mirror of
https://github.com/chen-gz/food_detection.git
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114 lines
3.0 KiB
Plaintext
114 lines
3.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import csv\n",
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"from sklearn.utils import shuffle\n",
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"food_raw_data, non_food_raw_data, food_rectify = [], [], []\n",
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"food_data, non_food_data = [], []\n",
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"\n",
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"with open(\"food_rectify.csv\") as f:\n",
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" reader = csv.reader(f)\n",
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" for row in reader:\n",
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" food_rectify.append(row)\n",
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"\n",
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"with open(\"food.csv\") as f:\n",
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" reader = csv.reader(f)\n",
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" for row in reader:\n",
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" food_raw_data.append(row)\n",
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" \n",
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"with open(\"no_food.csv\") as f:\n",
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" reader = csv.reader(f)\n",
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" for row in reader:\n",
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" non_food_raw_data.append(row)\n",
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" \n",
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"food_data = food_raw_data\n",
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"\n",
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"for i in non_food_raw_data:\n",
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" if i[0] not in food_rectify:\n",
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" non_food_data.append(i)\n",
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" else:\n",
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" food_data.append(i)\n",
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"\n",
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"food_data = shuffle(food_data)\n",
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"non_food_data = shuffle(non_food_data)\n",
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"\n",
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"ratio = 0.75 \n",
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"train_food_len = int(len(food_data) * ratio)\n",
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"train_non_food_len = train_food_len\n",
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"\n",
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"test_food_len = len(food_data) - train_food_len\n",
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"test_non_food_len = int(len(non_food_data) * (1 - ratio))\n",
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"\n",
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"\n",
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"train_food = food_data[0:train_food_len]\n",
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"test_food = food_data[train_food_len:train_food_len + test_food_len]\n",
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"\n",
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"train_non_food = non_food_data[0:train_non_food_len]\n",
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"test_non_food = non_food_data[train_non_food_len:train_non_food_len + test_non_food_len]\n",
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"\n",
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"with open('train_food.csv', 'w') as f:\n",
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" write = csv.writer(f)\n",
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" write.writerows(train_food)\n",
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" \n",
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"with open('train_non_food.csv', 'w') as f:\n",
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" write = csv.writer(f)\n",
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" write.writerows(train_non_food )\n",
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"\n",
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"with open('test_food.csv', 'w') as f:\n",
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" write = csv.writer(f)\n",
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" write.writerows(test_food )\n",
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"with open('test_non_food.csv', 'w') as f:\n",
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" write = csv.writer(f)\n",
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" write.writerows(test_non_food)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = [i for i in range(10)]\n",
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"print(a)\n",
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"print(a[0:4])\n",
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"print(a[4:7])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"non_food_raw_data"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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