109 lines
3.7 KiB
R
109 lines
3.7 KiB
R
# A script to use Google Cloud Vision to OCR/parse/mangle Collections label-images
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# Note!
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# - this may take >30 seconds per label-image
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# - running >1000 API calls/month incurs a fee
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# (c) 2019 The Field Museum - MIT License (https://opensource.org/licenses/MIT)
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# https://github.com/fieldmuseum/Collections-OCR
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library(googleCloudVisionR) # NOTE - requires API Key / Service Account
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library(tidyr)
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library(readr)
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library(stringr)
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# library(magick)
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# get list of local JPG & JPEG image files [REVERT]
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imagelist <- list.files(path = "images/", pattern = ".jp|.JP")
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imagenames <- gsub(".jp.*|.JP.*", "", imagelist)
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# # Prompt user for input/output batch directory names?
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# image_dir <- readline("Paste the path for the image directory: ")
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# Retrieve OCR text ####
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# Setup table for OCRed text
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imagesOCR <- data.frame("image" = rep("", NROW(imagelist)),
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"imagesize_MB" = rep("", NROW(imagelist)),
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"ocr_start" = rep("", NROW(imagelist)),
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"ocr_duration" = rep("", NROW(imagelist)),
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"line_count" = rep("", NROW(imagelist)),
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"text" = rep("", NROW(imagelist)),
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stringsAsFactors = F)
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imagesOCR$line_count <- as.integer(imagesOCR$line_count)
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# setup output dir
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# # add image_dir if use prompt above
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if (!dir.exists("ocr_text")) { # paste0(image_dir, "_out")
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dir.create("ocr_text") # paste0(image_dir, "_out")
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print("output directory created")
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} else {
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print("output directory already exists")
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}
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# Loop through each label-image
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for (i in 1:NROW(imagelist)) {
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# # If files are over 20MB, uncomment this to lower quality + avoid error?
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# ### NOTE! This will overwrite image with lower-quality file.
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#
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# if (file.info(paste0("images/", imagelist[i]))$size > 20000000) {
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# image_write(image_read(paste0("images/", imagelist[i])),
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# path = paste0("images/", imagelist[i]),
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# quality = 80)
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# OCR image
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# ### NOTE! This can take over ~30s per image
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print(paste(i, "- starting OCR -", Sys.time()))
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imagesOCR$ocr_start[i] <- as.character(Sys.time())
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start <- Sys.time()
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ocr_list <- gcv_get_image_annotations(imagePaths = paste0("images/", imagelist[i]),
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feature = "DOCUMENT_TEXT_DETECTION") #,
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# savePath = paste0("ocr_text/",
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# imagenames[i], "_text.csv"))
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print(paste(i, "- finishing OCR -", Sys.time()))
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end <- Sys.time()
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# Add raw text to dataframe
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imagesOCR$text[i] <- ocr_list$description
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# Add OCR duration (in seconds), & text-lines per image, filename, filesize (in MB)
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imagesOCR$ocr_duration[i] <- as.integer(end) - as.integer(start)
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imagesOCR$line_count[i] <- str_count(ocr_list$description, "\n+")
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imagesOCR$image[i] <- imagelist[i]
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imagesOCR$imagesize_MB[i] <- round(file.info(paste0("images/",
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imagelist[i]))$size
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/ 1000000, 2)
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# show progress
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print(paste(i, "- done -", Sys.time()))
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# rate limit to max of 240/min (Vision API limit = 1800/min)
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Sys.sleep(0.25)
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}
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# split text lines to separate columns
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ocrText <- separate(imagesOCR, text,
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into = paste0("Line",
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seq(1:max(imagesOCR$line_count, na.rm = T))),
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# into = seq(1:20), # if need consistent NCOL
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sep = "(\n)+",
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extra = "merge", fill = "right")
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# export CSV
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write.csv(ocrText,
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paste0("ocrText-",
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gsub("\\s+|:", "", Sys.time()),
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# image_dir,
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".csv"),
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na = "",
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row.names = F) |