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https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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写了一半的dt
This commit is contained in:
FEIJINTI
parent
39d3aa4b59
commit
8caffa55bc
@ -23,10 +23,6 @@
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"import scipy.io\n",
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"import cv2\n",
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"import numpy as np\n",
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"import pickle\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"# %matplotlib notebook\n",
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"from main_test import pony_run\n",
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"from models import AnonymousColorDetector\n",
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"from utils import lab_scatter"
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],
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239
05_model_training.ipynb
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239
05_model_training.ipynb
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@ -0,0 +1,239 @@
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{
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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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"# 训练像素模型\n",
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"用这个文件可以训练出需要使用的光谱像素点模型"
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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": "#%% 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": 22,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pickle\n",
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"from utils import read_envi_ascii\n",
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"from config import Config\n",
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"from models import ManualTree"
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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": "markdown",
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"source": [
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"# 一些变量"
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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": "#%% 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": 23,
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"outputs": [],
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"source": [
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"data_path = r'data/envi20220802.txt'\n",
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"name_dict = {'tobacco': 1, 'yantou':2, 'kazhi':3, 'bomo':4, 'jiaodai':5, 'background':0}"
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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": "markdown",
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"source": [
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"# 构建数据集"
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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": "#%% 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": 24,
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"outputs": [],
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"source": [
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"data = read_envi_ascii(data_path)"
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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": 25,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"zibian (569, 448)\n",
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"tobacco (1457, 448)\n",
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"yantou (354, 448)\n",
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"kazhi (449, 448)\n",
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"bomo (1154, 448)\n",
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"jiaodai (566, 448)\n",
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"background (1235, 448)\n"
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]
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}
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],
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"source": [
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"_ = [print(class_name, d.shape) for class_name, d in data.items()]"
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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": 26,
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"outputs": [],
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"source": [
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"data_x = [d for class_name, d in data.items() if class_name in name_dict.keys()]\n",
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"data_y = [np.ones((d.shape[0], ))*name_dict[class_name] for class_name, d in data.items() if class_name in name_dict.keys()]\n",
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"data_x, data_y = np.concatenate(data_x), np.concatenate(data_y)"
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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": "markdown",
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"source": [
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"## 取出需要的22个特征谱段"
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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": "#%% 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": 27,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"这些是现在的数据: (5215, 448) (5215,)\n",
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"截取其中需要的部分后: (5215, 22) (5215,)\n"
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]
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}
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],
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"source": [
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"print(\"这些是现在的数据: \", data_x.shape, data_y.shape)\n",
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"data_x_cut = data_x[..., Config.bands]\n",
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"print(\"截取其中需要的部分后: \", data_x_cut.shape, data_y.shape)"
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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": "markdown",
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"source": [
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"## 进行样本平衡"
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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": "#%% 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": 30,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"这是重采样后的数据: (8742, 22) (8742,)\n"
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]
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}
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],
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"source": [
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"from imblearn.over_sampling import RandomOverSampler\n",
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"ros = RandomOverSampler(random_state=0)\n",
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"x_resampled, y_resampled = ros.fit_resample(data_x_cut, data_y)\n",
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"print('这是重采样后的数据: ', x_resampled.shape, y_resampled.shape)"
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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": "markdown",
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"source": [
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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": "#%% md\n"
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}
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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55
utils.py
55
utils.py
@ -140,6 +140,61 @@ def size_threshold(img, blk_size, threshold):
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return mask
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def read_envi_ascii(file_name, save_xy=False, hdr_file_name=None):
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"""
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Read envi ascii file. Use ENVI ROI Tool -> File -> output ROIs to ASCII...
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:param file_name: file name of ENVI ascii file
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:param hdr_file_name: hdr file name for a "BANDS" vector in the output
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:param save_xy: save the x, y position on the first two cols of the result vector
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:return: dict {class_name: vector, ...}
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"""
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number_line_start_with = "; Number of ROIs: "
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roi_name_start_with, roi_npts_start_with = "; ROI name: ", "; ROI npts: "
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data_start_with = "; ID"
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class_num, class_names, class_nums, vectors = 0, [], [], []
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with open(file_name, 'r') as f:
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for line_text in f:
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if line_text.startswith(number_line_start_with):
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class_num = int(line_text[len(number_line_start_with):])
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elif line_text.startswith(roi_name_start_with):
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class_names.append(line_text[len(roi_name_start_with):-1])
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elif line_text.startswith(roi_npts_start_with):
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class_nums.append(int(line_text[len(roi_name_start_with):-1]))
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elif line_text.startswith(data_start_with):
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col_list = list(filter(None, line_text[1:].split(" ")))
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assert (len(class_names) == class_num) and (len(class_names) == len(class_nums))
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break
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elif line_text.startswith(";"):
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continue
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for vector_rows in class_nums:
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vector_str = ''
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for i in range(vector_rows):
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vector_str += f.readline()
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vector = np.fromstring(vector_str, dtype=np.float, sep=" ").reshape(-1, len(col_list))
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assert vector.shape[0] == vector_rows
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vector = vector[:, 3:] if not save_xy else vector[:, 1:]
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vectors.append(vector)
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f.readline() # suppose to read a blank line
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if hdr_file_name is not None:
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bands = []
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with open(hdr_file_name, 'r') as f:
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start_bands = False
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for line_text in f:
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if start_bands:
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if line_text.endswith(",\n"):
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bands.append(float(line_text[:-2]))
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else:
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bands.append(float(line_text))
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break
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elif line_text.startswith("wavelength ="):
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start_bands = True
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bands = np.array(bands, dtype=np.float)
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vectors.append(bands)
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class_names.append("BANDS")
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return dict(zip(class_names, vectors))
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if __name__ == '__main__':
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color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): "bejing", (0, 255, 0): "hongdianxian",
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(255, 0, 255): "chengsebangbangtang", (0, 255, 255): "lvdianxian"}
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