Poor man's attempt at pointilism with python

Pointillism.ipynb

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   "id": "cf91c81c-03c4-4357-ae7f-ba969c859c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm.notebook import tqdm\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38b28cd8-00ea-4e42-a0c4-e57853ce7866",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94e20b00-867c-4bdf-8b15-d07c949088f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def smudge(img, img_new, x, y, radius, rng):\n",
    "    Nx, Ny = img.shape[:2]\n",
    "    idx_x, idx_y = np.array([\n",
    "        [x + dx, y + dy]\n",
    "        for dx in range(max(-radius, -x), min(radius + 1, Nx - x))\n",
    "        for dy in range(max(-radius, -y), min(radius + 1, Ny - y))\n",
    "        if ((dx ** 2 + dy ** 2) / (radius ** 2)) ** 2 <= rng.random()\n",
    "    ]).T\n",
    "    for layer in range(img.shape[2]):\n",
    "        idx_l = [layer] * len(idx_x)\n",
    "        mean = np.mean(img[idx_x, idx_y, idx_l])\n",
    "        std = np.std(img[idx_x, idx_y, idx_l]) / 5\n",
    "        img_new[idx_x, idx_y, idx_l] = np.clip(\n",
    "            rng.normal(mean - std + rng.random() * 2 * std, std, len(idx_l)), 0, 1\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5155ba4-08ed-4997-980e-50a7328d4a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '/tmp/Coral_Tree_Monsoon_Mallalli_Falls_Hassan_Jun24_A7CR_01575.jpg'\n",
    "\n",
    "# Source: Wikimedia Commons (https://commons.wikimedia.org/wiki/File:Coral_Tree_Monsoon_Mallalli_Falls_Hassan_Jun24_A7CR_01575.jpg)\n",
    "# Author: Timothy A. Gonsalves (https://commons.wikimedia.org/wiki/User:Tagooty)\n",
    "# License: Creative Commons Attribution-Share Alike 4.0 International "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b416730e-d7f7-476c-b71d-ce739af88611",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "80266f73fc1d48d5b236b5a3012d435c",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/100000 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.array(Image.open(filename)) / 255.\n",
    "img_new = img * 1\n",
    "\n",
    "for i in tqdm(range(100000)):\n",
    "    x, y = [rng.integers(N) for N in img.shape[:2]]\n",
    "    if (img[x][y] != img_new[x][y]).all():\n",
    "        continue\n",
    "    radius = rng.integers(12, 20)\n",
    "    smudge(img, img_new, x, y, radius, rng)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "f4a8fca7-0db6-40f9-bd8b-0f768ecde033",
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