From f7c88d3c2a1e43f39b9cce238ccd06c298cb4542 Mon Sep 17 00:00:00 2001 From: FEIJINTI <83849113+FEIJINTI@users.noreply.github.com> Date: Thu, 21 Jul 2022 17:13:44 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=86=E7=A6=BB=E9=A2=84=E6=B5=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 02_classification.ipynb | 2 +- 02_classification.py | 31 +++++++++++-------------------- 04_multi_classification.ipynb | 22 +++++++++++----------- 3 files changed, 23 insertions(+), 32 deletions(-) diff --git a/02_classification.ipynb b/02_classification.ipynb index 8d72e6d..18552e7 100644 --- a/02_classification.ipynb +++ b/02_classification.ipynb @@ -37,7 +37,7 @@ "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", "\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',(0, 255, 0): \"zibian\"} # 颜色对应的类别\n", "# color_dict = {(0, 0, 255): \"yangeng\"}\n", "# color_dict = {(255, 0, 0): 'beijing'}\n", "color_dict = {(0, 255, 0): \"zibian\"}\n", diff --git a/02_classification.py b/02_classification.py index 6e55f0b..ef3ad4a 100644 --- a/02_classification.py +++ b/02_classification.py @@ -1,4 +1,3 @@ -# %% import numpy as np import scipy from imblearn.under_sampling import RandomUnderSampler @@ -6,12 +5,12 @@ from models import AnonymousColorDetector from utils import read_labeled_img # %% -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 +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 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 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"} +color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing', (0, 255, 0): "zibian"} # 颜色对应的类别 +# color_dict = {(0, 0, 255): "yangeng"} # color_dict = {(255, 0, 0): 'beijing'} # color_dict = {(0, 255, 0): "zibian"} label_index = {"yangeng": 1, "beijing": 0, "zibian": 2} # 类别对应的序号 @@ -32,11 +31,13 @@ if show_samples: # %% 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)} +if len(dataset) > 1: + 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)} # %% md ## 模型训练 # %% @@ -53,14 +54,4 @@ 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() +model.save() \ No newline at end of file diff --git a/04_multi_classification.ipynb b/04_multi_classification.ipynb index f1ed8ac..9e85ffb 100644 --- a/04_multi_classification.ipynb +++ b/04_multi_classification.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 44, "outputs": [], "source": [ "import datetime\n", @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 45, "outputs": [], "source": [ "img_path = r\"C:\\Users\\FEIJINTI\\Desktop\\721\\zazhi\\Image_2022_0721_1351_38_946-002034.bmp\"\n", @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 46, "outputs": [], "source": [ "img = cv2.imread(img_path)[:, :, ::-1]\n", @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 47, "outputs": [], "source": [ "t_detector = AnonymousColorDetector(file_path=model_path[1])\n", @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 48, "outputs": [], "source": [ "z_detector = AnonymousColorDetector(file_path=model_path[2])\n", @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 49, "outputs": [], "source": [ "result = 1 - (b_result | t_result | z_result)" @@ -115,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 50, "outputs": [ { "data": { @@ -128,16 +128,16 @@ { "data": { "text/plain": "", - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 36, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 50, "outputs": [], "source": [], "metadata": {