{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Color Detector\n",
    "\n",
    "\n",
    "\n",
    "This notebook shows primary color analysis of color image using K-Means algorithm.\n",
    "The output are N primary colors and their corresponding percentage.\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": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-15T11:10:06.893784Z",
     "iopub.status.busy": "2024-01-15T11:10:06.893592Z",
     "iopub.status.idle": "2024-01-15T11:10:06.901045Z",
     "shell.execute_reply": "2024-01-15T11:10:06.900549Z"
    }
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   "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",
    "    # 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": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-15T11:10:06.903564Z",
     "iopub.status.busy": "2024-01-15T11:10:06.903204Z",
     "iopub.status.idle": "2024-01-15T11:10:20.918642Z",
     "shell.execute_reply": "2024-01-15T11:10:20.917797Z"
    }
   },
   "outputs": [],
   "source": [
    "import ammico\n",
    "from ammico import utils as mutils\n",
    "from ammico import display as mdisplay\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select a subset of image files to try the color analysis on, see the `limit` keyword. The `find_files` function finds image files within a given directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-15T11:10:20.922071Z",
     "iopub.status.busy": "2024-01-15T11:10:20.921343Z",
     "iopub.status.idle": "2024-01-15T11:10:20.926372Z",
     "shell.execute_reply": "2024-01-15T11:10:20.925792Z"
    }
   },
   "outputs": [],
   "source": [
    "# Here you need to provide the path to your google drive folder\n",
    "# or local folder containing the images\n",
    "image_dict = mutils.find_files(\n",
    "    path=\"data/\",\n",
    "    limit=10,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "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 color detection results provided by `colorgram` and `colour` 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": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-15T11:10:20.929815Z",
     "iopub.status.busy": "2024-01-15T11:10:20.929407Z",
     "iopub.status.idle": "2024-01-15T11:10:20.965670Z",
     "shell.execute_reply": "2024-01-15T11:10:20.964719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8057/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fd90adf4cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis_explorer = mdisplay.AnalysisExplorer(image_dict)\n",
    "analysis_explorer.run_server(port = 8057)"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-15T11:10:20.968930Z",
     "iopub.status.busy": "2024-01-15T11:10:20.968358Z",
     "iopub.status.idle": "2024-01-15T11:10:26.328182Z",
     "shell.execute_reply": "2024-01-15T11:10:26.327484Z"
    }
   },
   "outputs": [],
   "source": [
    "for key in image_dict.keys():\n",
    "    image_dict[key] = ammico.colors.ColorDetector(image_dict[key]).analyse_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-15T11:10:26.331271Z",
     "iopub.status.busy": "2024-01-15T11:10:26.330894Z",
     "iopub.status.idle": "2024-01-15T11:10:26.334703Z",
     "shell.execute_reply": "2024-01-15T11:10:26.334030Z"
    }
   },
   "outputs": [],
   "source": [
    "image_df = ammico.get_dataframe(image_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-15T11:10:26.338776Z",
     "iopub.status.busy": "2024-01-15T11:10:26.338287Z",
     "iopub.status.idle": "2024-01-15T11:10:26.350669Z",
     "shell.execute_reply": "2024-01-15T11:10:26.350034Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>red</th>\n",
       "      <th>green</th>\n",
       "      <th>blue</th>\n",
       "      <th>yellow</th>\n",
       "      <th>cyan</th>\n",
       "      <th>orange</th>\n",
       "      <th>purple</th>\n",
       "      <th>pink</th>\n",
       "      <th>brown</th>\n",
       "      <th>grey</th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/106349S_por.png</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/102141_2_eng.png</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.05</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/102730_eng.png</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                filename   red  green  blue  yellow  cyan  orange  purple  \\\n",
       "0   data/106349S_por.png  0.01   0.01  0.05     0.0     0       0    0.22   \n",
       "1  data/102141_2_eng.png  0.04   0.03  0.02     0.0     0       0    0.01   \n",
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       "\n",
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       "0   0.0   0.03  0.68   0.00   0.00  \n",
       "1   0.0   0.32  0.23   0.31   0.05  \n",
       "2   0.0   0.00  0.66   0.02   0.27  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-15T11:10:26.353181Z",
     "iopub.status.busy": "2024-01-15T11:10:26.352811Z",
     "iopub.status.idle": "2024-01-15T11:10:26.357012Z",
     "shell.execute_reply": "2024-01-15T11:10:26.356396Z"
    }
   },
   "outputs": [],
   "source": [
    "image_df.to_csv(\"data_out.csv\")"
   ]
  }
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