【Demo03】和_AI_PK_看誰比較會認_IVE_成員.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yenlung/AI-Demo/blob/master/%E3%80%90Demo03%E3%80%91%E5%92%8C_AI_PK_%E7%9C%8B%E8%AA%B0%E6%AF%94%E8%BC%83%E6%9C%83%E8%AA%8D_IVE_%E6%88%90%E5%93%A1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s9pLGjFDxiHk"
      },
      "source": [
        "*** 請先做完以下的基本設定 ***\n",
        "\n",
        "用 Colab 打開後, 登入 Google 帳號 (如果還沒有登入) 請在「檔案」選「在雲端硬碟中儲存副本」, 檔名請自取, 副檔名保留 `.ipynb` 即可。這樣這個程式才完全屬於你的。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XzcNS_KUxsBF"
      },
      "outputs": [],
      "source": [
        "# 成員英文名, 這是因為資料夾名稱用中文有時會出問題\n",
        "members_en = \"yujin,wonyoung,gaeul,rei,liz,leeseo\"\n",
        "\n",
        "# 成員中文名, 顯示時用的名稱\n",
        "members_zh = \"兪真유진,員瑛원영,秋天가을,Liz리즈,Rei레이,李瑞이서\"\n",
        "\n",
        "# APP 的名稱, 可自由更改\n",
        "title = \"IVE 成員 PK 戰\"\n",
        "\n",
        "# APP 的說明, 可自由更改\n",
        "description=\"和 AI PK IVE 成員的辨識能力\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3kOgfmzLNr1"
      },
      "source": [
        "### 1. 安裝 DeepFace 套件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1BxDCV7Dyka",
        "outputId": "0e9d5d94-dfb8-44e0-808e-3bf7249e5228"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "\u001b[?25hCollecting retina-face>=0.0.1 (from deepface)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting gunicorn>=20.1.0 (from deepface)\n",
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            "Building wheels for collected packages: fire\n",
            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fire: filename=fire-0.5.0-py2.py3-none-any.whl size=116934 sha256=42ba429fb23c58a23dc48cf097b97fb3cc23a08aa1bda3b4ea6a79a922c6287b\n",
            "  Stored in directory: /root/.cache/pip/wheels/90/d4/f7/9404e5db0116bd4d43e5666eaa3e70ab53723e1e3ea40c9a95\n",
            "Successfully built fire\n",
            "Installing collected packages: gunicorn, fire, mtcnn, retina-face, deepface\n",
            "Successfully installed deepface-0.0.85 fire-0.5.0 gunicorn-21.2.0 mtcnn-0.1.1 retina-face-0.0.14\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio\n",
        "!pip install deepface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4IqkIcaPKq_P"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7fHtd0tayZU"
      },
      "source": [
        "接下來讀入需要的套件。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MU1ReDALSNr",
        "outputId": "ace45d6b-c0c1-4dce-c861-48eb22beb55f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "24-03-07 01:19:57 - Directory /root/.deepface created\n",
            "24-03-07 01:19:57 - Directory /root/.deepface/weights created\n"
          ]
        }
      ],
      "source": [
        "from deepface import DeepFace\n",
        "from PIL import Image\n",
        "import gradio as gr\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPAj--7OPPG3"
      },
      "source": [
        "### 2. 創造資料夾\n",
        "\n",
        "你想創造的成員, 用逗號隔開。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UdY2mqOuLhTi"
      },
      "outputs": [],
      "source": [
        "# name 這個字典就是用來對應英文和中文名字的\n",
        "\n",
        "members = members_en.split(',')\n",
        "name_zh = members_zh.split(',')\n",
        "member_name = dict(zip(members, name_zh))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aatequS7PbeZ"
      },
      "outputs": [],
      "source": [
        "# prompt: 建立 photos 資料夾, 並為上面 members 串列中毎個都建一個資料夾。\n",
        "\n",
        "for member in members:\n",
        "    os.makedirs(f'./photos/{member}', exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xEFOGON9QZmH"
      },
      "outputs": [],
      "source": [
        "photo_folder = \"/content/photos/\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlyTMEN60sIA"
      },
      "source": [
        "### *** 請開始在每個成員的資料夾下，放入至少一張該成員的照片 ***\n",
        "\n",
        "接著就可以一路執行到最後!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Crl3FyTxWahC"
      },
      "source": [
        "### 3. 用 Gradio 打造 PK 程式"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "royma0guVl16"
      },
      "outputs": [],
      "source": [
        "# 用 DeepFace 辨識是哪為成員\n",
        "\n",
        "def rec(img):\n",
        "    df = DeepFace.find(img, db_path=photo_folder,\n",
        "                       enforce_detection=False)\n",
        "    try:\n",
        "        result = df[0].identity[0].split('/')[-2]\n",
        "    except:\n",
        "        result = \"我不知道\"\n",
        "    return result\n",
        "\n",
        "# 回傳中文名稱\n",
        "\n",
        "def pk(img, name):\n",
        "    result = member_name[rec(img)]\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8n21meIJEu6O"
      },
      "source": [
        "### 5. 用 Gradio 打造 PK 程式"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tEkB72ZuFPBB"
      },
      "outputs": [],
      "source": [
        "inp1 = gr.Image(label=\"QUIZ\", type=\"filepath\")\n",
        "inp2 = gr.Dropdown(\n",
        "            name_zh,\n",
        "            label=\"你的答案:\"\n",
        "        )\n",
        "out = gr.Text(label=\"我的答案:\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "id": "hc4wcRw9FTFs",
        "outputId": "7d9f34f2-11b2-4e2c-d803-29af8d77c09b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://662d17e8a3d77a7485.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://662d17e8a3d77a7485.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "24-03-07 01:36:53 - find function duration 0.7080485820770264 seconds\n",
            "24-03-07 01:37:57 - find function duration 1.2975664138793945 seconds\n",
            "24-03-07 01:38:39 - find function duration 0.7401201725006104 seconds\n"
          ]
        }
      ],
      "source": [
        "iface = gr.Interface(pk,\n",
        "                     inputs=[inp1, inp2],\n",
        "                     outputs=out,\n",
        "                     title=title,\n",
        "                     description=description)\n",
        "\n",
        "iface.launch(share=True, debug=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IYM-V-NciVQk"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM2E8eVw5pT3ev/rNHl4KAB",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}