AMMICO/ammico/notebooks/facial_expressions.ipynb

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   "id": "0",
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   "source": [
    "# Facial Expression recognition with DeepFace"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "Facial expressions can be detected using [DeepFace](https://github.com/serengil/deepface) and [RetinaFace](https://github.com/serengil/retinaface).\n",
    "\n",
    "The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
    "\n",
    "After that, we can import `ammico` and read in the files given a folder path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if running on google colab\n",
    "# flake8-noqa-cell\n",
    "import os\n",
    "\n",
    "if \"google.colab\" in str(get_ipython()):\n",
    "    # update python version\n",
    "    # install setuptools\n",
    "    # %pip install setuptools==61 -qqq\n",
    "    # uninstall some pre-installed packages due to incompatibility\n",
    "    %pip uninstall tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint -y -qqq\n",
    "    # install ammico\n",
    "    %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
    "    # mount google drive for data and API key\n",
    "    from google.colab import drive\n",
    "\n",
    "    drive.mount(\"/content/drive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ammico"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "We select a subset of image files to try facial expression detection on, see the `limit` keyword. The `find_files` function finds image files within a given directory and initialize the main dictionary that contains all information for the images and is updated through each subsequent analysis::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here you need to provide the path to your google drive folder\n",
    "# or local folder containing the images\n",
    "mydict = ammico.find_files(\n",
    "    path=\"/content/drive/MyDrive/misinformation-data/\",\n",
    "    limit=10,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can skip this and directly export a csv file in the step below.\n",
    "Here, we display the face recognition results provided by the DeepFace and RetinaFace libraries. Click on the tabs to see the results in the right sidebar. You may need to increment the `port` number if you are already running several notebook instances on the same server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_explorer = ammico.AnalysisExplorer(mydict)\n",
    "analysis_explorer.run_server(port = 8050)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "Instead of inspecting each of the images, you can also directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in mydict.keys():\n",
    "    mydict[key] = ammico.EmotionDetector(mydict[key]).analyse_image()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "These steps are required to convert the dictionary of dictionarys into a dictionary with lists, that can be converted into a pandas dataframe and exported to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ammico.get_dataframe(mydict)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "Check the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "Write the csv file - here you should provide a file path and file name for the csv file to be written."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
   ]
  }
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