NanjingForestryUniversity/supermachine--tomato-passion_fruit
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supermachine--tomato-passio.../spectrum/02picture.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2024-03-18T12:58:36.021344Z",
"start_time": "2024-03-18T12:58:35.489144Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import cv2 as cv\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n",
"<class 'numpy.ndarray'>\n",
"(39911424,)\n",
"(87, 2048, 224)\n",
"<class 'numpy.ndarray'>\n",
"[[[1686. 1654. 1654. ... 1654. 1622. 1654.]\n",
" [1689. 1673. 1673. ... 1689. 1689. 1673.]\n",
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" ...\n",
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"\n",
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]
}
],
"source": [
"##合成真彩色图像\n",
"#读取raw文件\n",
"imagePath ='data/new/yellow/1.raw' \n",
"print(type(imagePath))\n",
"rawImage = np.fromfile(imagePath,\"uint16\")\n",
"print(type(rawImage))\n",
"rawShape = rawImage.shape\n",
"print(rawShape)\n",
"formatImage = np.zeros((87,2048,224))\n",
"for row in range(0,87):\n",
" for dim in range(0,224):\n",
" formatImage[row,:,dim] = rawImage[(dim + row*224) * 2048:(dim + 1 + row*224)* 2048]\n",
"print(formatImage.shape)\n",
"print(type(formatImage))\n",
"print(formatImage)\n",
"\n",
"#选择通道数为12/46/96的三个通道相当于rgb三通道的图片\n",
"# imgR = formatImage[:,:,88]\n",
"# imgG = formatImage[:,:,58]\n",
"# imgB = formatImage[:,:,31]\n",
"# \n",
"# rgbImg = cv.merge([imgR,imgG,imgB])\n",
"# print(rgbImg.shape)\n",
"# print(type(rgbImg))\n",
"# cv.imshow('test',rgbImg)\n",
"# cv.waitKey(0)\n",
"# cv.destroyAllWindows()\n",
"# plt.imshow(rgbImg)\n",
"# #保存\n",
"# plt.savefig('result/1_ref.png')\n",
"\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-03-18T13:06:05.055643Z",
"start_time": "2024-03-18T13:06:04.869067Z"
}
},
"id": "3443037608cc60f6"
},
{
"cell_type": "code",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n",
"<class 'numpy.ndarray'>\n",
"(50462720,)\n"
]
}
],
"source": [
"##合成真彩色图像\n",
"#读取raw文件\n",
"imagePath = 'data/数据/原光源（条光）/黄果（成熟）/1.raw' \n",
"print(type(imagePath))\n",
"rawImage = np.fromfile(imagePath,\"float16\")\n",
"print(type(rawImage))\n",
"rawShape = rawImage.shape\n",
"print(rawShape)\n",
"formatImage = np.zeros((110,2048,224))\n",
"# 假设 rawImage 已经正确读取\n",
"raw_length = len(rawImage) # 获取 rawImage 的实际长度\n",
"\n",
"for row in range(0, 110):\n",
" for dim in range(0, 224):\n",
" start_index = (dim + row*224) * 2048\n",
" end_index = (dim + 1 + row*224) * 2048\n",
" \n",
" # 检查索引是否在 rawImage 的边界内\n",
" if start_index < raw_length and end_index <= raw_length:\n",
" formatImage[row,:,dim] = rawImage[start_index:end_index]\n",
" else:\n",
" print(f\"索引超出范围: 行 {row}, 波段 {dim}\")\n",
"\n",
"# for row in range(0,110):\n",
"# for dim in range(0,224):\n",
"# # formatImage[row,:,dim] = rawImage[(dim + row*224) * 2048:(dim + 1 + row*224)* 2048]\n",
" \n",
"#选择通道数为12/46/96的三个通道相当于rgb三通道的图片\n",
"# imgR = formatImage[:,:,31]\n",
"# imgG = formatImage[:,:,58]\n",
"# imgB = formatImage[:,:,88]\n",
"# \n",
"# rgbImg = cv.merge([imgR,imgG,imgB])\n",
"# print(rgbImg.shape)\n",
"# print(type(rgbImg))\n",
"# cv.imshow('test',rgbImg)\n",
"# cv.waitKey(0)\n",
"# cv.destroyAllWindows()\n",
"# plt.imshow(rgbImg)\n",
"# #保存\n",
"# plt.savefig('result/1_ref.png')"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-03-18T03:26:37.014093Z",
"start_time": "2024-03-18T03:26:36.769031Z"
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},
"id": "283f370b2401221b",
"execution_count": 5
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(928, 1200, 3)\n",
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"(848, 1200, 3)\n",
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"(902, 1200, 3)\n",
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"<class 'numpy.ndarray'>\n",
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"(747, 1200, 3)\n",
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"(791, 1200, 3)\n",
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"(723, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(797, 1200, 3)\n",
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"(760, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(701, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(770, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(637, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(729, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(762, 1200, 3)\n",
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"(732, 1200, 3)\n",
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"(820, 1200, 3)\n",
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"(772, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(772, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(747, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(770, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(645, 1200, 3)\n",
"<class 'numpy.ndarray'>\n",
"(756, 1200, 3)\n",
"<class 'numpy.ndarray'>\n"
]
}
],
"source": [
"##循环合成真彩色图像\n",
"# 指定文件夹路径\n",
"folder_path = 'data' # 替换成你的文件夹路径\n",
"# mask_out_path = 'result' # 替换成你的掩码图像文件夹路径\n",
"\n",
"# 获取文件夹中的所有文件\n",
"files = os.listdir(folder_path)\n",
"\n",
"\n",
"for i in range(1, 40):\n",
" with open(folder_path + '/' + str(i) + '_ref.hdr', 'r') as hdr_file:\n",
" lines = hdr_file.readlines()\n",
" for line in lines:\n",
" if line.startswith('lines'):\n",
" height = int(line.split()[-1])\n",
" elif line.startswith('samples'):\n",
" width = int(line.split()[-1])\n",
" elif line.startswith('bands'):\n",
" bands = int(line.split()[-1])\n",
" raw_image = np.fromfile(folder_path + '/' + str(i) + '_ref.raw', dtype='float32')\n",
" \n",
" formatImage = np.zeros((height, width, bands))\n",
" for row in range(0, height):\n",
" for dim in range(0, bands):\n",
" formatImage[row,:,dim] = raw_image[(dim + row*bands) * width:(dim + 1 + row*bands)* width]\n",
" #选择通道数为12/46/96的三个通道相当于rgb三通道的图片\n",
" imgR = formatImage[:,:,12]\n",
" imgG = formatImage[:,:,46]\n",
" imgB = formatImage[:,:,96]\n",
" \n",
" rgbImg = cv.merge([imgR,imgG,imgB])\n",
" print(rgbImg.shape)\n",
" print(type(rgbImg))\n",
" \n",
" Img_Name = \"./tcimage/\" + str(i)+ \".png\"\n",
" cv.imwrite(Img_Name, rgbImg*255)\n",
" # cv.imshow('test',rgbImg)\n",
" # cv.waitKey(0)\n",
" # cv.destroyAllWindows()\n",
"# #读取raw文件\n",
"# imagePath = 'data/1_ref.raw' \n",
"# print(type(imagePath))\n",
"# rawImage = np.fromfile(imagePath,\"float32\")\n",
"# print(type(rawImage))\n",
"# rawShape = rawImage.shape\n",
"# print(rawShape)\n",
"# formatImage = np.zeros((928,1200,224))\n",
"# for row in range(0,928):\n",
"# for dim in range(0,224):\n",
"# formatImage[row,:,dim] = rawImage[(dim + row*224) * 1200:(dim + 1 + row*224)* 1200]\n",
"# #选择通道数为12/46/96的三个通道相当于rgb三通道的图片\n",
"# imgR = formatImage[:,:,12]\n",
"# imgG = formatImage[:,:,46]\n",
"# imgB = formatImage[:,:,96]\n",
" \n",
"# rgbImg = cv.merge([imgR,imgG,imgB])\n",
"# print(rgbImg.shape)\n",
"# print(type(rgbImg))\n",
"# cv.imshow('test',rgbImg)\n",
"# cv.waitKey(0)\n",
"# cv.destroyAllWindows()\n",
"# plt.imshow(rgbImg)\n",
"# #保存\n",
"# plt.savefig('result/1_ref.png')\n",
"\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-24T02:56:13.173734400Z",
"start_time": "2023-11-24T02:50:24.958389400Z"
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