mirror of
https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
synced 2025-11-08 06:13:53 +00:00
加入紫边，分离模型
This commit is contained in:
FEIJINTI
parent
62440b5bb9
commit
6444e923e6
@ -2,21 +2,17 @@
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"cells": [
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": []
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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": 5,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": null,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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@ -24,7 +20,13 @@
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"from imblearn.under_sampling import RandomUnderSampler\n",
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"from models import AnonymousColorDetector\n",
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"from utils import read_labeled_img"
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]
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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@ -35,8 +37,11 @@
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"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",
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"dataset_file = \"data/dataset/dataset_2022-07-20_10-04.mat\"\n",
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"\n",
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"color_dict = {(0, 0, 255): \"yangeng\", (255, 0, 0): 'beijing'} # 颜色对应的类别\n",
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"label_index = {\"yangeng\": 1, \"beijing\": 0} # 类别对应的序号\n",
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"# color_dict = {(0, 0, 255): \"yangeng\", (255, 0, 0): 'beijing'} # 颜色对应的类别\n",
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"color_dict = {(0, 0, 255): \"yangeng\"}\n",
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"color_dict = {(255, 0, 0): 'beijing'}\n",
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"color_dict = {(0, 255, 0): \"zibian\"}\n",
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"label_index = {\"yangeng\": 1, \"beijing\": 0, \"zibian\":2} # 类别对应的序号\n",
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"show_samples = False # 是否展示样本\n",
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"\n",
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"# 定义一些训练量\n",
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@ -57,26 +62,16 @@
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"## 读取数据"
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],
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 10,
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: 'data/dataset/label'",
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"output_type": "error",
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[0;31mFileNotFoundError\u001B[0m Traceback (most recent call last)",
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"\u001B[0;32m/var/folders/wh/kr5c3dr12834pfk3j7yqnrq40000gn/T/ipykernel_30867/1942905945.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mdataset\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mread_labeled_img\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata_dir\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcolor_dict\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mcolor_dict\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mis_ps_color_space\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\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[0;32mif\u001B[0m \u001B[0mshow_samples\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[0;32mfrom\u001B[0m \u001B[0mutils\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mlab_scatter\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 4\u001B[0m \u001B[0mlab_scatter\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mclass_max_num\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m30000\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mis_3d\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mis_ps_color_space\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
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"\u001B[0;32m~/PycharmProjects/tobacco_color/utils.py\u001B[0m in \u001B[0;36mread_labeled_img\u001B[0;34m(dataset_dir, color_dict, ext, is_ps_color_space)\u001B[0m\n\u001B[1;32m 37\u001B[0m \u001B[0;34m:\u001B[0m\u001B[0;32mreturn\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0m字典形式的数据集\u001B[0m\u001B[0;34m{\u001B[0m\u001B[0mlabel\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mvector\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mn\u001B[0m \u001B[0mx\u001B[0m \u001B[0;36m3\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0mvector为lab色彩空间\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 38\u001B[0m \"\"\"\n\u001B[0;32m---> 39\u001B[0;31m img_names = [img_name for img_name in os.listdir(os.path.join(dataset_dir, 'label'))\n\u001B[0m\u001B[1;32m 40\u001B[0m if img_name.endswith(ext)]\n\u001B[1;32m 41\u001B[0m \u001B[0mtotal_dataset\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mMergeDict\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",
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"\u001B[0;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: 'data/dataset/label'"
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]
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}
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],
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"execution_count": null,
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"outputs": [],
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"source": [
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"dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)\n",
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"if show_samples:\n",
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@ -96,25 +91,16 @@
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"## 数据平衡化"
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],
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 11,
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'dataset' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)",
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"\u001B[0;32m/var/folders/wh/kr5c3dr12834pfk3j7yqnrq40000gn/T/ipykernel_30867/603974095.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[0mrus\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mRandomUnderSampler\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrandom_state\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mx_list\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_list\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconcatenate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mv\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mk\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mv\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtolist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 3\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconcatenate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mones\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mv\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0mlabel_index\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mk\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mk\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mv\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtolist\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[0mx_resampled\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_resampled\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mrus\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit_resample\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx_list\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_list\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[0mdataset\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0;34m\"inside\"\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx_resampled\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",
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"\u001B[0;31mNameError\u001B[0m: name 'dataset' is not defined"
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]
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}
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],
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"execution_count": null,
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"outputs": [],
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"source": [
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"rus = RandomUnderSampler(random_state=0)\n",
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"x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \\\n",
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"## 模型训练"
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],
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 12,
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'dataset' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)",
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"\u001B[0;32m/var/folders/wh/kr5c3dr12834pfk3j7yqnrq40000gn/T/ipykernel_30867/1828483636.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[0;31m# 对数据进行预处理\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconcatenate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mv\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mk\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mv\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\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 3\u001B[0m \u001B[0mnegative_sample_num\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0;36m1.2\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mnegative_sample_num\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m \u001B[0;32melse\u001B[0m \u001B[0mnegative_sample_num\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[0mAnonymousColorDetector\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",
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"\u001B[0;31mNameError\u001B[0m: name 'dataset' is not defined"
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]
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}
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],
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"execution_count": null,
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"outputs": [],
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"source": [
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"# 对数据进行预处理\n",
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"x = np.concatenate([v for k, v in dataset.items()], axis=0)\n",
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}
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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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"outputs": [],
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"source": [
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"if train_from_existed:\n",
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" data = scipy.io.loadmat(dataset_file)\n",
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" x, y = data['x'], data['y'].ravel()\n",
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" model.fit(x, y=y, is_generate_negative=False, model_selection='dt')\n",
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"else:\n",
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" world_boundary = np.array([0, 0, 0, 255, 255, 255])\n",
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" model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,\n",
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" is_save_dataset=True, model_selection='dt')\n",
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"model.save()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 14,
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#!/usr/bin/env python
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# coding: utf-8
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# # 模型的训练
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# In[16]:
|
||||
|
||||
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||||
# %%
|
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import numpy as np
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import scipy
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from imblearn.under_sampling import RandomUnderSampler
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from models import AnonymousColorDetector
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from utils import read_labeled_img
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|
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# ## 读取数据与构建数据集
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# %%
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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
|
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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
|
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dataset_file = "data/dataset/dataset_2022-07-20_10-04.mat"
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||||
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# In[17]:
|
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# color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing'} # 颜色对应的类别
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||||
color_dict = {(0, 0, 255): "yangeng"}
|
||||
color_dict = {(255, 0, 0): 'beijing'}
|
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color_dict = {(0, 255, 0): "zibian"}
|
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label_index = {"yangeng": 1, "beijing": 0, "zibian": 2} # 类别对应的序号
|
||||
show_samples = False # 是否展示样本
|
||||
|
||||
|
||||
data_dir = "data/dataset"
|
||||
color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing'}
|
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label_index = {"yangeng": 1, "beijing": 0}
|
||||
# 定义一些训练量
|
||||
threshold = 5 # 正样本周围多大范围内的还算是正样本
|
||||
node_num = 20 # 如果使用ELM作为分类器物，有多少的节点
|
||||
negative_sample_num = None # None或者一个数字，对应生成的负样本数量
|
||||
# %% md
|
||||
## 读取数据
|
||||
# %%
|
||||
dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)
|
||||
if show_samples:
|
||||
from utils import lab_scatter
|
||||
|
||||
lab_scatter(dataset, class_max_num=30000, is_3d=True, is_ps_color_space=False)
|
||||
# %% md
|
||||
## 数据平衡化
|
||||
# %%
|
||||
rus = RandomUnderSampler(random_state=0)
|
||||
x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \
|
||||
np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()], axis=0).tolist()
|
||||
|
||||
x_resampled, y_resampled = rus.fit_resample(x_list, y_list)
|
||||
dataset = {"inside": np.array(x_resampled)}
|
||||
|
||||
# ## 模型训练
|
||||
|
||||
# In[18]:
|
||||
|
||||
|
||||
# 定义一些常量
|
||||
threshold = 5
|
||||
node_num = 20
|
||||
negative_sample_num = None # None或者一个数字
|
||||
world_boundary = np.array([0, 0, 0, 255, 255, 255])
|
||||
# %% md
|
||||
## 模型训练
|
||||
# %%
|
||||
# 对数据进行预处理
|
||||
x = np.concatenate([v for k, v in dataset.items()], axis=0)
|
||||
negative_sample_num = int(x.shape[0] * 1.2) if negative_sample_num is None else negative_sample_num
|
||||
|
||||
model = AnonymousColorDetector()
|
||||
|
||||
model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
|
||||
is_save_dataset=True, model_selection='dt')
|
||||
# data = scipy.io.loadmat('dataset_2022-07-19_15-07.mat')
|
||||
# x, y = data['x'], data['y'].ravel()
|
||||
# model.fit(x, y=y, is_generate_negative=False, model_selection='dt')
|
||||
|
||||
# %%
|
||||
if train_from_existed:
|
||||
data = scipy.io.loadmat(dataset_file)
|
||||
x, y = data['x'], data['y'].ravel()
|
||||
model.fit(x, y=y, is_generate_negative=False, model_selection='dt')
|
||||
else:
|
||||
world_boundary = np.array([0, 0, 0, 255, 255, 255])
|
||||
model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
|
||||
is_save_dataset=True, model_selection='dt')
|
||||
model.save()
|
||||
# %%
|
||||
if train_from_existed:
|
||||
data = scipy.io.loadmat(dataset_file)
|
||||
x, y = data['x'], data['y'].ravel()
|
||||
model.fit(x, y=y, is_generate_negative=False, model_selection='dt')
|
||||
else:
|
||||
world_boundary = np.array([0, 0, 0, 255, 255, 255])
|
||||
model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
|
||||
is_save_dataset=True, model_selection='dt')
|
||||
model.save()
|
||||
|
||||
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Reference in New Issue
Block a user