{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6eIeDemCSbdp"
   },
   "source": [
    "## $\\large{Quiz}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aPzuwxR5Sbd3"
   },
   "source": [
    "請使用 Laplacian Filter 將跑車的輪廓找出來<br>\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?export=view&id=13i8nSfDxiub6XQB0oMua6RcybWWEQdsr\" width=\"400\">  <img src=\"https://drive.google.com/uc?export=view&id=1iYWVAYf6gB5cHrzLikhkAgG9GFQ7h3Jo\" width=\"400\">\n",
    "\n",
    "Laplacian Filter (5x5)：\n",
    "$\\begin{bmatrix}\n",
    "0 & 0 &-1 & 0 & 0 \\\\\n",
    "0 & -1 & -2 & -1 & 0\\\\\n",
    "-1 & -2 & 16 & -2 & -1\\\\\n",
    "0 & -1 & -2 & -1 & 0\\\\\n",
    "0 & 0 &-1 & 0 & 0\n",
    "\\end{bmatrix}$"
   ]
  },
  {
   "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": "fydrLdmJSbd7"
   },
   "outputs": [],
   "source": [
    "path = \"car.png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jssGweacVGXj"
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zHYkvh9VSbeC"
   },
   "outputs": [],
   "source": [
    "def load_img(path):\n",
    "    img = cv2.imread(path)[:,:,::-1]\n",
    "    laplacian_filter = np.array([[0, 0, -1, 0, 0],\n",
    "                                 [0, -1, -2, -1, 0],\n",
    "                                 [-1, -2, 16, -2, -1],\n",
    "                                 [0, -1, -2, -1, 0],\n",
    "                                 [0, 0, -1, 0, 0]])\n",
    "    edge = cv2.filter2D(img, -1, laplacian_filter)\n",
    "    return edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O4TewCfxSbeG"
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "quiz_img = cv2.imread(\"car_ans.png\")[:,:,::-1]\n",
    "assert np.array_equiv(load_img(path), quiz_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Hnsf3QKrVlQj"
   },
   "outputs": [],
   "source": []
  }
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