create_masks_axial_18_09_2020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from PIL import Image, ImageDraw\n",
    "import json\n",
    "import matplotlib.pyplot as plt \n",
    "import cv2\n",
    "from jsonmerge import merge\n",
    "from tifffile import imsave\n",
    "import os\n",
    "from os.path import splitext\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_lst = ['Fifth_3',\n",
    "              'First_10',\n",
    "              'Forth_6',\n",
    "              'Second_0',\n",
    "              'Second_2',\n",
    "              'Second_4',\n",
    "              'Sixth_2',\n",
    "              'Sixth_3']\n",
    "\n",
    "# folder_lst = ['Fifth_5',\n",
    "#               'Sixth_1']\n",
    "\n",
    "# folder_lst = ['Fifth_2',\n",
    "#               'Forth_3',\n",
    "#               'Second_7',\n",
    "#               'Second_9',\n",
    "#               'Sixth_9']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "for folder_name in folder_lst:\n",
    "    dir_ = '/home/natasha/unet4/axial_data/train/' + folder_name + '/'\n",
    "    json1 = 'T2Tra_' + folder_name + '_s.json'\n",
    "    json2 = 'T2Tra_' + folder_name + '_h.json'\n",
    "    \n",
    "    with open(dir_ + json1) as file:\n",
    "        j1 = json.load(file)\n",
    "        \n",
    "    with open(dir_ + json2) as file:\n",
    "        j2 = json.load(file)\n",
    "    \n",
    "    raw_data = merge(j1, j2)\n",
    "    all_keys = []\n",
    "\n",
    "    for key in raw_data['_via_img_metadata'].keys():\n",
    "        all_keys.append(key)\n",
    "\n",
    "    pics_path = dir_ + 'pictures/'\n",
    "    masks_path = dir_ + 'masks/'\n",
    "\n",
    "    if not os.path.exists(masks_path):\n",
    "        os.makedirs(masks_path)\n",
    "\n",
    "    fucked_imgs = []\n",
    "    zero_files = []\n",
    "    not_found = []\n",
    "    for k in range(0, len(all_keys)):\n",
    "        filename = raw_data['_via_img_metadata'][all_keys[k]]['filename']\n",
    "\n",
    "        try:\n",
    "            image = Image.open(pics_path + filename)\n",
    "            width = image.size[0] \n",
    "            height = image.size[1]\n",
    "\n",
    "            img1 = Image.new('L',(width, height), 0)\n",
    "            img2 = Image.new('L',(width, height), 0)\n",
    "            img3 = Image.new('L',(width, height), 0)\n",
    "            img4 = Image.new('L',(width, height), 0)    \n",
    "            img5 = Image.new('L',(width, height), 0)\n",
    "            img6 = Image.new('L',(width, height), 0)\n",
    "            for i in range(len(raw_data['_via_img_metadata'][all_keys[k]]['regions'])):\n",
    "                try:\n",
    "                    xs = raw_data['_via_img_metadata'][all_keys[k]]['regions'][i]['shape_attributes']['all_points_x']\n",
    "                    ys = raw_data['_via_img_metadata'][all_keys[k]]['regions'][i]['shape_attributes']['all_points_y']\n",
    "                    all_pairs = []\n",
    "                    for j in range(len(ys)):\n",
    "                        all_pairs.append((xs[j], ys[j]))\n",
    "\n",
    "                    if raw_data['_via_img_metadata'][all_keys[k]]['regions'][i]['region_attributes']['A3'] == 'disc':\n",
    "                        ImageDraw.Draw(img1).polygon(all_pairs, outline=1, fill=1)\n",
    "\n",
    "                    if raw_data['_via_img_metadata'][all_keys[k]]['regions'][i]['region_attributes']['A3'] == 'spinal canal':\n",
    "                        ImageDraw.Draw(img2).polygon(all_pairs, outline=1, fill=1) \n",
    "                        \n",
    "                    if raw_data['_via_img_metadata'][all_keys[k]]['regions'][i]['region_attributes']['A3'] == 'nerv':\n",
    "                        ImageDraw.Draw(img3).polygon(all_pairs, outline=1, fill=1)\n",
    "                        \n",
    "                    if raw_data['_via_img_metadata'][all_keys[k]]['regions'][i]['region_attributes']['A3'] == 'neural foramen':\n",
    "                        ImageDraw.Draw(img4).polygon(all_pairs, outline=1, fill=1)\n",
    "                        \n",
    "                    if raw_data['_via_img_metadata'][all_keys[k]]['regions'][i]['region_attributes']['A3'] == 'size of spinal canal':\n",
    "                        ImageDraw.Draw(img5).polygon(all_pairs, outline=1, fill=1)\n",
    "\n",
    "                    if raw_data['_via_img_metadata'][all_keys[k]]['regions'][i]['region_attributes']['A3'] == 'size of herniation':\n",
    "                        ImageDraw.Draw(img6).polygon(all_pairs, outline=1, fill=1)\n",
    "\n",
    "\n",
    "                except:\n",
    "                    fucked_imgs.append(filename)\n",
    "                    \n",
    "            mask1 = np.array(img1)\n",
    "            mask2 = np.array(img2)\n",
    "            mask3 = np.array(img3)\n",
    "            mask4 = np.array(img4)\n",
    "            mask5 = np.array(img5)\n",
    "            mask6 = np.array(img6)\n",
    "\n",
    "#             mask = mask1 + 2 * mask2 + 3 * mask3 + 4 * mask4 + 5 * mask5 + 6 * mask6\n",
    "            mask = np.dstack((mask1, mask2, mask3, mask4, mask5, mask6))\n",
    "            if len(np.unique(mask)) == 1:\n",
    "                zero_files.append(filename)\n",
    "\n",
    "            \n",
    "        except:\n",
    "            not_found.append(filename)\n",
    "        \n",
    "\n",
    "        if filename not in not_found:\n",
    "            if filename not in fucked_imgs:\n",
    "                if filename not in zero_files:\n",
    "                    if len(np.unique(mask)) > 1:\n",
    "                        filename = filename[:-3] + 'npy'\n",
    "#                         print(filename)\n",
    "#                         print(np.unique(mask))\n",
    "                        np.save(masks_path + filename, mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82, 82, 0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zero_files), len(fucked_imgs), len(not_found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00088.PNG',\n",
       " '00092.PNG',\n",
       " '00126.PNG',\n",
       " '0157.PNG',\n",
       " '00158.PNG',\n",
       " '00160.PNG',\n",
       " '00201.PNG',\n",
       " '00209.PNG',\n",
       " '00223.PNG',\n",
       " '00224.PNG',\n",
       " '00227.PNG',\n",
       " '00228.PNG',\n",
       " '00241.PNG',\n",
       " '0256.PNG',\n",
       " '00257.PNG',\n",
       " '00261.PNG',\n",
       " '00265.PNG',\n",
       " '0269.PNG',\n",
       " '00272.PNG',\n",
       " '00274.PNG',\n",
       " '00279.PNG',\n",
       " '00292.PNG',\n",
       " '00295.PNG',\n",
       " '00302.PNG',\n",
       " '00303.PNG',\n",
       " '0678.PNG',\n",
       " '00784.PNG',\n",
       " '00789.PNG',\n",
       " '00797.PNG',\n",
       " '0841.PNG',\n",
       " '0845.PNG',\n",
       " '0905.PNG',\n",
       " '00907.PNG',\n",
       " '0913.PNG',\n",
       " '0935.PNG',\n",
       " '00936.PNG',\n",
       " '00944.PNG',\n",
       " '00982.PNG',\n",
       " '00987.PNG',\n",
       " '0987.PNG',\n",
       " '00992.PNG',\n",
       " '01000.PNG',\n",
       " '01005.PNG',\n",
       " '01018.PNG',\n",
       " '1023.PNG',\n",
       " '01029.PNG',\n",
       " '01036.PNG',\n",
       " '1045.PNG',\n",
       " '01050.PNG',\n",
       " '01055.PNG',\n",
       " '01058.PNG',\n",
       " '1066.PNG',\n",
       " '1078.PNG']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(broken_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = filename[:-3] + 'npy'\n",
    "np.save(masks_path + filename, mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "img6.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "folder_name = Second_2\n",
      "# not present in folder from json = 44\n",
      "# imgs in json = 312\n",
      "not present files = ['00268.PNG', '00269.PNG', '0269.PNG', '00272.PNG', '00273.PNG', '00274.PNG', '00276.PNG', '0276.PNG', '00282.PNG', '0283.PNG', '00285.PNG', '00286.PNG', '00287.PNG', '0287.PNG', '00289.PNG', '00292.PNG', '0292.PNG', '00293.PNG', '00295.PNG', '00297.PNG', '0297.PNG', '00298.PNG', '00300.PNG', '0300.PNG', '00301.PNG', '00302.PNG', '00303.PNG', '00304.PNG', '00324.PNG', '00327.PNG', '0327.PNG', '00329.PNG', '00332.PNG', '0332.PNG', '00334.PNG', '00335.PNG', '00362.PNG', '0362.PNG', '00363.PNG', '00365.PNG', '00366.PNG', '00367.PNG', '0367.PNG', '00369.PNG']\n"
     ]
    }
   ],
   "source": [
    "print(f'folder_name = {folder_name}')\n",
    "print(f'# not present in folder from json = {len(zero_files)}')\n",
    "print(f'# imgs in json = {len(all_keys)}')\n",
    "print(f'not present files = {zero_files}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs_dir = '/home/natasha/unet4/axial_data/train/Fifth_3/pictures/'\n",
    "masks_dir = '/home/natasha/unet4/axial_data/train/Fifth_3/masks/'\n",
    "ids = [splitext(file)[0] for file in os.listdir(imgs_dir) if not file.startswith('.')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00211.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00211.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0287.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0287.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01225.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01225.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0354.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0354.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00437.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00437.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1098.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1098.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0154.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0154.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0348.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0348.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01095.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01095.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01138.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01138.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1271.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1271.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01233.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01233.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00428.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00428.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00365.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00365.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00350.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00350.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1346.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1346.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00387.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00387.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00247.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00247.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00422.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00422.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1251.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1251.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01274.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01274.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00439.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00439.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1256.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1256.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01289.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01289.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0380.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0380.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00322.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00322.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1300.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1300.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01271.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01271.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0322.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0322.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0395.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0395.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1343.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1343.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00314.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00314.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01107.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01107.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01123.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01123.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1334.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1334.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0390.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0390.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01238.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01238.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00153.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00153.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00246.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00246.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/0428.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/0428.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00392.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00392.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1132.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1132.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00417.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00417.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01321.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01321.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1186.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1186.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01317.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01317.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00434.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00434.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00405.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00405.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/00353.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/00353.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01335.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01335.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1204.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1204.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/1207.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/1207.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01268.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01268.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/Sixth_2/masks/01246.npy/home/natasha/unet4/axial_data/train/Sixth_2/pictures/01246.PNG\n",
      "\n",
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      "/home/natasha/unet4/axial_data/train/First_10/masks/01106.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01106.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1164.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1164.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1160.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1160.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01005.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01005.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1025.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1025.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01042.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01042.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1124.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1124.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1082.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1082.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1074.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1074.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1029.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1029.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/0995.npy/home/natasha/unet4/axial_data/train/First_10/pictures/0995.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01097.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01097.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1067.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1067.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1089.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1089.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01038.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01038.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1006.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1006.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1150.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1150.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1072.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1072.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01058.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01058.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01069.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01069.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01101.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01101.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1139.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1139.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01154.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01154.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1018.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1018.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01093.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01093.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/00981.npy/home/natasha/unet4/axial_data/train/First_10/pictures/00981.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01113.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01113.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01088.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01088.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/1126.npy/home/natasha/unet4/axial_data/train/First_10/pictures/1126.PNG\n",
      "\n",
      "/home/natasha/unet4/axial_data/train/First_10/masks/01151.npy/home/natasha/unet4/axial_data/train/First_10/pictures/01151.PNG\n",
      "\n"
     ]
    }
   ],
   "source": [
    "imgs_dir = '/home/natasha/unet4/axial_data/train/'\n",
    "folders = os.listdir(imgs_dir)\n",
    "mask_paths = []\n",
    "img_paths = []\n",
    "for i, folder in enumerate(folders):\n",
    "    path = os.path.join(imgs_dir, folder, 'pictures')\n",
    "    ids = [splitext(file)[0] for file in os.listdir(path) if not file.startswith('.')]\n",
    "#     print(f'path = {path}')\n",
    "    for idx in ids:\n",
    "        mask_paths.append(os.path.join(imgs_dir, folder, 'masks', str(idx)) + '.npy')\n",
    "        img_paths.append(os.path.join(imgs_dir, folder, 'pictures', str(idx)) + '.PNG')\n",
    "    \n",
    "for i, _ in enumerate(mask_paths):\n",
    "    print(f'{mask_paths[i]}{img_paths[i]}\\n')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/natasha/unet4/axial_data/train/Sixth_2 ['00211', '0287', '01225', '0354', '00437', '1098', '0154', '0348', '01095', '01138', '1271', '01233', '00428', '00365', '00350', '1346', '00387', '00247', '00422', '1251', '01274', '00439', '1256', '01289', '0380', '00322', '1300', '01271', '0322', '0395', '1343', '00314', '01107', '01123', '1334', '0390', '01238', '00153', '00246', '0428', '00392', '1132', '00417', '01321', '1186', '01317', '00434', '00405', '00353', '01335', '1204', '1207', '01268', '01246', '1127', '01200', '1143', '1233', '01192', '1108', '00406', '01214', '1153', '1118', '00155', '01340', '00312', '0433', '00403', '00440', '00420', '01219', '01131', '1248', '00400', '1223', '1313', '1091', '01355', '0438', '0398', '01241', '00425', '01159', '01187', '00285', '1276', '0367', '1354', '1179', '1243', '00248', '00320', '0345', '0351', '01197', '00127', '01141', '1169', '00367', '00363', '01117', '1290', '0385', '00360', '00401', '00287', '01103', '01179', '00332', '01120', '00366', '01205', '01350', '01256', '00351', '1190', '0312', '00424', '00218', '01128', '1265', '00352', '01312', '1137', '1280', '01284', '0315', '00213', '01144', '1221', '0406', '1216', '01149', '00217', '01302', '0418', '01292', '1174', '00390', '00157', '1211', '0248', '1268', '00319', '00348', '1123', '00429', '00362', '00329', '01087', '00128', '0327', '01328', '1228', '00431', '1236', '0219', '00357', '00419', '00383', '1310', '1295', '00391', '01172', '00286', '01251', '01168', '01307', '01263', '01092', '00382', '1156', '00421', '00334', '00130', '1329', '1285', '1240', '01184', '01112', '0319', '01164', '0215', '0362', '0357', '1198', '01228', '0423', '1103', '0332', '01222', '1113', '1159', '00317', '1148', '1307', '0415', '1201', '00214', '01135', '00395', '00356', '1164', '1339', '00386', '00219', '0401', '1349', '0130', '00327', '01154']\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-6578b00ab5fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpath_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpath_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_id\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath_id\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mmask_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_id\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'masks'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'{path_id[1]}.npy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mimg_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_id\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pictures'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'{path_id[1]}.PNG'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "for path_id in path_ids:\n",
    "    print(path_id[0], path_id[1])\n",
    "    mask_file = glob(os.path.join(path_id[0], 'masks', f'{path_id[1]}.npy'))[0]\n",
    "    img_file = glob(os.path.join(path_id[0], 'pictures', f'{path_id[1]}.PNG'))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "expected str, bytes or os.PathLike object, not _io.TextIOWrapper",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-0f21f19b8d4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msplitext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3.7/posixpath.py\u001b[0m in \u001b[0;36msplitext\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msplitext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m     \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0msep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mb'/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not _io.TextIOWrapper"
     ]
    }
   ],
   "source": [
    "splitext(file)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['00437', '00428', '00460', '00422', '00439', '00409', '0480', '0395', '00490', '00497', '00448', '0428', '00392', '00417', '00506', '00434', '00405', '00415', '0510', '00451', '00444', '0487', '0494', '0515', '00406', '0469', '0433', '00403', '00440', '00420', '0501', '00486', '00400', '0438', '0398', '00425', '00459', '00484', '00476', '00408', '00401', '00471', '00457', '00498', '0490', '00424', '00481', '00505', '0410', '00499', '0406', '00456', '0418', '00503', '0453', '00475', '00487', '00414', '0476', '0409', '00429', '00502', '00466', '00480', '00513', '00431', '00473', '00514', '0443', '00465', '00419', '0506', '00463', '00494', '00421', '0472', '00443', '0450', '00510', '00500', '00412', '0423', '0464', '00492', '00449', '00507', '0458', '00485', '00493', '0415', '00395', '00413', '00454', '00495', '0401', '00515', '00396', '00452', '00511', '00468']\n"
     ]
    }
   ],
   "source": [
    "print(ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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