Day004_geometric_transform_HW.ipynb

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   "source": [
    "# 作業\n",
    "\n",
    "實作本篇提到的三大概念\n",
    "\n",
    "- 翻轉：實作上下左右的翻轉\n",
    "- 縮放：比較鄰近差值與雙立方插值 (或雙線性插值) 的圖片品質\n",
    "- 平移：建立 Translation Transformation Matrix 來做平移"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "img_path = 'lena.png'\n",
    "img = cv2.imread(img_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 上下左右翻轉圖片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 水平翻轉 (horizontal)\n",
    "img_hflip = cv2.flip(img,1)\n",
    "\n",
    "# 垂直翻轉 (vertical)\n",
    "img_vflip = cv2.flip(img,0)\n",
    "\n",
    "# 水平 + 垂直翻轉\n",
    "img_hvflip = cv2.flip(img,-1)\n",
    "\n",
    "# 組合 + 顯示圖片\n",
    "hflip = np.hstack((img, img_hflip))\n",
    "vflip = np.hstack((img_vflip, img_hvflip))\n",
    "img_flip = np.vstack((hflip, vflip))\n",
    "while True:\n",
    "    cv2.imshow('flip image', img_flip)\n",
    "    k = cv2.waitKey(0)\n",
    "    if k == 27:\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 縮放圖片\n",
    "\n",
    "### 放大\n",
    "\n",
    "我們先透過縮小圖片去壓縮原有圖片保有的資訊，再放大比較不同方法之間的速度與圖片品質"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INTER_NEAREST zoom cost 0.05007433891296387\n",
      "INTER_CUBIC zoom cost 0.050122737884521484\n"
     ]
    }
   ],
   "source": [
    "# 將圖片縮小成原本的 20%\n",
    "img_test = cv2.resize(img, None, fx=0.2, fy=0.2)\n",
    "\n",
    "# 將圖片放大為\"小圖片\"的 8 倍大 = 原圖的 1.6 倍大\n",
    "fx, fy = 8, 8\n",
    "\n",
    "# 鄰近差值 scale + 計算花費時間\n",
    "start_time = time.time()\n",
    "img_area_scale =cv2.resize(img,None,fx=fx,fy=fy,interpolation=cv2.INTER_NEAREST) \n",
    "print('INTER_NEAREST zoom cost {}'.format(time.time() - start_time))\n",
    "\n",
    "# 雙立方差補 scale + 計算花費時間\n",
    "start_time = time.time()\n",
    "img_cubic_scale = cv2.resize(img,None,fx=fx,fy=fy,interpolation=cv2.INTER_CUBIC)\n",
    "print('INTER_CUBIC zoom cost {}'.format(time.time() - start_time))\n",
    "\n",
    "# 組合 + 顯示圖片\n",
    "img_zoom = np.hstack((img_area_scale, img_cubic_scale))\n",
    "while True:\n",
    "    cv2.imshow('zoom image', img_zoom)\n",
    "    k = cv2.waitKey(0)\n",
    "    if k == 27:\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 平移幾何轉換"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 設定 translation transformation matrix\n",
    "# x 平移 50 pixel; y 平移 100 pixel\n",
    "M = np.array([[1,0,50],[0,1,100]],dtype=np.float32)\n",
    "shift_img = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))\n",
    "\n",
    "# 組合 + 顯示圖片\n",
    "img_shift = np.hstack((img, shift_img))\n",
    "while True:\n",
    "    cv2.imshow('shift image', img_shift)\n",
    "    k = cv2.waitKey(0)\n",
    "    if k == 27:\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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