{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TA-aiacademy/course_3.0/blob/main/05_CVCNN/Part1_Intro_CV/05-2_Quiz_ans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PXmQmDurBXv0"
      },
      "source": [
        "## $\\large{Quiz}$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRlD1qLRBXv9"
      },
      "source": [
        "請將左圖中的龍貓完成底下這幾個影像處理，以得到右圖<br>\n",
        "1. 以一半影像長、寬減 1 的 index 作為中心點，將旋轉 45 度角\n",
        "2. 使用鏡像反射補值法\n",
        "3. 將影像放大 2 倍\n",
        "\n",
        "![](https://hackmd.io/_uploads/rkOHU2DG6.png)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rZ65aUaxN4Mg"
      },
      "outputs": [],
      "source": [
        "# upload Data\n",
        "!wget -q https://github.com/TA-aiacademy/course_3.0/releases/download/CVCNN_Data/CVCNN_part1.zip\n",
        "!unzip -q CVCNN_part1.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YqpUdo7tBXwA"
      },
      "outputs": [],
      "source": [
        "path = \"totoro.png\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bu7tyjsZEKuv"
      },
      "outputs": [],
      "source": [
        "import cv2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pt8RwnfWEQjb"
      },
      "outputs": [],
      "source": [
        "def load_img(path):\n",
        "    img = cv2.imread(path)[:,:,::-1]\n",
        "    h, w, _ = img.shape\n",
        "    center = (w//2-1, h//2-1)\n",
        "    M = cv2.getRotationMatrix2D(center, 45, 1)\n",
        "    rotate_img = cv2.warpAffine(img, M, (w*2, h*2), borderMode=cv2.BORDER_REFLECT)\n",
        "    return rotate_img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S-GyAo2RGJve"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "quiz_img = cv2.imread(\"totoro_ans.png\")[:,:,::-1]\n",
        "assert np.array_equiv(load_img(path), quiz_img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LOF8kIhkFSj3"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "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": "3.7.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}