mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 06:44:02 +00:00
249 lines
7.6 KiB
Plaintext
249 lines
7.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-03-18T12:58:36.021344Z",
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"start_time": "2024-03-18T12:58:35.489144Z"
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}
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},
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"source": [
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"import numpy as np\n",
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"import cv2\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import cv2 as cv\n",
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"import os"
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"source": [
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"##合成真彩色图像\n",
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"#读取raw文件\n",
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"imagePath ='data/new/yellow/1.raw' \n",
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"print(type(imagePath))\n",
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"rawImage = np.fromfile(imagePath,\"uint16\")\n",
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"print(type(rawImage))\n",
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"rawShape = rawImage.shape\n",
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"print(rawShape)\n",
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"formatImage = np.zeros((87,2048,224))\n",
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"for row in range(0,87):\n",
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" for dim in range(0,224):\n",
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" formatImage[row,:,dim] = rawImage[(dim + row*224) * 2048:(dim + 1 + row*224)* 2048]\n",
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"print(formatImage.shape)\n",
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"print(type(formatImage))\n",
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"print(formatImage)\n",
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"\n",
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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\n",
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"# imgR = formatImage[:,:,88]\n",
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"# imgG = formatImage[:,:,58]\n",
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"# imgB = formatImage[:,:,31]\n",
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"# \n",
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"# rgbImg = cv.merge([imgR,imgG,imgB])\n",
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"# print(rgbImg.shape)\n",
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"# print(type(rgbImg))\n",
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"# cv.imshow('test',rgbImg)\n",
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"# cv.waitKey(0)\n",
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"# cv.destroyAllWindows()\n",
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"# plt.imshow(rgbImg)\n",
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"# #保存\n",
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"# plt.savefig('result/1_ref.png')\n",
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"\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-18T13:06:05.055643Z",
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"start_time": "2024-03-18T13:06:04.869067Z"
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}
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},
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"id": "3443037608cc60f6",
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"##合成真彩色图像\n",
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"#读取raw文件\n",
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"imagePath = 'data/数据/原光源（条光）/黄果（成熟）/1.raw' \n",
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"print(type(imagePath))\n",
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"rawImage = np.fromfile(imagePath,\"float16\")\n",
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"print(type(rawImage))\n",
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"rawShape = rawImage.shape\n",
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"print(rawShape)\n",
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"formatImage = np.zeros((110,2048,224))\n",
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"# 假设 rawImage 已经正确读取\n",
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"raw_length = len(rawImage) # 获取 rawImage 的实际长度\n",
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"\n",
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"for row in range(0, 110):\n",
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" for dim in range(0, 224):\n",
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" start_index = (dim + row*224) * 2048\n",
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" end_index = (dim + 1 + row*224) * 2048\n",
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" \n",
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" # 检查索引是否在 rawImage 的边界内\n",
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" if start_index < raw_length and end_index <= raw_length:\n",
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" formatImage[row,:,dim] = rawImage[start_index:end_index]\n",
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" else:\n",
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" print(f\"索引超出范围: 行 {row}, 波段 {dim}\")\n",
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"\n",
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"# for row in range(0,110):\n",
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"# for dim in range(0,224):\n",
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"# # formatImage[row,:,dim] = rawImage[(dim + row*224) * 2048:(dim + 1 + row*224)* 2048]\n",
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" \n",
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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\n",
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"# imgR = formatImage[:,:,31]\n",
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"# imgG = formatImage[:,:,58]\n",
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"# imgB = formatImage[:,:,88]\n",
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"# \n",
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"# rgbImg = cv.merge([imgR,imgG,imgB])\n",
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"# print(rgbImg.shape)\n",
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"# print(type(rgbImg))\n",
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"# cv.imshow('test',rgbImg)\n",
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"# cv.waitKey(0)\n",
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"# cv.destroyAllWindows()\n",
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"# plt.imshow(rgbImg)\n",
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"# #保存\n",
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"# plt.savefig('result/1_ref.png')"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-18T03:26:37.014093Z",
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"start_time": "2024-03-18T03:26:36.769031Z"
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}
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},
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"id": "283f370b2401221b",
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"execution_count": 5,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"source": [
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"##循环合成真彩色图像\n",
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"# 指定文件夹路径\n",
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"folder_path = 'data' # 替换成你的文件夹路径\n",
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"# mask_out_path = 'result' # 替换成你的掩码图像文件夹路径\n",
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"\n",
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"# 获取文件夹中的所有文件\n",
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"files = os.listdir(folder_path)\n",
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"\n",
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"\n",
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"for i in range(1, 40):\n",
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" with open(folder_path + '/' + str(i) + '_ref.hdr', 'r') as hdr_file:\n",
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" lines = hdr_file.readlines()\n",
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" for line in lines:\n",
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" if line.startswith('lines'):\n",
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" height = int(line.split()[-1])\n",
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" elif line.startswith('samples'):\n",
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" width = int(line.split()[-1])\n",
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" elif line.startswith('bands'):\n",
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" bands = int(line.split()[-1])\n",
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" raw_image = np.fromfile(folder_path + '/' + str(i) + '_ref.raw', dtype='float32')\n",
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" \n",
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" formatImage = np.zeros((height, width, bands))\n",
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" for row in range(0, height):\n",
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" for dim in range(0, bands):\n",
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" formatImage[row,:,dim] = raw_image[(dim + row*bands) * width:(dim + 1 + row*bands)* width]\n",
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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\n",
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" imgR = formatImage[:,:,12]\n",
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" imgG = formatImage[:,:,46]\n",
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" imgB = formatImage[:,:,96]\n",
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" \n",
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" rgbImg = cv.merge([imgR,imgG,imgB])\n",
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" print(rgbImg.shape)\n",
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" print(type(rgbImg))\n",
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" \n",
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" Img_Name = \"./tcimage/\" + str(i)+ \".png\"\n",
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" cv.imwrite(Img_Name, rgbImg*255)\n",
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" # cv.imshow('test',rgbImg)\n",
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" # cv.waitKey(0)\n",
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" # cv.destroyAllWindows()\n",
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"# #读取raw文件\n",
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"# imagePath = 'data/1_ref.raw' \n",
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"# print(type(imagePath))\n",
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"# rawImage = np.fromfile(imagePath,\"float32\")\n",
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"# print(type(rawImage))\n",
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"# rawShape = rawImage.shape\n",
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"# print(rawShape)\n",
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"# formatImage = np.zeros((928,1200,224))\n",
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"# for row in range(0,928):\n",
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"# for dim in range(0,224):\n",
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"# formatImage[row,:,dim] = rawImage[(dim + row*224) * 1200:(dim + 1 + row*224)* 1200]\n",
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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\n",
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"# imgR = formatImage[:,:,12]\n",
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"# imgG = formatImage[:,:,46]\n",
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"# imgB = formatImage[:,:,96]\n",
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" \n",
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"# rgbImg = cv.merge([imgR,imgG,imgB])\n",
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"# print(rgbImg.shape)\n",
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"# print(type(rgbImg))\n",
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"# cv.imshow('test',rgbImg)\n",
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"# cv.waitKey(0)\n",
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"# cv.destroyAllWindows()\n",
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"# plt.imshow(rgbImg)\n",
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"# #保存\n",
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"# plt.savefig('result/1_ref.png')\n",
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"\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-11-24T02:56:13.173734400Z",
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"start_time": "2023-11-24T02:50:24.958389400Z"
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}
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},
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"id": "15a3889f7bf1917f",
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [],
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"metadata": {
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"collapsed": false
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},
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"id": "17b929b3de3fa2ed",
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [],
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"metadata": {
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"collapsed": false
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},
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"id": "e00c8e929ee57b60",
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"outputs": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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"nbformat": 4,
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"nbformat_minor": 5
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