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[local] 데이터 증식 test 준비과정
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@dragon11112222
dragon11112222 committed Nov 23, 2023
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361 changes: 361 additions & 0 deletions 231123imageAugmentation.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 버스만 있는 이미지"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"valid_total_df = pd.read_csv('valid_total_txt_label.csv')\n",
"\n",
"condition1 = valid_total_df['car_cnt'].isnull()\n",
"condition2 = valid_total_df['truck_cnt'].isnull()\n",
"\n",
"busonlyList = list(valid_total_df[(condition1) & (condition2)]['txt_in_valid_folder'].values)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Suwon_CH05_20200720_1830_MON_9m_NH_highway_OW5_sunny_FHD_066.txt',\n",
" 'Suwon_CH05_20200720_1830_MON_9m_NH_highway_OW5_sunny_FHD_085.txt',\n",
" 'Suwon_CH06_20200722_1900_WED_9m_NH_highway_OW5_sunny_FHD_033.txt',\n",
" 'Suwon_CH10_20201213_0752_SUN_9m_NH_highway_OW5_snow_FHD_041.txt',\n",
" 'Suwon_CH10_20201213_0752_SUN_9m_NH_highway_OW5_snow_FHD_082.txt']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"\n",
"valid_filename = []\n",
"for i in busonlyList:\n",
" basename = os.path.basename(i)\n",
" valid_filename.append(basename)\n",
"valid_filename"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from glob import glob\n",
"import cv2\n",
"import numpy as np\n",
"\n",
"\n",
"def glob_files(path, file_type=\"*\"):\n",
" search_string = os.path.join(path, file_type)\n",
" files = glob(search_string)\n",
"\n",
" # print('searching ', path)\n",
" paths = []\n",
" for f in files:\n",
" if os.path.isdir(f):\n",
" sub_paths = glob_files(f + '/')\n",
" paths += sub_paths\n",
" else:\n",
" paths.append(f)\n",
"\n",
" # We sort the images in alphabetical order to match them\n",
" # to the annotation files\n",
" paths.sort()\n",
"\n",
" return paths\n",
"\n",
"\n",
"# IMAGE_SIZE = 600\n",
"\n",
"def load_images(path):\n",
" files = glob_files(path, \"*.png\")\n",
"\n",
" # print(files)\n",
" X_data = []\n",
" for file in files:\n",
" image = cv2.imread(file)\n",
" # print(image.shape)\n",
" # x = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)\n",
"\n",
" X_data.append(image)\n",
" \n",
" return np.array(X_data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5, 1920, 1080, 3)\n"
]
}
],
"source": [
"basePath_image = 'highway/valid/image/'\n",
"\n",
"X_test = load_images(basePath_image)\n",
"print(X_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"WIDTH = 1080\n",
"HEIGHT = 1920\n",
"\n",
"def load_labels(path):\n",
" files = glob_files(path, \"*.txt\")\n",
"\n",
" Y_data = []\n",
" for file in files:\n",
" with open(file) as f:\n",
" lines = f.readlines()\n",
"\n",
" boxes = []\n",
" for line in lines:\n",
" tokens = line.split()\n",
"\n",
" class_id = int(tokens[0])\n",
" xc = float(tokens[1]) * WIDTH\n",
" yc = float(tokens[2]) * HEIGHT\n",
" width = float(tokens[3]) * WIDTH\n",
" height = float(tokens[4]) * HEIGHT\n",
"\n",
" boxes.append(np.array([class_id, xc, yc, width, height]))\n",
" # print(class_id, xc, yc, width, height)\n",
"\n",
" Y_data.append(np.array(boxes))\n",
" # print(lines)\n",
" return Y_data ## 이미지마다 탐지 대상 개수가 달라서(행 개수가 달라서) 정해진 shape의 넘파이 어레이로 변환할 수 없다! 변환하려면 패딩 등이 필요.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## car, truck, bus 가 모두 포함된 이미지"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>txt_in_valid_folder</th>\n",
" <th>car_cnt</th>\n",
" <th>truck_cnt</th>\n",
" <th>bus_cnt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
" <td>5.0</td>\n",
" <td>9.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
" <td>8.0</td>\n",
" <td>7.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>highway/valid/label/Suwon_CH01_20200720_1830_M...</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9290</th>\n",
" <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9311</th>\n",
" <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9312</th>\n",
" <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9315</th>\n",
" <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9324</th>\n",
" <td>highway/valid/label/Suwon_CH10_20201213_0752_S...</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1175 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" txt_in_valid_folder car_cnt truck_cnt \\\n",
"2 highway/valid/label/Suwon_CH01_20200720_1830_M... 5.0 9.0 \n",
"3 highway/valid/label/Suwon_CH01_20200720_1830_M... 8.0 7.0 \n",
"5 highway/valid/label/Suwon_CH01_20200720_1830_M... 9.0 4.0 \n",
"6 highway/valid/label/Suwon_CH01_20200720_1830_M... 9.0 4.0 \n",
"7 highway/valid/label/Suwon_CH01_20200720_1830_M... 7.0 4.0 \n",
"... ... ... ... \n",
"9290 highway/valid/label/Suwon_CH10_20201213_0752_S... 2.0 1.0 \n",
"9311 highway/valid/label/Suwon_CH10_20201213_0752_S... 2.0 2.0 \n",
"9312 highway/valid/label/Suwon_CH10_20201213_0752_S... 1.0 1.0 \n",
"9315 highway/valid/label/Suwon_CH10_20201213_0752_S... 1.0 1.0 \n",
"9324 highway/valid/label/Suwon_CH10_20201213_0752_S... 4.0 1.0 \n",
"\n",
" bus_cnt \n",
"2 1.0 \n",
"3 1.0 \n",
"5 1.0 \n",
"6 1.0 \n",
"7 3.0 \n",
"... ... \n",
"9290 1.0 \n",
"9311 1.0 \n",
"9312 1.0 \n",
"9315 1.0 \n",
"9324 1.0 \n",
"\n",
"[1175 rows x 4 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"condition3 = valid_total_df['car_cnt'].notnull()\n",
"condition4 = valid_total_df['truck_cnt'].notnull()\n",
"condition5 = valid_total_df['bus_cnt'].notnull()\n",
"\n",
"valid_notnull_df = valid_total_df[(condition3) & (condition4) & (condition5)]\n",
"valid_notnull_df[valid_notnull_df['car_cnt'] <= 10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "yolov8",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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