In [122]:
import numpy as np
threshold = 200 #the average of the darkest values must be _below_ this to count (0 is darkest, 255 is lightest)
obviousness = 50 #how many of the darkest pixels to include (1 would mean a single dark pixel triggers it)
from PIL import Image
from pytesseract import image_to_string
def find_line(vals):
#implement edge detection once, use many times
for i,tmp in enumerate(vals):
tmp.sort()
average = float(sum(tmp[:obviousness]))/len(tmp[:obviousness])
if average <= threshold:
return i
return i #i is left over from failed threshold finding, it is the bounds
def getbox(img):
#get the bounding box of the interesting part of a PIL image object
#this is done by getting the darekest of the R, G or B value of each pixel
#and finding were the edge gest dark/colored enough
#returns a tuple of (left,upper,right,lower)
width, height = img.size #for making a 2d array
retval = [0,0,width,height] #values will be disposed of, but this is a black image's box
pixels = list(img.getdata())
vals = [] #store the value of the darkest color
for pixel in pixels:
vals.append(min(pixel)) #the darkest of the R,G or B values
#make 2d array
vals = np.array([vals[i * width:(i + 1) * width] for i in xrange(height)])
#start with upper bounds
forupper = vals.copy()
retval[1] = find_line(forupper)
#next, do lower bounds
forlower = vals.copy()
forlower = np.flipud(forlower)
retval[3] = height - find_line(forlower)
#left edge, same as before but roatate the data so left edge is top edge
forleft = vals.copy()
forleft = np.swapaxes(forleft,0,1)
retval[0] = find_line(forleft)
#and right edge is bottom edge of rotated array
forright = vals.copy()
forright = np.swapaxes(forright,0,1)
forright = np.flipud(forright)
retval[2] = width - find_line(forright)
if retval[0] >= retval[2] or retval[1] >= retval[3]:
print "error, bounding box is not legit"
return None
return tuple(retval)
def crop_image(image_name):
image = Image.open(image_name)
box = getbox(image)
#print "result is: ",box
result = image.crop(box)
cropped_image_name = 'cropped_'+image_name
result.save(cropped_image_name)
#result.show()
ratio = (box[2]-box[0])/float(box[3]-box[1])
return (cropped_image_name, ratio)
In [123]:
image_to_process, ratio = crop_image('pan2.jpg')
In [124]:
ratio
Out[124]:
In [125]:
if ratio < 1:
img = Image.open(image_to_process)
img2 = img.rotate(90, expand=True)
img2.save(image_to_process)
t = Image.open(image_to_process)
text_from_image = image_to_string(t, lang='eng')
if "INCOME" in text_from_image:
pass
else:
img = Image.open(image_to_process)
img2 = img.rotate(180, expand=True)
img2.save(image_to_process)
t = Image.open(image_to_process)
text_from_image = image_to_string(t, lang='eng')
In [126]:
list_of_words = text_from_image.split('\n')
In [128]:
list_of_words
Out[128]:
In [118]:
cleaned_words = []
for word in list_of_words:
stripped_word = word.strip()
if len(stripped_word)>1:
cleaned_words.append(stripped_word)
In [119]:
for i, word in enumerate(cleaned_words):
if "INCOME" in word:
break
In [120]:
user_name = cleaned_words[3]
fathers_name = cleaned_words[4]
dob = cleaned_words[5].replace(' ', '')
pan_card_no = cleaned_words[7].replace(' ', '')
In [121]:
user_name, fathers_name, dob, pan_card_no
Out[121]:
In [80]:
dob.replace(' ', '')
Out[80]:
In [ ]: