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https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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分离预测
This commit is contained in:
FEIJINTI
parent
6444e923e6
commit
b1be6eeb49
@ -12,7 +12,7 @@
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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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"execution_count": 7,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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@ -30,16 +30,16 @@
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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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"execution_count": 8,
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"outputs": [],
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"source": [
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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\n",
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"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",
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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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"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, 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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@ -70,7 +70,7 @@
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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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"execution_count": 9,
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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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@ -99,14 +99,15 @@
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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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"execution_count": 10,
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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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"if len(dataset) > 1:\n",
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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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" np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()], axis=0).tolist()\n",
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"x_resampled, y_resampled = rus.fit_resample(x_list, y_list)\n",
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"dataset = {\"inside\": np.array(x_resampled)}"
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" x_resampled, y_resampled = rus.fit_resample(x_list, y_list)\n",
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" dataset = {\"inside\": np.array(x_resampled)}"
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],
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"metadata": {
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"collapsed": false,
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@ -129,7 +130,7 @@
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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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"execution_count": 11,
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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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@ -146,39 +147,35 @@
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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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"execution_count": 12,
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'train_from_existed' 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_30867/103680055.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",
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"\u001B[0;31mNameError\u001B[0m: name 'train_from_existed' is not defined"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"1923it [00:00, 5114.42it/s] "
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]
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},
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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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" precision recall f1-score support\n",
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"\n",
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" 0 0.99 0.99 0.99 577\n",
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" 1 0.99 0.99 0.99 481\n",
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"\n",
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" accuracy 0.99 1058\n",
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" macro avg 0.99 0.99 0.99 1058\n",
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"weighted avg 0.99 0.99 0.99 1058\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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@ -12,8 +12,8 @@ dataset_file = "data/dataset/dataset_2022-07-20_10-04.mat"
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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"}
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color_dict = {(255, 0, 0): 'beijing'}
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color_dict = {(0, 255, 0): "zibian"}
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# 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
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show_samples = False # 是否展示样本
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 3,
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"outputs": [],
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"source": [
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"import datetime\n",
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@ -37,7 +37,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 4,
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"outputs": [],
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"source": [
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"model_path = 'models/dt_2022-07-19_16-03.model'\n",
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@ -52,7 +52,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 5,
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"outputs": [
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{
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"data": {
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@ -65,7 +65,7 @@
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{
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"data": {
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"text/plain": "<IPython.core.display.HTML object>",
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"text/html": "<div id='7cca4d64-83fc-4ab9-9e9c-2e378a71e01e'></div>"
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"text/html": "<div id='2ffc90a2-1e1c-4280-91f7-d085a8204883'></div>"
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},
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"metadata": {},
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"output_type": "display_data"
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@ -81,7 +81,7 @@
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{
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"data": {
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"text/plain": "<IPython.core.display.HTML object>",
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"text/html": "<div id='b1888224-2f86-48e2-9725-4e1a56eeb4a3'></div>"
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"metadata": {},
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"output_type": "display_data"
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@ -102,10 +102,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 10,
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"outputs": [],
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"source": [
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"dataset_path = 'data/dataset/dataset_2022-07-19_16-03.mat'"
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"dataset_path = 'data/dataset_old/dataset_2022-07-19_16-03.mat'"
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],
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"metadata": {
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"collapsed": false,
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@ -116,7 +116,7 @@
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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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"execution_count": 11,
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"outputs": [],
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"source": [
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"new_dataset_path = datetime.datetime.now().strftime(\"data/dataset/dataset_%Y-%m-%d_%H-%M.mat\")"
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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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"execution_count": 12,
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"outputs": [],
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"source": [
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"data = scipy.io.loadmat(dataset_path)\n",
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@ -145,7 +145,7 @@
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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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"execution_count": 13,
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"outputs": [],
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"source": [
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"nimg = img[result == 0]"
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@ -159,7 +159,7 @@
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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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"execution_count": 14,
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"outputs": [
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{
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"data": {
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@ -172,7 +172,7 @@
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{
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"data": {
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"text/plain": "<IPython.core.display.HTML object>",
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"text/html": "<div id='8f2acac1-57e7-41a6-9cc3-3d514a75fcd7'></div>"
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"text/html": "<div id='cdb3654b-8248-4ed4-897c-b920b33dbd8c'></div>"
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"metadata": {},
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"output_type": "display_data"
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"execution_count": 28,
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"outputs": [],
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"source": [
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"scipy.io.savemat(new_dataset_path, {'x': x, 'y': y})"
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"# scipy.io.savemat(new_dataset_path, {'x': x, 'y': y})"
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],
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"metadata": {
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"collapsed": false,
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193
04_multi_classification.ipynb
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193
04_multi_classification.ipynb
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