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Move to model number and name
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@hyun06000
hyun06000 committed Nov 7, 2021
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0 dev/data/post_proc/Untitled.ipynb → ...ata/post_proc/visualize_and_compare.ipynb
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3 changes: 3 additions & 0 deletions release/data_processing/EDA_vis_tools/README.md
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- [x] wandb visualization : 상현
- [ ] annotation 겹친 후에 픽셀 수 : 상현
- [ ] test csv 만 있으면 시각화 가능하도록 구현 : 상현
235 changes: 235 additions & 0 deletions release/data_processing/EDA_vis_tools/csv_visualization.ipynb
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337 changes: 337 additions & 0 deletions ...cessing/EDA_vis_tools/images_with_masks/.ipynb_checkpoints/json_analisys-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"from easydict import EasyDict as edict\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"common_json_path = \"/opt/ml/segmentation/input/data/\"\n",
"train_all_json_path = common_json_path + \"train_all.json\"\n",
"train_json_path = common_json_path + \"train.json\"\n",
"val_json_path = common_json_path + \"val.json\"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['info', 'licenses', 'images', 'categories', 'annotations'])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open(train_all_json_path,'r') as f:\n",
" train_all_json = json.load(f)\n",
"\n",
"train_all_json.keys()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start number ::: 0002 || end number ::: 1259\n",
"total number ::: 949\n",
"start number ::: 0001 || end number ::: 2005\n",
"total number ::: 1561\n",
"start number ::: 0002 || end number ::: 1000\n",
"total number ::: 762\n"
]
}
],
"source": [
"train_all_json = edict(train_all_json)\n",
"batch_01, batch_02, batch_03 = [], [], []\n",
"for im in train_all_json.images:\n",
" batch, f_name = im.file_name.split('/')\n",
" if batch == \"batch_01_vt\":\n",
" batch_01.append(f_name.split('.')[0])\n",
" elif batch == \"batch_02_vt\":\n",
" batch_02.append(f_name.split('.')[0])\n",
" elif batch == \"batch_03\":\n",
" batch_03.append(f_name.split('.')[0])\n",
" else:\n",
" print(\"unexpected case ::: \", f_name)\n",
"\n",
"batch_01.sort()\n",
"print(\"start number ::: \",batch_01[0],\" || end number ::: \",batch_01[-1])\n",
"print(\"total number ::: \",len(batch_01))\n",
"batch_02.sort()\n",
"print(\"start number ::: \",batch_02[0],\" || end number ::: \",batch_02[-1])\n",
"print(\"total number ::: \",len(batch_02))\n",
"batch_03.sort()\n",
"print(\"start number ::: \",batch_03[0],\" || end number ::: \",batch_03[-1])\n",
"print(\"total number ::: \",len(batch_03))\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3272"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"949+1561+762"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['info', 'licenses', 'images', 'categories', 'annotations'])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open(train_json_path,'r') as f:\n",
" train_json = json.load(f)\n",
"\n",
"train_json.keys()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start number ::: 0003 || end number ::: 1259\n",
"total number ::: 739\n",
"start number ::: 0001 || end number ::: 2005\n",
"total number ::: 1264\n",
"start number ::: 0002 || end number ::: 1000\n",
"total number ::: 614\n"
]
}
],
"source": [
"train_json = edict(train_json)\n",
"batch_01, batch_02, batch_03 = [], [], []\n",
"for im in train_json.images:\n",
" batch, f_name = im.file_name.split('/')\n",
" if batch == \"batch_01_vt\":\n",
" batch_01.append(f_name.split('.')[0])\n",
" elif batch == \"batch_02_vt\":\n",
" batch_02.append(f_name.split('.')[0])\n",
" elif batch == \"batch_03\":\n",
" batch_03.append(f_name.split('.')[0])\n",
" else:\n",
" print(\"unexpected case ::: \", f_name)\n",
"\n",
"batch_01.sort()\n",
"print(\"start number ::: \",batch_01[0],\" || end number ::: \",batch_01[-1])\n",
"print(\"total number ::: \",len(batch_01))\n",
"batch_02.sort()\n",
"print(\"start number ::: \",batch_02[0],\" || end number ::: \",batch_02[-1])\n",
"print(\"total number ::: \",len(batch_02))\n",
"batch_03.sort()\n",
"print(\"start number ::: \",batch_03[0],\" || end number ::: \",batch_03[-1])\n",
"print(\"total number ::: \",len(batch_03))\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2617"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"739+1264+614"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['info', 'licenses', 'images', 'categories', 'annotations'])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open(val_json_path,'r') as f:\n",
" val_json = json.load(f)\n",
"\n",
"val_json.keys()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start number ::: 0002 || end number ::: 1255\n",
"total number ::: 210\n",
"start number ::: 0002 || end number ::: 1998\n",
"total number ::: 297\n",
"start number ::: 0011 || end number ::: 0995\n",
"total number ::: 148\n"
]
}
],
"source": [
"val_json = edict(val_json)\n",
"batch_01, batch_02, batch_03 = [], [], []\n",
"for im in val_json.images:\n",
" batch, f_name = im.file_name.split('/')\n",
" if batch == \"batch_01_vt\":\n",
" batch_01.append(f_name.split('.')[0])\n",
" elif batch == \"batch_02_vt\":\n",
" batch_02.append(f_name.split('.')[0])\n",
" elif batch == \"batch_03\":\n",
" batch_03.append(f_name.split('.')[0])\n",
" else:\n",
" print(\"unexpected case ::: \", f_name)\n",
"\n",
"batch_01.sort()\n",
"print(\"start number ::: \",batch_01[0],\" || end number ::: \",batch_01[-1])\n",
"print(\"total number ::: \",len(batch_01))\n",
"batch_02.sort()\n",
"print(\"start number ::: \",batch_02[0],\" || end number ::: \",batch_02[-1])\n",
"print(\"total number ::: \",len(batch_02))\n",
"batch_03.sort()\n",
"print(\"start number ::: \",batch_03[0],\" || end number ::: \",batch_03[-1])\n",
"print(\"total number ::: \",len(batch_03))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"655"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"210+297+148"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20.01833740831296\n"
]
}
],
"source": [
"print(100*655/3272)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "57de06900712cf18029ae1aa25e596fdd00c5bfb0417441f21d75cec62e8b0cb"
},
"kernelspec": {
"display_name": "Python 3.10.0 64-bit ('EDA': conda)",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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