mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-08 22:34:00 +00:00
112 lines
4.3 KiB
Python
112 lines
4.3 KiB
Python
import numpy as np
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import cv2
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import copy
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import pandas as pd
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import os
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# 指定文件夹路径
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folder_path = 'data' # 替换成你的文件夹路径
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mask_folder_path = 'result' # 替换成你的掩码图像文件夹路径
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# 获取文件夹中的所有文件
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files = os.listdir(folder_path)
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files1 = os.listdir(mask_folder_path)
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D =[]
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for i in range(39, 40):
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with open(folder_path + '/' + str(i) + '_ref.hdr', 'r') as hdr_file:
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lines = hdr_file.readlines()
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for line in lines:
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if line.startswith('lines'):
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height = int(line.split()[-1])
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elif line.startswith('samples'):
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width = int(line.split()[-1])
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elif line.startswith('bands'):
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bands = int(line.split()[-1])
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raw_image = np.fromfile(folder_path + '/' + str(i) + '_ref.raw', dtype='float32')
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mask = cv2.imread(mask_folder_path + '/' + str(i) + '.tiff', cv2.IMREAD_GRAYSCALE)
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formatImage = np.zeros((height, width, bands))
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for row in range(0, height):
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for dim in range(0, bands):
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formatImage[row,:,dim] = raw_image[(dim + row*bands) * width:(dim + 1 + row*bands)* width]
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# 创建一个和光谱图像形状相同的全零数组，用于存储西红柿区域的光谱图像
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tomato_spectrum_image = np.zeros_like(formatImage)
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# 将掩码图像扩展到和光谱图像形状相同，以便进行按位与操作
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mask = np.stack([mask]*bands, axis=2)
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# 使用掩码提取西红柿区域的光谱图像
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tomato_spectrum_image = np.where(mask == 255, formatImage, 0)
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# print(tomato_spectrum_image.shape)
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# cv2.imshow('', tomato_spectrum_image[:, :, 145])
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# cv2.waitKey(0)
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data_save = []
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for i in range(0, 224):
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data = copy.deepcopy(tomato_spectrum_image[:, :, i])
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data[data>0] = 1
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num = np.sum(data)
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print(num)
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average_values_tomato = np.sum(tomato_spectrum_image[:, :, i])/num
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print("西红柿区域各波段的平均值:", average_values_tomato)
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data_save.append(average_values_tomato)
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print(data_save)
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D.append(data_save)
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# 创建一个DataFrame来存储结果
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D_array = np.array(D).reshape(224, 1)
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df = pd.DataFrame(D_array)
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# 将DataFrame导出为Excel文件
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df.to_excel('test39.xlsx', index=False)
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#选择通道数为12/46/96的三个通道相当于rgb三通道的图片
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# imgR = formatImage[:,:,12]
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# imgG = formatImage[:,:,46]
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# imgB = formatImage[:,:,96]
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#
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# rgbImg = cv2.merge([imgR, imgG, imgB])
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# cv2.imshow('test', rgbImg)
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# cv2.waitKey()
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#
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# # 使用图像的宽度、高度和波段数来重塑raw光谱图像的形状
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# height = 756 # 替换成你的图像高度
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# width = 1200 # 替换成你的图像宽度
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# bands = 224 # 替换成你的图像波段数
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# spectrum_image = raw_image.reshape((height, width, bands))
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# # 读取mask掩码图像，假设掩码图像是一个二值图像，西红柿的区域为255，其他区域为0
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# mask = cv2.imread(mask_file_path, cv2.IMREAD_GRAYSCALE)
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# # 创建一个和光谱图像形状相同的全零数组，用于存储西红柿区域的光谱图像
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# tomato_spectrum_image = np.zeros_like(spectrum_image)
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#
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# # 将掩码图像扩展到和光谱图像形状相同，以便进行按位与操作
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# mask = np.stack([mask]*bands, axis=2)
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#
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# # 使用掩码提取西红柿区域的光谱图像
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# tomato_spectrum_image = np.where(mask == 255, spectrum_image, 0)
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# print(tomato_spectrum_image.shape)
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# # data_save = []
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# # for i in range(0, 224):
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# # data = copy.deepcopy(tomato_spectrum_image[:, :, i])
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# # data[data>0] = 1
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# # num = np.sum(data)
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# # print(num)
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# # average_values_tomato = np.sum(tomato_spectrum_image[:, :, i])/num
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# # print("西红柿区域各波段的平均值:", average_values_tomato)
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# # data_save.append(average_values_tomato)
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# # print(data_save)
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# # 计算每个谱段上西红柿的光谱信息均值
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# # average_values_tomato = np.mean(tomato_spectrum_image, axis=(0, 1))
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#
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# # print(tomato_spectrum_image[:, :, 145])
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#
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# # cv2.imshow('', tomato_spectrum_image[:, :, 145])
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# # cv2.waitKey(0)
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# # 打印结果
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# # print("西红柿区域各波段的平均值:", average_values_tomato)
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# # import pandas as pd
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# #
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# # # 创建一个DataFrame来存储结果
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# # df = pd.DataFrame(average_values_tomato, columns=['Average Spectral Values'])
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# # 将DataFrame导出为Excel文件
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# df.to_excel('39.xlsx', index=False) |