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分离预测
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FEIJINTI
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@ -37,7 +37,7 @@
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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\", (255, 0, 0): 'beijing',(0, 255, 0): \"zibian\"} # 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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@ -1,4 +1,3 @@
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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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@ -6,12 +5,12 @@ from models import AnonymousColorDetector
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from utils import read_labeled_img
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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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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
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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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# 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 = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing', (0, 255, 0): "zibian"} # 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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label_index = {"yangeng": 1, "beijing": 0, "zibian": 2} # 类别对应的序号
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@ -32,9 +31,11 @@ if show_samples:
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# %% md
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## 数据平衡化
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# %%
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if len(dataset) > 1:
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rus = RandomUnderSampler(random_state=0)
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x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \
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np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()], axis=0).tolist()
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np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()],
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axis=0).tolist()
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x_resampled, y_resampled = rus.fit_resample(x_list, y_list)
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dataset = {"inside": np.array(x_resampled)}
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# %% md
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@ -54,13 +55,3 @@ else:
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model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
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is_save_dataset=True, model_selection='dt')
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model.save()
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# %%
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if train_from_existed:
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data = scipy.io.loadmat(dataset_file)
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x, y = data['x'], data['y'].ravel()
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model.fit(x, y=y, is_generate_negative=False, model_selection='dt')
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else:
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world_boundary = np.array([0, 0, 0, 255, 255, 255])
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model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
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is_save_dataset=True, model_selection='dt')
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model.save()
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@ -14,7 +14,7 @@
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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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"execution_count": 44,
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"outputs": [],
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"source": [
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"import datetime\n",
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@ -39,7 +39,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 45,
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"outputs": [],
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"source": [
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"img_path = r\"C:\\Users\\FEIJINTI\\Desktop\\721\\zazhi\\Image_2022_0721_1351_38_946-002034.bmp\"\n",
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@ -54,7 +54,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 46,
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"outputs": [],
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"source": [
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"img = cv2.imread(img_path)[:, :, ::-1]\n",
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@ -71,7 +71,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 47,
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"outputs": [],
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"source": [
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"t_detector = AnonymousColorDetector(file_path=model_path[1])\n",
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@ -86,7 +86,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 48,
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"outputs": [],
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"source": [
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"z_detector = AnonymousColorDetector(file_path=model_path[2])\n",
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@ -101,7 +101,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 49,
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"outputs": [],
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"source": [
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"result = 1 - (b_result | t_result | z_result)"
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@ -115,7 +115,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 50,
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"outputs": [
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{
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"data": {
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@ -128,16 +128,16 @@
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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='cec7c80e-e50e-4a78-858f-b58ac7b4a36e'></div>"
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"text/html": "<div id='71e0e832-f293-40bc-9913-301125493d2e'></div>"
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": "<matplotlib.image.AxesImage at 0x24e9025e170>"
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"text/plain": "<matplotlib.image.AxesImage at 0x24e8faaa440>"
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},
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"execution_count": 36,
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"execution_count": 50,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -158,7 +158,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 50,
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"outputs": [],
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"source": [],
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"metadata": {
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