LoadingBeeDataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "770351d3",
   "metadata": {},
   "source": [
    "## Loading a dataset into Scivision\n",
    "\n",
    "Example of loading the BeeImage dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "502b1405",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scivision import default_catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e95645da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>tasks</th>\n",
       "      <th>domains</th>\n",
       "      <th>url</th>\n",
       "      <th>format</th>\n",
       "      <th>labels_provided</th>\n",
       "      <th>institution</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data-002</td>\n",
       "      <td>None</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>(oceanography,)</td>\n",
       "      <td>https://github.com/scotthosking/intake-plankto...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>(CEFAS,)</td>\n",
       "      <td>(help-needed, 2D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data-003</td>\n",
       "      <td>Koala</td>\n",
       "      <td>(TaskEnum.object_detection, TaskEnum.classific...</td>\n",
       "      <td>(computer-vision,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>(Wikipedia by Erik Veland,)</td>\n",
       "      <td>(demo,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data-004</td>\n",
       "      <td>Sample dataset of Plankton required to demonst...</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>(computer-vision,)</td>\n",
       "      <td>https://raw.githubusercontent.com/alan-turing-...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>(Centre for Environment, Fisheries and Aquacul...</td>\n",
       "      <td>(2D, plankton, ecology, environmental-science)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>data-005</td>\n",
       "      <td>Sample dataset of satellite imagery required t...</td>\n",
       "      <td>(TaskEnum.classificiation, TaskEnum.segmentation)</td>\n",
       "      <td>(computer-vision, earth-observation, ecology)</td>\n",
       "      <td>https://github.com/MartinSJRogers/VEdge_Detect...</td>\n",
       "      <td>image</td>\n",
       "      <td>False</td>\n",
       "      <td>(Cambridge University,)</td>\n",
       "      <td>(2D, satellite, remote-sensing, ecology, envir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>data-007</td>\n",
       "      <td>A labelled dataset of honeybee images, from Ka...</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>(nature, insects, bee-keeping)</td>\n",
       "      <td>https://github.com/nbarlowATI/honeybee_data_repo</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(bees, biology, classification)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       name                                        description  \\\n",
       "2  data-002                                               None   \n",
       "3  data-003                                              Koala   \n",
       "4  data-004  Sample dataset of Plankton required to demonst...   \n",
       "5  data-005  Sample dataset of satellite imagery required t...   \n",
       "7  data-007  A labelled dataset of honeybee images, from Ka...   \n",
       "\n",
       "                                               tasks  \\\n",
       "2                        (TaskEnum.classificiation,)   \n",
       "3  (TaskEnum.object_detection, TaskEnum.classific...   \n",
       "4                        (TaskEnum.classificiation,)   \n",
       "5  (TaskEnum.classificiation, TaskEnum.segmentation)   \n",
       "7                        (TaskEnum.classificiation,)   \n",
       "\n",
       "                                         domains  \\\n",
       "2                                (oceanography,)   \n",
       "3                             (computer-vision,)   \n",
       "4                             (computer-vision,)   \n",
       "5  (computer-vision, earth-observation, ecology)   \n",
       "7                 (nature, insects, bee-keeping)   \n",
       "\n",
       "                                                 url format  labels_provided  \\\n",
       "2  https://github.com/scotthosking/intake-plankto...  image             True   \n",
       "3  https://github.com/alan-turing-institute/scivi...  image             True   \n",
       "4  https://raw.githubusercontent.com/alan-turing-...  image             True   \n",
       "5  https://github.com/MartinSJRogers/VEdge_Detect...  image            False   \n",
       "7   https://github.com/nbarlowATI/honeybee_data_repo  image             True   \n",
       "\n",
       "                                         institution  \\\n",
       "2                                           (CEFAS,)   \n",
       "3                        (Wikipedia by Erik Veland,)   \n",
       "4  (Centre for Environment, Fisheries and Aquacul...   \n",
       "5                            (Cambridge University,)   \n",
       "7                           (alan-turing-institute,)   \n",
       "\n",
       "                                                tags  \n",
       "2                                  (help-needed, 2D)  \n",
       "3                                            (demo,)  \n",
       "4     (2D, plankton, ecology, environmental-science)  \n",
       "5  (2D, satellite, remote-sensing, ecology, envir...  \n",
       "7                    (bees, biology, classification)  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compatible_datasources = default_catalog.compatible_datasources(\"image-classifiers\").to_dataframe()\n",
    "compatible_datasources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f3bca86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>name</th>\n",
       "      <th>description</th>\n",
       "      <th>tasks</th>\n",
       "      <th>domains</th>\n",
       "      <th>url</th>\n",
       "      <th>format</th>\n",
       "      <th>labels_provided</th>\n",
       "      <th>institution</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>data-007</td>\n",
       "      <td>A labelled dataset of honeybee images, from Ka...</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>(nature, insects, bee-keeping)</td>\n",
       "      <td>https://github.com/nbarlowATI/honeybee_data_repo</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(bees, biology, classification)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name                                        description  \\\n",
       "7  data-007  A labelled dataset of honeybee images, from Ka...   \n",
       "\n",
       "                         tasks                         domains  \\\n",
       "7  (TaskEnum.classificiation,)  (nature, insects, bee-keeping)   \n",
       "\n",
       "                                                url format  labels_provided  \\\n",
       "7  https://github.com/nbarlowATI/honeybee_data_repo  image             True   \n",
       "\n",
       "                institution                             tags  \n",
       "7  (alan-turing-institute,)  (bees, biology, classification)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_datasource = compatible_datasources.loc[compatible_datasources['name'] == 'data-007']\n",
    "target_datasource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5816f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scivision import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee3ab789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnyUrl('https://github.com/nbarlowATI/honeybee_data_repo', scheme='https', host='github.com', tld='com', host_type='domain', path='/nbarlowATI/honeybee_data_repo')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_url = target_datasource['url'].item()\n",
    "data_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d2025b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/yaml": "data:\n  args:\n    path: https://raw.githubusercontent.com/nbarlowATI/honeybee_data_repo/main/.scivision/data.yml\n  description: ''\n  driver: intake.catalog.local.YAMLFileCatalog\n  metadata: {}\n",
      "text/plain": [
       "data:\n",
       "  args:\n",
       "    path: https://raw.githubusercontent.com/nbarlowATI/honeybee_data_repo/main/.scivision/data.yml\n",
       "  description: ''\n",
       "  driver: intake.catalog.local.YAMLFileCatalog\n",
       "  metadata: {}\n"
      ]
     },
     "metadata": {
      "application/json": {
       "root": "data"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_config = load_dataset(data_url)\n",
    "data_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a28d641",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nbarlow/miniforge3/envs/kaggle-bee-env/lib/python3.8/site-packages/xarray/core/dataarray.py:1195: FutureWarning: None value for 'chunks' is deprecated. It will raise an error in the future. Use instead '{}'\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "images = data_config.honeybee().to_dask()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51cd0ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image = images[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acb82408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x28a986070>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4df1d79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}