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FEIJINTI 2022-07-21 17:10:11 +08:00
parent 6444e923e6
commit b1be6eeb49
4 changed files with 249 additions and 59 deletions
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85
02_classification.ipynb
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@ -12,7 +12,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 7,
"outputs": [], "outputs": [],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
@ -30,16 +30,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"outputs": [], "outputs": [],
"source": [ "source": [
"train_from_existed = True # 是否从现有数据训练，如果是的话，那就从dataset_file训练，否则就用data_dir里头的数据\n", "train_from_existed = False # 是否从现有数据训练，如果是的话，那就从dataset_file训练，否则就用data_dir里头的数据\n",
"data_dir = \"data/dataset\" # 数据集，文件夹下必须包含`img`和`label`两个文件夹，放置相同文件名的图片和label\n", "data_dir = \"data/dataset\" # 数据集，文件夹下必须包含`img`和`label`两个文件夹，放置相同文件名的图片和label\n",
"dataset_file = \"data/dataset/dataset_2022-07-20_10-04.mat\"\n", "dataset_file = \"data/dataset/dataset_2022-07-20_10-04.mat\"\n",
"\n", "\n",
"# color_dict = {(0, 0, 255): \"yangeng\", (255, 0, 0): 'beijing'} # 颜色对应的类别\n", "# color_dict = {(0, 0, 255): \"yangeng\", (255, 0, 0): 'beijing'} # 颜色对应的类别\n",
"color_dict = {(0, 0, 255): \"yangeng\"}\n", "# color_dict = {(0, 0, 255): \"yangeng\"}\n",
"color_dict = {(255, 0, 0): 'beijing'}\n", "# color_dict = {(255, 0, 0): 'beijing'}\n",
"color_dict = {(0, 255, 0): \"zibian\"}\n", "color_dict = {(0, 255, 0): \"zibian\"}\n",
"label_index = {\"yangeng\": 1, \"beijing\": 0, \"zibian\":2} # 类别对应的序号\n", "label_index = {\"yangeng\": 1, \"beijing\": 0, \"zibian\":2} # 类别对应的序号\n",
"show_samples = False # 是否展示样本\n", "show_samples = False # 是否展示样本\n",
@ -70,7 +70,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 9,
"outputs": [], "outputs": [],
"source": [ "source": [
"dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)\n", "dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)\n",
@ -99,14 +99,15 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 10,
"outputs": [], "outputs": [],
"source": [ "source": [
"rus = RandomUnderSampler(random_state=0)\n", "if len(dataset) > 1:\n",
"x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \\\n", " rus = RandomUnderSampler(random_state=0)\n",
" np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()], axis=0).tolist()\n", " x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \\\n",
"x_resampled, y_resampled = rus.fit_resample(x_list, y_list)\n", " np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()], axis=0).tolist()\n",
"dataset = {\"inside\": np.array(x_resampled)}" " x_resampled, y_resampled = rus.fit_resample(x_list, y_list)\n",
" dataset = {\"inside\": np.array(x_resampled)}"
], ],
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
@ -129,7 +130,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 11,
"outputs": [], "outputs": [],
"source": [ "source": [
"# 对数据进行预处理\n", "# 对数据进行预处理\n",
@ -146,39 +147,35 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 12,
"outputs": [],
"source": [
"if train_from_existed:\n",
" data = scipy.io.loadmat(dataset_file)\n",
" x, y = data['x'], data['y'].ravel()\n",
" model.fit(x, y=y, is_generate_negative=False, model_selection='dt')\n",
"else:\n",
" world_boundary = np.array([0, 0, 0, 255, 255, 255])\n",
" model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,\n",
" is_save_dataset=True, model_selection='dt')\n",
"model.save()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 14,
"outputs": [ "outputs": [
{ {
"ename": "NameError", "name": "stderr",
"evalue": "name 'train_from_existed' is not defined", "output_type": "stream",
"output_type": "error", "text": [
"traceback": [ "1923it [00:00, 5114.42it/s] "
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", ]
"\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)", },
"\u001B[0;32m/var/folders/wh/kr5c3dr12834pfk3j7yqnrq40000gn/T/ipykernel_30867/103680055.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0;32mif\u001B[0m \u001B[0mtrain_from_existed\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 2\u001B[0m \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mscipy\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mio\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mloadmat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset_file\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 3\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdata\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'x'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdata\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'y'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mravel\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 4\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0my\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mis_generate_negative\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmodel_selection\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'dt'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 5\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msave\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", {
"\u001B[0;31mNameError\u001B[0m: name 'train_from_existed' is not defined" "name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.99 0.99 577\n",
" 1 0.99 0.99 0.99 481\n",
"\n",
" accuracy 0.99 1058\n",
" macro avg 0.99 0.99 0.99 1058\n",
"weighted avg 0.99 0.99 0.99 1058\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
] ]
} }
], ],

4
02_classification.py
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@ -12,8 +12,8 @@ dataset_file = "data/dataset/dataset_2022-07-20_10-04.mat"
# color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing'} # 颜色对应的类别 # color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing'} # 颜色对应的类别
color_dict = {(0, 0, 255): "yangeng"} color_dict = {(0, 0, 255): "yangeng"}
color_dict = {(255, 0, 0): 'beijing'} # color_dict = {(255, 0, 0): 'beijing'}
color_dict = {(0, 255, 0): "zibian"} # color_dict = {(0, 255, 0): "zibian"}
label_index = {"yangeng": 1, "beijing": 0, "zibian": 2} # 类别对应的序号 label_index = {"yangeng": 1, "beijing": 0, "zibian": 2} # 类别对应的序号
show_samples = False # 是否展示样本 show_samples = False # 是否展示样本

26
03_data_update.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 3,
"outputs": [], "outputs": [],
"source": [ "source": [
"import datetime\n", "import datetime\n",
@ -37,7 +37,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 4,
"outputs": [], "outputs": [],
"source": [ "source": [
"model_path = 'models/dt_2022-07-19_16-03.model'\n", "model_path = 'models/dt_2022-07-19_16-03.model'\n",
@ -52,7 +52,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 5,
"outputs": [ "outputs": [
{ {
"data": { "data": {
@ -65,7 +65,7 @@
{ {
"data": { "data": {
"text/plain": "<IPython.core.display.HTML object>", "text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id='7cca4d64-83fc-4ab9-9e9c-2e378a71e01e'></div>" "text/html": "<div id='2ffc90a2-1e1c-4280-91f7-d085a8204883'></div>"
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
@ -81,7 +81,7 @@
{ {
"data": { "data": {
"text/plain": "<IPython.core.display.HTML object>", "text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id='b1888224-2f86-48e2-9725-4e1a56eeb4a3'></div>" "text/html": "<div id='a9e43e6c-8500-444b-9c77-886843c0cbeb'></div>"
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
@ -102,10 +102,10 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 10,
"outputs": [], "outputs": [],
"source": [ "source": [
"dataset_path = 'data/dataset/dataset_2022-07-19_16-03.mat'" "dataset_path = 'data/dataset_old/dataset_2022-07-19_16-03.mat'"
], ],
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
@ -116,7 +116,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 11,
"outputs": [], "outputs": [],
"source": [ "source": [
"new_dataset_path = datetime.datetime.now().strftime(\"data/dataset/dataset_%Y-%m-%d_%H-%M.mat\")" "new_dataset_path = datetime.datetime.now().strftime(\"data/dataset/dataset_%Y-%m-%d_%H-%M.mat\")"
@ -130,7 +130,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 12,
"outputs": [], "outputs": [],
"source": [ "source": [
"data = scipy.io.loadmat(dataset_path)\n", "data = scipy.io.loadmat(dataset_path)\n",
@ -145,7 +145,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 13,
"outputs": [], "outputs": [],
"source": [ "source": [
"nimg = img[result == 0]" "nimg = img[result == 0]"
@ -159,7 +159,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 14,
"outputs": [ "outputs": [
{ {
"data": { "data": {
@ -172,7 +172,7 @@
{ {
"data": { "data": {
"text/plain": "<IPython.core.display.HTML object>", "text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id='8f2acac1-57e7-41a6-9cc3-3d514a75fcd7'></div>" "text/html": "<div id='cdb3654b-8248-4ed4-897c-b920b33dbd8c'></div>"
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
@ -224,7 +224,7 @@
"execution_count": 28, "execution_count": 28,
"outputs": [], "outputs": [],
"source": [ "source": [
"scipy.io.savemat(new_dataset_path, {'x': x, 'y': y})" "# scipy.io.savemat(new_dataset_path, {'x': x, 'y': y})"
], ],
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,

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04_multi_classification.ipynb Normal file
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