ejfox · August 25, 2024 21:59
diff --git a/folder_to_blogpost.js b/folder_to_blogpost.js
 'use strict'
 require('dotenv').config()

 const path = require('path')
 const fs = require('fs')
 const cloudinary = require('cloudinary').v2
 const ExifReader = require('exifreader')
 const exifErrors = ExifReader.errors
 const _ = require('lodash')
 const exifDate = require('exif-date').parse

 // HOW TO USE THIS SCRIPT
 // 1 - Set up Cloudinary, which is used to upload
 //     the photos in the folder
 //
 // 2 - Get a bunch of photos, ideally from Lightroom
 //     and with captions, and export them so their
 //     max filesize is 10mb each, which is the max
 //     filesize on Cloudinary's free plan
 //
 // 3 - Run `node folder-to-blogpost.js ~/photo-folder-path/ post-slug`

 /*
 This script is designed to automate the process of creating a blog post from a folder of images. It performs the following steps:

 1. The script first loads environment variables from a .env file using the dotenv package. These variables are used to configure the Cloudinary API, which is used to upload images.

 2. The script expects the path to a folder of images and a post slug as command-line arguments. If no post slug is provided, the name of the folder is used as the slug.

 3. The script reads the contents of the specified folder. It ignores any subdirectories and any files that are not JPEG images.

 4. For each JPEG image, the script extracts EXIF data using the exifreader package. This data includes the model of the camera, the date and time the photo was taken, the aperture and shutter speed, and any description or caption embedded in the image. The script also uses the exif-date package to parse the date and time from the EXIF data.

 5. The script then uploads each image to Cloudinary using the cloudinary package. The URL of the uploaded image is added to the data for that image.

 6. Once all images have been processed and uploaded, the script generates a Markdown file for the blog post. Each image is included in the post using Markdown's image syntax, and the caption (if any) is included as plain text underneath the image.

 7. The script writes the Markdown for the blog post to a .md file in the 'content/photos/' directory. The name of the file is the post slug provided as a command-line argument (or the name of the folder if no slug was provided).

 8. The script also writes a .json file containing the data for each image, including the local and Cloudinary URLs and the EXIF data. This file can be used for additional processing or for debugging.

 9. Finally, the script exits. If any errors occur during the execution of the script (such as failing to read the folder or upload an image), the script logs the error and exits immediately.
 */

 cloudinary.config({
  cloud_name: process.env.CLOUDINARY_CLOUD_NAME,
  api_key: process.env.CLOUDINARY_API_KEY,
  api_secret: process.env.CLOUDINARY_API_SECRET,
 })

 const photoFolderObject = {
  files: [],
 }

 if (process.argv.length < 3) {
  console.log('No arguments')
  process.exit()
 }

 // Okay, we are gonna pass in a folder path via CLI args
 //
 //const filePath = process.argv[2]
 const folderPath = process.argv[2]
 const splitPath = folderPath.split('/')
 console.log('split path: ' + JSON.stringify(splitPath))
 const folder = splitPath[splitPath.length - 2]

 if (!process.argv[3]) {
  console.log(`No name provided, using the folder name: ${folder}`)
  photoFolderObject.title = folder
  photoFolderObject.slug = folder
 } else {
  photoFolderObject.title = process.argv[3]
  photoFolderObject.slug = process.argv[3]
 }

 // Gonna read through every file in that folder
 fs.readdir(folderPath, function (err, files) {
  if (err) {
    console.error('Error', err)
    process.exit(1)
  }

  files.forEach(function (file, i) {
    const filePath = path.join(folderPath, file)
    const fileType = filePath.split('.')[1]
    const stat = fs.statSync(filePath)
    if (stat && stat.isDirectory()) {
      // ignore
    } else if (fileType === 'jpg' || fileType === 'jpeg') {
      processFile(filePath)
    } else {
      // Don't process non jpg files
    }
  })

  // Okay now we should have built an array of JSON objects
  // Each JSON object should have:
  // + image path
  // + EXIF data in an object

  // console.log(JSON.stringify(photoFolderObject))

  //
  // Now we want to actually take those .jpgs and upload them to cloudinary
  // And actually, also to S3 if we could, and maybe some smaller versions to Dropbox
  photoFolderObject.files = uploadAllPhotosToCloudinary(
    photoFolderObject,
    function (newPhotoFolderObj) {
      console.log(newPhotoFolderObj)
      // let blogPostHtml = makeBlogPost(newPhotoFolderObj);
      // Write that html to a .json file with a bunch of cool stuff in it

      const photoPostDestination = 'content/photos/'

      newPhotoFolderObj.date = newPhotoFolderObj.files[0].date

      fs.writeFileSync(
        photoPostDestination + folder + '.json',
        JSON.stringify(photoFolderObject, null, '\t'),
      )

      const blogPostMarkdown = makeBlogPost(newPhotoFolderObj)
      fs.writeFileSync(
        photoPostDestination + folder + '.md',
        blogPostMarkdown,
      )

      process.exit(0)
    },
  )
  // Then we get our new uploaded URLs and put them back in the image objects
  // So now we have a local path and an uploaded path and EXIF data
  //
  // Now we need to make a blogpost style .json that has all of that stuff laid out
  // It's probably gonna need to generate HTML now that I think about it
  //
  // Then put that in /content/photo/ as a .json file and we are golden

  // console.log(photoFolderObject)
 })

 function makeBlogPost(photoFolderObject) {
  let postMarkdown = ''
  _.each(photoFolderObject.files, (p) => {
    let photoMarkdown = ''
    photoMarkdown += `![Image](${p.cloudinaryUrl})\n`
    if (p.caption) {
      photoMarkdown += `${p.caption}\n`
    }
    postMarkdown += photoMarkdown
  })
  return postMarkdown
 }

 function uploadAllPhotosToCloudinary(photoFolderObject, cb) {
  let uploadPromises = photoFolderObject.files.map((file, i) => {
    console.log('Uploading ', file.path)
    return new Promise((resolve, reject) => {
      cloudinary.uploader.upload(file.path, (err, res) => {
        if (err) {
          console.log('Error uploading: ', err)
          reject(err)
        } else {
          photoFolderObject.files[i].cloudinaryUrl = res.url
          resolve()
        }
      })
    })
  })

  Promise.all(uploadPromises)
    .then(() => {
      console.log(`Successfully uploaded ${photoFolderObject.files.length} files`)
      cb(photoFolderObject)
    })
    .catch(err => {
      console.log('Error uploading files: ', err)
    })

  return photoFolderObject.files
 }

 // Then run that file through the EXIF reader and extract EXIF data
 function processFile(filePath) {
  // console.log('Processing file: ', filePath)
  const fileData = fs.readFileSync(filePath)

  const tags = ExifReader.load(fileData.buffer)

  delete tags['MakerNote']

  // Mostly we want the date/time of capture
  // And also the GPS latitude and longitude, if they exist
  // Oh and also the aperture and shutter speed
  //
  // Almost forgot the most important part: the caption
  let photoObject = {}

  const {
    Model,
    DateTime,
    FNumber,
    ISOSpeedRatings,
    Description,
    FocalLengthIn35mmFilm,
    ShutterSpeedValue,
    ExposureTime,
  } = tags

  // photoObject.tags = tags
  photoObject.tags = {
    Model,
    DateTime,
    FNumber,
    ISOSpeedRatings,
    Description,
    FocalLengthIn35mmFilm,
    ShutterSpeedValue,
    ExposureTime,
  }
  if (tags['Caption/Abstract']) {
    photoObject.caption = tags['Caption/Abstract'].description
  }

  photoObject.date = exifDate(tags['DateTime'].value[0])
  photoObject.tags.date = photoObject.date
  photoObject.path = filePath

  photoFolderObject.files.push(photoObject)
 }
	'use strict'
	require('dotenv').config()

	const path = require('path')
	const fs = require('fs')
	const cloudinary = require('cloudinary').v2
	const ExifReader = require('exifreader')
	const exifErrors = ExifReader.errors
	const _ = require('lodash')
	const exifDate = require('exif-date').parse

	// HOW TO USE THIS SCRIPT
	// 1 - Set up Cloudinary, which is used to upload
	// the photos in the folder
	//
	// 2 - Get a bunch of photos, ideally from Lightroom
	// and with captions, and export them so their
	// max filesize is 10mb each, which is the max
	// filesize on Cloudinary's free plan
	//
	// 3 - Run `node folder-to-blogpost.js ~/photo-folder-path/ post-slug`

	/*
	This script is designed to automate the process of creating a blog post from a folder of images. It performs the following steps:

	1. The script first loads environment variables from a .env file using the dotenv package. These variables are used to configure the Cloudinary API, which is used to upload images.

	2. The script expects the path to a folder of images and a post slug as command-line arguments. If no post slug is provided, the name of the folder is used as the slug.

	3. The script reads the contents of the specified folder. It ignores any subdirectories and any files that are not JPEG images.

	4. For each JPEG image, the script extracts EXIF data using the exifreader package. This data includes the model of the camera, the date and time the photo was taken, the aperture and shutter speed, and any description or caption embedded in the image. The script also uses the exif-date package to parse the date and time from the EXIF data.

	5. The script then uploads each image to Cloudinary using the cloudinary package. The URL of the uploaded image is added to the data for that image.

	6. Once all images have been processed and uploaded, the script generates a Markdown file for the blog post. Each image is included in the post using Markdown's image syntax, and the caption (if any) is included as plain text underneath the image.

	7. The script writes the Markdown for the blog post to a .md file in the 'content/photos/' directory. The name of the file is the post slug provided as a command-line argument (or the name of the folder if no slug was provided).

	8. The script also writes a .json file containing the data for each image, including the local and Cloudinary URLs and the EXIF data. This file can be used for additional processing or for debugging.

	9. Finally, the script exits. If any errors occur during the execution of the script (such as failing to read the folder or upload an image), the script logs the error and exits immediately.
	*/

	cloudinary.config({
	cloud_name: process.env.CLOUDINARY_CLOUD_NAME,
	api_key: process.env.CLOUDINARY_API_KEY,
	api_secret: process.env.CLOUDINARY_API_SECRET,
	})

	const photoFolderObject = {
	files: [],
	}

	if (process.argv.length < 3) {
	console.log('No arguments')
	process.exit()
	}

	// Okay, we are gonna pass in a folder path via CLI args
	//
	//const filePath = process.argv[2]
	const folderPath = process.argv[2]
	const splitPath = folderPath.split('/')
	console.log('split path: ' + JSON.stringify(splitPath))
	const folder = splitPath[splitPath.length - 2]

	if (!process.argv[3]) {
	console.log(`No name provided, using the folder name: ${folder}`)
	photoFolderObject.title = folder
	photoFolderObject.slug = folder
	} else {
	photoFolderObject.title = process.argv[3]
	photoFolderObject.slug = process.argv[3]
	}

	// Gonna read through every file in that folder
	fs.readdir(folderPath, function (err, files) {
	if (err) {
	console.error('Error', err)
	process.exit(1)
	}

	files.forEach(function (file, i) {
	const filePath = path.join(folderPath, file)
	const fileType = filePath.split('.')[1]
	const stat = fs.statSync(filePath)
	if (stat && stat.isDirectory()) {
	// ignore
	} else if (fileType === 'jpg' \|\| fileType === 'jpeg') {
	processFile(filePath)
	} else {
	// Don't process non jpg files
	}
	})

	// Okay now we should have built an array of JSON objects
	// Each JSON object should have:
	// + image path
	// + EXIF data in an object

	// console.log(JSON.stringify(photoFolderObject))

	//
	// Now we want to actually take those .jpgs and upload them to cloudinary
	// And actually, also to S3 if we could, and maybe some smaller versions to Dropbox
	photoFolderObject.files = uploadAllPhotosToCloudinary(
	photoFolderObject,
	function (newPhotoFolderObj) {
	console.log(newPhotoFolderObj)
	// let blogPostHtml = makeBlogPost(newPhotoFolderObj);
	// Write that html to a .json file with a bunch of cool stuff in it

	const photoPostDestination = 'content/photos/'

	newPhotoFolderObj.date = newPhotoFolderObj.files[0].date

	fs.writeFileSync(
	photoPostDestination + folder + '.json',
	JSON.stringify(photoFolderObject, null, '\t'),
	)

	const blogPostMarkdown = makeBlogPost(newPhotoFolderObj)
	fs.writeFileSync(
	photoPostDestination + folder + '.md',
	blogPostMarkdown,
	)

	process.exit(0)
	},
	)
	// Then we get our new uploaded URLs and put them back in the image objects
	// So now we have a local path and an uploaded path and EXIF data
	//
	// Now we need to make a blogpost style .json that has all of that stuff laid out
	// It's probably gonna need to generate HTML now that I think about it
	//
	// Then put that in /content/photo/ as a .json file and we are golden

	// console.log(photoFolderObject)
	})

	function makeBlogPost(photoFolderObject) {
	let postMarkdown = ''
	_.each(photoFolderObject.files, (p) => {
	let photoMarkdown = ''
	photoMarkdown += `![Image](${p.cloudinaryUrl})\n`
	if (p.caption) {
	photoMarkdown += `${p.caption}\n`
	}
	postMarkdown += photoMarkdown
	})
	return postMarkdown
	}

	function uploadAllPhotosToCloudinary(photoFolderObject, cb) {
	let uploadPromises = photoFolderObject.files.map((file, i) => {
	console.log('Uploading ', file.path)
	return new Promise((resolve, reject) => {
	cloudinary.uploader.upload(file.path, (err, res) => {
	if (err) {
	console.log('Error uploading: ', err)
	reject(err)
	} else {
	photoFolderObject.files[i].cloudinaryUrl = res.url
	resolve()
	}
	})
	})
	})

	Promise.all(uploadPromises)
	.then(() => {
	console.log(`Successfully uploaded ${photoFolderObject.files.length} files`)
	cb(photoFolderObject)
	})
	.catch(err => {
	console.log('Error uploading files: ', err)
	})

	return photoFolderObject.files
	}

	// Then run that file through the EXIF reader and extract EXIF data
	function processFile(filePath) {
	// console.log('Processing file: ', filePath)
	const fileData = fs.readFileSync(filePath)

	const tags = ExifReader.load(fileData.buffer)

	delete tags['MakerNote']

	// Mostly we want the date/time of capture
	// And also the GPS latitude and longitude, if they exist
	// Oh and also the aperture and shutter speed
	//
	// Almost forgot the most important part: the caption
	let photoObject = {}

	const {
	Model,
	DateTime,
	FNumber,
	ISOSpeedRatings,
	Description,
	FocalLengthIn35mmFilm,
	ShutterSpeedValue,
	ExposureTime,
	} = tags

	// photoObject.tags = tags
	photoObject.tags = {
	Model,
	DateTime,
	FNumber,
	ISOSpeedRatings,
	Description,
	FocalLengthIn35mmFilm,
	ShutterSpeedValue,
	ExposureTime,
	}
	if (tags['Caption/Abstract']) {
	photoObject.caption = tags['Caption/Abstract'].description
	}

	photoObject.date = exifDate(tags['DateTime'].value[0])
	photoObject.tags.date = photoObject.date
	photoObject.path = filePath

	photoFolderObject.files.push(photoObject)
	}