分离预测

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",

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", (255, 0, 0): 'beijing', (0, 255, 0): "zibian"}  # 颜色对应的类别
-color_dict = {(0, 0, 255): "yangeng"}
+# 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
@@ -32,9 +31,11 @@ if show_samples:
 # %% md
 ## 数据平衡化
 # %%
+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()
+                     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
@@ -54,13 +55,3 @@ else:
     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()

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": "<IPython.core.display.HTML object>",
-      "text/html": "<div id='cec7c80e-e50e-4a78-858f-b58ac7b4a36e'></div>"
+      "text/html": "<div id='71e0e832-f293-40bc-9913-301125493d2e'></div>"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": "<matplotlib.image.AxesImage at 0x24e9025e170>"
+      "text/plain": "<matplotlib.image.AxesImage at 0x24e8faaa440>"
      },
-     "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": {