{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from PIL import Image, ImageFilter, ImageMath\n",
    "\n",
    "import numpy as np\n",
    "import skimage\n",
    "from sklearn.feature_extraction import image\n",
    "\n",
    "STUDY_PATH = \"/media/cifs/shares/SRS/storage/tmp/MRI With_Without Contrast--Brain_53820330\"\n",
    "\n",
    "MODEL_PATH = '/home/xfr/nni/model-5-64/TwNuKtj7/best_zdoyO.pth'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/media/cifs/shares/SRS/storage/tmp/MRI With_Without Contrast--Brain_53820330/export--873570386.jpg\n",
      "<PIL.Image.Image image mode=L size=512x512 at 0x7FE087FC8350>\n",
      "[[34 34 32 ... 35 34 33]\n",
      " [34 43 40 ... 45 40 42]\n",
      " [29 49 47 ... 42 39 47]\n",
      " ...\n",
      " [31 48 49 ... 45 42 41]\n",
      " [33 51 49 ... 43 48 54]\n",
      " [36 56 52 ... 61 58 60]]\n"
     ]
    }
   ],
   "source": [
    "for jpg_file in sorted(os.listdir(STUDY_PATH)):\n",
    "    jpg_path = os.path.join(STUDY_PATH, jpg_file)\n",
    "    print(jpg_path)\n",
    "    img = Image.open(jpg_path).convert('L')\n",
    "    print(img)\n",
    "    data = np.array(img)\n",
    "    print(data)\n",
    "    \n",
    "#     blocks = skimage.util.shape.view_as_blocks(data, (300, 300))\n",
    "#     print(blocks.shape)\n",
    "    \n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BaseEstimator', 'PatchExtractor', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_compute_gradient_3d', '_compute_n_patches', '_make_edges_3d', '_mask_edges_weights', '_to_graph', 'as_strided', 'check_array', 'check_random_state', 'extract_patches', 'extract_patches_2d', 'grid_to_graph', 'img_to_graph', 'np', 'numbers', 'product', 'reconstruct_from_patches_2d', 'sparse']\n",
      "(2, 2, 250, 250)\n"
     ]
    }
   ],
   "source": [
    "print(dir(image))\n",
    "# extract_patches\n",
    "\n",
    "patches = image.extract_patches(data, patch_shape=250, extraction_step=250)\n",
    "print(patches.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "\n",
    "\n",
    "# # chop it up\n",
    "# I, J = map(np.arange, (200, 200), data.shape[:2], (200, 200))\n",
    "# chops = [np.split(row, J, axis=1) for row in np.split(data, I, axis=0)]\n",
    "\n",
    "# print(I,J)\n",
    "# print(len(chops))\n",
    "# print(chops[0])\n",
    "# # do something with the bits\n",
    "\n",
    "# predictions = [chop-(i+j)*(chop>>3) for j, row in enumerate(chops) for i, chop in enumerate(row)]\n",
    "\n",
    "# # unflatten predictions\n",
    "# def nest(data, template):\n",
    "#     data = iter(data)\n",
    "#     return [[next(data) for _ in row] for row in template]\n",
    "\n",
    "# pred_lol = nest(predictions, chops)\n",
    "\n",
    "\n",
    "# # almost builtin reconstruction\n",
    "# def np_block_2D(chops):\n",
    "#     return np.block([[[x] for x in row] for row in chops])\n",
    "\n",
    "# recon = np_block_2D(pred_lol)\n",
    "# Image.fromarray(recon).save('demo.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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