def extract_bu(original, x, w, y, h, num):
"""
Extract base units from layout units consisting of written text.
Args:
original: The input image.
x: X-coordinate of the bounding box.
w: Width of the bounding box.
y: Y-coordinate of the bounding box.
h: Height of the bounding box.
num: The number of contour.
Returns:
The layout unit identifier and the base units contained within it.
"""
# Extract the region defined by the bounding box
roi = original[y: y + h, x: x + w]
# Convert the region of interest into grayscale
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
# Perform thresholding using Otsu's method
(t, thresholded) = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
# Resize the thresholded image to 200% of the original
resized = imutils.resize(thresholded, width=2 * w)
# Feed the resized image to Pytesser
content = pytesser.mat_to_string(resized)
unicode_content = unicode(content, 'utf-8')
# TODO Use APNR techniques to measure font size at this stage
# - seek lines of text using aspect ratio of detected bounding boxes
# - use morphological operations to cut individual characters from the image
# - calculate their size and take the most common one (this should cover both normal & all caps)
# Tokenize sentences for base units
bu = Generate.tokenize(unicode_content)
# Return the extracted base units
return num, bu
def extract_bu(original, x, w, y, h, num):
"""
Extract base units from layout units consisting of written text.
Args:
original: The input image.
x: X-coordinate of the bounding box.
w: Width of the bounding box.
y: Y-coordinate of the bounding box.
h: Height of the bounding box.
num: The number of contour.
Returns:
The layout unit identifier and the base units contained within it.
"""
# Extract the region defined by the bounding box
roi = original[y:y + h, x:x + w]
# Convert the region of interest into grayscale
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
# Perform thresholding using Otsu's method
(t, thresholded) = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
# Resize the thresholded image to 200% of the original
resized = imutils.resize(thresholded, width=2 * w)
# Feed the resized image to Pytesser
content = pytesser.mat_to_string(resized)
unicode_content = unicode(content, 'utf-8')
# TODO Use APNR techniques to measure font size at this stage
# - seek lines of text using aspect ratio of detected bounding boxes
# - use morphological operations to cut individual characters from the image
# - calculate their size and take the most common one (this should cover both normal & all caps)
# Tokenize sentences for base units
bu = Generate.tokenize(unicode_content)
# Return the extracted base units
return num, bu
def extract_bu(original, x, w, y, h, num):
"""
Extract base units from layout units consisting of written text.
Args:
original: The input image.
x: X-coordinate of the bounding box.
w: Width of the bounding box.
y: Y-coordinate of the bounding box.
h: Height of the bounding box.
num: The number of contour.
Returns:
The layout unit identifier and the base units contained within it.
"""
# Extract the region defined by the bounding box
roi = original[y: y + h, x: x + w]
# Convert the region of interest into grayscale
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
# Perform thresholding using Otsu's method
(t, thresholded) = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
# Resize the thresholded image to 200% of the original
resized = imutils.resize(thresholded, width=2 * w)
# Feed the resized image to Pytesser
content = pytesser.mat_to_string(resized)
unicode_content = unicode(content, 'utf-8')
# Tokenize sentences for base units
bu = tokenize(unicode_content)
# Return the extracted base units
return num, bu
def extract_bu(original, x, w, y, h, num):
"""
Extract base units from layout units consisting of written text.
Args:
original: The input image.
x: X-coordinate of the bounding box.
w: Width of the bounding box.
y: Y-coordinate of the bounding box.
h: Height of the bounding box.
num: The number of contour.
Returns:
The layout unit identifier and the base units contained within it.
"""
# Extract the region defined by the bounding box
roi = original[y:y + h, x:x + w]
# Convert the region of interest into grayscale
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
# Perform thresholding using Otsu's method
(t, thresholded) = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
# Resize the thresholded image to 200% of the original
resized = imutils.resize(thresholded, width=2 * w)
# Feed the resized image to Pytesser
content = pytesser.mat_to_string(resized)
unicode_content = unicode(content, 'utf-8')
# Tokenize sentences for base units
bu = tokenize(unicode_content)
# Return the extracted base units
return num, bu
import numpy
import cv2
import time
import pytesser
print "Automatic Apple ID Creator\n"
index = 24
while index < 26:
path = "captcha/captcha" + str(index) + ".jpg"
img = cv2.imread(path)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#detect text in the image
text = pytesser.mat_to_string(gray_img)
print(text)
cv2.imshow('Image', gray_img)
key = cv2.waitKey(113)
time.sleep(0.25)
cv2.destroyAllWindows()
index = index + 1
import numpy
import cv2
import time
import pytesser
print "Automatic Apple ID Creator\n"
index = 24
while index < 26:
path = "captcha/captcha" + str(index) + ".jpg"
img = cv2.imread(path)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#detect text in the image
text = pytesser.mat_to_string(gray_img)
print(text)
cv2.imshow('Image', gray_img)
key = cv2.waitKey(113)
time.sleep(0.25)
cv2.destroyAllWindows()
index = index + 1
