mirror of
https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
synced 2025-11-08 22:33:54 +00:00
更换模型路径
This commit is contained in:
FEIJINTI
parent
f415a480a6
commit
5ace1f858b
@ -12,7 +12,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 3,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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@ -30,7 +30,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 4,
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"outputs": [],
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"source": [
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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\n",
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@ -38,8 +38,8 @@
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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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@ -70,7 +70,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 5,
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"outputs": [],
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"source": [
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"dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)\n",
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@ -99,7 +99,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 6,
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"outputs": [],
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"source": [
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"if len(dataset) > 1:\n",
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@ -130,7 +130,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 7,
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"outputs": [],
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"source": [
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"# 对数据进行预处理\n",
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@ -147,84 +147,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 8,
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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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]
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},
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{
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@ -233,19 +162,12 @@
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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 2706\n",
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" 1 1.00 1.00 1.00 2255\n",
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" 0 1.00 1.00 1.00 11523\n",
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" 1 1.00 1.00 1.00 9603\n",
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"\n",
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" accuracy 1.00 4961\n",
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" macro avg 1.00 1.00 1.00 4961\n",
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"weighted avg 1.00 1.00 1.00 4961\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 21126\n",
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" macro avg 1.00 1.00 1.00 21126\n",
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"weighted avg 1.00 1.00 1.00 21126\n",
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"\n"
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]
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}
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@ -23,7 +23,7 @@ class Config:
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blk_model_path = r"./models/rf_4x4_c22_20_sen8_8.model"
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# rgb模型参数
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rgb_tobacco_model_path = r"models/tobacco_dt_2022-07-21_16-30.model"
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rgb_background_model_path = r"models/beijing_dt_2022-07-21_16-44.model"
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rgb_tobacco_model_path = r"models/tobacco_dt_2022-07-26_15-57.model"
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rgb_background_model_path = r"models/background_dt_2022-07-27_08-11.model"
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threshold_low, threshold_high = 5, 255
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threshold_s = 175
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8
main.py
8
main.py
@ -9,8 +9,7 @@ import cv2
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def main():
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spec_detector = SpecDetector(blk_model_path=Config.blk_model_path,
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pixel_model_path=Config.pixel_model_path)
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spec_detector = SpecDetector(blk_model_path=Config.blk_model_path, pixel_model_path=Config.pixel_model_path)
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rgb_detector = RgbDetector(tobacco_model_path=Config.rgb_tobacco_model_path,
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background_model_path=Config.rgb_background_model_path)
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total_len = Config.nRows * Config.nCols * Config.nBands * 4 # float型变量, 4个字节
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@ -43,9 +42,8 @@ def main():
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os.close(fd_rgb)
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# 识别
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t1 = time.time()
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img_data = np.frombuffer(data_total, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1)).transpose(0,
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2,
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1)
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img_data = np.frombuffer(data_total, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1)) \
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.transpose(0, 2, 1)
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rgb_data = np.frombuffer(rgb_data_total, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
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# 光谱识别
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mask = spec_detector.predict(img_data)
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