def search_in_presentations(self):
ppt_instance, slide_counter = win32com.client.Dispatch(
'PowerPoint.Application'), 0
for index_file, file_path in enumerate(
self.results['files_to_search_inside']['presentation']):
print('Searching in presentation file {} of {}...'.format(
index_file + 1,
len(self.results['files_to_search_inside']['presentation'])))
read_only, has_title, window = False, False, False
prs = ppt_instance.Presentations.open(file_path, read_only,
has_title, window)
self.results['file_slide_sizes'][file_path] = (
prs.PageSetup.SlideWidth, prs.PageSetup.SlideHeight)
for index_slide, Slide in enumerate(prs.Slides):
for index_shape, Shape in enumerate(Slide.Shapes):
slide_string = 'Slide ' + str(index_slide + 1)
object_string = 'Object ' + str(index_shape + 1)
if Shape.HasTextFrame:
if Shape.TextFrame.HasText:
paragraphs_specialchars_removed = [
p.Text for p in
Shape.TextFrame.TextRange.Paragraphs()
if (p.Text != '\r')
]
for index_paragraph, Paragraph in enumerate(
paragraphs_specialchars_removed):
for search_string in self.parameters[
'search_strings']:
occurrences = nm.count_text_occurrences(
Paragraph, search_string,
self.parameters['case_sensitive'],
self.parameters['whole_phrase_only'])
if occurrences > 0:
slide_counter += 1
if str(file_path) not in list(
self.results[
'containing_file_paths']
[search_string]
['presentation'].keys()):
self.results[
'containing_file_paths'][
search_string][
'presentation'][str(
file_path)] = {}
paragraph_string = 'Paragraph ' + str(
index_paragraph + 1)
occurrences_string = str(
occurrences
) + ' occurrence' if occurrences == 1 else str(
occurrences) + ' occurrences'
combined_string = object_string + ', ' + paragraph_string + ', ' + occurrences_string
if slide_string in list(
self.results[
'containing_file_paths']
[search_string]['presentation'][
str(file_path)].keys()):
self.results[
'containing_file_paths'][
search_string][
'presentation'][str(
file_path
)][slide_string].append(
combined_string)
else:
self.results[
'containing_file_paths'][
search_string][
'presentation'][str(
file_path
)][slide_string] = [
combined_string
]
if Shape.Type in [3, 21, 28, 11, 13] and self.parameters[
'search_in_doc_images'] and self.parameters[
'allow_OCR']:
img_fp = str(
self.temp_directory / '{}_{}_{}.jpg'.format(
str(Path(file_path).stem).replace('.', ''),
slide_string, object_string))
Shape.Export(img_fp, 3)
try:
image_text = OCR_utils.image_to_text(
img_fp, language='eng')
except:
image_text = ''
for search_string in self.parameters['search_strings']:
occurrences = nm.count_text_occurrences(
image_text, search_string,
self.parameters['case_sensitive'],
self.parameters['whole_phrase_only'])
occurrences_string = str(
occurrences
) + ' occurrence' if occurrences == 1 else str(
occurrences) + ' occurrences'
combined_string = object_string + ' (image), ' + occurrences_string
if occurrences > 0:
if str(file_path) not in list(
self.results['containing_file_paths']
[search_string]['presentation'].keys()):
self.results['containing_file_paths'][
search_string]['presentation'][str(
file_path)] = {}
if slide_string in list(
self.results['containing_file_paths']
[search_string]['presentation'][str(
file_path)].keys()):
self.results['containing_file_paths'][
search_string]['presentation'][str(
file_path)][slide_string].append(
combined_string)
else:
self.results['containing_file_paths'][
search_string]['presentation'][str(
file_path)][slide_string] = [
combined_string
]
os.remove(img_fp)
def search_in_pdfs(self, file_path):
# Read the text with tika and parse into individual pages with BeautifulSoup
# TODO: test with 1 page pdf
pages = pdf_utils.tika_read()
# If the file contains 4 or more consecutive symbols, it is probably encrypted
file_probably_encrypted = False
for page_text in pages:
if page_text is None or pdf_utils.is_probably_encrypted(
page_text, n_consecutive_symbols=4):
file_probably_encrypted = True
break
# 1. If probably encrypted and OCR allowed, analyse the pages with Tesseract OCR (Optical Character Recognition)
if file_probably_encrypted and self.parameters['allow_OCR']:
self.results['pdf_reading_steps'][file_path].append(
'encrypted: used page OCR')
page_image_filepaths = OCR_utils.pdf_pages_to_images(
file_path, self.temp_directory, 'jpg'
) # Convert pdf pages to image files and save list of their filepaths
for i, image_fp in enumerate(
page_image_filepaths
): # Use OCR on each page to get a text string for each
page_text = OCR_utils.image_to_text(image_fp, language='eng')
for search_string in self.parameters['search_strings']:
line_numbers = nm.count_text_occurrences(
page_text,
search_string,
self.parameters['case_sensitive'],
self.parameters['whole_phrase_only'],
get_line_numbers=True)
if len(line_numbers) > 0:
if str(file_path) not in list(
self.results['containing_file_paths']
[search_string]['fancytext'].keys()):
self.results['containing_file_paths'][
search_string]['fancytext'][str(
file_path)] = {}
self.results['containing_file_paths'][search_string][
'fancytext'][str(file_path)]['page ' +
str(i +
1)] = line_numbers
# Delete the temporary image files created
if page_image_filepaths:
for fp in page_image_filepaths:
os.remove(fp)
# 2. If probably encrypted but cannot use OCR, add to failed file paths store
elif file_probably_encrypted and not self.parameters['allow_OCR']:
self.results['failed_file_paths']['fancytext'][(
str(file_path)
)] = 'File appears to be encrypted and OCR has not been allowed/is not available.'
self.results['pdf_reading_steps'][file_path].append(
'encrypted: OCR not allowed')
# 3. If probably not encrypted, analyse the tika text of each page
else:
self.results['pdf_reading_steps'][file_path].append(
'unencrypted: analyse tika text')
for i, page_text in enumerate(pages):
for search_string in self.parameters['search_strings']:
line_numbers = nm.count_text_occurrences(
page_text,
search_string,
self.parameters['case_sensitive'],
self.parameters['whole_phrase_only'],
get_line_numbers=True)
if len(line_numbers) > 0:
if str(file_path) not in list(
self.results['containing_file_paths']
[search_string]['fancytext'].keys()):
self.results['containing_file_paths'][
search_string]['fancytext'][str(
file_path)] = {}
self.results['containing_file_paths'][search_string][
'fancytext'][str(file_path)]['page ' +
str(i +
1)] = line_numbers
# Check if the pdf has any images - if desired, these can be analysed separately with OCR (not needed in encrypted case)
if self.parameters['search_in_doc_images'] and self.parameters[
'allow_OCR']:
self.results['pdf_reading_steps'][file_path].append(
'unencrypted: search in images')
n_images, saved_image_filepaths = pdf_utils.count_extract_pdf_images(
file_path, save_images=True)
if n_images > 0:
for j, image_fp in enumerate(saved_image_filepaths):
image_text = OCR_utils.image_to_text(image_fp,
language='eng')
for search_string in self.parameters['search_strings']:
occurrences = nm.count_text_occurrences(
image_text, search_string,
self.parameters['case_sensitive'],
self.parameters['whole_phrase_only'])
if occurrences > 0:
page_number = Path(file_path).stem.split(
'_page_')[-1]
if str(file_path) not in list(
self.results['containing_file_paths']
[search_string]['fancytext'].keys()):
self.results['containing_file_paths'][
search_string]['fancytext'][str(
file_path)] = {}
self.results['containing_file_paths'][
search_string]['fancytext'][str(
file_path
)]['image {} on page {}'.format(
j + 1, page_number
)] = '{} occurrences'.format(occurrences)