  --- processed\n",
            "5 / 44  --- processed\n",
            "6 / 44  --- processed\n",
            "7 / 44  --- processed\n",
            "8 / 44  --- processed\n",
            "9 / 44  --- processed\n",
            "10 / 44  --- processed\n",
            "11 / 44  --- processed\n",
            "12 / 44  --- processed\n",
            "13 / 44  --- processed\n",
            "14 / 44  --- processed\n",
            "15 / 44  --- processed\n",
            "16 / 44  --- processed\n",
            "17 / 44  --- processed\n",
            "18 / 44  --- processed\n",
            "19 / 44  --- processed\n",
            "20 / 44  --- processed\n",
            "21 / 44  --- processed\n",
            "22 / 44  --- processed\n",
            "23 / 44  --- processed\n",
            "24 / 44  --- processed\n",
            "25 / 44  --- processed\n",
            "26 / 44  --- processed\n",
            "27 / 44  --- processed\n",
            "28 / 44  --- processed\n",
            "29 / 44  --- processed\n",
            "30 / 44  --- processed\n",
            "31 / 44  --- processed\n",
            "32 / 44  --- processed\n",
            "33 / 44  --- processed\n",
            "34 / 44  --- processed\n",
            "35 / 44  --- processed\n",
            "36 / 44  --- processed\n",
            "37 / 44  --- processed\n",
            "38 / 44  --- processed\n",
            "39 / 44  --- processed\n",
            "40 / 44  --- processed\n",
            "41 / 44  --- processed\n",
            "42 / 44  --- processed\n",
            "43 / 44  --- processed\n",
            "44 / 44  --- processed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`test`"
      ],
      "metadata": {
        "id": "1EGtiCyRLXPO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "count = 0\n",
        "for key in test_sects:\n",
        "  # brain sect\n",
        "  temp_sect = np.zeros((256,256,3))\n",
        "  for x in range(3):\n",
        "    temp_sect[:,:,x] = (test_sects[key]/np.max(test_sects[key]))*255\n",
        "  cv2.imwrite(parent_fold+'test/Gray/'+str(count)+'.png', temp_sect)\n",
        "  # mask sects\n",
        "  temp_masks = test_masks[key]\n",
        "  cv2.imwrite(parent_fold+'test/masks2test/0_'+str(count)+'.png', np.uint8(temp_masks[0]>0)*255)\n",
        "  cv2.imwrite(parent_fold+'test/masks2test/1_'+str(count)+'.png', np.uint8(temp_masks[1]>0)*255)\n",
        "  cv2.imwrite(parent_fold+'test/masks2test/2_'+str(count)+'.png', np.uint8(temp_masks[2]>0)*255)\n",
        "  cv2.imwrite(parent_fold+'test/masks2test/3_'+str(count)+'.png', np.uint8(temp_masks[3]>0)*255)\n",
        "  count+=1\n",
        "  print(count,'/',len(test_sects),' --- processed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yzNfFgxrGqyN",
        "outputId": "84c7b077-fbda-4afd-9b45-c23f4a20ef87"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 / 10  --- processed\n",
            "2 / 10  --- processed\n",
            "3 / 10  --- processed\n",
            "4 / 10  --- processed\n",
            "5 / 10  --- processed\n",
            "6 / 10  --- processed\n",
            "7 / 10  --- processed\n",
            "8 / 10  --- processed\n",
            "9 / 10  --- processed\n",
            "10 / 10  --- processed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RprLD5qeKlYv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}