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https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
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242bb9a71b
fix:修复在20240627test4中的classifier.py的analyze_tomato函数中white_defect的函数忘记传递两个阈值量的错误；修复analyze_tomato函数中的叶片实例分割存在的问题，解决由于变量污染引起的分割错误；
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/6/17 下午3:36
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# @Author : TG
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# @File : config.py
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# @Software: PyCharm
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from root_dir import ROOT_DIR
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class Config:
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#文件相关参数
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#预热参数
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n_spec_rows, n_spec_cols, n_spec_bands = 25, 30, 13
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n_rgb_rows, n_rgb_cols, n_rgb_bands = 613, 800, 3
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tomato_img_dir = ROOT_DIR / 'models' / 'TO.bmp'
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passion_fruit_img_dir = ROOT_DIR / 'models' / 'PF.bmp'
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#模型路径
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#糖度模型
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brix_model_path = ROOT_DIR / 'models' / 'passion_fruit.joblib'
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#图像分类模型
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imgclassifier_model_path = ROOT_DIR / 'models' / 'resnet18pf20240705.pth'
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imgclassifier_class_indices_path = ROOT_DIR / 'models' / 'class_indices.json'
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#番茄初版实例分割叶片模型，实例分割叶片
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# toseg_weights = ROOT_DIR / 'weights' / 'raw_seg_best.pt'
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#20240711番茄新版实例分割模型，实例分割叶片、果蒂、果脐处
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toseg_weights = ROOT_DIR / 'weights' / '20240711_seg_best.pt'
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#番茄初版裂口目标检测模型，原始包含刀划伤的模型
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# toobj_path = ROOT_DIR / 'weights' / 'raw_obj_best.pt'
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#20240711番茄新版裂口目标检测模型，去除刀划伤的模型
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# toobj_path = ROOT_DIR / 'weights' / '20240711_obj_best.pt'
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#20240712番茄新版裂口目标检测模型，去除刀划伤的模型，新增加坑状破损样本
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toobj_path = ROOT_DIR / 'weights' / '20240712_obj_best.pt'
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#classifer.py参数
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#tomato
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find_reflection_threshold = 190
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extract_g_r_factor = 1.5
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#passion_fruit
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hue_value = 37
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hue_delta = 10
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value_target = 25
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value_delta = 10
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#提取绿色像素参数
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low_H = 0
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low_S = 100
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low_V = 0
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high_H = 60
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high_S = 180
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high_V = 60
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#spec_predict
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#筛选谱段并未使用，在qt取数据时已经筛选
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selected_bands = [8, 9, 10, 48, 49, 50, 77, 80, 103, 108, 115, 143, 145]
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#data_processing
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#根据标定数据计算的参数,实际长度/像素长度，单位cm
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pixel_length_ratio = 6.3/425
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#绿叶面积阈值，高于此阈值认为连通域是绿叶
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area_threshold = 20000
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#百香果密度（g/cm^3）
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density = 0.652228972
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#百香果面积比例，每个像素代表的实际面积（cm^2）
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area_ratio = 0.00021973702422145334
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#def analyze_tomato
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#s_l通道阈值
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threshold_s_l = 180
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threshold_fore_g_r_t = 20
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