diff --git "a/data_exploration-2.ipynb" "b/data_exploration-2.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cv2\n",
+ "import os\n",
+ "from PIL import Image\n",
+ "from tqdm import tqdm\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from torchvision import transforms"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# creating train and test dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def getData(type):\n",
+ " df = list()\n",
+ " directory = f'D-Fire/{type}/labels' \n",
+ " n = len(os.listdir(directory))\n",
+ " for filename in tqdm(os.listdir(directory)):\n",
+ " f = os.path.join(directory, filename)\n",
+ " # print(f)\n",
+ "\n",
+ " image = filename[:-3] + 'jpg'\n",
+ " # print(image)\n",
+ " # break\n",
+ " img = Image.open(f'D-Fire/{type}/images/{image}')\n",
+ " width, height = img.size\n",
+ " # print(width, height)\n",
+ " # plt.imshow(img)\n",
+ " # plt.show()\n",
+ " # break\n",
+ " pre = [image, width, height]\n",
+ " if os.path.getsize(f) == 0:\n",
+ " dp = pre + [2]\n",
+ " df.append(dp)\n",
+ " else:\n",
+ " with open(f) as fp:\n",
+ " lines = fp.readlines()\n",
+ " for line in lines:\n",
+ " line = line.split()\n",
+ " line = list(map(float, line))\n",
+ " if len(line) != 0:\n",
+ " line[0] = int(line[0])\n",
+ " # line.insert(0, image)\n",
+ " dp = pre + line\n",
+ " df.append(dp)\n",
+ " fp.close()\n",
+ " return df, n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 17221/17221 [00:04<00:00, 4029.38it/s]\n",
+ "100%|██████████| 4306/4306 [00:00<00:00, 5038.54it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get train and test data\n",
+ "train, n_train = getData(\"train\")\n",
+ "df_train = pd.DataFrame(train, columns= [\"Image\", \"Width\", \"Height\", \"Label\", \"x_min\", \"y_min\", \"x_max\", \"y_max\"])\n",
+ "test, n_test = getData(\"test\")\n",
+ "df_test = pd.DataFrame(test, columns= [\"Image\", \"Width\", \"Height\", \"Label\", \"x_min\", \"y_min\", \"x_max\", \"y_max\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " Height | \n",
+ " Label | \n",
+ " x_min | \n",
+ " y_min | \n",
+ " x_max | \n",
+ " y_max | \n",
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "images = np.concatenate((class_0, class_1, class_2))\n",
+ "num_images = len(images)\n",
+ "\n",
+ "fig, axes = plt.subplots(2, 3, figsize=(20, 6)) # 2 rows, 3 columns for 6 images\n",
+ "\n",
+ "for i in range(num_images):\n",
+ " row, col = divmod(i, 3)\n",
+ " path = f\"D-Fire/train/images/{images[i][0]}\"\n",
+ " axes[row, col].imshow(cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB))\n",
+ " if images[i][3] == 0:\n",
+ " label = \"Smoke\"\n",
+ " elif images[i][3] == 1:\n",
+ " label = \"Fire\"\n",
+ " else:\n",
+ " label = \"Neither\"\n",
+ " axes[row, col].set_title(label)\n",
+ " axes[row, col].axis('off')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# data split exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "group_tr = df_train.groupby(\"Label\").count().iloc[:, 0].to_numpy()\n",
+ "group_tr_ratio = group_tr / n_train\n",
+ "group_te = df_test.groupby(\"Label\").count().iloc[:, 0].to_numpy()\n",
+ "group_te_ratio = group_te / n_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# statistics on data ratio split\n",
+ "x = np.arange(3)\n",
+ "plt.bar(x, group_tr_ratio, color ='r', width = 0.25,\n",
+ " edgecolor ='grey', label ='Train')\n",
+ "x = [x + 0.25 for x in x]\n",
+ "plt.bar(x, group_te_ratio, color ='b', width = 0.25,\n",
+ " edgecolor ='grey', label ='Test')\n",
+ "plt.xlabel('Labels')\n",
+ "plt.ylabel('Proprtion Ratio')\n",
+ "plt.xticks([0.15, 1.15, 2.15], [\"Smoke\", \"Fire\", \"None\"])\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# total count for different classes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Smoke | \n",
+ " Fire | \n",
+ " Neither | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Train | \n",
+ " 9550 | \n",
+ " 11814 | \n",
+ " 7833 | \n",
+ "
\n",
+ " \n",
+ " | Test | \n",
+ " 2315 | \n",
+ " 2878 | \n",
+ " 2005 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "