openpiv_gpu.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Openpiv_Python_Cython_GPU_demo.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "private_outputs": true,
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/OpenPIV/openpiv-python-gpu/blob/master/Openpiv_Python_Cython_GPU_demo.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "Gw1l_0dyfncc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "!/opt/bin/nvidia-smi\n",
        "!wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb\n",
        "!dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb 2> /dev/null\n",
        "!apt-key add /var/cuda-repo-8-0-local-ga2/7fa2af80.pub\n",
        "!apt-get update\n",
        "!apt-get install -qq cuda gcc-5 g++-5 -y\n",
        "!ln -s /usr/bin/gcc-5 /usr/local/cuda/bin/gcc\n",
        "!ln -s /usr/bin/g++-5 /usr/local/cuda/bin/g++\n",
        "!/usr/local/cuda/bin/nvcc --version"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bztUxphUiWAR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# put nvcc on PATH for PyCUDA install\n",
        "import os\n",
        "work_dir = os.getcwd()\n",
        "if \"/usr/local/cuda/bin\" not in os.environ['PATH'].split(':'):\n",
        "  os.environ['PATH'] += \":/usr/local/cuda/bin\"\n",
        "  \n",
        "! echo $PATH\n",
        "! nvcc --version\n",
        "\n",
        "! pwd\n",
        "! ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ez_JnGBhQaV2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# install pycuda using pip\n",
        "! pip install pycuda"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kWhscug26_Ju",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# check to make sure PyCUDA installed properly\n",
        "import pycuda.autoinit"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nhsxCUgqNZzE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install cython"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "70rcCaKVv0N-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!# install scikits-cuda from github as the pip version cant find cusolver\n",
        "!git clone https://github.com/lebedov/scikit-cuda.git\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0TKcC1MEeZWy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#!ls\n",
        "os.chdir(work_dir)\n",
        "os.chdir('scikit-cuda')\n",
        "%run setup.py install\n",
        "%run setup.py test\n",
        "\n",
        "# add scikit-cuda to the PYTHONPATH\n",
        "import sys\n",
        "sys.path.append(os.getcwd())\n",
        "os.chdir(work_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ixK9-lJbgs6a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! pip install progressbar"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Rpdhrze08fXY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#!git clone https://github.com/CameronDallas5000/openpiv-python.git\n",
        "!git clone https://github.com/OpenPIV/openpiv-python-gpu.git\n",
        "os.chdir('openpiv-python-gpu')\n",
        "sys.path.append(os.getcwd())\n",
        "!python setup.py build_ext --inplace\n",
        "!python setup.py install"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NU2fc-TFNmxP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import openpiv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X_RvxJFuOZue",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from openpiv import tools, process, validation, filters, scaling, pyprocess\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from IPython.display import display"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LCqvHHd-9vB_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/CameronDallas5000/openpiv-python/master/openpiv/examples/tutorial-part1/exp1_001_a.bmp\n",
        "!wget https://raw.githubusercontent.com/CameronDallas5000/openpiv-python/master/openpiv/examples/tutorial-part1/exp1_001_b.bmp\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tyghpYPBTc_q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# from google.colab import files\n",
        "# files.upload() # choose exp1_001_b.bmp and _c.bmp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IR92ibRrTilS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "frame_a  = tools.imread( 'exp1_001_a.bmp' )\n",
        "frame_b  = tools.imread( 'exp1_001_b.bmp' )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "582KZU21TwGm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(1,2,figsize=(10,8))\n",
        "ax[0].imshow(frame_a,cmap=plt.cm.gray)\n",
        "ax[1].imshow(frame_b,cmap=plt.cm.gray)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rpMmy19nIZPA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eYd4k2DfIaBw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# OpenPIV Python version"
      ]
    },
    {
      "metadata": {
        "id": "wOC0zYVHT5Mm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "winsize = 32 # pixels\n",
        "searchsize = 64  # pixels, search in image B\n",
        "overlap = 12 # pixels\n",
        "dt = 0.02 # sec"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AgZ_Kp3XUAp1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), \n",
        "                                                     window_size=winsize, overlap=overlap, dt=dt, \n",
        "                                                     search_area_size=searchsize, sig2noise_method='peak2peak' )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y2J7gaXpUEfy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x, y = pyprocess.get_coordinates( image_size=frame_a.shape, window_size=searchsize, overlap=overlap )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ykcjY9-TUHpa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tools.save(x, y, u, v, np.zeros_like(u), 'exp1_001.txt' ) # no masking, all values are valid"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QiTJUjGlISN3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# OpenPIV Python-GPU version"
      ]
    },
    {
      "metadata": {
        "id": "HN6VXtEPmYnp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from progressbar import *\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VrXv0SR7l4uD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import openpiv.gpu_process\n",
        "reload(openpiv.gpu_process)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uH-F9Ryphvyp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# gpu code parametes\n",
        "min_window_size = 32\n",
        "overlap_ratio = 0.5\n",
        "coarse_factor = 1\n",
        "nb_iter_max = 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Dj-oo1BLUrLA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# First time is slow as the GPU modules need to compile. Once they are compiled, they stay compiled.\n",
        "#Every time you run this after the first time it will be fast.\n",
        "x, y, u, v, mask = openpiv.gpu_process.WiDIM( frame_a.astype(np.int32), frame_b.astype(np.int32), np.ones(frame_a.shape, dtype=np.int32),\n",
        "                                                     min_window_size, \n",
        "                                                     overlap_ratio,\n",
        "                                                     coarse_factor,\n",
        "                                                     dt,\n",
        "                                                     nb_iter_max = nb_iter_max)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "o6931RnJehtP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tools.save(x, y, u, v, np.zeros_like(u), 'exp1_001_gpu.txt' )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aITMf5f2Iozd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# OpenPIV Python-CPU (Cython) version"
      ]
    },
    {
      "metadata": {
        "id": "eqzZ_a6xHQOn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x, y, u, v, mask = openpiv.process.WiDIM( frame_a.astype(np.int32), frame_b.astype(np.int32), np.ones(frame_a.shape, dtype=np.int32),\n",
        "                                                     min_window_size, \n",
        "                                                     overlap_ratio,\n",
        "                                                     coarse_factor,\n",
        "                                                     dt,\n",
        "                                                     nb_iter_max = nb_iter_max)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X57HJpHCHxt-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tools.save(x, y, u, v, np.zeros_like(u), 'exp1_001_cpu.txt' )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KlUXKOsRIF-G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tools.display_vector_field('exp1_001.txt', scale=5000, width=0.0025)\n",
        "tools.display_vector_field('exp1_001_cpu.txt', scale=5000, width=0.0025)\n",
        "tools.display_vector_field('exp1_001_gpu.txt', scale=5000, width=0.0025)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9iI7CFnkIKPy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}