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<body>
<div id="content">
<h1 class="title">PDF Parsing</h1>
<div id="table-of-contents">
<h2>Table of Contents</h2>
<div id="text-table-of-contents">
<ul>
<li><a href="#org533e16a">1. Preparing our data</a>
<ul>
<li><a href="#orga09ea5b">1.1. Converting PDFs to images</a></li>
<li><a href="#orgf3a14b3">1.2. Detecting image orientation and applying rotation.</a></li>
</ul>
</li>
<li><a href="#org8657733">2. Detecting tables</a></li>
<li><a href="#orgda5b77b">3. OCR tables</a>
<ul>
<li>
<ul>
<li><a href="#org111c988">3.0.1. Blur</a></li>
<li><a href="#org08523db">3.0.2. Threshold</a></li>
<li><a href="#orgcfbb819">3.0.3. Finding the vertical and horizontal lines of the table</a></li>
<li><a href="#orge26e613">3.0.4. Finding the contours</a></li>
<li><a href="#org39c0a09">3.0.5. Sorting the bounding rectangles</a></li>
<li><a href="#orgb00b770">3.0.6. Cropping each cell to the text</a></li>
<li><a href="#orgd24a937">3.0.7. OCR each cell</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#org8689ce0">4. Files</a>
<ul>
<li><a href="#org91ea732">4.1. setup.py</a></li>
<li><a href="#orgf115626">4.2. table_image_ocr</a>
<ul>
<li><a href="#org8765709">4.2.1. table_image_ocr/__init__.py</a></li>
<li><a href="#org8d0619f">4.2.2. table_image_ocr/util.py</a></li>
<li><a href="#orga454dca">4.2.3. table_image_ocr/prepare_pdfs.py</a></li>
<li><a href="#org076a34b">4.2.4. table_image_ocr/extract_tables.py</a></li>
<li><a href="#org1b2f268">4.2.5. table_image_ocr/extract_cells_from_table.py</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#orgde56bd1">5. Utils</a></li>
</ul>
</div>
</div>
<div id="outline-container-org533e16a" class="outline-2">
<h2 id="org533e16a"><span class="section-number-2">1</span> Preparing our data</h2>
<div class="outline-text-2" id="text-1">
</div>
<div id="outline-container-orga09ea5b" class="outline-3">
<h3 id="orga09ea5b"><span class="section-number-3">1.1</span> Converting PDFs to images</h3>
<div class="outline-text-3" id="text-1-1">
<p>
Not all pdfs need to be sent through OCR to extract the text content. If you can
click and drag to highlight text in the pdf, then the tools in this library
probably aren&rsquo;t necessary.
</p>
<p>
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>.
</p>
<div class="org-src-container">
<pre class="src src-python" id="org1bef3d0"><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
</pre>
</div>
</div>
</div>
<div id="outline-container-orgf3a14b3" class="outline-3">
<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">
<p>
Tesseract can detect orientation and we can then use <a href="https://www.imagemagick.org/script/mogrify.php">ImageMagick&rsquo;s mogrify</a> to
rotate the image.
</p>
<p>
Here&rsquo;s an example of the output we get from orientation detection with
Tesseract.
</p>
<pre class="example">
➜ example/ tesseract --psm 0 example-000.png -
Page number: 0
Orientation in degrees: 90
Rotate: 270
Orientation confidence: 26.86
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&rsquo;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> &lt;&lt;blur&gt;&gt;
<span style="background-color: #282c34;"> </span> &lt;&lt;threshold&gt;&gt;
<span style="background-color: #282c34;"> </span> &lt;&lt;lines-of-table&gt;&gt;
<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) &gt; 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
&lt;&lt;detect-table&gt;&gt;
<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&rsquo;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&rsquo;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)
&lt;&lt;blur&gt;&gt;
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&rsquo;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">&lt;&lt;threshold&gt;&gt;
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&rsquo;s a wierd issue with the results of the example below when it&rsquo;s
evaluated as part of an export or a full-buffer evaluation. If you evaluate the
example by itself, it looks the way it&rsquo;s intended. If you evaluate it as part of
an entire buffer evaluation, it&rsquo;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">&lt;&lt;lines-of-table&gt;&gt;
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 &ldquo;Opening&rdquo; 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&rsquo;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 &lt; r[<span style="color: #da8548; font-weight: bold;">2</span>] <span style="color: #51afef;">and</span> MIN_RECT_HEIGHT &lt; 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&rsquo;ve thought of a straightforward algorithm for it, but it could probably be
made more efficient.
</p>
<p>
We&rsquo;ll find the most rectangle with the most top-left corner. Then we&rsquo;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&rsquo;ll sort those rectangles by the x
value of their center. We&rsquo;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 &lt; c1_center &lt; 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&rsquo;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)
&lt;&lt;blur&gt;&gt;
&lt;&lt;threshold&gt;&gt;
&lt;&lt;lines-of-table&gt;&gt;
&lt;&lt;bounding-rects&gt;&gt;
&lt;&lt;sort-contours&gt;&gt;
<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 &lt; r[<span style="color: #da8548; font-weight: bold;">2</span>] <span style="color: #51afef;">and</span> MIN_RECT_HEIGHT &lt; 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 &lt; c1_center &lt; 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">&lt;&lt;extract-cells-<span style="color: #51afef;">from</span>-table&gt;&gt;
<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&rsquo;s probobly just a few pixels,
much fewer than the width of the text. If that&rsquo;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
&lt;&lt;crop-to-text&gt;&gt;
<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&rsquo;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)
&lt;&lt;ocr-image&gt;&gt;
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;">'&gt;=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) &gt; 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 &lt; r[<span style="color: #da8548; font-weight: bold;">2</span>] <span style="color: #51afef;">and</span> MIN_RECT_HEIGHT &lt; 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 &lt; c1_center &lt; 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>
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</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&rsquo;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;">&amp;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>
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