PhD_test_Csapo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PhD-test_Csapo.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBZjW_VrL5gT",
        "colab_type": "text"
      },
      "source": [
        "Welcome to this Project Laboratory test! This is a Jupyter notebook, where we have access to a Linux computer. \n",
        "If you write a command to a code cell starting with \"!\", it will be executed, after pressing Shift+Enter on the cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZomCA2_WMI4f",
        "colab_type": "code",
        "outputId": "c60d4e7f-07bb-4502-9f3f-aae36311c7ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# list files in current directory\n",
        "!ls -al"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 16\n",
            "drwxr-xr-x 1 root root 4096 Feb  5 18:37 .\n",
            "drwxr-xr-x 1 root root 4096 Feb  7 14:43 ..\n",
            "drwxr-xr-x 1 root root 4096 Feb  5 18:37 .config\n",
            "drwxr-xr-x 1 root root 4096 Feb  5 18:37 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vx2koqi1MOU4",
        "colab_type": "text"
      },
      "source": [
        " otherwise, this is a fully working Python3 environment. you can write your own commands, and execute them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayMAqJGsKQAN",
        "colab_type": "code",
        "outputId": "b15bc60b-fb95-4a39-9925-0a40edf1acd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "# example for doing some simple plot\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "t = np.arange(0, 10, 0.001)\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "plt.plot(np.sin(t) + np.cos(3*t), color='r')\n",
        "plt.plot(0.01*t*t, color='g')\n",
        "plt.xlabel('Time (s)')\n",
        "plt.ylabel('Amplitude')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Amplitude')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jhYwmaV5z7bXwwAMwapRcrMJVaqqsM6lTx3UkweXzyZ3/4cOR0/ZKRZfx4+V4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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cjj0Dx6ULOcQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPvuCJ7JMYVm",
        "colab_type": "text"
      },
      "source": [
        "now it is your turn: try to solve any of the tasks below (as many as you like  - note that some of them are relatively easy, while others can seem to be a bit difficult, but none of them requires a PhD :)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQqr2E7qMoms",
        "colab_type": "text"
      },
      "source": [
        "Task 1.a: Download any arbitrary 128x128px image, display it and print the RGB channels of the image as numbers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-igWc62M-RL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ud4icvS_NBEr",
        "colab_type": "text"
      },
      "source": [
        "Task 1.b: Calculate the mean and variance of the pictures by channels and convert them to 0 mean and 1 variance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsLL8aGQNIty",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08rR-qDhNKii",
        "colab_type": "text"
      },
      "source": [
        "Task 2.a: Read an arbitrary WAV file (speech/music) and plot its waveform, as a function of time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_qqtF41NaLB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81OV-M3PNb-B",
        "colab_type": "text"
      },
      "source": [
        "Task 2.b: Plot the spectrogram of the previous WAV file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVf5H_bwNbEw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Apd9pbpwNtEw",
        "colab_type": "text"
      },
      "source": [
        "Task 3.a: Download a sample AVI video of the vocal tract (recorded with 2D rtMRI), load it into a Python array, and plot every 20th frame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XVuumcAN5i4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget http://smartlab.tmit.bme.hu/csapo/ultrasound/usctimit_mri_f1_146_150.avi\n",
        "!ls -al"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNkHyEb5PIm1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zsmHNytPQpy",
        "colab_type": "text"
      },
      "source": [
        "Task 3.b: calculate and show that region in the MRI video, where there is larger movement than in other parts\n",
        "\n",
        "Some help:\n",
        "*   calculate the standard deviation across time\n",
        "*   try to use colors for visualization\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1NrBsjzP3Kv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSfd2w75P99t",
        "colab_type": "text"
      },
      "source": [
        "Task 4.a: Download a sample ultrasound tongue image sequence, where data is stored as raw scanlines. Load it into a Python array, and plot every 100th frame.\n",
        "\n",
        "Some help:\n",
        "\n",
        "\n",
        "\n",
        "*   the .ULT file contains raw ultrasound images after each other\n",
        "*   each pixel is stored as a 1 byte unsigned int, which is actually a grayscale pixel intensity\n",
        "*   size of the raw ultrasound images is 64 x 842 pixels\n",
        "*   there are ??? number of images after each other - basically an uncompressed video\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NP5jowEQP5X_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget http://smartlab.tmit.bme.hu/csapo/ultrasound/sample_female1_002.ult\n",
        "!ls -al"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6liJeaVFQaPI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lztXwso-Qe-U",
        "colab_type": "text"
      },
      "source": [
        "Task 4.b: On the above tongue ultrasound video, calculate the RMS difference across the consecutive frames and plot this as a function of time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTyMBiprTZlP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FefA2qhtTY8S",
        "colab_type": "text"
      },
      "source": [
        "Task 5.a: Train a deep neural network (any kind: fully connected / convolutional / recurrent / GAN / etc.) to classify the MNIST written digits.\n",
        "\n",
        "You can use any framework that you like (e.g. tensorflow, keras, pytorch)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhz3Nfw5Tw8P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oji3-FjTzL_",
        "colab_type": "text"
      },
      "source": [
        "Task 5.b: Write with your own hand several digits on a paper, take a photo, put it here, and try to use the above neural network to predict which image is shown there."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqnsE2A4UFj9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# your solution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pi_e0bBVNkQ",
        "colab_type": "text"
      },
      "source": [
        "If you are done, please send me back the shared link to your solutions in a similar Jupyter notebook format."
      ]
    }
  ]
}