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ImagesExtractor.py
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ImagesExtractor.py
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'''Extract images from PDF.
Both raster images and vector graphics are considered:
* Normal images like jpeg or png could be extracted with method ``page.get_text('rawdict')``
and ``Page.get_images()``. Note the process for png images with alpha channel.
* Vector graphics are actually composed of a group of paths, represented by operators like
``re``, ``m``, ``l`` and ``c``. They're detected by finding the contours with ``opencv``.
'''
import logging
import fitz
from ..common.Collection import Collection
from ..common.share import BlockType
from ..common.algorithm import (recursive_xy_cut, inner_contours, xy_project_profile)
class ImagesExtractor:
'''Extract images from PDF.'''
def __init__(self, page:fitz.Page) -> None:
'''Extract images from PDF page.
Args:
page (fitz.Page): pdf page to extract images.
'''
self._page = page
def clip_page_to_pixmap(self,
bbox:fitz.Rect=None,
rm_image:bool=False,
zoom:float=3.0):
'''Clip page pixmap according to ``bbox``.
Args:
bbox (fitz.Rect, optional): Target area to clip. Defaults to None, i.e. entire page.
Note that ``bbox`` depends on un-rotated page CS, while clipping page is based on
the final page.
rm_image (bool): remove images or not.
zoom (float, optional): Improve resolution by this rate. Defaults to 3.0.
Returns:
fitz.Pixmap: The extracted pixmap.
'''
# hide text and images
stream_dict = self._hide_page_text_and_images(self._page, rm_text=True, rm_image=rm_image)
if bbox is None:
clip_bbox = self._page.rect
# transform to the final bbox when page is rotated
elif self._page.rotation:
clip_bbox = bbox * self._page.rotation_matrix
else:
clip_bbox = bbox
clip_bbox = self._page.rect & clip_bbox
# improve resolution
# - https://pymupdf.readthedocs.io/en/latest/faq.html#how-to-increase-image-resolution
# - https://github.com/pymupdf/PyMuPDF/issues/181
matrix = fitz.Matrix(zoom, zoom)
pix = self._page.get_pixmap(clip=clip_bbox, matrix=matrix) # type: fitz.Pixmap
# recovery page if hide text
doc = self._page.parent
for xref, stream in stream_dict.items(): doc.update_stream(xref, stream)
return pix
def clip_page_to_dict(self,
bbox:fitz.Rect=None,
rm_image:bool=False,
clip_image_res_ratio:float=3.0):
'''Clip page pixmap (without text) according to ``bbox`` and convert to source image.
Args:
bbox (fitz.Rect, optional): Target area to clip. Defaults to None, i.e. entire page.
rm_image (bool): remove images or not.
clip_image_res_ratio (float, optional): Resolution ratio of clipped bitmap.
Defaults to 3.0.
Returns:
list: A list of image raw dict.
'''
pix = self.clip_page_to_pixmap(bbox=bbox, rm_image=rm_image, zoom=clip_image_res_ratio)
return self._to_raw_dict(pix, bbox)
def extract_images(self, clip_image_res_ratio:float=3.0):
'''Extract normal images with ``Page.get_images()``.
Args:
clip_image_res_ratio (float, optional): Resolution ratio of clipped bitmap.
Defaults to 3.0.
Returns:
list: A list of extracted and recovered image raw dict.
.. note::
``Page.get_images()`` contains each image only once, which may less than the
real count of images in a page.
'''
# pdf document
doc = self._page.parent
rotation = self._page.rotation
# The final view might be formed by several images with alpha channel only,
# as shown in issue-123.
# It's still inconvenient to extract the original alpha/mask image, as a compromise,
# extract the equivalent image by clipping the union page region for now.
# https://github.com/dothinking/pdf2docx/issues/123
# step 1: collect images: [(bbox, item), ..., ]
ic = Collection()
for item in self._page.get_images(full=True):
item = list(item)
item[-1] = 0
# find all occurrences referenced to this image
rects = self._page.get_image_rects(item)
unrotated_page_bbox = self._page.cropbox # note the difference to page.rect
for bbox in rects:
# ignore small images
if bbox.get_area()<=4: continue
# ignore images outside page
if not unrotated_page_bbox.intersects(bbox): continue
# collect images
ic.append((bbox, item))
# step 2: group by intersection
fun = lambda a, b: a[0].intersects(b[0])
groups = ic.group(fun)
# step 3: check each group
images = []
for group in groups:
# clip page with the union bbox of all intersected images
if len(group) > 1:
clip_bbox = fitz.Rect()
for (bbox, item) in group: clip_bbox |= bbox
raw_dict = self.clip_page_to_dict(clip_bbox, False, clip_image_res_ratio)
else:
bbox, item = group[0]
# Regarding images consist of alpha values only, the turquoise color shown in
# the PDF is not part of the image, but part of PDF background.
# So, just to clip page pixmap according to the right bbox
# https://github.com/pymupdf/PyMuPDF/issues/677
# It's not safe to identify images with alpha values only,
# - colorspace is None, for pymupdf <= 1.23.8
# - colorspace is always Colorspace(CS_RGB), for pymupdf==1.23.9-15 -> issue
# - colorspace is Colorspace(CS_), for pymupdf >= 1.23.16
# So, use extracted image info directly.
# image item: (xref, smask, width, height, bpc, colorspace, ...), e.g.,
# (19, 0, 331, 369, 1, '', '', 'Im1', 'FlateDecode', 0)
# (20, 24, 1265, 1303, 8, 'DeviceRGB', '', 'Im2', 'FlateDecode', 0)
# (21, 0, 331, 369, 1, '', '', 'Im3', 'CCITTFaxDecode', 0)
# (22, 25, 1265, 1303, 8, 'DeviceGray', '', 'Im4', 'DCTDecode', 0)
# (23, 0, 1731, 1331, 8, 'DeviceGray', '', 'Im5', 'DCTDecode', 0)
if item[5]=='':
raw_dict = self.clip_page_to_dict(bbox, False, clip_image_res_ratio)
# normal images
else:
# recover image, e.g., handle image with mask, or CMYK color space
pix = self._recover_pixmap(doc, item)
# rotate image with opencv if page is rotated
raw_dict = self._to_raw_dict(pix, bbox)
if rotation:
raw_dict['image'] = self._rotate_image(pix, -rotation)
images.append(raw_dict)
return images
def detect_svg_contours(self,
min_svg_gap_dx:float,
min_svg_gap_dy:float,
min_w:float,
min_h:float):
'''Find contour of potential vector graphics.
Args:
min_svg_gap_dx (float): Merge svg if the horizontal gap is less than this value.
min_svg_gap_dy (float): Merge svg if the vertical gap is less than this value.
min_w (float): Ignore contours if the bbox width is less than this value.
min_h (float): Ignore contours if the bbox height is less than this value.
Returns:
list: A list of potential svg region: (external_bbox, inner_bboxes:list).
'''
import cv2 as cv
# clip page and convert to opencv image
pixmap = self.clip_page_to_pixmap(rm_image=True, zoom=1.0)
src = self._pixmap_to_cv_image(pixmap)
# gray and binary
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
_, binary = cv.threshold(gray, 253, 255, cv.THRESH_BINARY_INV)
# external bbox: split images with recursive xy cut
external_bboxes = recursive_xy_cut(binary, min_dx=min_svg_gap_dx, min_dy=min_svg_gap_dy)
# inner contours
grouped_inner_bboxes = [inner_contours(binary, bbox, min_w, min_h)
for bbox in external_bboxes]
# combined external and inner contours
groups = list(zip(external_bboxes, grouped_inner_bboxes))
# plot detected images for debug
debug = False
if debug:
# plot projection profile for each sub-image
for i, (x0, y0, x1, y1) in enumerate(external_bboxes):
arr = xy_project_profile(src[y0:y1, x0:x1, :], binary[y0:y1, x0:x1])
cv.imshow(f'sub-image-{i}', arr)
for bbox, inner_bboxes in groups:
# plot external bbox
x0, y0, x1, y1 = bbox
cv.rectangle(src, (x0, y0), (x1, y1), (255,0,0), 1)
# plot inner bbox
for u0, v0, u1, v1 in inner_bboxes:
cv.rectangle(src, (u0, v0), (u1, v1), (0,0,255), 1)
cv.imshow("img", src)
cv.waitKey(0)
return groups
@staticmethod
def _to_raw_dict(image:fitz.Pixmap, bbox:fitz.Rect):
'''Store Pixmap ``image`` to raw dict.
Args:
image (fitz.Pixmap): Pixmap to store.
bbox (fitz.Rect): Boundary box the pixmap.
Returns:
dict: Raw dict of the pixmap.
'''
return {
'type': BlockType.IMAGE.value,
'bbox': tuple(bbox),
'width': image.width,
'height': image.height,
'image': image.tobytes()
}
@staticmethod
def _rotate_image(pixmap:fitz.Pixmap, rotation:int):
'''Rotate image represented by image bytes.
Args:
pixmap (fitz.Pixmap): Image to rotate.
rotation (int): Rotation angle.
Return: image bytes.
'''
import cv2 as cv
import numpy as np
# convert to opencv image
img = ImagesExtractor._pixmap_to_cv_image(pixmap)
h, w = img.shape[:2] # get image height, width
# calculate the center of the image
x0, y0 = w//2, h//2
# default scale value for now -> might be extracted from PDF page property
scale = 1.0
# rotation matrix
matrix = cv.getRotationMatrix2D((x0, y0), rotation, scale)
# calculate the final dimension
cos = np.abs(matrix[0, 0])
sin = np.abs(matrix[0, 1])
# compute the new bounding dimensions of the image
W = int((h * sin) + (w * cos))
H = int((h * cos) + (w * sin))
# adjust the rotation matrix to take into account translation
matrix[0, 2] += (W / 2) - x0
matrix[1, 2] += (H / 2) - y0
# perform the rotation holding at the center
rotated_img = cv.warpAffine(img, matrix, (W, H))
# convert back to bytes
_, im_png = cv.imencode('.png', rotated_img)
return im_png.tobytes()
@staticmethod
def _hide_page_text_and_images(page:fitz.Page, rm_text:bool, rm_image:bool):
'''Hide page text and images.'''
# NOTE: text might exist in both content stream and form object stream
# - content stream, i.e. direct page content
# - form object, i.e. contents referenced by this page
xref_list = [xref for (xref, name, invoker, bbox) in page.get_xobjects()]
xref_list.extend(page.get_contents())
# (1) hide text
# render Tr: set the text rendering mode
# - 3: neither fill nor stroke the text -> invisible
# read more:
# - https://github.com/pymupdf/PyMuPDF/issues/257
# - https://www.adobe.com/content/dam/acom/en/devnet/pdf/pdfs/pdf_reference_archives/PDFReference.pdf
def hide_text(stream):
res = stream
found = False
# set 3 Tr to text block
for k in ['BT', 'Tm', 'Td', '2 Tr']:
bk = k.encode()
if bk in stream:
found = True
res = res.replace(bk, f'{k} 3 Tr'.encode())
return res, found
# (2) hide image
# https://github.com/pymupdf/PyMuPDF/issues/338
def hide_images(stream):
res = stream
found = False
# image names, e.g. [[270, 0, 261, 115, 8, 'DeviceRGB', '', 'Im1', 'DCTDecode']]
img_names = [item[7] for item in page.get_images(full=True)]
for k in img_names:
bk = f'/{k} Do'.encode()
if bk in stream:
found = True
res = res.replace(bk, b'')
return res, found
doc = page.parent # type: fitz.Document
source = {}
for xref in xref_list:
src = doc.xref_stream(xref)
# try to hide text
stream, found_text = hide_text(src) if rm_text else (src, False)
# try to hide images
stream, found_images = hide_images(stream) if rm_image else (stream, False)
if found_text or found_images:
doc.update_stream(xref, stream)
source[xref] = src # save original stream
return source
@staticmethod
def _recover_pixmap(doc:fitz.Document, item:list):
"""Restore pixmap with soft mask considered.
References:
* https://pymupdf.readthedocs.io/en/latest/document.html#Document.getPageImageList
* https://pymupdf.readthedocs.io/en/latest/faq.html#how-to-handle-stencil-masks
* https://github.com/pymupdf/PyMuPDF/issues/670
Args:
doc (fitz.Document): pdf document.
item (list): image instance of ``page.get_images()``.
Returns:
fitz.Pixmap: Recovered pixmap with soft mask considered.
"""
# data structure of `item`:
# (xref, smask, width, height, bpc, colorspace, ...)
x = item[0] # xref of PDF image
s = item[1] # xref of its /SMask
# base image
pix = fitz.Pixmap(doc, x)
# reconstruct the alpha channel with the smask if exists
if s > 0:
mask = fitz.Pixmap(doc, s)
if pix.alpha:
temp = fitz.Pixmap(pix, 0) # make temp pixmap w/o the alpha
pix = None # release storage
pix = temp
# check dimension
if pix.width==mask.width and pix.height==mask.height:
pix = fitz.Pixmap(pix, mask) # now compose final pixmap
else:
logging.warning('Ignore image due to inconsistent size of color and mask pixmaps: %s', item)
# we may need to adjust something for CMYK pixmaps here ->
# recreate pixmap in RGB color space if necessary
# NOTE: pix.colorspace may be None for images with alpha channel values only
if 'CMYK' in item[5].upper():
pix = fitz.Pixmap(fitz.csRGB, pix)
return pix
@staticmethod
def _pixmap_to_cv_image(pixmap:fitz.Pixmap):
'''Convert fitz Pixmap to opencv image.
Args:
pixmap (fitz.Pixmap): PyMuPDF Pixmap.
'''
import cv2 as cv
import numpy as np
img_byte = pixmap.tobytes()
return cv.imdecode(np.frombuffer(img_byte, np.uint8), cv.IMREAD_COLOR)