My project involves reading text from a bunch of PDF form files for which I'm using PyPDF2 open source library. There is no issue in getting the text data as follows:
reader = PdfReader("data/test.pdf")
cnt = len(reader.pages)
print("reading pdf (%d pages)" % cnt)
page = reader.pages[cnt-1]
lines = page.extract_text().splitlines()
print("%d lines extracted..." % len(lines))
However, this text doesn't contain the checked statuses of the radio and checkboxes. I just get normal text (like Yes No or Check-1 Check-2 Check-3 for example) instead of these values.
I also tried the reader.get_fields() and reader.get_form_text_fields() methods as described in their documentation but they return empty values. I also tried reading it through annotations but no "/Annots" found on the page. When I open the PDF in a notepad++ to see its meta data, this is what I get:
%PDF-1.4
%²³´µ
%Generated by ExpertPdf v9.2.2
It appears to me that these checkboxes aren't usual form fields used in PDF but appear similar to HTML elements. Is there any way to extract these fields using python?
Finally, I've also tried pdfminer.six, the other popular pdf library for python with same results.
It seems like the PDF you have contains XFA forms, which are not supported by the pdfminer.six library.
You may try this tool to extract the XFA forms.
If your PDF does not contain XFA forms, you can extract the form field and value data using the following snippet from pdfminer.six docs
from pdfminer.pdfparser import PDFParser
from pdfminer.pdfdocument import PDFDocument
from pdfminer.pdftypes import resolve1
from pdfminer.psparser import PSLiteral, PSKeyword
from pdfminer.utils import decode_text
data = {}
def decode_value(value):
# decode PSLiteral, PSKeyword
if isinstance(value, (PSLiteral, PSKeyword)):
value = value.name
# decode bytes
if isinstance(value, bytes):
value = decode_text(value)
return value
with open(file_path, 'rb') as fp:
parser = PDFParser(fp)
doc = PDFDocument(parser)
res = resolve1(doc.catalog)
if 'AcroForm' not in res:
raise ValueError("No AcroForm Found")
fields = resolve1(doc.catalog['AcroForm'])['Fields'] # may need further resolving
for f in fields:
field = resolve1(f)
name, values = field.get('T'), field.get('V')
# decode name
name = decode_text(name)
# resolve indirect obj
values = resolve1(values)
# decode value(s)
if isinstance(values, list):
values = [decode_value(v) for v in values]
else:
values = decode_value(values)
data.update({name: values})
print(name, values)
If the above code throws No AcroForm found error, that means your PDF most probably contains XFA forms.
Related
I need to process some PDF files and add their form field contents in a database.
This document has not Security Method set, as I can see in the PDF Viewer document properties.
I tried the suggestions I found here.
When I test using pdfminer (or pdfminer.six), I didn't get an error message, but it didn't retrieve any field.
Using PyPDF2, I get the error message: "file has not been decrypted."
This is the pdfminer code:
import sys
from pdfminer.pdfparser import PDFParser
from pdfminer.pdfdocument import PDFDocument
from pdfminer.pdftypes import resolve1
fname=r'D:\Atrium\Projects\CTFC\psgf\database\19022021\formulari-dinamic-redaccio-plans-simples-gestio-forestal_Filled.pdf'
fp = open(fname, 'rb')
parser = PDFParser(fp)
doc = PDFDocument(parser)
fields = resolve1(doc.catalog['AcroForm'])['Fields']
for i in fields:
field = resolve1(i)
name, value = field.get('T'), field.get('V')
print('{0}: {1}'.format(name, value))
print('Done!')
A sample file can be download here.
How can I do to obtain field names and content?
As mkl explained, my PDF files store form data in XFA form, a deprecated format. The XFA is an array of XML docs and I have to procure field names in each one of these docs.
I used PyPDF2 library to do that:
import PyPDF2 as pypdf
import xml.etree.ElementTree as ET
fname=r'form.pdf'
def findInDict(needle, haystack):
xlas = []
for key in haystack.keys():
try:
value=haystack[key]
except:
continue
if key==needle:
return value
if isinstance(value,dict):
x=findInDict(needle,value)
if x is not None:
return x
pdfobject=open(fname,'rb')
pdf=pypdf.PdfFileReader(pdfobject)
xfaparts=findInDict('/XFA',pdf.resolvedObjects)
for xfa in xfaparts:
if isinstance(xfa,pypdf.generic.IndirectObject):
xml = str(xfa.getObject().getData())
## Then process XML to find form tags
I need to read data from hundreds of PDF forms. These forms have all text entry boxes, the forms are not editable. I have been trying to use Python and PyPDF2 to read these forms to a CSV file (since the ultimate goal is an excel database.
I have tried using acrobats export as csv function, but this is extremely slow as each form has 4 embedded images that export as plaintext. I have the following code,
from PyPDF2 import PdfFileReader
infile = "FormSample.pdf"
pdf_reader = PdfFileReader(open(infile, "rb"))
with open('exportharvest.csv','w') as exportharvestcsv:
dictionary = pdf_reader.getFields(fileobj = exportharvestcsv)
textfields = pdf_reader.getFormTextFields()
dest = pdf_reader.getNamedDestinations()
print(dest)
The issue with the above code is as follows: the getFields command only gets the ~4 digital signature fields in the form (form has ~300 entries). Is there some way to instruct python to look through all the fields? I know the field names in the document as they are listed when I export to pdf.
getFormTextFields() returns a dictionary of {}
getNamedDestinations() returns a dictionary of {}
Thanks for any help.
From my experience pyPDF is slow as well.
this here should do what you want:
from PyPDF2 import PdfFileReader
from pprint import pprint
pdf_file_name = 'formdocument.pdf'
f = PdfFileReader(pdf_file_name)
fields = f.getFields()
fdfinfo = dict((k, v.get('/V', '')) for k, v in fields.items())
pprint(fdfinfo)
with open('test.csv', 'w') as f2:
for key in fdfinfo.keys():
if type(key)==type("string") and type(str(fdfinfo[key]))==type("string"):
f2.write('"'+key+'","'+fdfinfo[key]+'"\n')
I have to convert whole pdf to text. i have seen at many places converting pdf to text but particular page.
from PyPDF2 import PdfFileReader
import os
def text_extractor(path):
with open(os.path.join(path,file), 'rb') as f:
pdf = PdfFileReader(f)
###Here i can specify page but i need to convert whole pdf without specifying pages###
page = pdf.getPage(0)
text = page.extractText()
print(text)
if __name__ == '__main__':
path="C:\\Users\\AAAA\\Desktop\\BB"
for file in os.listdir(path):
if not file.endswith(".pdf"):
continue
text_extractor(path)
How to convert whole pdf file to text without using getpage()??
You may want to use textract as this answer recommends to get the full document if all you want is the text.
If you want to use PyPDF2 then you can first get the number of pages then iterate over each page such as:
from PyPDF2 import PdfFileReader
import os
def text_extractor(path):
with open(os.path.join(path,file), 'rb') as f:
pdf = PdfFileReader(f)
###Here i can specify page but i need to convert whole pdf without specifying pages###
text = ""
for page_num in range(pdf.getNumPages()):
page = pdf.getPage(page_num)
text += page.extractText()
print(text)
if __name__ == '__main__':
path="C:\\Users\\AAAA\\Desktop\\BB"
for file in os.listdir(path):
if not file.endswith(".pdf"):
continue
text_extractor(path)
Though you may want to remember which page the text came from in which case you could use a list:
page_text = []
for page_num in range(pdf.getNumPages()): # For each page
page = pdf.getPage(page_num) # Get that page's reference
page_text.append(page.extractText()) # Add that page to our array
for page in page_text:
print(page) # print each page
You could use tika to accomplish this task, but the output needs a little cleaning.
from tika import parser
parse_entire_pdf = parser.from_file('mypdf.pdf', xmlContent=True)
parse_entire_pdf = parse_entire_pdf['content']
print (parse_entire_pdf)
This answer uses PyPDF2 and encode('utf-8') to keep the output per page together.
from PyPDF2 import PdfFileReader
def pdf_text_extractor(path):
with open(path, 'rb') as f:
pdf = PdfFileReader(f)
# Get total pdf page number.
totalPageNumber = pdf.numPages
currentPageNumber = 0
while (currentPageNumber < totalPageNumber):
page = pdf.getPage(currentPageNumber)
text = page.extractText()
# The encoding put each page on a single line.
# type is <class 'bytes'>
print(text.encode('utf-8'))
#################################
# This outputs the text to a list,
# but it doesn't keep paragraphs
# together
#################################
# output = text.encode('utf-8')
# split = str(output, 'utf-8').split('\n')
# print (split)
#################################
# Process next page.
currentPageNumber += 1
path = 'mypdf.pdf'
pdf_text_extractor(path)
Try pdfreader. You can extract either plain text or decoded text containing "pdf markdown":
from pdfreader import SimplePDFViewer, PageDoesNotExist
fd = open(you_pdf_file_name, "rb")
viewer = SimplePDFViewer(fd)
plain_text = ""
pdf_markdown = ""
try:
while True:
viewer.render()
pdf_markdown += viewer.canvas.text_content
plain_text += "".join(viewer.canvas.strings)
viewer.next()
except PageDoesNotExist:
pass
PDF is a page-oriented format & therefore you'll need to deal with the concept of pages.
What makes it perhaps even more difficult, you're not guaranteed that the text excerpts you're able to extract are extracted in the same order as they are presented on the page: PDF allows one to say "put this text within a 4x3 box situated 1" from the top, with a 1" left margin.", and then I can put the next set of text somewhere else on the same page.
Your extractText() function simply gets the extracted text blocks in document order, not presentation order.
Tables are notoriously difficult to extract in a common, meaningful way... You see them as tables, PDF sees them as text blocks placed on the page with little or no relationship.
Still, getPage() and extractText() are good starting points & if you have simply formatted pages, they may work fine.
I found out a very simple way to do this.
You have to follow this steps:
Install PyPDF2 :To do this step if you use Anaconda, search for Anaconda Prompt and digit the following command, you need administrator permission to do this.
pip install PyPDF2
If you're not using Anaconda you have to install pip and put its path
to your cmd or terminal.
Python Code: This following code shows how to convert a pdf file very easily:
import PyPDF2
with open("pdf file path here",'rb') as file_obj:
pdf_reader = PyPDF2.PdfFileReader(file_obj)
raw = pdf_reader.getPage(0).extractText()
print(raw)
I just used pdftotext module to get this done easily.
import pdftotext
# Load your PDF
with open("test.pdf", "rb") as f:
pdf = pdftotext.PDF(f)
# creating a text file after iterating through all pages in the pdf
file = open("test.txt", "w")
for page in pdf:
file.write(page)
file.close()
Link: https://github.com/manojitballav/pdf-text
I have 5 image fields in my model , imageA, imageB, imageC, imageD and imageE
I am trying to save the images in the following manner.The image are of type Base64ImageField
images=["imageA","imageB","imageC","imageD","imageE"]
for field in images:
if field in serializer.validated_data:
content = serializer.validated_data[field]
dict = {field : content}
modelJob.objects.filter(id=modjob.id).update(**dict)
In the above code content contains the raw data.I am trying to update the image using the dict I created (the key is the field name and value is the content).
However the images saved in the imageField of the model are raw and not an actual image. How can I fix this ? This is what my serializer looks like
class Serializer_Custom_RX(serializers.ModelSerializer):
imageA = Base64ImageField(max_length=None, use_url=True, )
imageB = Base64ImageField(max_length=None, use_url=True, )
imageC = Base64ImageField(max_length=None, use_url=True, )
imageD = Base64ImageField(max_length=None, use_url=True, )
class Meta:
model = modelTest
fields = [
'title',
'zip',
'imageA','imageB','imageC','imageD',
]
More info:
If I do something like this
modelJob.instance.imageA.save(content=content,name="image.jpeg")
it works fine and the problem is solved.However there are two problems with this approach first of all I do not know the extension. How do I extract an extension ? I am just guessing a jpeg here and it works. The next thing is Ill have to check for imageA,B,C,D and E if they exist and then save each one individually. If I could come up with a dynamic solution close to something that I have that would work as well. This is what my jsondata looks like that I am posting
{
"title" : "Some Title",
"zip":12345,
"imageA":"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"
}
The images encoding should come from the client, this is how you can know which format of each one has. Example:
base_64 = "data:image/gif;base64,R0lGODlhPQBEAPeoAJosM//AwO/AwHVYZ/z595kzAP/s7P+goOXMv8+fhw/v739......
You will receive it from the client and you know that is a .gif
Once you have validated the extensions and the base64 you can convert it to images and save it in your OS:
Convert string in base64 to image and save on filesystem in Python or Decoding base64 from POST to use in PIL
Once you have the images in your OS, you can link them to your ImageField in the model changing the name property: Set Django's FileField to an existing file
I hope that is clear and helpful!!
Here is how I solved this problem. None of the answers above had this information
However there are two problems with this approach first of all I do
not know the extension.How do I extract an extension ?
The extension can be extracted by using the following code
from PIL import Image
decodedbytes = base64.decodebytes(str.encode(image_content))
image_stream = io.BytesIO(decodedbytes)
image = Image.open(image_stream)
filetype = image.format #Contains the extension
The next thing is Ill have to check for imageA,B,C,D and E if they
exist and then save each one individually. If I could come up with a
dynamic solution close to something that I have that would work as
well.
The solution to this was simple to use
getattr(job_inst, field).save(content=data , name="img"+filetype)
Short answer is :
import imghdr
extension = imghdr.what(file_name, decoded_file)
ref : https://docs.python.org/2/library/imghdr.html OR https://docs.python.org/3/library/imghdr.html
Basically import imghdr is the key in function Base64ImageField.get_file_extension to get / extract the extension of the function.
With below class extend / code you don't need to do modelJob.instance.imageA.save(content=content,name="image.jpeg")
You need to add this class in your codebase to call or for trial purpose you can add in same Serializer class file itself.
from django.core.files.base import ContentFile
import base64
import six
import uuid
class Base64ImageField(serializers.ImageField):
"""
A Django REST framework field for handling image-uploads through raw post data.
It uses base64 for encoding and decoding the contents of the file.
Heavily based on
https://github.com/tomchristie/django-rest-framework/pull/1268
Updated for Django REST framework 3.
"""
def to_internal_value(self, data):
# Check if this is a base64 string
if isinstance(data, six.string_types):
# Check if the base64 string is in the "data:" format
if 'data:' in data and ';base64,' in data:
# Break out the header from the base64 content
header, data = data.split(';base64,')
# Try to decode the file. Return validation error if it fails.
try:
decoded_file = base64.b64decode(data)
except TypeError:
self.fail('invalid_image')
# Generate file name:
file_name = str(uuid.uuid4())[:12] # 12 characters are more than enough.
# Get the file name extension:
file_extension = self.get_file_extension(file_name, decoded_file)
complete_file_name = "%s.%s" % (file_name, file_extension, )
data = ContentFile(decoded_file, name=complete_file_name)
return super(Base64ImageField, self).to_internal_value(data)
def get_file_extension(self, file_name, decoded_file):
import imghdr
extension = imghdr.what(file_name, decoded_file)
extension = "jpg" if extension == "jpeg" else extension
return extension
One more information is you can have
Base64ImageField(
max_length=None,
use_url=True,
required=False,
allow_null=True,
allow_empty_file=True
)
these params in case you want to make this optional.
NOTE :: I had got this code from StackOverflow only, but not remembered from where I got this I had liked this answer too.
When I print a PDF from any of my source PDFs, the file size drops and removes the text boxes presents in form. In short, it flattens the file.
This is behavior I want to achieve.
The following code to create a PDF using another PDF as a source (the one I want to flatten), it writes the text boxes form as well.
Can I get a PDF without the text boxes, flatten it? Just like Adobe does when I print a PDF as a PDF.
My other code looks something like this minus some things:
import os
import StringIO
from pyPdf import PdfFileWriter, PdfFileReader
from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter
directory = os.path.join(os.getcwd(), "source") # dir we are interested in
fif = [f for f in os.listdir(directory) if f[-3:] == 'pdf'] # get the PDFs
for i in fif:
packet = StringIO.StringIO()
can = canvas.Canvas(packet, pagesize=letter)
can.rotate(-90)
can.save()
packet.seek(0)
new_pdf = PdfFileReader(packet)
fname = os.path.join('source', i)
existing_pdf = PdfFileReader(file(fname, "rb"))
output = PdfFileWriter()
nump = existing_pdf.getNumPages()
page = existing_pdf.getPage(0)
for l in range(nump):
output.addPage(existing_pdf.getPage(l))
page.mergePage(new_pdf.getPage(0))
outputStream = file("out-"+i, "wb")
output.write(outputStream)
outputStream.close()
print fName + " written as", i
Summing up: I have a pdf, I add a text box to it, covering up info and adding new info, and then I print a pdf from that pdf. The text box becomes not editable or moveable any longer. I wanted to automate that process but everything I tried still allowed that text box to be editable.
If installing an OS package is an option, then you could use pdftk with its python wrapper pypdftk like this:
import pypdftk
pypdftk.fill_form('filled.pdf', out_file='flattened.pdf', flatten=True)
You would also need to install the pdftk package, which on Ubuntu could be done like this:
sudo apt-get install pdftk
The pypdftk library can by downloaded from PyPI:
pip install pypdftk
Update: pdftk was briefly removed from Ubuntu in version 18.04, but it seems it is back since 20.04.
A simple but more of a round about way it to covert the pdf to images than to put those image into a pdf.
You'll need pdf2image and PIL
Like So
from pdf2image import convert_from_path
from PIL import Image
images = convert_from_path('temp.pdf')
im1 = images[0]
images.pop(0)
pdf1_filename = "flattened.pdf"
im1.save(pdf1_filename, "PDF" ,resolution=100.0, save_all=True, append_images=images)
Edit:
I created a library to do this called fillpdf
pip install fillpdf
from fillpdf import fillpdfs
fillpdfs.flatten_pdf('input.pdf', 'newflat.pdf')
Per the Adobe Docs, you can change the Bit Position of the Editable Form Fields to 1 to make the field ReadOnly. I provided a full solution here, but it uses Django:
https://stackoverflow.com/a/55301804/8382028
Adobe Docs (page 552):
https://www.adobe.com/content/dam/acom/en/devnet/pdf/pdfs/pdf_reference_archives/PDFReference.pdf
Use PyPDF2 to fill the fields, then loop through the annotations to change the bit position:
from io import BytesIO
from PyPDF2 import PdfFileReader, PdfFileWriter
from PyPDF2.generic import BooleanObject, NameObject, NumberObject
# open the pdf
input_stream = open("YourPDF.pdf", "rb")
reader = PdfFileReader(input_stream, strict=False)
if "/AcroForm" in reader.trailer["/Root"]:
reader.trailer["/Root"]["/AcroForm"].update(
{NameObject("/NeedAppearances"): BooleanObject(True)}
)
writer = PdfFileWriter()
writer.set_need_appearances_writer()
if "/AcroForm" in writer._root_object:
# Acro form is form field, set needs appearances to fix printing issues
writer._root_object["/AcroForm"].update(
{NameObject("/NeedAppearances"): BooleanObject(True)}
)
data_dict = dict() # this is a dict of your form values
writer.addPage(reader.getPage(0))
page = writer.getPage(0)
# update form fields
writer.updatePageFormFieldValues(page, data_dict)
for j in range(0, len(page["/Annots"])):
writer_annot = page["/Annots"][j].getObject()
for field in data_dict:
if writer_annot.get("/T") == field:
# make ReadOnly:
writer_annot.update({NameObject("/Ff"): NumberObject(1)})
output_stream = BytesIO()
writer.write(output_stream)
# output_stream is your flattened PDF
A solution that goes for Windows as well, converts many pdf pages and flatens the chackbox values as well. For some reason #ViaTech code did not work in my pc (Windows7 python 3.8)
Followed #ViaTech indications and used extensively #hchillon code from this post
from PyPDF2 import PdfFileReader, PdfFileWriter
from PyPDF2.generic import BooleanObject, NameObject, IndirectObject, TextStringObject, NumberObject
def set_need_appearances_writer(writer):
try:
catalog = writer._root_object
# get the AcroForm tree and add "/NeedAppearances attribute
if "/AcroForm" not in catalog:
writer._root_object.update({
NameObject("/AcroForm"): IndirectObject(len(writer._objects), 0, writer)})
need_appearances = NameObject("/NeedAppearances")
writer._root_object["/AcroForm"][need_appearances] = BooleanObject(True)
return writer
except Exception as e:
print('set_need_appearances_writer() catch : ', repr(e))
return writer
class PdfFileFiller(object):
def __init__(self, infile):
self.pdf = PdfFileReader(open(infile, "rb"), strict=False)
if "/AcroForm" in self.pdf.trailer["/Root"]:
self.pdf.trailer["/Root"]["/AcroForm"].update(
{NameObject("/NeedAppearances"): BooleanObject(True)})
# newvals and newchecks have keys have to be filled. '' is not accepted
def update_form_values(self, outfile, newvals=None, newchecks=None):
self.pdf2 = MyPdfFileWriter()
trailer = self.pdf.trailer['/Root'].get('/AcroForm', None)
if trailer:
self.pdf2._root_object.update({
NameObject('/AcroForm'): trailer})
set_need_appearances_writer(self.pdf2)
if "/AcroForm" in self.pdf2._root_object:
self.pdf2._root_object["/AcroForm"].update(
{NameObject("/NeedAppearances"): BooleanObject(True)})
for i in range(self.pdf.getNumPages()):
self.pdf2.addPage(self.pdf.getPage(i))
self.pdf2.updatePageFormFieldValues(self.pdf2.getPage(i), newvals)
for j in range(0, len(self.pdf.getPage(i)['/Annots'])):
writer_annot = self.pdf.getPage(i)['/Annots'][j].getObject()
for field in newvals:
writer_annot.update({NameObject("/Ff"): NumberObject(1)})
self.pdf2.updatePageFormCheckboxValues(self.pdf2.getPage(i), newchecks)
with open(outfile, 'wb') as out:
self.pdf2.write(out)
class MyPdfFileWriter(PdfFileWriter):
def __init__(self):
super().__init__()
def updatePageFormCheckboxValues(self, page, fields):
for j in range(0, len(page['/Annots'])):
writer_annot = page['/Annots'][j].getObject()
for field in fields:
writer_annot.update({NameObject("/Ff"): NumberObject(1)})
origin = ## Put input pdf path here
destination = ## Put output pdf path here, even if the file does not exist yet
newchecks = {} # A dict with all checkbox values that need to be changed
newvals = {'':''} # A dict with all entry values that need to be changed
# newvals dict has to be equal to {'':''} in case that no changes are needed
c = PdfFileFiller(origin)
c.update_form_values(outfile=destination, newvals=newvals, newchecks=newchecks)
print('PDF has been created\n')
I had trouble flattening a form that I had entered content into using pdfrw (How to Populate Fillable PDF's with Python) and found that I had to add an additional step using generate_fdf (pdftk flatten loses fillable field data).
os.system('pdftk '+outtemp+' generate_fdf output '+outfdf)
os.system('pdftk '+outtemp+' fill_form '+outfdf+' output '+outpdf)
I came to this solution because I was able to flatten a file just fine using ghostscript's pdf2ps followed by ps2pdf on my Mac, but the quality had low resolution when I ran it on an Amazon Linux instance. I couldn't figure out why that was the case and so moved to the pdftk solution.