PLANT_PEO_IMAGE_PREPROCESSING.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda\n",
      "GPU activate --> Count of using GPUs: 4\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import cv2\n",
    "import time\n",
    "import timm\n",
    "import copy \n",
    "import random\n",
    "import argparse\n",
    "import datetime\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "from sklearn import cluster\n",
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "\n",
    "seed = 10\n",
    "device = '0,1,2,3'\n",
    "\n",
    "os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)  # if use multi-GPU\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.benchmark = False\n",
    "np.random.seed(seed)\n",
    "random.seed(seed)\n",
    "\n",
    "\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"  # Arrange GPU devices starting from 0\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")    \n",
    "print('Device: %s' % device)\n",
    "if (device.type == 'cuda') or (torch.cuda.device_count() > 1):\n",
    "    print('GPU activate --> Count of using GPUs: %s' % torch.cuda.device_count())\n",
    "\n",
    "\n",
    "seed = 10\n",
    "suffix = (datetime.datetime.now() + datetime.timedelta(hours=9)).strftime(\"%y%m%d_%H%M\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_train_data(data_dir, mode='train'):\n",
    "    img_path_list = []\n",
    "    meta_path_list = []\n",
    "    label_list = []\n",
    "    \n",
    "    for case_name in os.listdir(data_dir):\n",
    "        current_path = os.path.join(data_dir, case_name)\n",
    "        if os.path.isdir(current_path):\n",
    "            # get image path\n",
    "            img_path_list.extend(glob(os.path.join(current_path, 'image', '*.jpg')))\n",
    "            img_path_list.extend(glob(os.path.join(current_path, 'image', '*.png')))\n",
    "            \n",
    "            # get meta path\n",
    "            meta_path_list.extend(glob(os.path.join(current_path, 'meta', '*.csv')))\n",
    "            \n",
    "            # get label\n",
    "            label_df = pd.read_csv(current_path+'/label.csv')\n",
    "            label_list.extend(label_df['leaf_weight'])\n",
    "                \n",
    "    return img_path_list, meta_path_list, label_list\n",
    "\n",
    "def get_test_data(data_dir):\n",
    "        \n",
    "    # get image path\n",
    "    img_path_list = glob(os.path.join(data_dir, 'image', '*.jpg'))\n",
    "    img_path_list.extend(glob(os.path.join(data_dir, 'image', '*.png')))\n",
    "    img_path_list.sort(key=lambda x:int(x.split('/')[-1].split('.')[0]))\n",
    "    \n",
    "    # get meta path\n",
    "    meta_path_list = glob(os.path.join(data_dir, 'meta', '*.csv'))\n",
    "    \n",
    "    return img_path_list, meta_path_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_img_path, all_meta_path, all_label = get_train_data('/data/KIST_PLANT/train')\n",
    "test_img_path, test_meta_path = get_test_data('/data/KIST_PLANT/test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = 1\n",
    "col = 3\n",
    "n_clusters = 3\n",
    "\n",
    "for idx in range(10,20):\n",
    "    plt.figure(figsize=(25,15))\n",
    "    img = cv2.imread(test_img_path[idx])[:,:,::-1]\n",
    "    img = cv2.resize(img, (512, 512))\n",
    "    \n",
    "    # -----------------------------------------------------------\n",
    "    plt.subplot(row, col, 1)    \n",
    "    plt.imshow(img)\n",
    "    img_n = img.reshape(img.shape[0]*img.shape[1], img.shape[2])\n",
    "    \n",
    "    # -----------------------------------------------------------\n",
    "    agglo = cluster.FeatureAgglomeration(n_clusters=n_clusters).fit(img_n)\n",
    "    X_reduced = agglo.transform(img_n)\n",
    "\n",
    "    pic2show = X_reduced.reshape(img.shape[0], img.shape[1], n_clusters)\n",
    "    pic2show = pic2show.astype('uint8')\n",
    "\n",
    "    ret, mask = cv2.threshold(pic2show[:,:,0], 120, 255, cv2.THRESH_BINARY)\n",
    "    mask_inv = cv2.bitwise_not(mask)\n",
    "\n",
    "    plt.subplot(row, col, 2)  \n",
    "    plt.imshow(mask_inv, cmap='gray')\n",
    "    \n",
    "    # -----------------------------------------------------------\n",
    "    img_mask = cv2.bitwise_and(img, img, mask = mask)\n",
    "    img_mask_inv = cv2.bitwise_and(img, img, mask = mask_inv)\n",
    "    plt.subplot(row, col, 3)  \n",
    "    plt.imshow(img_mask_inv)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 460/460 [08:59<00:00,  1.17s/it]\n"
     ]
    }
   ],
   "source": [
    "modes = ['TRAIN', 'TEST']\n",
    "datas = [all_img_path, test_img_path]\n",
    "\n",
    "for mode, dset in zip(modes, datas):\n",
    "    \n",
    "    n_clusters = 3    \n",
    "    foreground_sum_list = []\n",
    "\n",
    "    for idx in tqdm(range(len(dset)), total=len(dset)):\n",
    "\n",
    "        img = cv2.imread(dset[idx])[:,:,::-1]\n",
    "        img = cv2.resize(img, (512, 512))\n",
    "\n",
    "        img_n = img.reshape(img.shape[0]*img.shape[1], img.shape[2])\n",
    "        agglo = cluster.FeatureAgglomeration(n_clusters=n_clusters).fit(img_n)\n",
    "        X_reduced = agglo.transform(img_n)\n",
    "\n",
    "        pic2show = X_reduced.reshape(img.shape[0], img.shape[1], n_clusters)\n",
    "        pic2show = pic2show.astype('uint8')\n",
    "\n",
    "        ret, mask = cv2.threshold(pic2show[:,:,0], 120, 255, cv2.THRESH_BINARY)\n",
    "        mask_inv = cv2.bitwise_not(mask)\n",
    "        \n",
    "        cv2.imwrite(\"/home/SY_LEE/KIST_PLANT/DATA_BACKGROUND_{}/\".format(mode) + dset[idx].split('/')[-1].split('.')[0] + \"_background.png\", mask_inv)\n",
    "        foreground_sum_list.append(mask_inv.sum() / ( img.shape[0] * img.shape[1]))\n",
    "\n",
    "    foreground_sum_df = pd.DataFrame(foreground_sum_list, columns=['image_sum'])\n",
    "    foreground_sum_df.to_csv('{}_foreground_sum_df.csv'.format(mode), index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(30,12))\n",
    "\n",
    "row = 1\n",
    "col = 3\n",
    "\n",
    "t = np.arange(0, 512)\n",
    "t_func = 0.43 * t\n",
    "\n",
    "# -----------------------------------------------------------\n",
    "plt.subplot(row, col, 1)  \n",
    "plt.scatter(all_label, foreground_sum_list)\n",
    "plt.plot(t, t, '--')\n",
    "plt.xlabel('leaf_weight')\n",
    "plt.ylabel('segment_sum')\n",
    "# -----------------------------------------------------------\n",
    "\n",
    "plt.subplot(row, col, 2)  \n",
    "plt.scatter(all_label, foreground_sum_list)\n",
    "plt.plot(t, t_func, '--')\n",
    "plt.xlabel('leaf_weight')\n",
    "plt.ylabel('segment_sum')\n",
    "plt.ylim(0, 200)\n",
    "\n",
    "# -----------------------------------------------------------\n",
    "plt.subplot(row, col, 3)  \n",
    "t = np.arange(0, 512)\n",
    "plt.scatter(all_label, np.log1p(foreground_sum_list))\n",
    "plt.plot(t, np.log1p(t_func), '--')\n",
    "plt.xlabel('leaf_weight')\n",
    "plt.ylabel('segment_sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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