{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Discussion: Anti-aliasing Effect \n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "According to the paper \"On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation\" by @Richard Zhang, the selection of image resizing library and kernel matter to the Fréchet Inception (FID) score consistency because of the aliasing effect. (See: https://www.cs.cmu.edu/~clean-fid/)\n",
    "\n",
    "Thus, we recommend to use anti-aliasing from beacon_aug."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import beacon_aug as BA\n",
    "BA.properties.isAntiAliasing(BA.Resize, library= \"torchvision\",interpolation = \"linear\" )"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "InterpolationMode.BILINEAR\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1. Discussion on \"resize\" operator\n",
    "\n",
    "Whether or not the operator is anti-aliasing  depends on the PSNR of original and rescaled image\n",
    "As example showing below, only PSNR of operator-interpolation combinations > 18dB are anti-aliasing.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import cv2\n",
    "import torchvision.transforms.functional as torch_f\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import beacon_aug as BA\n",
    "\n",
    "\n",
    "library = 'torchvision'\n",
    "interpolation='linear'\n",
    "\n",
    "def psnr_uint8(img0, img1):\n",
    "\treturn -10*np.log10(np.mean((img0/255. - img1/255.)**2))\n",
    "\n",
    "def resize_psnr(library='torchvision', interpolation='linear'):\n",
    "\timg_np = np.zeros((128, 128, 3), dtype='uint8')+255\n",
    "\timg_np = cv2.circle(img_np, (128//2, 128//2), 50, (0, 0, 0), 1)\n",
    "\n",
    "\t# interpolation_all = [\"nearest\", \"linear\", \"area\", \"cubic\", \"Lanczos\", \"hamming\"]\n",
    "\tinterpolation_dict = {\"albumentations\":\n",
    "\t\t\t{\"nearest\": cv2.INTER_NEAREST,\n",
    "\t\t\t\"linear\": cv2.INTER_LINEAR,\n",
    "\t\t\t\"area\": cv2.INTER_AREA,\n",
    "\t\t\t\"cubic\": cv2.INTER_CUBIC,\n",
    "\t\t\t\"Lanczos\": cv2.INTER_LANCZOS4,\n",
    "\t\t\t},\n",
    "\t\t\"imgaug\":\n",
    "\t\t\t{\"nearest\": \"nearest\",\n",
    "\t\t\t\"linear\": \"linear\",\n",
    "\t\t\t\"cubic\": \"cubic\",\n",
    "\t\t\t\"area\": \"area\"\n",
    "\t\t\t},\n",
    "\t\t\"torchvision\":\n",
    "\t\t\t{\"nearest\": torch_f.InterpolationMode.NEAREST,\n",
    "\t\t\t\"linear\": torch_f.InterpolationMode.BILINEAR,\n",
    "\t\t\t\"box\": torch_f.InterpolationMode.BOX,\n",
    "\t\t\t\"cubic\": torch_f.InterpolationMode.BICUBIC,\n",
    "\t\t\t\"Lanczos\": torch_f.InterpolationMode.LANCZOS,\n",
    "\t\t\t\"hamming\": torch_f.InterpolationMode.HAMMING,\n",
    "\t\t\t}}\n",
    "\tinterp = interpolation_dict[library][interpolation] if library in interpolation_dict else interpolation\n",
    "\t# print(interp)\n",
    "\n",
    "\top = BA.Resize(p=1, library=library, interpolation=interp, height=16, width=16)\n",
    "\top_up = BA.Resize(p=1, library=library, interpolation=interp, height=128, width=128)\n",
    "\n",
    "\timg_resized = op(image=img_np)[\"image\"].copy()\n",
    "\timg_up = op_up(image=img_resized)[\"image\"].copy()\n",
    "\n",
    "\treturn psnr_uint8(img_np, img_up), img_up\n",
    "\n",
    "\n",
    "img_np = np.zeros((128, 128, 3), dtype='uint8')+255\n",
    "img_np = cv2.circle(img_np, (128//2, 128//2), 50, (0, 0, 0), 1)\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(3,7,1)\n",
    "plt.imshow(img_np)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "\n",
    "for (ii,interp) in enumerate(['nearest', 'linear', 'box', 'cubic', 'Lanczos', 'hamming']):\n",
    "\tplt.subplot(3,7,1+ii+1)\n",
    "\tpsnr, img_up = resize_psnr(library='torchvision', interpolation=interp)\n",
    "\tplt.imshow(img_up)\n",
    "\tplt.xticks([])\n",
    "\tplt.yticks([])\n",
    "\tplt.title('%.2f [tv, %s]'%(psnr, interp))\n",
    "\n",
    "for (ii,interp) in enumerate(['nearest', 'linear', 'cubic', 'area']):\n",
    "\tplt.subplot(3,7,8+ii+1)\n",
    "\tpsnr, img_up = resize_psnr(library='imgaug', interpolation=interp)\n",
    "\tplt.imshow(img_up)\n",
    "\tplt.xticks([])\n",
    "\tplt.yticks([])\n",
    "\tplt.title('%.2f [ia, %s]'%(psnr, interp))\n",
    "\n",
    "for (ii,interp) in enumerate(['nearest', 'linear', 'area', 'cubic', 'Lanczos']):\n",
    "\tplt.subplot(3,7,15+ii+1)\n",
    "\tpsnr, img_up = resize_psnr(library='albumentations', interpolation=interp)\n",
    "\tplt.imshow(img_up)\n",
    "\tplt.xticks([])\n",
    "\tplt.yticks([])\n",
    "\tplt.title('%.2f [alb, %s]'%(psnr, interp))\n",
    "\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2. Discussion on \"affine transform\" operator\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "\n",
    "interpolation_dict = {\"albumentations\":\n",
    "                      {\"nearest\": cv2.INTER_NEAREST,\n",
    "                       \"linear\": cv2.INTER_LINEAR,\n",
    "                       \"area\": cv2.INTER_AREA,\n",
    "                       \"cubic\": cv2.INTER_CUBIC,\n",
    "                       \"Lanczos\": cv2.INTER_LANCZOS4,\n",
    "                       },\n",
    "                      \"imgaug-skimage\":\n",
    "                            {\"nearest\": 0,\n",
    "                            \"linear\": 1,\n",
    "                            \"cubic\": 3,\n",
    "                            \"quartic\": 4,\n",
    "                            \"quintic\":5.\n",
    "                            },\n",
    "                       \"imgaug-cv2\":\n",
    "                            {\"nearest\": 0,\n",
    "                            \"linear\": 1,\n",
    "                            \"cubic\": 3,\n",
    "                            },\n",
    "                      \"torchvision\":\n",
    "                        {\"nearest\": torch_f.InterpolationMode.NEAREST,\n",
    "                        \"linear\": torch_f.InterpolationMode.BILINEAR,\n",
    "                        \"box\": torch_f.InterpolationMode.BOX,\n",
    "                        \"cubic\": torch_f.InterpolationMode.BICUBIC,\n",
    "                        \"Lanczos\": torch_f.InterpolationMode.LANCZOS,\n",
    "                        \"hamming\": torch_f.InterpolationMode.HAMMING,\n",
    "                        },\n",
    "                      \"keras\":  \n",
    "                        {\"zero\": 0,\n",
    "                        \"linear\": 1,\n",
    "                        \"quartic\": 2,\n",
    "                        \"cubic\": 3,\n",
    "                        }\n",
    "                       }\n",
    "interpolation_all = [\"zero\",\"nearest\", \"linear\", \"area\",\"cubic\",\"quartic\", \"Lanczos\",\"hamming\"]\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(len(interpolation_dict), len(interpolation_all)+1, figsize=(12,6))\n",
    "for i_k, kernel in enumerate( interpolation_all):\n",
    "    for i_l, library in enumerate(interpolation_dict):\n",
    "        ax[i_l][0].text(0.3, 0.5, library)\n",
    "        ax[i_l][0].axis(\"off\")\n",
    "        if kernel in interpolation_dict[library]:\n",
    "            interp= interpolation_dict[library][kernel]\n",
    "            \n",
    "            if library == \"albumentations\":\n",
    "                aug = BA.Affine(p=1,library=library, scale=0.25, interpolation= interp)\n",
    "            elif library == \"imgaug-cv2\" : #library == \"imgaug\" or \"torchvision\"\n",
    "                aug = BA.Affine(p=1,library=\"imgaug\", scale = 0.25, order = interp,backend=\"cv2\")\n",
    "            elif library == \"imgaug-skimage\" : #library == \"imgaug\" or \"torchvision\"\n",
    "                aug = BA.Affine(p=1,library=\"imgaug\", scale = 0.25, order = interp,backend=\"skimage\")\n",
    "            elif library == \"torch\":\n",
    "                aug = BA.Affine(p=1,library=library, scale= (0.25, 0.25), interpolation= interp)\n",
    "            elif library == \"keras\":\n",
    "                aug = BA.Affine(p=1,library=library, zx=4,zy=4, order=interp)     \n",
    "\n",
    "            img_resized = aug(image=img_np)[\"image\"].copy()\n",
    "            ax[i_l, i_k+1].imshow(img_resized[48:48+32,48:48+32 ])\n",
    "            ax[i_l, i_k+1].set_title(kernel )\n",
    "        ax[i_l,i_k+1].axis('off')\n",
    "        \n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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