I'd like to use PyMuPDF : I'd like to split a pdf, with for each splitted file, a file named with the name of the bookmark, with only page
I've succefully my files, for exemple 4 PDF files for a 4 pages PDF source.... but in the several pdf, I don't have one page but with a random number of page ?
import sys, fitz
file = '/home/ilyes/Bulletins_Originaux.pdf'
bookmark = ''
try:
doc = fitz.open(file)
toc = doc.getToC(simple = True)
except Exception as e:
print(e)
for i in range(len(toc)):
documentPdfCible=toc[i][1]
documentPdfCibleSansSlash=documentPdfCible.replace("/","-")
numeroPage=toc[i][2]
pagedebut=numeroPage
pagefin=numeroPage + 1
print (pagedebut)
print (pagefin)
doc2 = fitz.open(file)
doc2.insertPDF(doc, from_page = pagedebut, to_page = pagefin, start_at = 0)
doc2.save('/home/ilyes/' + documentPdfCibleSansSlash + ".pdf")
doc2.close
Could you tell me what's wrong ?
Maybee because I use always "doc2" in the loop ?
Thanks you,
Abou Ilyès
Seems weird, that you open the same document twice.
You open your pdf file at doc = fitz.open(file) and again at doc2 = fitz.open(file).
Then you insert pages into the same file by doc2.insertPDF(doc, from_page = pagedebut, to_page = pagefin, start_at = 0).
Of course the doc files toc will get messed up completely by "randomly" inserting pages.
I recommend to replace doc2 = fitz.open(file) with doc2 = fitz.open()
This will create an empty "in memory" pdf (see the documentation), in which you can then insert the pages you need from doc. Then save this as a new pdf by its bookmark title by running
doc2.save('/home/ilyes/' + documentPdfCibleSansSlash + ".pdf")
Related
I am trying to split a PDF file by finding a key word of text and then grabbing that page the key word is on and the following 4 pages after, so total of 5 pages, and splitting them from that original PDF and putting them into their own PDF so the new PDF will have those 5 pages only, then loop through again find that key text again because its repeated further down the original PDF X amount of times, grabbing that page plus the 4 after and putting into its own PDF.
Example: key word is found on page 7 the first loop so need page 7 and also pages 8-11 and put those 5 pages 7-11 into a pdf file,
the next loop they key word is found on page 12 so need page 12 and pages 13-16 so pages 12-16 split onto their own pdf at this point it has created 2 separate pdfs
the below code finds the key word and puts it into its own pdf file but only got it for that one page not sure how to include the range
import os
from PyPDF2 import PdfFileReader, PdfFileWriter
path = "example.pdf"
fname = os.path.basename(path)
reader = PdfFileReader(path)
for page_number in range(reader.getNumPages()):
writer = PdfFileWriter()
writer.addPage(reader.getPage(page_number))
text = reader.getPage(page_number).extractText()
text_stripped = text.replace("\n", "")
print(text_stripped)
if text_stripped.find("Disregarded Branch") != (-1):
output_filename = f"{fname}_page_{page_number + 1}.pdf"
with open(output_filename, "wb") as out:
writer.write(out)
print(f"Created: {output_filename}")
disclaimer: I am the author of borb, the library used in this answer.
I think your question comes down to 2 common functionalities:
find the location of a given piece of text
merge/split/extract pages from a PDF
For the first part, there is a good tutorial in the examples repo.
You can find it here. I'll repeat one of the examples here for completeness.
import typing
from borb.pdf.document.document import Document
from borb.pdf.pdf import PDF
from borb.toolkit.text.simple_text_extraction import SimpleTextExtraction
def main():
# read the Document
doc: typing.Optional[Document] = None
l: SimpleTextExtraction = SimpleTextExtraction()
with open("output.pdf", "rb") as in_file_handle:
doc = PDF.loads(in_file_handle, [l])
# check whether we have read a Document
assert doc is not None
# print the text on the first Page
print(l.get_text_for_page(0))
if __name__ == "__main__":
main()
This example extracts all the text from page 0 of the PDF. of course you could simply iterate over all pages, and check whether a given page contains the keyword you're looking for.
For the second part, you can find a good example in the examples repository. This is the link. This example (and subsequent example) takes you through the basics of frankensteining a PDF from various sources.
The example I copy/paste here will show you how to build a PDF by alternatively picking a page from input document 1, and input document 2.
import typing
from borb.pdf.document.document import Document
from borb.pdf.pdf import PDF
import typing
from decimal import Decimal
from borb.pdf.document.document import Document
from borb.pdf.page.page import Page
from borb.pdf.pdf import PDF
def main():
# open doc_001
doc_001: typing.Optional[Document] = Document()
with open("output_001.pdf", "rb") as pdf_file_handle:
doc_001 = PDF.loads(pdf_file_handle)
# open doc_002
doc_002: typing.Optional[Document] = Document()
with open("output_002.pdf", "rb") as pdf_file_handle:
doc_002 = PDF.loads(pdf_file_handle)
# create new document
d: Document = Document()
for i in range(0, 10):
p: typing.Optional[Page] = None
if i % 2 == 0:
p = doc_001.get_page(i)
else:
p = doc_002.get_page(i)
d.append_page(p)
# write
with open("output_003.pdf", "wb") as pdf_file_handle:
PDF.dumps(pdf_file_handle, d)
if __name__ == "__main__":
main()
You've almost got it!
import os
from PyPDF2 import PdfFileReader, PdfFileWriter
def create_4page_pdf(base_pdf_path, start):
reader = PdfFileReader(base_pdf_path)
writer = PdfFileWriter()
for i in range(4):
index = start + i
if index < len(reader.pages):
page = reader.pages[index]
writer.addPage(page)
fname = os.path.basename(base_pdf_path)
output_filename = f"{fname}_page_{start + 1}.pdf"
with open(output_filename, "wb") as out:
writer.write(out)
print(f"Created: {output_filename}")
def main(base_pdf_path="example.pdf"):
base_pdf_path = "example.pdf"
reader = PdfFileReader(base_pdf_path)
for page_number, page in enumerate(reader.pages):
text = page.extractText()
text_stripped = text.replace("\n", "")
print(text_stripped)
if text_stripped.find("Disregarded Branch") != (-1):
create_4page_pdf(base_pdf_path, page_number)
I am trying to merge two pdf's together. The other pdf is table of contents that I create manually using fpdf that links to specific pages. The other pdf is the text where the table of content links to.
from PyPDF2.merger imort PdfFileMerger
merger = PdfFileMerger()
merger.append("toc.pdf")
merger.append("temp.pdf")
merger.write("combined.pdf")
But I get the following error:
PdfReadWarning: Object 19 0 not defined. [pdf.py:1628]
Traceback (most recent call last):
...
...
raise utils.PdfReadError("Could not find object.")
PyPDF2.utils.PdfReadError: Could not find object.
I think the error comes as I have hyperlinks that point to nothing as the pages are not created. If I create the table of contents without the hyperlinks merging works correctly. Is there any way I can merge the files so that I preserve the hyperlinks?
To clarify: I believe that I can't add the content pdf's from the start to the table of contents as pyfpdf doesn't seem to have support for adding pdf files together.
Edit: more code
merger = PdfFileMerger()
pages = []
chapters = []
for file in pdfs:
read_pdf = PdfFileReader(file)
txt = read_pdf.getPage(0)
page_content = txt.extractText()
chapter = helper_functions.get_chapter_from_pdf_txt(page_content)
pages.append(read_pdf.getNumPages())
chapters.append(chapter)
merger.append(fileobj=file)
merger.write("temp.pdf")
pdfs.append("temp.pdf")
merger.close()
num_pages = sum(pages)
toc_len = 0
if toc_orientation == "P":
toc_len = math.ceil(len(pages) / 27)
if toc_orientation == "L":
toc_len = math.ceil(len(pages) / 17)
print(num_pages)
print(toc_len)
### Creating toc
toc = compile_toc(chapters, pages, orientation=toc_orientation)
pdf = PDF()
pdf.set_title("")
pdf.table_of_contents(toc, orientation=toc_orientation, create_hyperlink=True)
pdf.output("toc.pdf", 'F')
pdf.close()
time.sleep(2)
merger = PdfFileMerger()
merger.append(PdfFileReader(open("toc.pdf", 'rb')))
merger.append(PdfFileReader(open("temp.pdf", 'rb')))
merger.write("combined.pdf")
´´´
I have a conversion script, which converts pdf files and image files to text files. But it takes forever to run my script. It took me almost 48 hours to finished 2000 pdf documents. Right now, I have a pool of documents (around 12000+) that I need to convert. Based on my previous rate, I can't imagine how long will it take to finish the conversion using my code. I am wondering is there anything I can do/change with my code to make it run faster?
Here is the code that I used.
def tesseractOCR_pdf(pdf):
filePath = pdf
pages = convert_from_path(filePath, 500)
# Counter to store images of each page of PDF to image
image_counter = 1
# Iterate through all the pages stored above
for page in pages:
# Declaring filename for each page of PDF as JPG
# For each page, filename will be:
# PDF page 1 -> page_1.jpg
# PDF page 2 -> page_2.jpg
# PDF page 3 -> page_3.jpg
# ....
# PDF page n -> page_n.jpg
filename = "page_"+str(image_counter)+".jpg"
# Save the image of the page in system
page.save(filename, 'JPEG')
# Increment the counter to update filename
image_counter = image_counter + 1
# Variable to get count of total number of pages
filelimit = image_counter-1
# Create an empty string for stroing purposes
text = ""
# Iterate from 1 to total number of pages
for i in range(1, filelimit + 1):
# Set filename to recognize text from
# Again, these files will be:
# page_1.jpg
# page_2.jpg
# ....
# page_n.jpg
filename = "page_"+str(i)+".jpg"
# Recognize the text as string in image using pytesserct
text += str(((pytesseract.image_to_string(Image.open(filename)))))
text = text.replace('-\n', '')
#Delete all the jpg files that created from above
for i in glob.glob("*.jpg"):
os.remove(i)
return text
def tesseractOCR_img(img):
filePath = img
text = str(pytesseract.image_to_string(filePath,lang='eng',config='--psm 6'))
text = text.replace('-\n', '')
return text
def Tesseract_ALL(docDir, txtDir, troubleDir):
if docDir == "": docDir = os.getcwd() + "\\" #if no docDir passed in
for doc in os.listdir(docDir): #iterate through docs in doc directory
try:
fileExtension = doc.split(".")[-1]
if fileExtension == "pdf":
pdfFilename = docDir + doc
text = tesseractOCR_pdf(pdfFilename) #get string of text content of pdf
textFilename = txtDir + doc + ".txt"
textFile = open(textFilename, "w") #make text file
textFile.write(text) #write text to text file
else:
# elif (fileExtension == "tif") | (fileExtension == "tiff") | (fileExtension == "jpg"):
imgFilename = docDir + doc
text = tesseractOCR_img(imgFilename) #get string of text content of img
textFilename = txtDir + doc + ".txt"
textFile = open(textFilename, "w") #make text file
textFile.write(text) #write text to text file
except:
print("Error in file: "+ str(doc))
shutil.move(os.path.join(docDir, doc), troubleDir)
for filename in os.listdir(txtDir):
fileExtension = filename.split(".")[-2]
if fileExtension == "pdf":
os.rename(txtDir + filename, txtDir + filename.replace('.pdf', ''))
elif fileExtension == "tif":
os.rename(txtDir + filename, txtDir + filename.replace('.tif', ''))
elif fileExtension == "tiff":
os.rename(txtDir + filename, txtDir + filename.replace('.tiff', ''))
elif fileExtension == "jpg":
os.rename(txtDir + filename, txtDir + filename.replace('.jpg', ''))
docDir = "/drive/codingstark/Project/pdf/"
txtDir = "/drive/codingstark/Project/txt/"
troubleDir = "/drive/codingstark/Project/trouble_pdf/"
Tesseract_ALL(docDir, txtDir, troubleDir)
Does anyone know how can I edit my code to make it run faster?
I think a process pool would be perfect for your case.
First you need to figure out parts of your code that can run independent of each other, than you wrap it into a function.
Here is an example
from concurrent.futures import ProcessPoolExecutor
def do_some_OCR(filename):
pass
with ProcessPoolExecutor() as executor:
for file in range(file_list):
_ = executor.submit(do_some_OCR, file)
The code above will open a new process for each file and start processing things in parallel.
You can find the oficinal documentation here: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor
There is also an really awesome video that shows step-by-step how to use processes for exactly this: https://www.youtube.com/watch?v=fKl2JW_qrso
Here is a compact version of the function removing the file write stuff. I think this should work based on what I was reading on the APIs but I haven't tested this.
Note that I changed from string to list because adding to a list is MUCH less costly than appending to a string (See this about join vs concatenation
How slow is Python's string concatenation vs. str.join?) TLDR is that string concat makes a new string every time you are concatenating so with large strings you start having to copy many times.
Also, when you were calling replace each iteration on the string after concatenation, it was doing again creating a new string. So I moved that to operate on each string that is generated. Note that if for some reason that string '-\n' is an artifact that occured due to the concatenation previously, then it should be removed from where it is and placed here: return ''.join(pageText).replace('-\n','') but realize putting it there will be creating a new string with the join, then creating a whole new string from the replace.
def tesseractOCR_pdf(pdf):
pages = convert_from_path(pdf, 500)
# Counter to store images of each page of PDF to image
# Create an empty list for storing purposes
pageText = []
# Iterate through all the pages stored above will be a PIL Image
for page in pages:
# Recognize the text as string in image using pytesserct
# Add the text to a list while removing the -\n characters.
pageText.append(str(pytesseract.image_to_string(page)).replace('-\n',''))
return ''.join(pageText)
An even more compact one-liner version
def tesseractOCR_pdf(pdf):
#This takes each page of the pdf, extracts the text, removing -\n and combines the text.
return ''.join([str(pytesseract.image_to_string(page)).replace('-\n', '') for page in convert_from_path(pdf, 500)])
I have a list of pdf files and I need to highlight specific text on each page of these files and save a snapshot for each of the text instances.
So far I am able to highlight the text and save the entire page of a pdf file as a snapshot. But, I want to find the position of highlighted text and take a zoomed in the snapshot which will be more detailed compared to the full page snapshot.
I'm pretty sure there must be a solution to this problem. I am new to Python and hence I am not able to find it. I would be really grateful if someone can help me out with this.
I have tried using PyPDF2, Pymupdf libraries but I couldn't figure out the solution. I also tried highlighting by providing coordinates which works but couldn't find a way to get these coordinates as output.
[![Sample snapshot from the code[![\]\[1\]][1]][1]][1]
#import PyPDF2
import os
import fitz
from wand.image import Image
import csv
#import re
#from pdf2image import convert_from_path
check = r'C:\Users\Pradyumna.M\Desktop\Pradyumna\Automation\Intel Bytes\Create Source Docs\Sample Check 8 Apr 2019'
dir1 = check + '\\Source Docs\\'
dir2 = check + '\\Output\\'
dir = [dir1, dir2]
for x in dir:
try:
os.mkdir(x)
except FileExistsError:
print("Directory ", x, " already exists")
### READ PDF FILE
with open('upload1.csv', newline='') as myfile:
reader = csv.reader(myfile)
for row in reader:
rowarray = '; '.join(row)
src = rowarray.split("; ")
file = check + '\\' + src[4] + '.pdf'
print(file)
#pdfFileObj = open(file,'rb')
#pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
#print("Total number of pages: " + str(pdfReader.numPages))
doc = fitz.open(file)
print(src[5])
for i in range(int(src[5])-1, int(src[5])):
i = int(i)
page = doc[i]
print("Processing page: " + str(i))
text = src[3]
#SEARCH TEXT
print("Searching: " + text)
text_instances = page.searchFor(text)
for inst in text_instances:
highlight = page.addHighlightAnnot(inst)
file1 = check + '\\Output\\' + src[4] + '_output.pdf'
print(file1)
doc.save(file1, garbage=4, deflate=True, clean=True)
### Screenshot
with(Image(filename=file1, resolution=150)) as source:
images = source.sequence
newfilename = check + "\\Source Docs\\" + src[0] + '.jpeg'
Image(images[i]).save(filename=newfilename)
print("Screenshot of " + src[0] + " saved")
"couldn't find a way to get these coordinates as output"
- you can get the coordinates out by doing this:
for inst in text_instances:
print(inst)
inst are fitz.Rect objects which contain the top left and bottom right coordinates of the piece of text that was found. All the information is available in the docs.
I managed to highlight points and also save a cropped region using the following snippet of code. I am using python 3.7.1 and my output for fitz.version is ('1.14.13', '1.14.0', '20190407064320').
import fitz
doc = fitz.open("foo.pdf")
inst_counter = 0
for pi in range(doc.pageCount):
page = doc[pi]
text = "hello"
text_instances = page.searchFor(text)
five_percent_height = (page.rect.br.y - page.rect.tl.y)*0.05
for inst in text_instances:
inst_counter += 1
highlight = page.addHighlightAnnot(inst)
# define a suitable cropping box which spans the whole page
# and adds padding around the highlighted text
tl_pt = fitz.Point(page.rect.tl.x, max(page.rect.tl.y, inst.tl.y - five_percent_height))
br_pt = fitz.Point(page.rect.br.x, min(page.rect.br.y, inst.br.y + five_percent_height))
hl_clip = fitz.Rect(tl_pt, br_pt)
zoom_mat = fitz.Matrix(2, 2)
pix = page.getPixmap(matrix=zoom_mat, clip = hl_clip)
pix.writePNG(f"pg{pi}-hl{inst_counter}.png")
doc.close()
I tested this on a sample pdf that i peppered with "hello":
Some of the outputs from the script:
I composed the solution out of the following pages of the documentation:
Tutorial page to get introduced into the library
page.searchFor to figure out the return type of the searchFor method
fitz.Rect to understand what the returned objects from page.searchFor are
Collection of Recipes page (called faq in the URL) to figure out how to crop and save part of a pdf page
I am trying to copy elements of a doc from one doc file to other. The text part is easy, the images is where it gets tricky.
Attaching an image to explain the structure of the doc: Just some text and 1 image.
from docx import Document
import io
doc = Document('/Users/neha/Desktop/testing.docx')
new_doc = Document()
for elem in doc.element.body:
new_doc.element.body.append(elem)
new_doc.save('/Users/neha/Desktop/out.docx')
This gets me the whole structure of the doc in the new_doc but the image is still blank. Image below:
Good thing is I have the blank image in the right place so I thought of getting the byte level data from the previous image and insert it in the new doc. Here is how I extended the above code:
from docx import Document
import io
doc = Document('/Users/neha/Desktop/testing.docx')
new_doc = Document()
for elem in doc.element.body:
new_doc.element.body.append(elem)
im = doc.inline_shapes[0]
blip = im._inline.graphic.graphicData.pic.blipFill.blip
rId = blip.embed
doc_part = doc.part
image_part = doc_part.related_parts[rId]
bytes = image_part._blob #Here I get the byte level data for the image
im2 = new_doc.inline_shapes[0]
blip2 = im2._inline.graphic.graphicData.pic.blipFill.blip
rId2 = blip2.embed
document_part2 = new_doc.part
document_part2.related_parts[rId2]._blob = bytes
new_doc.save('/Users/neha/Desktop/out.docx')
But the image still shows empty in the new_doc. What should I do from here?
I figured out a solution a couple of days back. However the text loses formatting using this way, but the images are correctly placed.
So the idea is, for para in paras for the source doc, if there is text, I write it to dest doc. And if there is an inline image present, I add a unique identifier at that place in the dest doc (refer here to see how these identifiers work, and contexts in docxtpl). These identifiers and docxtpl proved to be particularly useful here. And then using those unique identifiers I create a 'context' (as shown below) which is basically a map mapping the unique identifier to its particular InlineImage, and finally I render this context..
Below is my code (Apologies for the unnecessary indentation, I copied it directly from my text editor, and shift+tab doesn't work here :P)
from docxtpl import DocxTemplate, InlineImage
import Document
import io
import xml.etree.ElementTree as ET
dest = DocxTemplate()
source = Document(source_path)
context = {}
ims = [im for im in source.inline_shapes]
im_addresses = []
im_streams = []
count = 0
for im in ims:
blip = im._inline.graphic.graphicData.pic.blipFill.blip
rId = blip.embed
doc_part = source.part
image_part = doc_part.related_parts[rId]
byte_data = image_part._blob
image_stream = io.BytesIO(byte_data)
im_streams.append(image_stream)
image_name = self.img_path+"img_"+"_"+str(count)+".jpeg"
with open(image_name, "wb") as fh:
fh.write(byte_data)
fh.close()
im_addresses.append(image_name)
count += 1
paras = source.paragraphs
im_idx = 0
for para in paras:
p = dest.add_paragraph()
r = p.add_run()
if(para.text):
r.add_text(para.text)
root = ET.fromstring(para._p.xml)
namespace = {'wp':"http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing"}
inlines = root.findall('.//wp:inline',namespace)
if(len(inlines) > 0):
uid = "img_"+str(im_idx)
r.add_text("{{ " + uid + " }}")
context[uid] = InlineImage(dest,im_addresses[im_idx])
im_idx += 1
try:
dest.render(context)
except Exception as e:
print(e)
dest.save(dest_path)
PS: If a paragraph has two images, this code will prove to be sub-optimal.. One will have to make some change in the following:
if(len(inlines) > 0):
uid = "img_"+str(im_idx)
r.add_text("{{ " + uid + " }}")
context[uid] = InlineImage(dest,im_addresses[im_idx])
im_idx += 1
Will have to add a for loop inside the if statement as well. Since I didn't need as usually my images were big enough, so they always came in different paragraphs. Just a side note for anyone who may need it..
Cheers!
You could try:
Extracting the images from the first document by unzipping the .docx file (per How can I search a word in a Word 2007 .docx file?)
Save those images to the file system (as foo.png, for instance)
Generate the new .docx file with Python and add the .png file using document.add_picture('foo.png').
This problem is solved by this package https://docxtpl.readthedocs.io/en/latest/