# Plot the table layoutΒΆ

```#!/usr/bin/env python

# Plot a comparison of original image and transcription results.
# Show the table structure found by textract.

import pickle
import argparse
from PIL import Image

import matplotlib
from matplotlib.backends.backend_agg import \
FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.patches
import numpy

# We're going to need the original image
im = Image.open("../../../samples/103.jpg")

fig=Figure(figsize=((im.size[0]/100)*1.06,
(im.size[1]/100)*1.04),
dpi=100,
facecolor=(0.88,0.88,0.88,1),
edgecolor=None,
linewidth=0.0,
frameon=False,
subplotpars=None,
tight_layout=None)
# Matplotlib magic
canvas=FigureCanvas(fig)
# Turn off the axis tics
ax_original.set_axis_off()
ax_result.set_axis_off()

# Put the original image in its half of the figure
ax_original.imshow(im)

# Load the JSON from Textract for this image
textract=pickle.load( open( "detection.pkl", "rb" ) )
# Convert block polygon dictionary to numpy array for matplotlib
def d2p(dct):
result=numpy.zeros((len(dct),2))
for idx in range(len(dct)):
result[idx,0]=dct[idx]['X']
result[idx,1]=1.0-dct[idx]['Y']
return result
# Convert block bounding box dictionary to numpy array for matplotlib
def b2p(dct):
result=numpy.zeros((4,2))
result[0,0]=dct['Left']
result[1,0]=dct['Left']+dct['Width']
result[2,0]=dct['Left']+dct['Width']
result[3,0]=dct['Left']
result[0,1]=1.0-dct['Top']
result[1,1]=1.0-dct['Top']
result[2,1]=1.0-dct['Top']-dct['Height']
result[3,1]=1.0-dct['Top']-dct['Height']
return result

# Draw the 'CELL' blocks - coloured by row and column index
zorder=10
for block in textract['Blocks']:
#print(block['BlockType'])
if block['BlockType'] != 'CELL': continue
#print('Y')
ccolour=(0,0,1)
if (block['ColumnIndex']+block['RowIndex'])%2==1:
ccolour=(1,0,0)
pp=matplotlib.patches.Polygon(d2p(block['Geometry']['Polygon']),
closed=True,
edgecolor=(0,0,0),
facecolor=ccolour,
fill=True,
linewidth=2,
alpha=0.2,
zorder=zorder)