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https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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屏蔽了左侧8个喷阀
This commit is contained in:
FEIJINTI
parent
36735a3d4f
commit
edd3b0f9c8
@ -18,7 +18,7 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\FEIJINTI\\miniconda3\\envs\\deepo\\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",
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"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",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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@ -54,8 +54,8 @@
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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',(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, 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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@ -169,7 +169,7 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"23961it [00:46, 519.87it/s] "
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"152147it [42:48, 59.24it/s] \n"
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]
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},
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{
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@ -178,19 +178,12 @@
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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 1.00 1.00 1.00 7188\n",
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" 1 1.00 1.00 1.00 5991\n",
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" 0 1.00 1.00 1.00 45644\n",
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" 1 1.00 1.00 1.00 38037\n",
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"\n",
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" accuracy 1.00 13179\n",
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" macro avg 1.00 1.00 1.00 13179\n",
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"weighted avg 1.00 1.00 1.00 13179\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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" accuracy 1.00 83681\n",
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" macro avg 1.00 1.00 1.00 83681\n",
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"weighted avg 1.00 1.00 1.00 83681\n",
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"\n"
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]
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}
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@ -200,7 +193,7 @@
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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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"else:8\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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@ -15,14 +15,20 @@
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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": 1,
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"outputs": [
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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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"C:\\Users\\FEIJINTI\\miniconda3\\envs\\deepo\\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",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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"ename": "NotImplementedError",
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"evalue": "开发时和机器上部署的路径不同，请注意选择rgb_tobacco_model_path、rgb_background_model_path、ai_path后删除本行",
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"output_type": "error",
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"traceback": [
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"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[1;31mNotImplementedError\u001B[0m Traceback (most recent call last)",
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"Input \u001B[1;32mIn [1]\u001B[0m, in \u001B[0;36m<cell line: 3>\u001B[1;34m()\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mnumpy\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mnp\u001B[39;00m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mpickle\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mutils\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m read_envi_ascii\n\u001B[0;32m 4\u001B[0m \u001B[38;5;66;03m# from config import Config\u001B[39;00m\n\u001B[0;32m 5\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mmodels\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m ManualTree\n",
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"File \u001B[1;32m~\\OneDrive - macrosolid\\PycharmProjects\\tobacco_color\\utils\\__init__.py:20\u001B[0m, in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 17\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mmatplotlib\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m pyplot \u001B[38;5;28;01mas\u001B[39;00m plt\n\u001B[0;32m 18\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mre\u001B[39;00m\n\u001B[1;32m---> 20\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mconfig\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Config\n\u001B[0;32m 23\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mnatural_sort\u001B[39m(l):\n\u001B[0;32m 24\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 25\u001B[0m \u001B[38;5;124;03m 自然排序\u001B[39;00m\n\u001B[0;32m 26\u001B[0m \u001B[38;5;124;03m :param l: 待排序\u001B[39;00m\n\u001B[0;32m 27\u001B[0m \u001B[38;5;124;03m :return:\u001B[39;00m\n\u001B[0;32m 28\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n",
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"File \u001B[1;32m~\\OneDrive - macrosolid\\PycharmProjects\\tobacco_color\\config.py:6\u001B[0m, in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mos\u001B[39;00m\n\u001B[0;32m 3\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mnumpy\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mnp\u001B[39;00m\n\u001B[1;32m----> 6\u001B[0m \u001B[38;5;28;01mclass\u001B[39;00m \u001B[38;5;21;01mConfig\u001B[39;00m:\n\u001B[0;32m 7\u001B[0m \u001B[38;5;66;03m# 文件相关参数\u001B[39;00m\n\u001B[0;32m 8\u001B[0m nRows, nCols, nBands \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m256\u001B[39m, \u001B[38;5;241m1024\u001B[39m, \u001B[38;5;241m22\u001B[39m\n\u001B[0;32m 9\u001B[0m nRgbRows, nRgbCols, nRgbBands \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1024\u001B[39m, \u001B[38;5;241m4096\u001B[39m, \u001B[38;5;241m3\u001B[39m\n",
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"File \u001B[1;32m~\\OneDrive - macrosolid\\PycharmProjects\\tobacco_color\\config.py:31\u001B[0m, in \u001B[0;36mConfig\u001B[1;34m()\u001B[0m\n\u001B[0;32m 28\u001B[0m spec_size_threshold \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m3\u001B[39m\n\u001B[0;32m 30\u001B[0m \u001B[38;5;66;03m# rgb模型参数\u001B[39;00m\n\u001B[1;32m---> 31\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m开发时和机器上部署的路径不同，请注意选择rgb_tobacco_model_path、rgb_background_model_path、ai_path后删除本行\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 32\u001B[0m \u001B[38;5;66;03m# rgb_tobacco_model_path = r\"weights/tobacco_dt_2022-08-27_14-43.model\" # 开发时的路径\u001B[39;00m\n\u001B[0;32m 33\u001B[0m \u001B[38;5;66;03m# rgb_tobacco_model_path = r\"/home/dt/tobacco-color/weights/tobacco_dt_2022-08-27_14-43.model\" # 机器上部署的路径\u001B[39;00m\n\u001B[0;32m 34\u001B[0m \u001B[38;5;66;03m# rgb_background_model_path = r\"weights/background_dt_2022-08-22_22-15.model\" # 开发时的路径\u001B[39;00m\n\u001B[0;32m 35\u001B[0m \u001B[38;5;66;03m# rgb_background_model_path = r\"/home/dt/tobacco-color/weights/background_dt_2022-08-22_22-15.model\" # 机器上部署的路径\u001B[39;00m\n\u001B[0;32m 36\u001B[0m threshold_low, threshold_high \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m10\u001B[39m, \u001B[38;5;241m230\u001B[39m\n",
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"\u001B[1;31mNotImplementedError\u001B[0m: 开发时和机器上部署的路径不同，请注意选择rgb_tobacco_model_path、rgb_background_model_path、ai_path后删除本行"
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]
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}
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],
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12
config.py
12
config.py
@ -20,23 +20,21 @@ class Config:
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# 光谱模型参数
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blk_size = 4 # 必须是2的倍数，不然会出错
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raise NotImplementedError("开发时和机器上部署的路径不同，请注意选择pixel_model_path、blk_model_path后删除本行")
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# pixel_model_path = r"./weights/pixel_2022-08-02_15-22.model" # 开发时的路径
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pixel_model_path = r"./weights/pixel_2022-08-02_15-22.model" # 开发时的路径
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# pixel_model_path = r"/home/dt/tobacco-color/weights/pixel_2022-08-02_15-22.model" # 机器上部署的路径
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# blk_model_path = r"./weights/rf_4x4_c22_20_sen8_9.model" # 开发时的路径
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blk_model_path = r"./weights/rf_4x4_c22_20_sen8_9.model" # 开发时的路径
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# blk_model_path = r"/home/dt/tobacco-color/weights/rf_4x4_c22_20_sen8_9.model" # 机器上部署的路径
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spec_size_threshold = 3
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# rgb模型参数
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raise NotImplementedError("开发时和机器上部署的路径不同，请注意选择rgb_tobacco_model_path、rgb_background_model_path、ai_path后删除本行")
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# rgb_tobacco_model_path = r"weights/tobacco_dt_2022-08-27_14-43.model" # 开发时的路径
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rgb_tobacco_model_path = r"weights/tobacco_dt_2022-08-27_14-43.model" # 开发时的路径
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# rgb_tobacco_model_path = r"/home/dt/tobacco-color/weights/tobacco_dt_2022-08-27_14-43.model" # 机器上部署的路径
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# rgb_background_model_path = r"weights/background_dt_2022-08-22_22-15.model" # 开发时的路径
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rgb_background_model_path = r"weights/background_dt_2022-08-22_22-15.model" # 开发时的路径
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# rgb_background_model_path = r"/home/dt/tobacco-color/weights/background_dt_2022-08-22_22-15.model" # 机器上部署的路径
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threshold_low, threshold_high = 10, 230
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threshold_s = 190 # 饱和度的最高允许值
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rgb_size_threshold = 4 # rgb的尺寸限制
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# ai_path = 'weights/best0827.pt' # 开发时的路径
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ai_path = 'weights/best0827.pt' # 开发时的路径
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# ai_path = '/home/dt/tobacco-color/weights/best0827.pt' # 机器上部署的路径
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ai_conf_threshold = 0.6
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1
main.py
1
main.py
@ -124,6 +124,7 @@ def main(only_spec=False, only_color=False, if_merge=False, interval_time=None,
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mask_rgb = rgb_detector.predict(rgb_data).astype(np.uint8)
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masks = [mask_spec, mask_rgb]
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# 进行多个喷阀的合并
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masks = [utils_customized.shield_valve(mask, left_shield=8) for mask in masks]
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masks = [utils_customized.valve_expend(mask) for mask in masks]
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mask_nums = sum([np.sum(np.sum(mask)) for mask in masks])
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log_time_count += 1
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@ -178,6 +178,14 @@ def valve_expend(img: np.ndarray) -> np.ndarray:
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return img
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def shield_valve(mask: np.ndarray, left_shield: int = -1, right_shield: int = -1) -> np.asarray:
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if (left_shield < mask.shape[1]) & (left_shield > 0):
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mask[:, :left_shield] = 0
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if (right_shield < mask.shape[1]) & (right_shield > 0):
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mask[:, -right_shield:] = 0
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return mask
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def valve_limit(mask: np.ndarray, max_valve_num: int) -> np.ndarray:
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"""
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用于限制阀门同时开启个数的函数
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@ -282,8 +290,8 @@ def valve_log(log_path: pathlib.Path, valve_num: [int, str]):
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将喷阀的开启次数记录到文件log_path当中。
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"""
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valve_str = "截至 " + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + f' 喷阀使用次数: {valve_num}.'
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with open(log_path, "w") as f:
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f.write(str(valve_str))
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with open(log_path, "a") as f:
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f.write(str(valve_str) + "\n")
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if __name__ == '__main__':
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