{ "cells": [ { "cell_type": "code", "execution_count": 6, "id": "05d4486c", "metadata": {}, "outputs": [], "source": [ "import csv\n", "import os\n", "import cv2\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 7, "id": "67a397be", "metadata": {}, "outputs": [], "source": [ "youtube_path = './train/labels/'\n", "youtube_vid_list = [file for file in os.listdir(youtube_path)]" ] }, { "cell_type": "code", "execution_count": 8, "id": "ae2da5ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['.ipynb_checkpoints',\n", " 'kbs_news_daegue - Trim',\n", " 'rest',\n", " 'water_main_break - Trim',\n", " 'water_main_break - Trim2 - Trim',\n", " 'ytn_news - Trim - Trim']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "youtube_vid_list" ] }, { "cell_type": "code", "execution_count": 9, "id": "14577dd8", "metadata": {}, "outputs": [], "source": [ "del youtube_vid_list[0]" ] }, { "cell_type": "code", "execution_count": 10, "id": "005e55b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['kbs_news_daegue - Trim',\n", " 'rest',\n", " 'water_main_break - Trim',\n", " 'water_main_break - Trim2 - Trim',\n", " 'ytn_news - Trim - Trim']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "youtube_vid_list" ] }, { "cell_type": "code", "execution_count": 12, "id": "57403119", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['0', '0.62792578125', '0.5845694444444445', '0.29002343750000004', '0.4039444444444444']]\n", "[['1', '0.62792578125', '0.5845694444444445', '0.29002343750000004', '0.4039444444444444']]\n", "[['0', '0.6413203125', '0.5886736111111112', '0.3168125', '0.41215277777777776']]\n", "[['1', '0.6413203125', '0.5886736111111112', '0.3168125', '0.41215277777777776']]\n", "[['0', '0.6413203125', '0.5886736111111112', '0.3168125', '0.41215277777777776']]\n", "[['1', '0.6413203125', '0.5886736111111112', 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'0.46397265625', '0.44905555555555554', '0.3864609375', '0.6273611111111111']]\n", "[['0', '0.46397265625', '0.44905555555555554', '0.3864609375', '0.6273611111111111']]\n", "[['1', '0.46397265625', '0.44905555555555554', '0.3864609375', '0.6273611111111111']]\n", "[['0', '0.46397265625', '0.44905555555555554', '0.3864609375', '0.6273611111111111']]\n", "[['1', '0.46397265625', '0.44905555555555554', '0.3864609375', '0.6273611111111111']]\n" ] } ], "source": [ "for folder in youtube_vid_list:\n", " label_files = [file for file in os.listdir(os.path.join(youtube_path,folder)) if file.endswith(\".txt\")]\n", " \n", " # convert xywh float to xyxy int\n", " label_array = []\n", " for idx,txt_file in enumerate(label_files):\n", " with open(os.path.join(youtube_path,folder,txt_file),'r+') as myfile:\n", " data = myfile.read()\n", " rows=data.split('\\n')\n", "\n", " data = []\n", " for row in rows:\n", " data.append(row.split(' '))\n", "\n", " print(data)\n", "\n", " for idx,row in enumerate(data):\n", " if row[0] == '0':\n", " data[idx][0] = '1'\n", "\n", " elif row[0] == '1':\n", " data[idx][0] = '0'\n", "\n", " print(data)\n", "\n", " string_data = ''\n", "\n", " for row in data:\n", " for col in row:\n", " string_data+=col+' '\n", " string_data+='\\n'\n", "\n", " string_data = string_data[:-2]\n", "\n", " myfile.seek(0)\n", " myfile.write(string_data)\n", " myfile.truncate()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7e933bf9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "yt38", "language": "python", "name": "yt38" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 5 }