Perturbation_3DCapsNet.ipynb

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  "metadata": {
    "colab": {
      "name": "Perturbation-3DCapsNet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "16EHFgDNmLNJZ25xOdqQzVMQOZdYExgLJ",
      "authorship_tag": "ABX9TyMz3X9EXRyJgUK6lq8bIr92",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dilaragokay/3D-point-capsule-networks/blob/colab/Perturbation_3DCapsNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_zvdaH4-drr"
      },
      "source": [
        "# 1. Install dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4kuG295KFjs"
      },
      "source": [
        "!sudo apt-get install libhdf5-dev\n",
        "!sudo pip install h5py\n",
        "!pip install open3d\n",
        "!pip install gdown"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0zoVlKAKcXx"
      },
      "source": [
        "%cd models/nndistance\n",
        "!python build.py install"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imynwMYfCMc5"
      },
      "source": [
        "# 2. Install ShapeNetPart Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wsXvYT4eKu9n"
      },
      "source": [
        "%cd ../../dataset\n",
        "!bash download_shapenet_part16_catagories.sh"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yg_fYo1lpoY7"
      },
      "source": [
        "# 3. Download checkpoints"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GF5nr99DNPg-"
      },
      "source": [
        "!mkdir ../checkpoints\n",
        "%cd ../checkpoints\n",
        "# Checkpoint link is obtained from https://github.com/yongheng1991/3D-point-capsule-networks/blob/master/README.md\n",
        "!gdown https://drive.google.com/uc?id=1so0OiVPS93n7Ed36yiMHQb-u0f1zJSzJ"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zISG0CBzqOnj"
      },
      "source": [
        "# 4. Perturb example point cloud\n",
        "<img src=\"https://i.ibb.co/jwGwVSV/Screenshot-2020-11-05-at-00-58-39.png\">\n",
        "\n",
        "*Image source: [1]*\n",
        "\n",
        "* I picked one object (plane) for the sake of simplicity.\n",
        "\n",
        "* After dynamic routing, 64 latent capsules are obtained. Since some of the capsules seem to have irregular points, plus, visualizing all of the capsules is not feasible, I've picked 3 capsules which seem to represent the object well. These capsules represent **wings, rudder, and bottom**. \n",
        "* Each capsule is a 64x1 vector. I've picked 3 features from these capsules. I've picked the ones on position 10, 20, and 30 (with no particular reason). [2] perturbs features in the capsules within [-0.5, 0.5]. I chose {-0.25, 0.25} in order not to have too many combinations to visualize.\n",
        "* At each attempt, I've changed one of the variables above and kept the rest constant. In a nutshell,\n",
        "\n",
        "```\n",
        "for capsule in capsule_set:\n",
        "    for feature in feature_set:\n",
        "        for amount in perturbations:\n",
        "            perturb the feature in the capsule by amount then save\n",
        "```\n",
        "\n",
        "Changes are in `viz_perturbation.py`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57_Hi34QNlv6"
      },
      "source": [
        "%cd ../../mini_example/AE/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4n5kBYWuN4Iz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3a2336a-fbe0-4032-907a-f6b283016f5d"
      },
      "source": [
        "!python viz_perturbation.py --model ../../checkpoints/shapenet_part_dataset_ae_200.pth"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(batch_size=8, latent_caps_size=64, latent_vec_size=64, model='../../checkpoints/shapenet_part_dataset_ae_200.pth', num_points=2048, prim_caps_size=1024, prim_vec_size=16)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dieQmcHwXdm"
      },
      "source": [
        "## 5. Show results in 3D plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcDtTTKitm_j"
      },
      "source": [
        "import open3d as o3d\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "import numpy as np\n",
        "import glob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2efaqLXtx9h"
      },
      "source": [
        "pcd_files = glob.glob('*.pcd')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuBciOvzuHmM"
      },
      "source": [
        "dictionary = {}\n",
        "for f in pcd_files:\n",
        "  key = '_'.join(f.split('_')[:2])\n",
        "  if key in dictionary:\n",
        "    dictionary[key].append(f)\n",
        "  else:\n",
        "    dictionary[key] = [f]\n",
        "\n",
        "for key in dictionary:\n",
        "  dictionary[key] = sorted(dictionary[key])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f3EZP_Pxw6X5"
      },
      "source": [
        "### Visualize the first point cloud"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85nHXknjPfqo"
      },
      "source": [
        "def plot_perturbations(minus_pert, zero_pert, plus_pert, specs):\n",
        "  # Initialize figure with 9 3D subplots\n",
        "  fig = make_subplots(\n",
        "      rows=3,\n",
        "      cols=1,\n",
        "      specs=[[{\"type\": \"scene\"}], [{\"type\": \"scene\"}], [{\"type\": \"scene\"}]],\n",
        "      shared_xaxes=True,\n",
        "      subplot_titles=(\"-0.25 perturbation\", \"No perturbation\", \"+0.25 perturbation\"))\n",
        "\n",
        "  # Generate data\n",
        "  pcd = o3d.io.read_point_cloud(minus_pert)\n",
        "  x1 = np.asarray(pcd.points)[:, 0]\n",
        "  y1 = np.asarray(pcd.points)[:, 1]\n",
        "  z1 = np.asarray(pcd.points)[:, 2]\n",
        "\n",
        "  c1 = np.asarray(pcd.colors)\n",
        "\n",
        "  pcd = o3d.io.read_point_cloud(zero_pert)\n",
        "  x2 = np.asarray(pcd.points)[:, 0]\n",
        "  y2 = np.asarray(pcd.points)[:, 1]\n",
        "  z2 = np.asarray(pcd.points)[:, 2]\n",
        "\n",
        "  c2 = np.asarray(pcd.colors)\n",
        "\n",
        "  pcd = o3d.io.read_point_cloud(plus_pert)\n",
        "  x3 = np.asarray(pcd.points)[:, 0]\n",
        "  y3 = np.asarray(pcd.points)[:, 1]\n",
        "  z3 = np.asarray(pcd.points)[:, 2]\n",
        "\n",
        "  c3 = np.asarray(pcd.colors)\n",
        "\n",
        "  # adding point clouds to subplots.\n",
        "  fig.add_trace(go.Scatter3d(x=x1, y=y1, z=z1,mode='markers',\n",
        "                                    marker=dict(\n",
        "          size=2,\n",
        "          color=c1,                # set color to an array/list of desired values\n",
        "          opacity=0.4\n",
        "      )),\n",
        "      row=1, col=1)\n",
        "\n",
        "  fig.add_trace(go.Scatter3d(x=x2, y=y2, z=z2, mode='markers',\n",
        "                                    marker=dict(\n",
        "          size=2,\n",
        "          color=c2,                # set color to an array/list of desired values\n",
        "          opacity=0.4\n",
        "      )),\n",
        "      row=2, col=1)\n",
        "\n",
        "  fig.add_trace(go.Scatter3d(x=x3, y=y3, z=z3, mode='markers',\n",
        "                                    marker=dict(\n",
        "          size=2,\n",
        "          color=c3,                # set color to an array/list of desired values\n",
        "          opacity=0.4\n",
        "      )),\n",
        "      row=3, col=1)\n",
        "\n",
        "  fig.update_layout(\n",
        "      title_text=specs,\n",
        "      height=1000,\n",
        "      width=1000\n",
        "  )\n",
        "\n",
        "  fig.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3rV7cV09me1"
      },
      "source": [
        "key = list(dictionary.keys())[0]  # key for the first point cloud\n",
        "\n",
        "point_clouds = dictionary[key]\n",
        "\n",
        "num_to_color = {\n",
        "    '29': 'red',\n",
        "    '40': 'green',\n",
        "    '16': 'blue'\n",
        "}\n",
        "\n",
        "num_to_name = {\n",
        "    '29': 'rudder',\n",
        "    '40': 'wings',\n",
        "    '16': 'bottom'\n",
        "}\n",
        "i = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0_vWP7K9zpL"
      },
      "source": [
        "Run the following cell as long as there are more combinations. All point clouds are not visualized at once as it can crash the browser."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MdTApmTz3MPv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3710e349-d275-442e-bd18-faae55b1c788"
      },
      "source": [
        "if i < len(point_clouds):\n",
        "  minus = point_clouds[i]\n",
        "  plus = point_clouds[i+1]\n",
        "  zero = point_clouds[i+2]\n",
        "  name_splitted = plus.split('_')\n",
        "  caps_no = name_splitted[2][4:]\n",
        "  feat_no = name_splitted[3][4:]\n",
        "  amount = name_splitted[5][:-4]\n",
        "  specs = \"Feature #{} of the capsule for {}. Capsule is shown in {}\".format(\n",
        "      feat_no,\n",
        "      num_to_name[caps_no],\n",
        "      num_to_color[caps_no])\n",
        "  plot_perturbations(minus, zero, plus, specs)\n",
        "  i+=3"
      ],
      "execution_count": null,
      "outputs": [
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          "metadata": {
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      "cell_type": "markdown",
      "metadata": {
        "id": "gFoyrNSsJ06P"
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      "source": [
        "# 6. Visualize precaptured images from 3 different viewpoints\n",
        "\n",
        "In images below, each 3x3 figure shows how changing certain feature in a certain capsule affects view from side, front, and top of the plane. In short, each subfigure has been organized the following way.  \n",
        "\n",
        "|       | side view | front view | top view |\n",
        "|-------|-----------|------------|----------|\n",
        "| -0.25 |      img     |     img       |    img      |\n",
        "| 0     |      img     |          img  |       img   |\n",
        "| 0.25  |      img     |         img   |       img   |\n",
        "\n",
        "Note that these figures have been captured by hand so not all these images have been captured from the same angle i.e. some images might be slightly rotated.\n",
        "\n",
        "## 6.1. Change in capsule that corresponds to the bottom of the plane (blue points)\n",
        "### 6.1.1. Perturbation on feature #10\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1ZJpBLMVfL3R1pLoge1ZiDrXN2OJiF_OX\">\n",
        "\n",
        "### 6.1.2. Perturbation on feature #20\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1tVz8mhOhHFsJbvZR3XnUzLSHxvHvzXws\">\n",
        "\n",
        "### 6.1.3. Perturbation on feature #30\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1fhTCQXEP25SrFT1kMR0ZrKoXFenRkYjC\">\n",
        "\n",
        "## 6.2. Change in capsule that corresponds to the rudder of the plane (red points)\n",
        "### 6.2.1. Perturbation on feature #10\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1uELPomS0IZw9IqrRoF5uCui6_uQdd8nr\">\n",
        "\n",
        "### 6.2.2. Perturbation on feature #20\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1yaLi7Ed7mI9ttixmxcAMcdlWub421vrd\">\n",
        "\n",
        "### 6.2.3. Perturbation on feature #30\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1jsvh0CbT263KbtarXo8zFAdxwdpwvIpr\">\n",
        "\n",
        "## 6.3. Change in capsule that corresponds to the wings of the plane (green points)\n",
        "### 6.3.1. Perturbation on feature #10\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=15VStNiDnyovnnWYCX9xQfs9hkdPHUul2\">\n",
        "\n",
        "### 6.3.2. Perturbation on feature #20\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1Z_kIv7yCX5inAnlQeQ5gxD06CATFjgkd\">\n",
        "\n",
        "### 6.3.3. Perturbation on feature #30\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1C87QsMgY0I8lsFIvaaU2PyJSm-vTU9kT\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PU1HsYtW3A0G"
      },
      "source": [
        "# 7. Observations (w/ [@evinpinar](https://github.com/evinpinar))\n",
        "\n",
        "* Changes in feature #30 of rudder,\n",
        "* Changes in feature #20 of wing,   \n",
        "* Changes in feature #30 of wing   \n",
        "causes rotation and translation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuBDV-ahs2Tk"
      },
      "source": [
        "# Reference\n",
        "\n",
        "[1] Zhao, Yongheng, et al. \"3D point capsule networks.\" *Proceedings of the IEEE conference on computer vision and pattern recognition*. 2019.  \n",
        "[2] Sabour, Sara, Nicholas Frosst, and Geoffrey E. Hinton. \"Dynamic routing between capsules.\" *Advances in neural information processing systems*. 2017."
      ]
    }
  ]
}