#!/usr/bin/env python
# Overplot transcription results on the original image.
# Show successful, erronious, and missing results.
import argparse
import pickle
from PIL import Image
import matplotlib
from matplotlib.backends.backend_agg import \
FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.patches
from matplotlib.cm import Set2
import numpy
from scipy.stats import gaussian_kde
import pandas
parser = argparse.ArgumentParser()
parser.add_argument("--source", help="Image file name",
type=str,default='modified.jpg')
parser.add_argument("--pickle", help="Pickled Textract results file name",
type=str,default='detection.pkl')
parser.add_argument("--benchmark", help="Benchmark results file name",
type=str,required=True)
parser.add_argument("--ndays", help="How many days in this month",
type=int,required=True)
parser.add_argument("--guides", help="Plot the day+hour guidelines",
type=bool,default=False)
parser.add_argument("--opimg", help="Image output file name",
default="oplot.png",
type=str,required=False)
parser.add_argument("--opstats", help="Statistics output file name",
default="stats.pkl",
type=str,required=False)
args = parser.parse_args()
# We're going to need the original image
im = Image.open(args.source)
# And the benchmark data
bm = pandas.read_csv(args.benchmark)
fig=Figure(figsize=((1*im.size[0]/100)*1.04,
(1*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)
ax_original=fig.add_axes([0.02,0.02,0.96,0.96],label='original')
ax_result=fig.add_axes([0.02,0.02,0.96,0.96],label='result')
# Matplotlib magic
canvas=FigureCanvas(fig)
# Turn off the axis tics
ax_original.set_axis_off()
ax_result.set_axis_off()
# Show the original image
ax_original.imshow(im)
# Load the JSON from Textract for this image
textract=pickle.load( open( args.pickle, "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
# Get bounding box centroid for text
def b2t(dct):
result=[0,0]
result[0]=dct['Left']+dct['Width']/2
result[1]=1.0-dct['Top']-dct['Height']/2
return result
# Make fake bounding box around point for missing items
def m2p(x,y):
result=numpy.zeros((4,2))
result[0,0]=x-0.014
result[0,1]=y-0.008
result[1,0]=x-0.014
result[1,1]=y+0.008
result[2,0]=x+0.014
result[2,1]=y+0.008
result[3,0]=x+0.014
result[3,1]=y-0.008
return result
# Do a few obvious fix-ups on the Textract output
def fixup(text):
# Insert the decimal point where it might have been missed
if len(text)==4:
text=text[0]+'.'+text[1:]
# Replace any non-digit with decimal point
for id in range(len(text)):
if not text[id].isdigit():
text=text[0:id]+'.'+text[id+1:]
return text
def hasNumbers(inputString):
return any(char.isdigit() for char in inputString)
# Find the column locations corresponding to hours,
# and the row locations corresponding to days
xc=[]
yc=[]
for block in textract['Blocks']:
if 'Text' in block and block['BlockType']=='WORD':
if not hasNumbers(block['Text']): continue
cent=b2t(block['Geometry']['BoundingBox'])
xc.append(cent[0])
yc.append(cent[1])
# Find clusters of points in a and y - these are the rows and columns
def get_columns(xc):
kde = gaussian_kde(xc,bw_method=0.02)
x_grid=numpy.linspace(0,1,1000)
dens=kde.evaluate(x_grid)
col=[]
for i in range(1,len(x_grid)-1):
if (dens[i]>1 and
dens[i]>dens[i-1] and
dens[i]>dens[i+1]):
col.append(x_grid[i])
return(col)
columns=get_columns(xc)
# If fewer than 26 columns we've lost some in the fold, add it back
while len(columns)<26:
columns.insert(0,columns[0]-(columns[1]-columns[0]))
columns=columns[1:(len(columns)-1)]
rows=get_columns(yc)
# Image might or might not have a row of hour numbers
if len(rows)==args.ndays+2:
rows=rows[1:(len(rows)-1)]
else:
rows=rows[1:(len(rows))]
# Draw the breaks
if args.guides==True:
for clm in columns:
ax_result.add_line(matplotlib.lines.Line2D(
xdata=(clm,clm),
ydata=(0,1),
linestyle='solid',
linewidth=1,
color=(1,0,0,1),
zorder=5))
for row in rows:
ax_result.add_line(matplotlib.lines.Line2D(
xdata=(0,1),
ydata=(row,row),
linestyle='solid',
linewidth=1,
color=(1,0,0,1),
zorder=5))
# Keep track of good, bad, and missing data
stats={'Total': 0,
'Good': 0,
'Bad': 0,
'Missing': 0}
# Make 2d array of blocks - for each row/column intersection
data_points=[[None] * len(columns) for i in range(len(rows))]
for block in textract['Blocks']:
if 'Text' in block and block['BlockType']=='WORD':
if (block['Geometry']['BoundingBox']['Width'] > 0.035 or
block['Geometry']['BoundingBox']['Width'] < 0.025 or
block['Geometry']['BoundingBox']['Height'] > 0.02 or
block['Geometry']['BoundingBox']['Height'] < 0.01): continue
at_column=None
for cmi in range(len(columns)):
clm=columns[cmi]
if (block['Geometry']['BoundingBox']['Left']<clm and
block['Geometry']['BoundingBox']['Left']+
block['Geometry']['BoundingBox']['Width']>clm):
at_column=cmi
break
if at_column is None: continue
for rwi in range(len(rows)):
row=rows[rwi]
if (1.0-block['Geometry']['BoundingBox']['Top']>row and
1.0-block['Geometry']['BoundingBox']['Top']-
block['Geometry']['BoundingBox']['Height']<row):
data_points[rwi][at_column]=block
break
# Draw a marker at every hour - either a text block or a missing marker
for rwi in range(len(rows)):
for cmi in range(len(columns)):
stats['Total'] += 1
if data_points[rwi][cmi] is None:
# Mark as missing
stats['Missing'] += 1
pp=matplotlib.patches.Polygon(m2p(columns[cmi],rows[rwi]),
closed=True,
fill=False,
hatch='/',
edgecolor=(1,0,0,1),
facecolor=(1,0,0,1),
linewidth=1.0,
alpha=1.0,
zorder=10)
ax_result.add_patch(pp)
else:
block=data_points[rwi][cmi]
# Compare the Textract value with the benchmark
try:
bmvalue=bm.iloc[len(rows)-rwi-1][cmi+1].replace("'","")
except:
bmvalue=0.0
if fixup(block['Text'])==bmvalue:
stats['Good'] += 1
bcol=(0.7,1,0.7,1)
else:
stats['Bad'] += 1
bcol=(1,0.5,0.5,1)
pp=matplotlib.patches.Polygon(d2p(block['Geometry']['Polygon']),
closed=True,
edgecolor=bcol,
facecolor=bcol,
fill=True,
linewidth=0.2,
alpha=0.85,
zorder=10)
ax_result.add_patch(pp)
# Text
txt_centroid=b2t(block['Geometry']['BoundingBox'])
angle=0
if (block['Geometry']['BoundingBox']['Height'] >
block['Geometry']['BoundingBox']['Width']*2):
angle=90
ax_result.text(txt_centroid[0],txt_centroid[1],
fixup(block['Text']),
fontsize=18,
verticalalignment='center',
horizontalalignment='center',
rotation=angle,
zorder=10)
# Draw the image
fig.savefig(args.opimg)
# Output the stats
print(stats)
with open( args.opstats, "wb" ) as pkf:
pickle.dump(stats, pkf )
