屏蔽了右侧10个喷阀

02_classification.ipynb

@@ -12,24 +12,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\FEIJINTI\\miniconda3\\envs\\cv\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training env\n"
-     ]
-    }
-   ],
+   "execution_count": 7,
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import scipy\n",
@@ -46,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "outputs": [],
    "source": [
     "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",
@@ -86,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "outputs": [],
    "source": [
     "dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)\n",
@@ -193,7 +177,7 @@
     "    data = scipy.io.loadmat(dataset_file)\n",
     "    x, y = data['x'], data['y'].ravel()\n",
     "    model.fit(x, y=y, is_generate_negative=False, model_selection='dt')\n",
-    "else:8\n",
+    "else:\n",
     "    world_boundary = np.array([0, 0, 0, 255, 255, 255])\n",
     "    model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,\n",
     "          is_save_dataset=True, model_selection='dt')\n",

main.py

@@ -124,7 +124,7 @@ def main(only_spec=False, only_color=False, if_merge=False, interval_time=None,
                 mask_rgb = rgb_detector.predict(rgb_data).astype(np.uint8)
             masks = [mask_spec, mask_rgb]
         # 进行多个喷阀的合并
-        masks = [utils_customized.shield_valve(mask, left_shield=10) for mask in masks]
+        masks = [utils_customized.shield_valve(mask, left_shield=10, right_shield=10) for mask in masks]
         masks = [utils_customized.valve_expend(mask) for mask in masks]
         mask_nums = sum([np.sum(np.sum(mask)) for mask in masks])
         log_time_count += 1