修改分割模块

shape_spectral.py

@@ -9,6 +9,7 @@ import get_glcm
 import os
 import cv2
 import numpy as np
+import copy
 from plantcv.plantcv._helpers import _iterate_analysis, _cv2_findcontours, _object_composition, _grayscale_to_rgb, \
     _scale_size
 from plantcv.plantcv import outputs, within_frame
@@ -332,7 +333,7 @@ def process_images(full_bil_path, mask, outdir='None', debug="None"):
     spectral_array = pcv.readimage(filename=args.image, mode='envi')
     #过曝区域掩膜
     bath_100 = spectral_array.array_data[:, :, 100]
-    bath_over = pcv.threshold.binary(gray_img=bath_100, threshold=11000, object_type='dark')
+    bath_over = pcv.threshold.binary(gray_img=bath_100, threshold=11000, object_type='dark')#
 
     # mask已经是16位标签掩膜，直接使用
     # 确保掩膜是正确的数据类型
@@ -367,8 +368,11 @@ def process_images(full_bil_path, mask, outdir='None', debug="None"):
     shape_img = size(img=binary_img, labeled_mask=labeled_mask, n_labels=num_valid)
 
     # 分析光谱反射率
-    # 应用过曝掩膜
-    labeled_spectral_mask = mask * bath_over_binary
+    # 应用过曝掩膜 ;如果采集的反射率大于1.1，则会被错误判断为过曝区域导致该样本的光谱没有被统计
+    # labeled_spectral_mask = mask * bath_over_binary
+    labeled_spectral_mask = mask
+
+
 
     spectral_hist = pcv.analyze.spectral_reflectance(
         hsi=spectral_array,
@@ -377,13 +381,16 @@ def process_images(full_bil_path, mask, outdir='None', debug="None"):
         label=None
     )
 
-    observations = pcv.outputs.observations
+    # 深拷贝观测结果，避免后续清空影响数据
+    observations = copy.deepcopy(pcv.outputs.observations)
+    # 立即清空全局缓存，防止跨文件/阶段累加
+    pcv.outputs.clear()
     # 将结果转换为列表
     combined_data = process_plantcv_outputs(observations)
 
     return combined_data
 
-# # 示例：批量处理指定路径下的光谱图像和掩膜文件
+# # # 示例：批量处理指定路径下的光谱图像和掩膜文件
 # bil_path = r'D:\WQ\test\Traindata-05'
 # mask_path = r'D:\WQ\test\mask'
 # outdir = r"D:\WQ\test"  # 输出文件夹路径