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1480 lines
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1480 lines
122 KiB
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</head>
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<body>
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<div id="content">
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<h1 class="title">PDF Parsing</h1>
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<div id="table-of-contents">
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<h2>Table of Contents</h2>
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<div id="text-table-of-contents">
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<ul>
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<li><a href="#org533e16a">1. Preparing our data</a>
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<ul>
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<li><a href="#orga09ea5b">1.1. Converting PDFs to images</a></li>
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<li><a href="#orgf3a14b3">1.2. Detecting image orientation and applying rotation.</a></li>
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</ul>
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</li>
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<li><a href="#org8657733">2. Detecting tables</a></li>
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<li><a href="#orgda5b77b">3. OCR tables</a>
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<ul>
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<li>
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<ul>
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<li><a href="#org111c988">3.0.1. Blur</a></li>
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<li><a href="#org08523db">3.0.2. Threshold</a></li>
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<li><a href="#orgcfbb819">3.0.3. Finding the vertical and horizontal lines of the table</a></li>
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<li><a href="#orge26e613">3.0.4. Finding the contours</a></li>
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<li><a href="#org39c0a09">3.0.5. Sorting the bounding rectangles</a></li>
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<li><a href="#orgb00b770">3.0.6. Cropping each cell to the text</a></li>
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<li><a href="#orgd24a937">3.0.7. OCR each cell</a></li>
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</ul>
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</li>
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</ul>
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</li>
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<li><a href="#org8689ce0">4. Files</a>
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<ul>
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<li><a href="#org91ea732">4.1. setup.py</a></li>
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<li><a href="#orgf115626">4.2. table_image_ocr</a>
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<ul>
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<li><a href="#org8765709">4.2.1. table_image_ocr/__init__.py</a></li>
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<li><a href="#org8d0619f">4.2.2. table_image_ocr/util.py</a></li>
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<li><a href="#orga454dca">4.2.3. table_image_ocr/prepare_pdfs.py</a></li>
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<li><a href="#org076a34b">4.2.4. table_image_ocr/extract_tables.py</a></li>
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<li><a href="#org1b2f268">4.2.5. table_image_ocr/extract_cells_from_table.py</a></li>
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</ul>
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</li>
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</ul>
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</li>
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<li><a href="#orgde56bd1">5. Utils</a></li>
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</ul>
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</div>
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</div>
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<div id="outline-container-org533e16a" class="outline-2">
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<h2 id="org533e16a"><span class="section-number-2">1</span> Preparing our data</h2>
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<div class="outline-text-2" id="text-1">
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</div>
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<div id="outline-container-orga09ea5b" class="outline-3">
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<h3 id="orga09ea5b"><span class="section-number-3">1.1</span> Converting PDFs to images</h3>
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<div class="outline-text-3" id="text-1-1">
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<p>
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Not all pdfs need to be sent through OCR to extract the text content. If you can
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click and drag to highlight text in the pdf, then the tools in this library
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probably aren’t necessary.
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</p>
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<p>
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This code calls out to <a href="https://manpages.debian.org/testing/poppler-utils/pdfimages.1.en.html">pdfimages</a> from <a href="https://poppler.freedesktop.org/">Poppler</a>.
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</p>
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<div class="org-src-container">
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<pre class="src src-python" id="org1bef3d0"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">pdf_to_images</span>(pdf_filepath):
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<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
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<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Turn a pdf into images</span>
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<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
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<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span>, <span style="color: #dcaeea;">filename</span> = os.path.split(pdf_filepath)
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<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">with</span> working_dir(directory):
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<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_filenames</span> = pdfimages(pdf_filepath)
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<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Since pdfimages creates a number of files named each for there page number</span>
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<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">and doesn't return us the list that it created</span>
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<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> [os.path.join(directory, f) <span style="color: #51afef;">for</span> f <span style="color: #51afef;">in</span> image_filenames]
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<span style="color: #51afef;">def</span> <span style="color: #c678dd;">pdfimages</span>(pdf_filepath):
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<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
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<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Uses the `pdfimages` utility from Poppler</span>
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<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> (https://poppler.freedesktop.org/). Creates images out of each page. Images</span>
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|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> are prefixed by their name sans extension and suffixed by their page number.</span>
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|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
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<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span>, <span style="color: #dcaeea;">filename</span> = os.path.split(pdf_filepath)
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|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">filename_sans_ext</span> = filename.split(<span style="color: #98be65;">".pdf"</span>)[<span style="color: #da8548; font-weight: bold;">0</span>]
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|
<span style="background-color: #282c34;"> </span> subprocess.run([<span style="color: #98be65;">"pdfimages"</span>, <span style="color: #98be65;">"-png"</span>, pdf_filepath, filename.split(<span style="color: #98be65;">".pdf"</span>)[<span style="color: #da8548; font-weight: bold;">0</span>]])
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<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_filenames</span> = find_matching_files_in_dir(filename_sans_ext, directory)
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<span style="background-color: #282c34;"> </span> logger.debug(<span style="color: #98be65;">"Converted {} into files:\n{}"</span>.<span style="color: #c678dd;">format</span>(pdf_filepath, <span style="color: #98be65;">"\n"</span>.join(image_filenames)))
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<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> image_filenames
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|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">find_matching_files_in_dir</span>(file_prefix, directory):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">files</span> = [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> filename
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> filename <span style="color: #51afef;">in</span> os.listdir(directory)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> re.match(r<span style="color: #98be65;">"{}.*\.png"</span>.<span style="color: #c678dd;">format</span>(re.escape(file_prefix)), filename)
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<span style="background-color: #282c34;"> </span> ]
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<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> files
|
|
</pre>
|
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</div>
|
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</div>
|
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</div>
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|
|
|
<div id="outline-container-orgf3a14b3" class="outline-3">
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<h3 id="orgf3a14b3"><span class="section-number-3">1.2</span> Detecting image orientation and applying rotation.</h3>
|
|
<div class="outline-text-3" id="text-1-2">
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<p>
|
|
Tesseract can detect orientation and we can then use <a href="https://www.imagemagick.org/script/mogrify.php">ImageMagick’s mogrify</a> to
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rotate the image.
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</p>
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<p>
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Here’s an example of the output we get from orientation detection with
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Tesseract.
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</p>
|
|
|
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<pre class="example">
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➜ example/ tesseract --psm 0 example-000.png -
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Page number: 0
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Orientation in degrees: 90
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Rotate: 270
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Orientation confidence: 26.86
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|
Script: Latin
|
|
Script confidence: 2.44
|
|
</pre>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python" id="org678f3f8"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">preprocess_img</span>(filepath):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Processing that involves running shell executables,</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> like mogrify to rotate.</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rotate</span> = get_rotate(filepath)
|
|
<span style="background-color: #282c34;"> </span> logger.debug(<span style="color: #98be65;">"Rotating {} by {}."</span>.<span style="color: #c678dd;">format</span>(filepath, rotate))
|
|
<span style="background-color: #282c34;"> </span> mogrify(filepath, rotate)
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">get_rotate</span>(image_filepath):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = (
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> subprocess.check_output([<span style="color: #98be65;">"tesseract"</span>, <span style="color: #98be65;">"--psm"</span>, <span style="color: #98be65;">"0"</span>, image_filepath, <span style="color: #98be65;">"-"</span>])
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> .decode(<span style="color: #98be65;">"utf-8"</span>)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> .split(<span style="color: #98be65;">"\n"</span>)
|
|
<span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = <span style="color: #c678dd;">next</span>(l <span style="color: #51afef;">for</span> l <span style="color: #51afef;">in</span> output <span style="color: #51afef;">if</span> <span style="color: #98be65;">"Rotate: "</span> <span style="color: #51afef;">in</span> l)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = output.split(<span style="color: #98be65;">": "</span>)[<span style="color: #da8548; font-weight: bold;">1</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> output
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">mogrify</span>(image_filepath, rotate):
|
|
<span style="background-color: #282c34;"> </span> subprocess.run([<span style="color: #98be65;">"mogrify"</span>, <span style="color: #98be65;">"-rotate"</span>, rotate, image_filepath])
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-org8657733" class="outline-2">
|
|
<h2 id="org8657733"><span class="section-number-2">2</span> Detecting tables</h2>
|
|
<div class="outline-text-2" id="text-2">
|
|
<p>
|
|
This answer from opencv.org was heavily referenced while writing the code around
|
|
table detection:
|
|
<a href="https://answers.opencv.org/question/63847/how-to-extract-tables-from-an-image/">https://answers.opencv.org/question/63847/how-to-extract-tables-from-an-image/</a>.
|
|
</p>
|
|
|
|
<p>
|
|
It’s much easier to OCR a table when the table is the only thing in the image.
|
|
This code detects tables in an image and returns a list of images of just the
|
|
tables, no surrounding text or noise.
|
|
</p>
|
|
|
|
<p>
|
|
The blurring, thresholding, and line detection is used here as well as later on
|
|
for cell extraction. They are good techniques for cleaning an image up in a way
|
|
that makes things like shape detection more accurate.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">find_tables</span>(image):
|
|
<span style="background-color: #282c34;"> </span> <<blur>>
|
|
<span style="background-color: #282c34;"> </span> <<threshold>>
|
|
<span style="background-color: #282c34;"> </span> <<lines-of-table>>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span>, <span style="color: #dcaeea;">heirarchy</span> = cv2.findContours(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE,
|
|
<span style="background-color: #282c34;"> </span> )
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MIN_TABLE_AREA</span> = 1e5
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours <span style="color: #51afef;">if</span> cv2.contourArea(c) > MIN_TABLE_AREA]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">perimeter_lengths</span> = [cv2.arcLength(c, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">epsilons</span> = [<span style="color: #da8548; font-weight: bold;">0.1</span> * p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> perimeter_lengths]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">approx_polys</span> = [cv2.approxPolyDP(c, e, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c, e <span style="color: #51afef;">in</span> <span style="color: #c678dd;">zip</span>(contours, epsilons)]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [cv2.boundingRect(a) <span style="color: #51afef;">for</span> a <span style="color: #51afef;">in</span> approx_polys]
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">The link where a lot of this code was borrowed from recommends an</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">additional step to check the number of "joints" inside this bounding rectangle.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">A table should have a lot of intersections. We might have a rectangular image</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">here though which would only have 4 intersections, 1 at each corner.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Leaving that step as a future </span><span style="color: #ECBE7B; font-weight: bold;">TODO</span><span style="color: #5B6268;"> if it is ever necessary.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">images</span> = [image[y:y+h, x:x+w] <span style="color: #51afef;">for</span> x, y, w, h <span style="color: #51afef;">in</span> bounding_rects]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> images
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">import</span> cv2
|
|
|
|
<<detect-table>>
|
|
|
|
<span style="color: #dcaeea;">image_filename</span> = <span style="color: #98be65;">"resources/examples/example-page.png"</span>
|
|
<span style="color: #dcaeea;">image</span> = cv2.imread(image_filename, cv2.IMREAD_GRAYSCALE)
|
|
<span style="color: #dcaeea;">image</span> = find_tables(image)[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table.png"</span>, image)
|
|
<span style="color: #98be65;">"resources/examples/example-table.png"</span>
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-orgda5b77b" class="outline-2">
|
|
<h2 id="orgda5b77b"><span class="section-number-2">3</span> OCR tables</h2>
|
|
<div class="outline-text-2" id="text-3">
|
|
<p>
|
|
Find the bounding box of each cell in the table. Run tesseract on each cell.
|
|
Print a comma seperated output.
|
|
</p>
|
|
|
|
<p>
|
|
We’ll start with an image shown at the end of the previous section.
|
|
</p>
|
|
</div>
|
|
|
|
<div id="outline-container-org111c988" class="outline-4">
|
|
<h4 id="org111c988"><span class="section-number-4">3.0.1</span> Blur</h4>
|
|
<div class="outline-text-4" id="text-3-0-1">
|
|
<p>
|
|
Blurring helps to make noise less noisy so that the overall structure of an
|
|
image is more detectable.
|
|
</p>
|
|
|
|
<p>
|
|
That gray row at the bottom is kind of noisy. If we don’t somehow clean it up,
|
|
OpenCV will detect all sorts of odd shapes in there and it will throw off our
|
|
cell detection.
|
|
</p>
|
|
|
|
<p>
|
|
Cleanup can be accomplished with a blur followed by some thresholding.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #dcaeea;">BLUR_KERNEL_SIZE</span> = (<span style="color: #da8548; font-weight: bold;">17</span>, <span style="color: #da8548; font-weight: bold;">17</span>)
|
|
<span style="color: #dcaeea;">STD_DEV_X_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
|
|
<span style="color: #dcaeea;">STD_DEV_Y_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
|
|
<span style="color: #dcaeea;">blurred</span> = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #dcaeea;">image</span> = ~cv2.imread(<span style="color: #98be65;">"resources/examples/example-table.png"</span>, cv2.IMREAD_GRAYSCALE)
|
|
<<blur>>
|
|
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-blurred.png"</span>, blurred)
|
|
<span style="color: #98be65;">"resources/examples/example-table-blurred.png"</span>
|
|
</pre>
|
|
</div>
|
|
|
|
|
|
<div class="figure">
|
|
<p><img src="resources/examples/example-table-blurred.png" alt="example-table-blurred.png" width="500px" height="100%" />
|
|
</p>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-org08523db" class="outline-4">
|
|
<h4 id="org08523db"><span class="section-number-4">3.0.2</span> Threshold</h4>
|
|
<div class="outline-text-4" id="text-3-0-2">
|
|
<p>
|
|
We’ve got a bunch of pixels that are gray. Thresholding will turn them all
|
|
either black or white. Having all black or white pixels lets us do morphological
|
|
transformations like erosion and dilation.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #dcaeea;">MAX_COLOR_VAL</span> = <span style="color: #da8548; font-weight: bold;">255</span>
|
|
<span style="color: #dcaeea;">BLOCK_SIZE</span> = <span style="color: #da8548; font-weight: bold;">15</span>
|
|
<span style="color: #dcaeea;">SUBTRACT_FROM_MEAN</span> = -<span style="color: #da8548; font-weight: bold;">2</span>
|
|
|
|
<span style="color: #dcaeea;">img_bin</span> = cv2.adaptiveThreshold(
|
|
<span style="background-color: #282c34;"> </span> ~blurred,
|
|
<span style="background-color: #282c34;"> </span> MAX_COLOR_VAL,
|
|
<span style="background-color: #282c34;"> </span> cv2.ADAPTIVE_THRESH_MEAN_C,
|
|
<span style="background-color: #282c34;"> </span> cv2.THRESH_BINARY,
|
|
<span style="background-color: #282c34;"> </span> BLOCK_SIZE,
|
|
<span style="background-color: #282c34;"> </span> SUBTRACT_FROM_MEAN,
|
|
)
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><<threshold>>
|
|
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-thresholded.png"</span>, img_bin)
|
|
<span style="color: #98be65;">"resources/examples/example-table-thresholded.png"</span>
|
|
</pre>
|
|
</div>
|
|
|
|
|
|
<div class="figure">
|
|
<p><img src="resources/examples/example-table-thresholded.png" alt="example-table-thresholded.png" width="500px" height="100%" />
|
|
</p>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-orgcfbb819" class="outline-4">
|
|
<h4 id="orgcfbb819"><span class="section-number-4">3.0.3</span> Finding the vertical and horizontal lines of the table</h4>
|
|
<div class="outline-text-4" id="text-3-0-3">
|
|
<p>
|
|
Note: There’s a wierd issue with the results of the example below when it’s
|
|
evaluated as part of an export or a full-buffer evaluation. If you evaluate the
|
|
example by itself, it looks the way it’s intended. If you evaluate it as part of
|
|
an entire buffer evaluation, it’s distorted.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #dcaeea;">vertical</span> = <span style="color: #dcaeea;">horizontal</span> = img_bin.copy()
|
|
<span style="color: #dcaeea;">SCALE</span> = <span style="color: #da8548; font-weight: bold;">5</span>
|
|
<span style="color: #dcaeea;">image_width</span>, <span style="color: #dcaeea;">image_height</span> = horizontal.shape
|
|
<span style="color: #dcaeea;">horizontal_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #c678dd;">int</span>(image_width / SCALE), <span style="color: #da8548; font-weight: bold;">1</span>))
|
|
<span style="color: #dcaeea;">horizontally_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
|
|
<span style="color: #dcaeea;">vertical_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #c678dd;">int</span>(image_height / SCALE)))
|
|
<span style="color: #dcaeea;">vertically_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
|
|
|
|
<span style="color: #dcaeea;">horizontally_dilated</span> = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">40</span>, <span style="color: #da8548; font-weight: bold;">1</span>)))
|
|
<span style="color: #dcaeea;">vertically_dilated</span> = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #da8548; font-weight: bold;">60</span>)))
|
|
|
|
<span style="color: #dcaeea;">mask</span> = horizontally_dilated + vertically_dilated
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><<lines-of-table>>
|
|
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-lines.png"</span>, mask)
|
|
<span style="color: #98be65;">"resources/examples/example-table-lines.png"</span>
|
|
</pre>
|
|
</div>
|
|
|
|
|
|
<div class="figure">
|
|
<p><img src="resources/examples/example-table-lines.png" alt="example-table-lines.png" width="500px" height="100%" />
|
|
</p>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-orge26e613" class="outline-4">
|
|
<h4 id="orge26e613"><span class="section-number-4">3.0.4</span> Finding the contours</h4>
|
|
<div class="outline-text-4" id="text-3-0-4">
|
|
<p>
|
|
Blurring and thresholding allow us to find the lines. Opening the lines allows
|
|
us to find the contours.
|
|
</p>
|
|
|
|
<p>
|
|
An “Opening” is an erosion followed by a dilation. Great examples and
|
|
descriptions of each morphological operation can be found at
|
|
<a href="https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html">https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html</a>.
|
|
</p>
|
|
|
|
<blockquote>
|
|
<p>
|
|
Contours can be explained simply as a curve joining all the continuous points
|
|
(along the boundary), having same color or intensity. The contours are a useful
|
|
tool for shape analysis and object detection and recognition.
|
|
</p>
|
|
</blockquote>
|
|
|
|
<p>
|
|
We can search those contours to find rectangles of certain size.
|
|
</p>
|
|
|
|
<p>
|
|
To do that, we can use OpenCV’s <code>approxPolyEP</code> function. It takes as arguments
|
|
the contour (list of contiguous points), and a number representing how different
|
|
the polygon perimeter length can be from the true perimeter length of the
|
|
contour. <code>0.1</code> (10%) seems to be a good value. The difference in perimeter
|
|
length between a 4-sided polygon and a 3-sided polygon is greater than 10% and
|
|
the difference between a 5+ sided polygon and a 4-sided polygon is less than
|
|
10%. So a 4-sided polygon is the polygon with the fewest sides that leaves the
|
|
difference in perimeter length within our 10% threshold.
|
|
</p>
|
|
|
|
<p>
|
|
Then we just get the bounding rectangle of that polygon and we have our cells!
|
|
</p>
|
|
|
|
<p>
|
|
We might need to do a little more filtering of those rectangles though. We might
|
|
have accidentally found some noise such as another image on the page or a title
|
|
header bar or something. If we know our cells are all within a certain size (by
|
|
area of pixels) then we can filter out the junk cells by removing cells
|
|
above/below certain sizes.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #dcaeea;">contours</span>, <span style="color: #dcaeea;">heirarchy</span> = cv2.findContours(
|
|
<span style="background-color: #282c34;"> </span> mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE,
|
|
)
|
|
|
|
<span style="color: #dcaeea;">perimeter_lengths</span> = [cv2.arcLength(c, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours]
|
|
<span style="color: #dcaeea;">epsilons</span> = [<span style="color: #da8548; font-weight: bold;">0.05</span> * p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> perimeter_lengths]
|
|
<span style="color: #dcaeea;">approx_polys</span> = [cv2.approxPolyDP(c, e, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c, e <span style="color: #51afef;">in</span> <span style="color: #c678dd;">zip</span>(contours, epsilons)]
|
|
|
|
<span style="color: #5B6268;"># </span><span style="color: #5B6268;">Filter out contours that aren't rectangular. Those that aren't rectangular</span>
|
|
<span style="color: #5B6268;"># </span><span style="color: #5B6268;">are probably noise.</span>
|
|
<span style="color: #dcaeea;">approx_rects</span> = [p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> approx_polys <span style="color: #51afef;">if</span> <span style="color: #c678dd;">len</span>(p) == <span style="color: #da8548; font-weight: bold;">4</span>]
|
|
<span style="color: #dcaeea;">bounding_rects</span> = [cv2.boundingRect(a) <span style="color: #51afef;">for</span> a <span style="color: #51afef;">in</span> approx_polys]
|
|
|
|
<span style="color: #5B6268;"># </span><span style="color: #5B6268;">Filter out rectangles that are too narrow or too short.</span>
|
|
<span style="color: #dcaeea;">MIN_RECT_WIDTH</span> = <span style="color: #da8548; font-weight: bold;">40</span>
|
|
<span style="color: #dcaeea;">MIN_RECT_HEIGHT</span> = <span style="color: #da8548; font-weight: bold;">10</span>
|
|
<span style="color: #dcaeea;">bounding_rects</span> = [
|
|
<span style="background-color: #282c34;"> </span> r <span style="color: #51afef;">for</span> r <span style="color: #51afef;">in</span> bounding_rects <span style="color: #51afef;">if</span> MIN_RECT_WIDTH < r[<span style="color: #da8548; font-weight: bold;">2</span>] <span style="color: #51afef;">and</span> MIN_RECT_HEIGHT < r[<span style="color: #da8548; font-weight: bold;">3</span>]
|
|
]
|
|
|
|
<span style="color: #5B6268;"># </span><span style="color: #5B6268;">The largest bounding rectangle is assumed to be the entire table.</span>
|
|
<span style="color: #5B6268;"># </span><span style="color: #5B6268;">Remove it from the list. We don't want to accidentally try to OCR</span>
|
|
<span style="color: #5B6268;"># </span><span style="color: #5B6268;">the entire table.</span>
|
|
<span style="color: #dcaeea;">largest_rect</span> = <span style="color: #c678dd;">max</span>(bounding_rects, key=<span style="color: #51afef;">lambda</span> r: r[<span style="color: #da8548; font-weight: bold;">2</span>] * r[<span style="color: #da8548; font-weight: bold;">3</span>])
|
|
<span style="color: #dcaeea;">bounding_rects</span> = [b <span style="color: #51afef;">for</span> b <span style="color: #51afef;">in</span> bounding_rects <span style="color: #51afef;">if</span> b <span style="color: #51afef;">is</span> <span style="color: #51afef;">not</span> largest_rect]
|
|
|
|
<span style="color: #dcaeea;">cells</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> bounding_rects]
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-org39c0a09" class="outline-4">
|
|
<h4 id="org39c0a09"><span class="section-number-4">3.0.5</span> Sorting the bounding rectangles</h4>
|
|
<div class="outline-text-4" id="text-3-0-5">
|
|
<p>
|
|
We want to process these from left-to-right, top-to-bottom.
|
|
</p>
|
|
|
|
<p>
|
|
I’ve thought of a straightforward algorithm for it, but it could probably be
|
|
made more efficient.
|
|
</p>
|
|
|
|
<p>
|
|
We’ll find the most rectangle with the most top-left corner. Then we’ll find all
|
|
of the rectangles that have a center that is within the top-y and bottom-y
|
|
values of that top-left rectangle. Then we’ll sort those rectangles by the x
|
|
value of their center. We’ll remove those rectangles from the list and repeat.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">cell_in_same_row</span>(c1, c2):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c1_center</span> = c1[<span style="color: #da8548; font-weight: bold;">1</span>] + c1[<span style="color: #da8548; font-weight: bold;">3</span>] - c1[<span style="color: #da8548; font-weight: bold;">3</span>] / <span style="color: #da8548; font-weight: bold;">2</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c2_bottom</span> = c2[<span style="color: #da8548; font-weight: bold;">1</span>] + c2[<span style="color: #da8548; font-weight: bold;">3</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c2_top</span> = c2[<span style="color: #da8548; font-weight: bold;">1</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> c2_top < c1_center < c2_bottom
|
|
|
|
<span style="color: #dcaeea;">orig_cells</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> cells]
|
|
<span style="color: #dcaeea;">rows</span> = []
|
|
<span style="color: #51afef;">while</span> cells:
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">first</span> = cells[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rest</span> = cells[<span style="color: #da8548; font-weight: bold;">1</span>:]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cells_in_same_row</span> = <span style="color: #c678dd;">sorted</span>(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> rest
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> cell_in_same_row(c, first)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ],
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> key=<span style="color: #51afef;">lambda</span> c: c[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
<span style="background-color: #282c34;"> </span> )
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">row_cells</span> = <span style="color: #c678dd;">sorted</span>([first] + cells_in_same_row, key=<span style="color: #51afef;">lambda</span> c: c[<span style="color: #da8548; font-weight: bold;">0</span>])
|
|
<span style="background-color: #282c34;"> </span> rows.append(row_cells)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cells</span> = [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> rest
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> <span style="color: #51afef;">not</span> cell_in_same_row(c, first)
|
|
<span style="background-color: #282c34;"> </span> ]
|
|
|
|
<span style="color: #5B6268;"># </span><span style="color: #5B6268;">Sort rows by average height of their center.</span>
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">avg_height_of_center</span>(row):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">centers</span> = [y + h - h / <span style="color: #da8548; font-weight: bold;">2</span> <span style="color: #51afef;">for</span> x, y, w, h <span style="color: #51afef;">in</span> row]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> <span style="color: #c678dd;">sum</span>(centers) / <span style="color: #c678dd;">len</span>(centers)
|
|
|
|
rows.sort(key=avg_height_of_center)
|
|
</pre>
|
|
</div>
|
|
|
|
<p>
|
|
To test if this code works, let’s try sorting the bounding rectangles and
|
|
numbering them from right to left, top to bottom.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">import</span> cv2
|
|
<span style="color: #dcaeea;">image</span> = cv2.imread(<span style="color: #98be65;">"resources/examples/example-table.png"</span>, cv2.IMREAD_GRAYSCALE)
|
|
<<blur>>
|
|
<<threshold>>
|
|
<<lines-of-table>>
|
|
<<bounding-rects>>
|
|
<<sort-contours>>
|
|
|
|
<span style="color: #dcaeea;">FONT_SCALE</span> = <span style="color: #da8548; font-weight: bold;">0.7</span>
|
|
<span style="color: #dcaeea;">FONT_COLOR</span> = (<span style="color: #da8548; font-weight: bold;">127</span>, <span style="color: #da8548; font-weight: bold;">127</span>, <span style="color: #da8548; font-weight: bold;">127</span>)
|
|
<span style="color: #51afef;">for</span> i, row <span style="color: #51afef;">in</span> <span style="color: #c678dd;">enumerate</span>(rows):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> j, cell <span style="color: #51afef;">in</span> <span style="color: #c678dd;">enumerate</span>(row):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">x</span>, <span style="color: #dcaeea;">y</span>, <span style="color: #dcaeea;">w</span>, <span style="color: #dcaeea;">h</span> = cell
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.putText(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> image,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #98be65;">"{},{}"</span>.<span style="color: #c678dd;">format</span>(i, j),
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> (<span style="color: #c678dd;">int</span>(x + w - w / <span style="color: #da8548; font-weight: bold;">2</span>), <span style="color: #c678dd;">int</span>(y + h - h / <span style="color: #da8548; font-weight: bold;">2</span>)),
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.FONT_HERSHEY_SIMPLEX,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> FONT_SCALE,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> FONT_COLOR,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #da8548; font-weight: bold;">2</span>,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> )
|
|
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-cells-numbered.png"</span>, image)
|
|
<span style="color: #98be65;">"resources/examples/example-table-cells-numbered.png"</span>
|
|
</pre>
|
|
</div>
|
|
|
|
|
|
<div class="figure">
|
|
<p><img src="resources/examples/example-table-cells-numbered.png" alt="example-table-cells-numbered.png" width="500px" height="100%" />
|
|
</p>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">extract_cell_images_from_table</span>(image):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">BLUR_KERNEL_SIZE</span> = (<span style="color: #da8548; font-weight: bold;">17</span>, <span style="color: #da8548; font-weight: bold;">17</span>)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">STD_DEV_X_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">STD_DEV_Y_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">blurred</span> = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MAX_COLOR_VAL</span> = <span style="color: #da8548; font-weight: bold;">255</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">BLOCK_SIZE</span> = <span style="color: #da8548; font-weight: bold;">15</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">SUBTRACT_FROM_MEAN</span> = -<span style="color: #da8548; font-weight: bold;">2</span>
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">img_bin</span> = cv2.adaptiveThreshold(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ~blurred,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> MAX_COLOR_VAL,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.ADAPTIVE_THRESH_MEAN_C,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.THRESH_BINARY,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> BLOCK_SIZE,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> SUBTRACT_FROM_MEAN,
|
|
<span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertical</span> = <span style="color: #dcaeea;">horizontal</span> = img_bin.copy()
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">SCALE</span> = <span style="color: #da8548; font-weight: bold;">5</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_width</span>, <span style="color: #dcaeea;">image_height</span> = horizontal.shape
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontal_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #c678dd;">int</span>(image_width / SCALE), <span style="color: #da8548; font-weight: bold;">1</span>))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontally_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertical_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #c678dd;">int</span>(image_height / SCALE)))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertically_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontally_dilated</span> = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">40</span>, <span style="color: #da8548; font-weight: bold;">1</span>)))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertically_dilated</span> = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #da8548; font-weight: bold;">60</span>)))
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">mask</span> = horizontally_dilated + vertically_dilated
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span>, <span style="color: #dcaeea;">heirarchy</span> = cv2.findContours(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE,
|
|
<span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">perimeter_lengths</span> = [cv2.arcLength(c, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">epsilons</span> = [<span style="color: #da8548; font-weight: bold;">0.05</span> * p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> perimeter_lengths]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">approx_polys</span> = [cv2.approxPolyDP(c, e, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c, e <span style="color: #51afef;">in</span> <span style="color: #c678dd;">zip</span>(contours, epsilons)]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Filter out contours that aren't rectangular. Those that aren't rectangular</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">are probably noise.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">approx_rects</span> = [p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> approx_polys <span style="color: #51afef;">if</span> <span style="color: #c678dd;">len</span>(p) == <span style="color: #da8548; font-weight: bold;">4</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [cv2.boundingRect(a) <span style="color: #51afef;">for</span> a <span style="color: #51afef;">in</span> approx_polys]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Filter out rectangles that are too narrow or too short.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MIN_RECT_WIDTH</span> = <span style="color: #da8548; font-weight: bold;">40</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MIN_RECT_HEIGHT</span> = <span style="color: #da8548; font-weight: bold;">10</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> r <span style="color: #51afef;">for</span> r <span style="color: #51afef;">in</span> bounding_rects <span style="color: #51afef;">if</span> MIN_RECT_WIDTH < r[<span style="color: #da8548; font-weight: bold;">2</span>] <span style="color: #51afef;">and</span> MIN_RECT_HEIGHT < r[<span style="color: #da8548; font-weight: bold;">3</span>]
|
|
<span style="background-color: #282c34;"> </span> ]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">The largest bounding rectangle is assumed to be the entire table.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Remove it from the list. We don't want to accidentally try to OCR</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">the entire table.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">largest_rect</span> = <span style="color: #c678dd;">max</span>(bounding_rects, key=<span style="color: #51afef;">lambda</span> r: r[<span style="color: #da8548; font-weight: bold;">2</span>] * r[<span style="color: #da8548; font-weight: bold;">3</span>])
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [b <span style="color: #51afef;">for</span> b <span style="color: #51afef;">in</span> bounding_rects <span style="color: #51afef;">if</span> b <span style="color: #51afef;">is</span> <span style="color: #51afef;">not</span> largest_rect]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cells</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> bounding_rects]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">def</span> <span style="color: #c678dd;">cell_in_same_row</span>(c1, c2):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c1_center</span> = c1[<span style="color: #da8548; font-weight: bold;">1</span>] + c1[<span style="color: #da8548; font-weight: bold;">3</span>] - c1[<span style="color: #da8548; font-weight: bold;">3</span>] / <span style="color: #da8548; font-weight: bold;">2</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c2_bottom</span> = c2[<span style="color: #da8548; font-weight: bold;">1</span>] + c2[<span style="color: #da8548; font-weight: bold;">3</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c2_top</span> = c2[<span style="color: #da8548; font-weight: bold;">1</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> c2_top < c1_center < c2_bottom
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">orig_cells</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> cells]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rows</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">while</span> cells:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">first</span> = cells[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rest</span> = cells[<span style="color: #da8548; font-weight: bold;">1</span>:]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cells_in_same_row</span> = <span style="color: #c678dd;">sorted</span>(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> rest
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> cell_in_same_row(c, first)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ],
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> key=<span style="color: #51afef;">lambda</span> c: c[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">row_cells</span> = <span style="color: #c678dd;">sorted</span>([first] + cells_in_same_row, key=<span style="color: #51afef;">lambda</span> c: c[<span style="color: #da8548; font-weight: bold;">0</span>])
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> rows.append(row_cells)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cells</span> = [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> rest
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> <span style="color: #51afef;">not</span> cell_in_same_row(c, first)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Sort rows by average height of their center.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">def</span> <span style="color: #c678dd;">avg_height_of_center</span>(row):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">centers</span> = [y + h - h / <span style="color: #da8548; font-weight: bold;">2</span> <span style="color: #51afef;">for</span> x, y, w, h <span style="color: #51afef;">in</span> row]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> <span style="color: #c678dd;">sum</span>(centers) / <span style="color: #c678dd;">len</span>(centers)
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> rows.sort(key=avg_height_of_center)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cell_images_rows</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> row <span style="color: #51afef;">in</span> rows:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cell_images_row</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> x, y, w, h <span style="color: #51afef;">in</span> row:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cell_images_row.append(image[y:y+h, x:x+w])
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cell_images_rows.append(cell_images_row)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> cell_images_rows
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><<extract-cells-<span style="color: #51afef;">from</span>-table>>
|
|
<span style="color: #dcaeea;">image</span> = cv2.imread(<span style="color: #98be65;">"resources/examples/example-table.png"</span>, cv2.IMREAD_GRAYSCALE)
|
|
<span style="color: #dcaeea;">cell_images_rows</span> = extract_cell_images_from_table(image)
|
|
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-cell-1-1.png"</span>, cell_images_rows[<span style="color: #da8548; font-weight: bold;">1</span>][<span style="color: #da8548; font-weight: bold;">1</span>])
|
|
<span style="color: #98be65;">"resources/examples/example-table-cell-1-1.png"</span>
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-orgb00b770" class="outline-4">
|
|
<h4 id="orgb00b770"><span class="section-number-4">3.0.6</span> Cropping each cell to the text</h4>
|
|
<div class="outline-text-4" id="text-3-0-6">
|
|
<p>
|
|
OCR with Tesseract works best when there is about 10 pixels of white border
|
|
around the text.
|
|
</p>
|
|
|
|
<p>
|
|
Our bounding rectangles may have picked up some stray pixels from the horizontal
|
|
and vertical lines of the cells in the table. It’s probobly just a few pixels,
|
|
much fewer than the width of the text. If that’s the case, then we can remove
|
|
that noise with a simple open morph.
|
|
</p>
|
|
|
|
<p>
|
|
Once the stray border pixels have been removed, we can expand our border using
|
|
<code>openMakeBorder</code>.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">crop_to_text</span>(image):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">kernel</span> = cv2.getStructuringElement(cv2.MORPH_CROSS, (<span style="color: #da8548; font-weight: bold;">4</span>, <span style="color: #da8548; font-weight: bold;">4</span>))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">opened</span> = cv2.morphologyEx(~image, cv2.MORPH_OPEN, kernel)
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span>, <span style="color: #dcaeea;">hierarchy</span> = cv2.findContours(opened, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [cv2.boundingRect(c) <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">The largest contour is certainly the text that we're looking for.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">largest_rect</span> = <span style="color: #c678dd;">max</span>(bounding_rects, key=<span style="color: #51afef;">lambda</span> r: r[<span style="color: #da8548; font-weight: bold;">2</span>] * r[<span style="color: #da8548; font-weight: bold;">3</span>])
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">x</span>, <span style="color: #dcaeea;">y</span>, <span style="color: #dcaeea;">w</span>, <span style="color: #dcaeea;">h</span> = largest_rect
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cropped</span> = image[y:y+h, x:x+w]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bordered</span> = cv2.copyMakeBorder(cropped, <span style="color: #da8548; font-weight: bold;">5</span>, <span style="color: #da8548; font-weight: bold;">5</span>, <span style="color: #da8548; font-weight: bold;">5</span>, <span style="color: #da8548; font-weight: bold;">5</span>, cv2.BORDER_CONSTANT, <span style="color: #a9a1e1;">None</span>, <span style="color: #da8548; font-weight: bold;">255</span>)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> bordered
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">import</span> cv2
|
|
<<crop-to-text>>
|
|
<span style="color: #dcaeea;">image</span> = cv2.imread(<span style="color: #98be65;">"resources/examples/example-table-cell-1-1.png"</span>, cv2.IMREAD_GRAYSCALE)
|
|
<span style="color: #dcaeea;">image</span> = crop_to_text(image)
|
|
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-cell-1-1-cropped.png"</span>, image)
|
|
<span style="color: #98be65;">"resources/examples/example-table-cell-1-1-cropped.png"</span>
|
|
</pre>
|
|
</div>
|
|
|
|
|
|
<div class="figure">
|
|
<p><img src="resources/examples/example-table-cell-1-1-cropped.png" alt="example-table-cell-1-1-cropped.png" width="200px" height="100%" />
|
|
</p>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-orgd24a937" class="outline-4">
|
|
<h4 id="orgd24a937"><span class="section-number-4">3.0.7</span> OCR each cell</h4>
|
|
<div class="outline-text-4" id="text-3-0-7">
|
|
<p>
|
|
If we cleaned up the images well enough, we might get some accurate OCR!
|
|
</p>
|
|
|
|
<p>
|
|
There is plenty that could have gone wrong along the way.
|
|
</p>
|
|
|
|
<p>
|
|
The first step to troubleshooting is to view the intermediate images and see if
|
|
there’s something about your image that is obviously abnormal, like some really
|
|
thick noise or a wrongly detected table.
|
|
</p>
|
|
|
|
<p>
|
|
If everything looks reasonable but the OCR is doing something like turning a
|
|
period into a comma, then you might need to do some custom Tesseract training.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">crop_to_text</span>(image):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">kernel</span> = cv2.getStructuringElement(cv2.MORPH_CROSS, (<span style="color: #da8548; font-weight: bold;">4</span>, <span style="color: #da8548; font-weight: bold;">4</span>))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">opened</span> = cv2.morphologyEx(~image, cv2.MORPH_OPEN, kernel)
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span>, <span style="color: #dcaeea;">hierarchy</span> = cv2.findContours(opened, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [cv2.boundingRect(c) <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">The largest contour is certainly the text that we're looking for.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">largest_rect</span> = <span style="color: #c678dd;">max</span>(bounding_rects, key=<span style="color: #51afef;">lambda</span> r: r[<span style="color: #da8548; font-weight: bold;">2</span>] * r[<span style="color: #da8548; font-weight: bold;">3</span>])
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">x</span>, <span style="color: #dcaeea;">y</span>, <span style="color: #dcaeea;">w</span>, <span style="color: #dcaeea;">h</span> = largest_rect
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cropped</span> = image[y:y+h, x:x+w]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bordered</span> = cv2.copyMakeBorder(cropped, <span style="color: #da8548; font-weight: bold;">5</span>, <span style="color: #da8548; font-weight: bold;">5</span>, <span style="color: #da8548; font-weight: bold;">5</span>, <span style="color: #da8548; font-weight: bold;">5</span>, cv2.BORDER_CONSTANT, <span style="color: #a9a1e1;">None</span>, <span style="color: #da8548; font-weight: bold;">255</span>)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> bordered
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">ocr_image</span>(image, config):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cropped</span> = crop_to_text(image)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> pytesseract.image_to_string(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ~cropped,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> config=config
|
|
<span style="background-color: #282c34;"> </span> )
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">import</span> pytesseract
|
|
<span style="color: #dcaeea;">image</span> = cv2.imread(<span style="color: #98be65;">"resources/examples/example-table-cell-1-1.png"</span>, cv2.IMREAD_GRAYSCALE)
|
|
<<ocr-image>>
|
|
ocr_image(image, <span style="color: #98be65;">"--psm 7"</span>)
|
|
</pre>
|
|
</div>
|
|
|
|
<pre class="example">
|
|
9.09
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-org8689ce0" class="outline-2">
|
|
<h2 id="org8689ce0"><span class="section-number-2">4</span> Files</h2>
|
|
<div class="outline-text-2" id="text-4">
|
|
<div class="org-src-container">
|
|
<pre class="src src-python">
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-org91ea732" class="outline-3">
|
|
<h3 id="org91ea732"><span class="section-number-3">4.1</span> setup.py</h3>
|
|
<div class="outline-text-3" id="text-4-1">
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">import</span> setuptools
|
|
|
|
<span style="color: #51afef;">with</span> <span style="color: #c678dd;">open</span>(<span style="color: #98be65;">"README.md"</span>, <span style="color: #98be65;">"r"</span>) <span style="color: #51afef;">as</span> fh:
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">long_description</span> = fh.read()
|
|
|
|
setuptools.setup(
|
|
<span style="background-color: #282c34;"> </span> name=<span style="color: #98be65;">"example-pkg-YOUR-USERNAME-HERE"</span>, <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Replace with your own username</span>
|
|
<span style="background-color: #282c34;"> </span> version=<span style="color: #98be65;">"0.0.1"</span>,
|
|
<span style="background-color: #282c34;"> </span> author=<span style="color: #98be65;">"Example Author"</span>,
|
|
<span style="background-color: #282c34;"> </span> author_email=<span style="color: #98be65;">"author@example.com"</span>,
|
|
<span style="background-color: #282c34;"> </span> description=<span style="color: #98be65;">"A small example package"</span>,
|
|
<span style="background-color: #282c34;"> </span> long_description=long_description,
|
|
<span style="background-color: #282c34;"> </span> long_description_content_type=<span style="color: #98be65;">"text/markdown"</span>,
|
|
<span style="background-color: #282c34;"> </span> url=<span style="color: #98be65;">"https://github.com/pypa/sampleproject"</span>,
|
|
<span style="background-color: #282c34;"> </span> packages=setuptools.find_packages(),
|
|
<span style="background-color: #282c34;"> </span> classifiers=[
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #98be65;">"Programming Language :: Python :: 3"</span>,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #98be65;">"License :: OSI Approved :: MIT License"</span>,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #98be65;">"Operating System :: OS Independent"</span>,
|
|
<span style="background-color: #282c34;"> </span> ],
|
|
<span style="background-color: #282c34;"> </span> python_requires=<span style="color: #98be65;">'>=3.6'</span>,
|
|
)
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-orgf115626" class="outline-3">
|
|
<h3 id="orgf115626"><span class="section-number-3">4.2</span> table_image_ocr</h3>
|
|
<div class="outline-text-3" id="text-4-2">
|
|
</div>
|
|
<div id="outline-container-org8765709" class="outline-4">
|
|
<h4 id="org8765709"><span class="section-number-4">4.2.1</span> table_image_ocr/__init__.py</h4>
|
|
<div class="outline-text-4" id="text-4-2-1">
|
|
<div class="org-src-container">
|
|
<pre class="src src-python">
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-org8d0619f" class="outline-4">
|
|
<h4 id="org8d0619f"><span class="section-number-4">4.2.2</span> table_image_ocr/util.py</h4>
|
|
<div class="outline-text-4" id="text-4-2-2">
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">from</span> contextlib <span style="color: #51afef;">import</span> contextmanager
|
|
<span style="color: #51afef;">import</span> functools
|
|
<span style="color: #51afef;">import</span> logging
|
|
<span style="color: #51afef;">import</span> os
|
|
<span style="color: #51afef;">import</span> tempfile
|
|
|
|
<span style="color: #51afef;">from</span> bs4 <span style="color: #51afef;">import</span> BeautifulSoup <span style="color: #51afef;">as</span> bs
|
|
<span style="color: #51afef;">import</span> requests
|
|
|
|
|
|
|
|
|
|
<span style="color: #dcaeea;">logger</span> = get_logger()
|
|
|
|
|
|
|
|
|
|
<span style="color: #ECBE7B;">@contextmanager</span>
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">working_dir</span>(directory):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">original_working_dir</span> = os.getcwd()
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">try</span>:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> os.chdir(directory)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">yield</span> directory
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">finally</span>:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> os.chdir(original_working_dir)
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">download</span>(url, filepath):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">response</span> = request_get(url)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">data</span> = response.content
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">with</span> <span style="color: #c678dd;">open</span>(filepath, <span style="color: #98be65;">"wb"</span>) <span style="color: #51afef;">as</span> f:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> f.write(data)
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">make_tempdir</span>(identifier):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> tempfile.mkdtemp(prefix=<span style="color: #98be65;">"{}_"</span>.<span style="color: #c678dd;">format</span>(identifier))
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-orga454dca" class="outline-4">
|
|
<h4 id="orga454dca"><span class="section-number-4">4.2.3</span> table_image_ocr/prepare_pdfs.py</h4>
|
|
<div class="outline-text-4" id="text-4-2-3">
|
|
<p>
|
|
Takes a variable number of pdf files and creates images out of each page of the
|
|
file using <code>pdfimages</code> from Poppler. Images are created in the same directory
|
|
that contains the pdf.
|
|
</p>
|
|
|
|
<p>
|
|
Prints each pdf followed by the images extracted from that pdf followed by a
|
|
blank line.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-shell">python -m pdf.prepare_pdfs /tmp/file1/file1.pdf /tmp/file2/file2.pdf ...
|
|
</pre>
|
|
</div>
|
|
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">import</span> argparse
|
|
<span style="color: #51afef;">import</span> logging
|
|
<span style="color: #51afef;">import</span> os
|
|
<span style="color: #51afef;">import</span> re
|
|
<span style="color: #51afef;">import</span> subprocess
|
|
<span style="color: #51afef;">import</span> sys
|
|
|
|
<span style="color: #51afef;">from</span> pdf.util <span style="color: #51afef;">import</span> request_get, working_dir, download, make_tempdir
|
|
|
|
|
|
|
|
|
|
<span style="color: #dcaeea;">logger</span> = get_logger()
|
|
|
|
<span style="color: #dcaeea;">parser</span> = argparse.ArgumentParser()
|
|
parser.add_argument(<span style="color: #98be65;">"files"</span>, nargs=<span style="color: #98be65;">"+"</span>)
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">main</span>(files):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">pdf_images</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> f <span style="color: #51afef;">in</span> files:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> pdf_images.append((f, pdf_to_images(f)))
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> pdf, images <span style="color: #51afef;">in</span> pdf_images:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> image <span style="color: #51afef;">in</span> images:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> preprocess_img(image)
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> pdf, images <span style="color: #51afef;">in</span> pdf_images:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">print</span>(<span style="color: #98be65;">"{}\n{}\n"</span>.<span style="color: #c678dd;">format</span>(pdf, <span style="color: #98be65;">"\n"</span>.join(images)))
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">pdf_to_images</span>(pdf_filepath):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Turn a pdf into images</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span>, <span style="color: #dcaeea;">filename</span> = os.path.split(pdf_filepath)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">with</span> working_dir(directory):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_filenames</span> = pdfimages(pdf_filepath)
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Since pdfimages creates a number of files named each for there page number</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">and doesn't return us the list that it created</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> [os.path.join(directory, f) <span style="color: #51afef;">for</span> f <span style="color: #51afef;">in</span> image_filenames]
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">pdfimages</span>(pdf_filepath):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Uses the `pdfimages` utility from Poppler</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> (https://poppler.freedesktop.org/). Creates images out of each page. Images</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> are prefixed by their name sans extension and suffixed by their page number.</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span>, <span style="color: #dcaeea;">filename</span> = os.path.split(pdf_filepath)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">filename_sans_ext</span> = filename.split(<span style="color: #98be65;">".pdf"</span>)[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
<span style="background-color: #282c34;"> </span> subprocess.run([<span style="color: #98be65;">"pdfimages"</span>, <span style="color: #98be65;">"-png"</span>, pdf_filepath, filename.split(<span style="color: #98be65;">".pdf"</span>)[<span style="color: #da8548; font-weight: bold;">0</span>]])
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_filenames</span> = find_matching_files_in_dir(filename_sans_ext, directory)
|
|
<span style="background-color: #282c34;"> </span> logger.debug(<span style="color: #98be65;">"Converted {} into files:\n{}"</span>.<span style="color: #c678dd;">format</span>(pdf_filepath, <span style="color: #98be65;">"\n"</span>.join(image_filenames)))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> image_filenames
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">find_matching_files_in_dir</span>(file_prefix, directory):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">files</span> = [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> filename
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> filename <span style="color: #51afef;">in</span> os.listdir(directory)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> re.match(r<span style="color: #98be65;">"{}.*\.png"</span>.<span style="color: #c678dd;">format</span>(re.escape(file_prefix)), filename)
|
|
<span style="background-color: #282c34;"> </span> ]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> files
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">preprocess_img</span>(filepath):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Processing that involves running shell executables,</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> like mogrify to rotate.</span>
|
|
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rotate</span> = get_rotate(filepath)
|
|
<span style="background-color: #282c34;"> </span> logger.debug(<span style="color: #98be65;">"Rotating {} by {}."</span>.<span style="color: #c678dd;">format</span>(filepath, rotate))
|
|
<span style="background-color: #282c34;"> </span> mogrify(filepath, rotate)
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">get_rotate</span>(image_filepath):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = (
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> subprocess.check_output([<span style="color: #98be65;">"tesseract"</span>, <span style="color: #98be65;">"--psm"</span>, <span style="color: #98be65;">"0"</span>, image_filepath, <span style="color: #98be65;">"-"</span>])
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> .decode(<span style="color: #98be65;">"utf-8"</span>)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> .split(<span style="color: #98be65;">"\n"</span>)
|
|
<span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = <span style="color: #c678dd;">next</span>(l <span style="color: #51afef;">for</span> l <span style="color: #51afef;">in</span> output <span style="color: #51afef;">if</span> <span style="color: #98be65;">"Rotate: "</span> <span style="color: #51afef;">in</span> l)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = output.split(<span style="color: #98be65;">": "</span>)[<span style="color: #da8548; font-weight: bold;">1</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> output
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">mogrify</span>(image_filepath, rotate):
|
|
<span style="background-color: #282c34;"> </span> subprocess.run([<span style="color: #98be65;">"mogrify"</span>, <span style="color: #98be65;">"-rotate"</span>, rotate, image_filepath])
|
|
|
|
<span style="color: #51afef;">if</span> <span style="color: #c678dd;">__name__</span> == <span style="color: #98be65;">"__main__"</span>:
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">args</span> = parser.parse_args()
|
|
<span style="background-color: #282c34;"> </span> main(args.files)
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-org076a34b" class="outline-4">
|
|
<h4 id="org076a34b"><span class="section-number-4">4.2.4</span> table_image_ocr/extract_tables.py</h4>
|
|
<div class="outline-text-4" id="text-4-2-4">
|
|
<div class="org-src-container">
|
|
<pre class="src src-shell">. ~/.virtualenvs/lotto_odds/bin/activate
|
|
python -m pdf.extract_tables <span style="color: #98be65;">"resources/examples/example-page.png"</span>
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">import</span> argparse
|
|
<span style="color: #51afef;">import</span> os
|
|
|
|
<span style="color: #51afef;">import</span> cv2
|
|
|
|
<span style="color: #dcaeea;">parser</span> = argparse.ArgumentParser()
|
|
parser.add_argument(<span style="color: #98be65;">"files"</span>, nargs=<span style="color: #98be65;">"+"</span>)
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">main</span>(files):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">results</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> f <span style="color: #51afef;">in</span> files:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span>, <span style="color: #dcaeea;">filename</span> = os.path.split(f)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image</span> = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">tables</span> = find_tables(image)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">files</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> i, table <span style="color: #51afef;">in</span> <span style="color: #c678dd;">enumerate</span>(tables):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">filename_sans_extension</span> = os.path.splitext(filename)[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">table_filename</span> = <span style="color: #98be65;">"{}-table-{:03d}.png"</span>.<span style="color: #c678dd;">format</span>(filename_sans_extension, i)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">table_filepath</span> = os.path.join(directory, table_filename)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> files.append(table_filepath)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.imwrite(table_filepath, table)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> results.append((f, files))
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> image_filename, table_filenames <span style="color: #51afef;">in</span> results:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">print</span>(<span style="color: #98be65;">"{}\n{}\n"</span>.<span style="color: #c678dd;">format</span>(image_filename, <span style="color: #98be65;">"\n"</span>.join(table_filenames)))
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">find_tables</span>(image):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">BLUR_KERNEL_SIZE</span> = (<span style="color: #da8548; font-weight: bold;">17</span>, <span style="color: #da8548; font-weight: bold;">17</span>)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">STD_DEV_X_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">STD_DEV_Y_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">blurred</span> = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MAX_COLOR_VAL</span> = <span style="color: #da8548; font-weight: bold;">255</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">BLOCK_SIZE</span> = <span style="color: #da8548; font-weight: bold;">15</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">SUBTRACT_FROM_MEAN</span> = -<span style="color: #da8548; font-weight: bold;">2</span>
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">img_bin</span> = cv2.adaptiveThreshold(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ~blurred,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> MAX_COLOR_VAL,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.ADAPTIVE_THRESH_MEAN_C,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.THRESH_BINARY,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> BLOCK_SIZE,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> SUBTRACT_FROM_MEAN,
|
|
<span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertical</span> = <span style="color: #dcaeea;">horizontal</span> = img_bin.copy()
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">SCALE</span> = <span style="color: #da8548; font-weight: bold;">5</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_width</span>, <span style="color: #dcaeea;">image_height</span> = horizontal.shape
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontal_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #c678dd;">int</span>(image_width / SCALE), <span style="color: #da8548; font-weight: bold;">1</span>))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontally_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertical_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #c678dd;">int</span>(image_height / SCALE)))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertically_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontally_dilated</span> = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">40</span>, <span style="color: #da8548; font-weight: bold;">1</span>)))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertically_dilated</span> = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #da8548; font-weight: bold;">60</span>)))
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">mask</span> = horizontally_dilated + vertically_dilated
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span>, <span style="color: #dcaeea;">heirarchy</span> = cv2.findContours(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE,
|
|
<span style="background-color: #282c34;"> </span> )
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MIN_TABLE_AREA</span> = 1e5
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours <span style="color: #51afef;">if</span> cv2.contourArea(c) > MIN_TABLE_AREA]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">perimeter_lengths</span> = [cv2.arcLength(c, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">epsilons</span> = [<span style="color: #da8548; font-weight: bold;">0.1</span> * p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> perimeter_lengths]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">approx_polys</span> = [cv2.approxPolyDP(c, e, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c, e <span style="color: #51afef;">in</span> <span style="color: #c678dd;">zip</span>(contours, epsilons)]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [cv2.boundingRect(a) <span style="color: #51afef;">for</span> a <span style="color: #51afef;">in</span> approx_polys]
|
|
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">The link where a lot of this code was borrowed from recommends an</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">additional step to check the number of "joints" inside this bounding rectangle.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">A table should have a lot of intersections. We might have a rectangular image</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">here though which would only have 4 intersections, 1 at each corner.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Leaving that step as a future </span><span style="color: #ECBE7B; font-weight: bold;">TODO</span><span style="color: #5B6268;"> if it is ever necessary.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">images</span> = [image[y:y+h, x:x+w] <span style="color: #51afef;">for</span> x, y, w, h <span style="color: #51afef;">in</span> bounding_rects]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> images
|
|
|
|
<span style="color: #51afef;">if</span> <span style="color: #c678dd;">__name__</span> == <span style="color: #98be65;">"__main__"</span>:
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">args</span> = parser.parse_args()
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">files</span> = args.files
|
|
<span style="background-color: #282c34;"> </span> main(files)
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-org1b2f268" class="outline-4">
|
|
<h4 id="org1b2f268"><span class="section-number-4">4.2.5</span> table_image_ocr/extract_cells_from_table.py</h4>
|
|
<div class="outline-text-4" id="text-4-2-5">
|
|
<div class="org-src-container">
|
|
<pre class="src src-shell">. ~/.virtualenvs/lotto_odds/bin/activate
|
|
python -m pdf.extract_cells_from_table <span style="color: #98be65;">"resources/examples/example-table.png"</span>
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-python"><span style="color: #51afef;">import</span> os
|
|
<span style="color: #51afef;">import</span> sys
|
|
|
|
<span style="color: #51afef;">import</span> cv2
|
|
<span style="color: #51afef;">import</span> pytesseract
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">main</span>(f):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">results</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span>, <span style="color: #dcaeea;">filename</span> = os.path.split(f)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">table</span> = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rows</span> = extract_cell_images_from_table(table)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cell_img_dir</span> = os.path.join(directory, <span style="color: #98be65;">"cells"</span>)
|
|
<span style="background-color: #282c34;"> </span> os.makedirs(cell_img_dir, exist_ok=<span style="color: #a9a1e1;">True</span>)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> i, row <span style="color: #51afef;">in</span> <span style="color: #c678dd;">enumerate</span>(rows):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> j, cell <span style="color: #51afef;">in</span> <span style="color: #c678dd;">enumerate</span>(row):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cell_filename</span> = <span style="color: #98be65;">"{:03d}-{:03d}.png"</span>.<span style="color: #c678dd;">format</span>(i, j)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">path</span> = os.path.join(cell_img_dir, cell_filename)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.imwrite(path, cell)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">print</span>(cell_filename)
|
|
|
|
|
|
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">extract_cell_images_from_table</span>(image):
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">BLUR_KERNEL_SIZE</span> = (<span style="color: #da8548; font-weight: bold;">17</span>, <span style="color: #da8548; font-weight: bold;">17</span>)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">STD_DEV_X_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">STD_DEV_Y_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">blurred</span> = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MAX_COLOR_VAL</span> = <span style="color: #da8548; font-weight: bold;">255</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">BLOCK_SIZE</span> = <span style="color: #da8548; font-weight: bold;">15</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">SUBTRACT_FROM_MEAN</span> = -<span style="color: #da8548; font-weight: bold;">2</span>
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">img_bin</span> = cv2.adaptiveThreshold(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ~blurred,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> MAX_COLOR_VAL,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.ADAPTIVE_THRESH_MEAN_C,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.THRESH_BINARY,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> BLOCK_SIZE,
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> SUBTRACT_FROM_MEAN,
|
|
<span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertical</span> = <span style="color: #dcaeea;">horizontal</span> = img_bin.copy()
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">SCALE</span> = <span style="color: #da8548; font-weight: bold;">5</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_width</span>, <span style="color: #dcaeea;">image_height</span> = horizontal.shape
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontal_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #c678dd;">int</span>(image_width / SCALE), <span style="color: #da8548; font-weight: bold;">1</span>))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontally_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertical_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #c678dd;">int</span>(image_height / SCALE)))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertically_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontally_dilated</span> = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">40</span>, <span style="color: #da8548; font-weight: bold;">1</span>)))
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertically_dilated</span> = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #da8548; font-weight: bold;">60</span>)))
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">mask</span> = horizontally_dilated + vertically_dilated
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span>, <span style="color: #dcaeea;">heirarchy</span> = cv2.findContours(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE,
|
|
<span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">perimeter_lengths</span> = [cv2.arcLength(c, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">epsilons</span> = [<span style="color: #da8548; font-weight: bold;">0.05</span> * p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> perimeter_lengths]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">approx_polys</span> = [cv2.approxPolyDP(c, e, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c, e <span style="color: #51afef;">in</span> <span style="color: #c678dd;">zip</span>(contours, epsilons)]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Filter out contours that aren't rectangular. Those that aren't rectangular</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">are probably noise.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">approx_rects</span> = [p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> approx_polys <span style="color: #51afef;">if</span> <span style="color: #c678dd;">len</span>(p) == <span style="color: #da8548; font-weight: bold;">4</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [cv2.boundingRect(a) <span style="color: #51afef;">for</span> a <span style="color: #51afef;">in</span> approx_polys]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Filter out rectangles that are too narrow or too short.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MIN_RECT_WIDTH</span> = <span style="color: #da8548; font-weight: bold;">40</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MIN_RECT_HEIGHT</span> = <span style="color: #da8548; font-weight: bold;">10</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> r <span style="color: #51afef;">for</span> r <span style="color: #51afef;">in</span> bounding_rects <span style="color: #51afef;">if</span> MIN_RECT_WIDTH < r[<span style="color: #da8548; font-weight: bold;">2</span>] <span style="color: #51afef;">and</span> MIN_RECT_HEIGHT < r[<span style="color: #da8548; font-weight: bold;">3</span>]
|
|
<span style="background-color: #282c34;"> </span> ]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">The largest bounding rectangle is assumed to be the entire table.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Remove it from the list. We don't want to accidentally try to OCR</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">the entire table.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">largest_rect</span> = <span style="color: #c678dd;">max</span>(bounding_rects, key=<span style="color: #51afef;">lambda</span> r: r[<span style="color: #da8548; font-weight: bold;">2</span>] * r[<span style="color: #da8548; font-weight: bold;">3</span>])
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [b <span style="color: #51afef;">for</span> b <span style="color: #51afef;">in</span> bounding_rects <span style="color: #51afef;">if</span> b <span style="color: #51afef;">is</span> <span style="color: #51afef;">not</span> largest_rect]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cells</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> bounding_rects]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">def</span> <span style="color: #c678dd;">cell_in_same_row</span>(c1, c2):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c1_center</span> = c1[<span style="color: #da8548; font-weight: bold;">1</span>] + c1[<span style="color: #da8548; font-weight: bold;">3</span>] - c1[<span style="color: #da8548; font-weight: bold;">3</span>] / <span style="color: #da8548; font-weight: bold;">2</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c2_bottom</span> = c2[<span style="color: #da8548; font-weight: bold;">1</span>] + c2[<span style="color: #da8548; font-weight: bold;">3</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">c2_top</span> = c2[<span style="color: #da8548; font-weight: bold;">1</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> c2_top < c1_center < c2_bottom
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">orig_cells</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> cells]
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rows</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">while</span> cells:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">first</span> = cells[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rest</span> = cells[<span style="color: #da8548; font-weight: bold;">1</span>:]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cells_in_same_row</span> = <span style="color: #c678dd;">sorted</span>(
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> rest
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> cell_in_same_row(c, first)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ],
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> key=<span style="color: #51afef;">lambda</span> c: c[<span style="color: #da8548; font-weight: bold;">0</span>]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> )
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">row_cells</span> = <span style="color: #c678dd;">sorted</span>([first] + cells_in_same_row, key=<span style="color: #51afef;">lambda</span> c: c[<span style="color: #da8548; font-weight: bold;">0</span>])
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> rows.append(row_cells)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cells</span> = [
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> rest
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> <span style="color: #51afef;">not</span> cell_in_same_row(c, first)
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ]
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Sort rows by average height of their center.</span>
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">def</span> <span style="color: #c678dd;">avg_height_of_center</span>(row):
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">centers</span> = [y + h - h / <span style="color: #da8548; font-weight: bold;">2</span> <span style="color: #51afef;">for</span> x, y, w, h <span style="color: #51afef;">in</span> row]
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> <span style="color: #c678dd;">sum</span>(centers) / <span style="color: #c678dd;">len</span>(centers)
|
|
<span style="background-color: #282c34;"> </span>
|
|
<span style="background-color: #282c34;"> </span> rows.sort(key=avg_height_of_center)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cell_images_rows</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> row <span style="color: #51afef;">in</span> rows:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">cell_images_row</span> = []
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> x, y, w, h <span style="color: #51afef;">in</span> row:
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cell_images_row.append(image[y:y+h, x:x+w])
|
|
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cell_images_rows.append(cell_images_row)
|
|
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> cell_images_rows
|
|
|
|
<span style="color: #51afef;">if</span> <span style="color: #c678dd;">__name__</span> == <span style="color: #98be65;">"__main__"</span>:
|
|
<span style="background-color: #282c34;"> </span> main(sys.argv[<span style="color: #da8548; font-weight: bold;">1</span>])
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="outline-container-orgde56bd1" class="outline-2">
|
|
<h2 id="orgde56bd1"><span class="section-number-2">5</span> Utils</h2>
|
|
<div class="outline-text-2" id="text-5">
|
|
<p>
|
|
The following code lets us specify a size for images when they are exported to
|
|
html.
|
|
</p>
|
|
|
|
<p>
|
|
Org supports specifying an export size for an image by putting the <code>#+ATTR_HTML:
|
|
:width 100px</code> before the image. But since our images are in a results drawer, we
|
|
need a way for our results drawer to do that for us automatically.
|
|
</p>
|
|
|
|
<p>
|
|
Adding <code>#+ATTR_HTML</code> after the beginning of the result block introduces a new
|
|
problem. Org-babel no longer recognizes the result as a result block and doesn’t
|
|
remove it when a src block is re-evaluated, so we end up just appending new
|
|
results on each evaluation.
|
|
</p>
|
|
|
|
<p>
|
|
There is nothing configurable that will tell org-babel to remove our line. But
|
|
we can define a function to do some cleanup and then add it as a before hook
|
|
with <code>advice-add</code>.
|
|
</p>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-emacs-lisp" id="org782d2f5"><span style="color: #51afef;">(</span><span style="color: #a9a1e1;">concat</span> <span style="color: #98be65;">"#+ATTR_HTML: :width "</span> width <span style="color: #98be65;">" :height "</span> height <span style="color: #98be65;">"\n[[file:"</span> text <span style="color: #98be65;">"]]"</span><span style="color: #51afef;">)</span>
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="org-src-container">
|
|
<pre class="src src-emacs-lisp"><span style="color: #51afef;">(</span><span style="color: #51afef;">defun</span> <span style="color: #c678dd;">remove-attributes-from-src-block-result</span> <span style="color: #c678dd;">(</span><span style="color: #ECBE7B;">&rest</span> args<span style="color: #c678dd;">)</span>
|
|
<span style="color: #c678dd;">(</span><span style="color: #51afef;">let</span> <span style="color: #98be65;">(</span><span style="color: #51afef;">(</span>location <span style="color: #c678dd;">(</span><span style="color: #c678dd;">org-babel-where-is-src-block-result</span><span style="color: #c678dd;">)</span><span style="color: #51afef;">)</span>
|
|
<span style="color: #51afef;">(</span>attr-regexp <span style="color: #98be65;">"[ ]*#\\+ATTR.*$"</span><span style="color: #51afef;">)</span><span style="color: #98be65;">)</span>
|
|
<span style="color: #98be65;">(</span><span style="color: #51afef;">when</span> location
|
|
<span style="color: #51afef;">(</span><span style="color: #51afef;">save-excursion</span>
|
|
<span style="color: #c678dd;">(</span><span style="color: #a9a1e1;">goto-char</span> location<span style="color: #c678dd;">)</span>
|
|
<span style="color: #c678dd;">(</span><span style="color: #51afef;">when</span> <span style="color: #98be65;">(</span><span style="color: #a9a1e1;">looking-at</span> <span style="color: #51afef;">(</span><span style="color: #a9a1e1;">concat</span> <span style="color: #dcaeea;">org-babel-result-regexp</span> <span style="color: #98be65;">".*$"</span><span style="color: #51afef;">)</span><span style="color: #98be65;">)</span>
|
|
<span style="color: #98be65;">(</span><span style="color: #c678dd;">next-line</span><span style="color: #98be65;">)</span>
|
|
<span style="color: #98be65;">(</span><span style="color: #51afef;">while</span> <span style="color: #51afef;">(</span><span style="color: #a9a1e1;">looking-at</span> attr-regexp<span style="color: #51afef;">)</span>
|
|
<span style="color: #51afef;">(</span><span style="color: #dcaeea;">kill-whole-line</span><span style="color: #51afef;">)</span><span style="color: #98be65;">)</span><span style="color: #c678dd;">)</span><span style="color: #51afef;">)</span><span style="color: #98be65;">)</span><span style="color: #c678dd;">)</span><span style="color: #51afef;">)</span>
|
|
|
|
<span style="color: #51afef;">(</span><span style="color: #c678dd;">advice-add</span> <span style="color: #51afef;">'</span><span style="color: #ECBE7B;">org-babel-remove-result</span> <span style="color: #c678dd;">:before</span> <span style="color: #51afef;">#'</span><span style="color: #ECBE7B;">remove-attributes-from-src-block-result</span><span style="color: #51afef;">)</span>
|
|
<span style="color: #51afef;">(</span><span style="color: #c678dd;">advice-add</span> <span style="color: #51afef;">'</span><span style="color: #ECBE7B;">org-babel-execute-src-block</span> <span style="color: #c678dd;">:before</span> <span style="color: #51afef;">#'</span><span style="color: #ECBE7B;">remove-attributes-from-src-block-result</span><span style="color: #51afef;">)</span>
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
<div id="postamble" class="status">
|
|
<p class="author">Author: Eric Ihli</p>
|
|
<p class="date">Created: 2020-04-10 Fri 13:49</p>
|
|
</div>
|
|
</body>
|
|
</html>
|