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widzenie-komputerowe-projekt/notepads/transform_data.ipynb

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model
2023-01-31 19:30:04 +01:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import shutil\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(r'C:\\Users\\PC\\Desktop\\wko_test_data\\train\\_classes.csv', encoding='utf-8')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"for category in list(data.columns[1:]):\n",
" os.mkdir(f\"./data/train/{category.strip().capitalize()}\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"for file in data.iloc:\n",
" file_name = file[0]\n",
" for item in list(file.items())[1:]:\n",
" if item[1]:\n",
" shutil.copyfile(rf'C:\\Users\\PC\\Desktop\\wko_test_data\\train\\{file_name}', f'./data/train/{item[0].strip().capitalize()}/{file_name}')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"data_test = pd.read_csv(r'C:\\Users\\PC\\Desktop\\wko_test_data\\test\\_classes.csv', encoding='utf-8')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"results = []\n",
"for file in data_test.iloc:\n",
" file_name = file[0]\n",
" for item in list(file.items())[1:]:\n",
" shutil.copyfile(rf'C:\\Users\\PC\\Desktop\\wko_test_data\\test\\{file_name}', f'./data/test/{file_name}')\n",
" if item[1]:\n",
" results.append({'filename': file_name, \"value\": item[0].strip().capitalize()})\n",
" continue\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"with open(\"./data/test_label.json\", 'w', encoding='utf-8') as f:\n",
" json.dump(results, f)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data_count = len(os.listdir(r'D:\\Michal\\studia\\wk_project_data\\Fish_Data\\images\\cropped'))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3362"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(data_count *0.8)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"results = []"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"os.listdir(r'D:\\Michal\\studia\\wk_project_data\\Fish_Data\\images\\cropped')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"for fish in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\Fish_Data\\images\\cropped')[:3362]:\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\Fish_Data\\images\\cropped\\{fish}', f'./new_data/train/Fish/{fish}')\n",
"for fish in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\Fish_Data\\images\\cropped')[3363:]:\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\Fish_Data\\images\\cropped\\{fish}', f'./new_data/test/{fish}')\n",
" results.append({'filename': fish, \"value\": 'Fish'})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"for jellyfish in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\jellyfish\\data\\val\\animal_jellyfish'):\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\jellyfish\\data\\val\\animal_jellyfish\\{jellyfish}', f'./new_data/train/Jellyfish/{jellyfish}')\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"for jellyfish in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\jellyfish\\data\\test\\animal_jellyfish'):\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\jellyfish\\data\\test\\animal_jellyfish\\{jellyfish}', f'./new_data/test/{jellyfish}')\n",
" results.append({'filename': jellyfish, \"value\": 'Jellyfish'})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"for lionfish in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\lionfish\\data\\val\\lionfish'):\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\lionfish\\data\\val\\lionfish\\{lionfish}', f'./new_data/train/Lionfish/{lionfish}')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"for lionfish in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\lionfish\\data\\test\\lionfish'):\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\lionfish\\data\\test\\lionfish\\{lionfish}', f'./new_data/test/{lionfish}')\n",
" results.append({'filename': lionfish, \"value\": 'Lionfish'})"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"for shark in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\shark\\data\\val\\animal_shark'):\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\shark\\data\\val\\animal_shark\\{shark}', f'./new_data/train/Shark/{shark}')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"for shark in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\shark\\data\\test\\animal_shark'):\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\shark\\data\\test\\animal_shark\\{shark}', f'./new_data/test/{shark}')\n",
" results.append({'filename': shark, \"value\": 'Shark'})"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"for stingray in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\stingray\\data\\val\\stingray'):\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\stingray\\data\\val\\stingray\\{stingray}', f'./new_data/train/Stingray/{stingray}')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"for stingray in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\stingray\\data\\test\\stingray'):\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\stingray\\data\\test\\stingray\\{stingray}', f'./new_data/test/{stingray}')\n",
" results.append({'filename': stingray, \"value\": 'Stingray'})"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1582"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(len(os.listdir(r'D:\\Michal\\studia\\wk_project_data\\turtle\\images')) *0.8)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"for turtle in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\turtle\\images')[:600]:\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\turtle\\images\\{turtle}', f'./new_data/train/Turtle/{turtle}')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"for turtle in os.listdir(r'D:\\Michal\\studia\\wk_project_data\\turtle\\images')[600:700]:\n",
" shutil.copyfile(rf'D:\\Michal\\studia\\wk_project_data\\turtle\\images\\{turtle}', f'./new_data/test/{turtle}')\n",
" results.append({'filename': turtle, \"value\": 'Turtle'})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(\"./new_data/test_labels.json\", 'w', encoding='utf-8') as f:\n",
" json.dump(results, f)"
]
rybki, gitignore, best models,
2023-02-01 10:23:06 +01:00
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"import cv2 as cv\n",
"from skimage.io import imread"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"results = []"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"\n",
"for dir in os.listdir('./new_data/train'):\n",
" for item in os.listdir(f'./new_data/train/{dir}'):\n",
" a = imread(f'./new_data/train/{dir}/{item}')\n",
" if len(a.shape) != 3:\n",
" results.append(f'./new_data/train/{dir}/{item}')\n",
" # os.remove(f'./new_data/train/{dir}/{item}')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(results)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"res = []"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"for dir in os.listdir('./new_data/test'):\n",
" a = imread(f'./new_data/test/{dir}')\n",
" if len(a.shape) != 3:\n",
" # os.remove(f'./new_data/test/{dir}')\n",
" results.append(f'./new_data/test/{dir}')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(res)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"import json"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1961 1902\n"
]
}
],
"source": [
"new_data = []\n",
"with open('./new_data/test_labels.json', 'r', encoding='utf-8') as f:\n",
" data= json.load(f)\n",
" files = os.listdir(\"./new_data/test\")\n",
" new_data = list(filter(lambda x: x['filename'] in files, data))\n",
" print(len(data), len(new_data))"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"with open('./new_data/test_labels2.json', 'w', encoding='utf-8') as f:\n",
" json.dump(new_data, f)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"600"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(os.listdir('./new_data/train/Turtle/'))"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"r = {}\n",
"for data in os.listdir(\"./new_data_2/train\"):\n",
" r[data] = len(os.listdir(f\"./new_data_2/train/{data}\"))"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Fish': 300,\n",
" 'Jellyfish': 300,\n",
" 'Lionfish': 300,\n",
" 'Shark': 300,\n",
" 'Stingray': 300,\n",
" 'Turtle': 300}"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"306"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(os.listdir(\"./new_data_2/test/\"))"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"import json\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"results = []\n",
"for type in os.listdir(\"./new_data/train\"):\n",
" count = 1\n",
" os.mkdir(f\"./new_data_2/train/{type}\")\n",
" for file in os.listdir(f\"./new_data/train/{type}\"):\n",
" if count <= 300:\n",
" shutil.copyfile(f\"./new_data/train/{type}/{file}\", f'./new_data_2/train/{type}/{file}')\n",
" if count <= 350 and count >= 300:\n",
" shutil.copyfile(f\"./new_data/train/{type}/{file}\", f'./new_data_2/test/{file}')\n",
" results.append({'filename': file, \"value\": type})\n",
" if count >= 350:\n",
" break\n",
" count += 1\n",
"with open(\"./new_data_2/test_labels.json\", \"w\", encoding='utf-8') as f:\n",
" json.dump(results, f)\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"306\n"
]
}
],
"source": [
"with open(\"./new_data_2/test_labels.json\", \"r\", encoding='utf-8') as f:\n",
" print(len(json.load(f)))\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Fish', 'Jellyfish', 'Lionfish', 'Shark', 'Stingray', 'Turtle']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(os.listdir('./new_data_2/train/'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import os, shutil, json"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"284"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from skimage.io import imread\n",
"\n",
"count = 0\n",
"for fish in os.listdir(\"../wk_project_data/Fish_Data/images/raw_images/\"):\n",
" a = imread(f'../wk_project_data/Fish_Data/images/raw_images/{fish}')\n",
" if len(a.shape) != 3:\n",
" os.remove(f'../wk_project_data/Fish_Data/images/raw_images/{fish}')\n",
" count +=1\n",
"count"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# old = os.listdir(\"./new_data_2/train/Fish/\")\n",
"for fish in os.listdir('../wk_project_data/Fish_Data/images/raw_images/')[:300]:\n",
" shutil.copyfile(f'../wk_project_data/Fish_Data/images/raw_images/{fish}', f'./new_data_2/train/Fish/{fish}')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"255 305\n"
]
}
],
"source": [
"results = []\n",
"with open(\"./new_data_2/test_labels.json\", \"r\", encoding='utf-8') as f:\n",
" a = json.load(f)\n",
" results = list(filter(lambda x: x['value'] != \"Fish\", a))\n",
" for fish in os.listdir('../wk_project_data/Fish_Data/images/raw_images/')[301:351]:\n",
" shutil.copyfile(f'../wk_project_data/Fish_Data/images/raw_images/{fish}', f'./new_data_2/test/{fish}')\n",
" results.append({'filename': fish, \"value\": 'Fish'})\n",
"print(len(a), len(results))\n",
"# for fish in os.listdir('./new_data_2/test/'):\n",
"# if fish not in [res['filename'] for res in results]:\n",
"# os.remove(f'./new_data_2/test/{fish}')\n",
"with open('./new_data_2/test_labels2.json', 'w', encoding='utf-8') as f:\n",
" json.dump(results, f)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"305"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(os.listdir(\"./new_data_2/test/\"))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"255\n"
]
}
],
"source": [
"results = []\n",
"old = os.listdir(\"./new_data_2/test/\")\n",
"with open(\"./new_data_2/test_labels.json\", \"r\", encoding='utf-8') as f:\n",
" a = json.load(f)\n",
" print(len(a))\n",
" results = list(filter(lambda x: x['value'] != \"Fish\", a))\n",
" # print(len(a), len(results))\n",
" # for file in old:\n",
" # if file not in [res['filename'] for res in results]:\n",
" # os.remove(f'./new_data_2/test/{file}')\n",
"# with open('./new_data_2/test_labels2.json', 'w', encoding='utf-8') as f:\n",
"# json.dump(results, f)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['aseraggodes_melanostictus_2.jpg',\n",
" 'aseraggodes_melanostictus_3.jpg',\n",
" 'atractoscion_aequidens_1.jpg',\n",
" 'atractoscion_aequidens_2.jpg',\n",
" 'atractoscion_aequidens_3.jpg',\n",
" 'atractoscion_aequidens_4.jpg',\n",
" 'atractoscion_aequidens_5.jpg',\n",
" 'atule_mate_1.jpg',\n",
" 'atule_mate_2.jpg',\n",
" 'atule_mate_3.jpg',\n",
" 'atule_mate_4.jpg',\n",
" 'atule_mate_5.jpg',\n",
" 'atule_mate_6.jpg',\n",
" 'auxis_rochei_1.jpg',\n",
" 'auxis_rochei_2.jpg',\n",
" 'auxis_rochei_3.jpg',\n",
" 'auxis_rochei_4.jpg',\n",
" 'auxis_rochei_5.jpg',\n",
" 'auxis_rochei_6.jpg',\n",
" 'auxis_thazard_1.jpg',\n",
" 'auxis_thazard_2.jpg',\n",
" 'auxis_thazard_3.jpg',\n",
" 'auxis_thazard_4.jpg',\n",
" 'auxis_thazard_5.jpg',\n",
" 'auxis_thazard_6.jpg',\n",
" 'auxis_thazard_7.jpg',\n",
" 'auxis_thazard_8.jpg',\n",
" 'BA472025AZL8.jpg',\n",
" 'BA5BMQ2M6X2R.jpg',\n",
" 'bathylagichthys_greyae_1.jpg',\n",
" 'bathylagichthys_greyae_2.jpg',\n",
" 'bathylagichthys_greyae_3.jpg',\n",
" 'BBUVLQAWO2J9.jpg',\n",
" 'BCNZZANYP0ZV.jpg',\n",
" 'BCTT754FXP33.jpg',\n",
" 'beryx_decadactylus_1.jpg',\n",
" 'beryx_decadactylus_2.jpg',\n",
" 'beryx_decadactylus_3.jpg',\n",
" 'beryx_decadactylus_4.jpg',\n",
" 'beryx_decadactylus_5.jpg',\n",
" 'beryx_decadactylus_6.jpg',\n",
" 'BFFL4FUMTZA5.jpg',\n",
" 'BG9GN3HB8COL.jpg',\n",
" 'BIGHPAJBP3PN.jpg',\n",
" 'BNBTC6NUO815.jpg',\n",
" 'bodianus_anthioides_1.jpg',\n",
" 'bodianus_anthioides_10.jpg',\n",
" 'bodianus_anthioides_11.jpg',\n",
" 'bodianus_anthioides_12.jpg',\n",
" 'bodianus_anthioides_13.jpg',\n",
" 'bodianus_anthioides_14.jpg',\n",
" 'bodianus_anthioides_15.jpg',\n",
" 'bodianus_anthioides_2.jpg',\n",
" 'bodianus_anthioides_3.jpg',\n",
" 'bodianus_anthioides_4.jpg',\n",
" 'bodianus_anthioides_5.jpg',\n",
" 'bodianus_anthioides_6.jpg',\n",
" 'bodianus_anthioides_7.jpg',\n",
" 'bodianus_anthioides_8.jpg',\n",
" 'BS58RK9Y7XRW.jpg',\n",
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},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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]
model
2023-01-31 19:30:04 +01:00
}
],
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classes for models, training
2023-02-01 18:42:47 +01:00
"version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
model
2023-01-31 19:30:04 +01:00
},
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}
Reference in New Issue Copy Permalink