def image_to_text(self, link):
try:
from PIL import Image
except:
import Image
self.r = mathpix.latex({
'src': mathpix.image_uri(link),
'formats': self.formats
})
'''except socket.error:
print('hi i am naresh')
pass'''
try:
self.element = self.r['latex_confidence']
if self.element != None:
self.confidence.append(self.element)
else:
self.confidence.append("0")
element = self.r['latex_simplified']
if element != None:
self.latex.append(element)
else:
self.latex.append("error")
element = self.r['text']
if element != None:
self.numerical_texts.append(element)
else:
self.numerical_texts.append("error")
except KeyError:
self.latex.append("error")
self.numerical_texts.append("error")
element = pytesseract.image_to_string(Image.open(link))
if element != None:
self.texts.append(element)
else:
self.texts.append("error")
self.data = pd.DataFrame(
list(
zip(self.numerical_texts, self.latex, self.confidence,
self.texts)),
columns=['numerical_text', 'latex', 'confidence', 'text'])
return self.data
def main(root):
savedir = '../out/'
list = os.listdir(root)
for filename in list:
path = os.path.join(rootdir, filename)
if not os.path.isfile(path):
break
portion = os.path.splitext(filename)
savename = portion[0] + '.txt'
savepath = os.path.join(savedir, savename)
r = mathpix.latex({
'src': mathpix.image_uri(path),
'formats': ['latex_simplified']
})
with open(savepath, 'w+') as f:
f.write(r['latex_simplified'])
print(savepath)
import mathpix
import json
#
# Example using Mathpix OCR with multiple result formats. We want to recognize
# both math and text in the image, so we pass the ocr parameter set to
# ['math', 'text']. This example returns the LaTeX text format, which
# starts in text mode instead of math mode, the latex_styled format,
# the asciimath format, and the mathml format. We define custom
# math delimiters for the text result so that the math is surrounded
# by dollar signs ("$").
#
r = mathpix.latex({
'src': mathpix.image_uri('../image/get.jpg'),
'ocr': ['math', 'text'],
'skip_recrop': True,
'formats': ['text', 'latex_styled', 'asciimath', 'mathml'],
'format_options': {
'text': {
'transforms': ['rm_spaces', 'rm_newlines'],
'math_delims': ['$', '$']
},
'latex_styled': {
'transforms': ['rm_spaces']
}
}
})
#
im = ImageGrab.grab()
while len(position) == 0:
with mouse.Listener(on_click=on_click) as listener:
listener.join()
bbox = (position[0], position[1], position[2], position[3])
print(bbox)
im.save('/tmp/latexTmp.jpg')
img = Image.open('/tmp/latexTmp.jpg')
cropped = img.crop(bbox)
cropped.save('/tmp/latexTmp.jpg')
try:
r.append(
mathpix.latex({
'src': mathpix.image_uri('/tmp/latexTmp.jpg'),
'formats': ['latex_simplified']
}))
except:
pass
r_new = []
for i in range(len(r)):
try:
islatexFormual = r[i]['latex_simplified'] != 0
if (islatexFormual):
r_new.append(r[i])
except:
pass
if (len(r_new) == 1):
formatDict = {}
calls += 1
if calls < 100:
pass
else:
print("Waiting for API")
time.sleep(60)
calls = 0
print(calls)
try:
r = mathpix.latex({'src': mathpix.image_uri(link), 'formats': formats})
except socket.error:
print(link)
pass
try:
element = r['latex_confidence']
if element != None:
confidence.append(element)
else:
confidence.append("0")
element = r['latex_simplified']
def pdftranslator():
finalResult = []
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'Mathpix-e6a478ab8826.json'
def listprocessor(words, pageNum):
# y-axis intervals A[p1by, p1ty] B[p2by, p2ty]
lines = []
currentLine = -1
for i in range(0, len(words) - 1):
sameLine = None # defaults to none
if not ((words[i + 1][1] - words[i][3] >= 0) or
(words[i][1] - words[i + 1][3] >=
0)): #(p2by-p1ty>=0||p1by-p2ty>=0):
oBot = max(words[i][1], words[i + 1][1]) #max(p1by,p2by)
oTop = min(words[i][3], words[i + 1][3]) #min(p1ty,p2ty)
overlap = oTop - oBot
overlapConst = 0.2
if overlap >= overlapConst * max(
words[i][3] - words[i][1],
words[i + 1][3] - words[i + 1][1]
): # overlapConst*max(p1ty-p1by, p2ty-p2by)): # checks if the words overlap
sameLine = True
else:
sameLine = False
else:
sameLine = False
if (sameLine == True):
# print("true") - for debugging purposes
lines[currentLine] = [
lines[currentLine][0],
min(lines[currentLine][1], words[i + 1][1]),
words[i + 1][2],
max(lines[currentLine][3], words[i + 1][3])
] #[p1tx, max(p1ty, p2ty), p2bx, min(p1by, p2by)])
else: # if not on the sameline
lines.append([
words[i + 1][0], words[i + 1][1], words[i + 1][2],
words[i + 1][3]
]) #([p2tx, p2ty, p2bx, p2by])
currentLine += 1
#print(lines)
lines.pop(0)
print(lines)
# Opens a image in RGB mode
im = Image.open(r"./preprocessed-data/preprocessed-data" +
str(pageNum) + ".jpg")
os.mkdir('/Users/matthewyang/PycharmProjects/MathPix3/processed-data' +
str(pageNum))
counter = 0
for line in lines:
im1 = im.crop((line[0] - 5, line[1] - 5, line[2] + 5, line[3] + 5))
saveName = 'crop' + str(counter) + '.jpg'
finalPath = os.path.join(
'/Users/matthewyang/PycharmProjects/MathPix3/processed-data' +
str(pageNum), saveName)
im1.save(finalPath, 'JPEG')
counter += 1
pages = convert_from_path('test.pdf', 750)
counta = 0
for page in pages:
page.save(
'./preprocessed-data/preprocessed-data' + str(counta) + '.jpg',
'JPEG')
counta += 1
# now crop based on the list
pageNum = 0
for filename in sorted(os.listdir('./preprocessed-data')):
if filename.endswith(".png") or filename.endswith(".jpg"):
#Text recognition
client = vision.ImageAnnotatorClient()
File_Name = filename
Folder_Path = r'/Users/matthewyang/PycharmProjects/MathPix3/preprocessed-data'
with io.open(os.path.join(Folder_Path, File_Name),
'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
response = client.text_detection(image=image)
#print(response)
texts = response.text_annotations
#print('Texts:')
bound_list = []
bound_list.append([100000, 100001, 99990, 99999])
i = 1
for text in texts:
#print('\n"{}"'.format(text.description))
vertices = ([
'({},{})'.format(vertex.x, vertex.y)
for vertex in text.bounding_poly.vertices
])
#print('bounds: {}'.format(','.join(vertices)))
bound_list.append('bounds: {}'.format(','.join(vertices)))
bound_list[i] = bound_list[i].strip('bounds: ')
bound_list[i] = bound_list[i].replace("(", "")
bound_list[i] = bound_list[i].replace(")", "")
bound_list[i] = [int(s) for s in bound_list[i].split(",")]
bound_list[i].pop(2)
bound_list[i].pop(2)
bound_list[i].pop(4)
bound_list[i].pop(4)
i += 1
# print(filename)
# print("\n")
print(bound_list)
print("\n")
listprocessor(bound_list, pageNum)
pageNum += 1
#!/usr/bin/env python3
#
# Example using Mathpix OCR with multiple result formats. We want to recognize
# both math and text in the image, so we pass the ocr parameter set to
# ['math', 'text']. This example returns the LaTeX text format, which
# starts in text mode instead of math mode, the latex_styled format,
# the asciimath format, and the mathml format. We define custom
# math delimiters for the text result so that the math is surrounded
# by dollar signs ("$").
#
#
#
#
# Create a for loop to go through all pngs generated.
for x in range(0, pageNum):
for filename in sorted(os.listdir('./processed-data' + str(x))):
r = mathpix.latex({
'src':
mathpix.image_uri(
os.path.join('./processed-data' + str(x), filename)),
'ocr': ['math', 'text'],
'skip_recrop':
True,
'formats': ['latex_styled'],
'format_options': {
'text': {
'transforms': ['rm_spaces', 'rm_newlines'],
'math_delims': ['$', '$']
}
}
})
#
# Note the actual results might be slighly different in LaTeX spacing or
# MathML attributes.
#
#
# print('Expected for r["text"]: "$-10 x^{2}+5 x-3$ and $-7 x+4$"')
# print('Expected for r["latex_styled"]: "-10 x^{2}+5 x-3 \\text { and }-7 x+4"')
# print('Expected for r["asciimath"]: "-10x^(2)+5x-3\\" and \\"-7x+4"')
# print('Expected for r["mathml"]: "<math><mo>\u2212</mo><mn>10</mn><msup><mi>x</mi><mn>2</mn></msup><mo>+</mo><mn>5</mn><mi>x</mi><mo>\u2212</mo><mn>3</mn><mtext>\u00a0and\u00a0</mtext><mo>\u2212</mo><mn>7</mn><mi>x</mi><mo>+</mo><mn>4</mn></math>"')
json_str = json.dumps(r, indent=4, sort_keys=True)
#print("\nResult object:", json_str)
output = json.loads(json_str)
finalResult.append(output['latex_styled'])
return finalResult
#!/usr/bin/env python3
import mathpix
import json
#
# Simple example of calling Mathpix OCR with ../images/algebra.jpg.
#
# We use the default ocr (math-only) and a single return format, latex_simplified.
#
# If you expect the image to be math and want to examine the result
# as math then the return format should either be latex_simplified,
# asciimath, or mathml. If you want to see the text in the image then
# you should include 'ocr': ['math', 'text'] as an argument and
# the return format should be either text or latex_styled
# (depending on whether you want to use the result as a paragraph or an equation).
#
r = mathpix.latex({
'src':
mathpix.image_uri(
'/home/jakob/Documents/Hackathon/lent21/api-examples/images/algebra.jpg'
),
'formats': ['latex_simplified']
})
print(json.dumps(r, indent=4, sort_keys=True))
assert (r['latex_simplified'] == '12 + 5 x - 8 = 12 x - 10')
import mathpix
import json
#
# Example using Mathpix OCR with multiple result formats. We want to recognize
# both math and text in the image, so we pass the ocr parameter set to
# ['math', 'text']. This example returns the LaTeX text format, which
# starts in text mode instead of math mode, the latex_styled format,
# the asciimath format, and the mathml format. We define custom
# math delimiters for the text result so that the math is surrounded
# by dollar signs ("$").
#
r = mathpix.latex({
'src': mathpix.image_uri('../images/mixed_text_math.jpg'),
'ocr': ['math', 'text'],
'skip_recrop': True,
'formats': ['text', 'latex_styled', 'asciimath', 'mathml'],
'format_options': {
'text': {
'transforms': ['rm_spaces', 'rm_newlines'],
'math_delims': ['$', '$']
},
'latex_styled': {
'transforms': ['rm_spaces']
}
}
})
#
#
# Example using Mathpix OCR with multiple result formats. We want to recognize
# both math and text in the image, so we pass the ocr parameter set to
# ['math', 'text']. This example returns the LaTeX text format, which
# starts in text mode instead of math mode, the latex_styled format,
# the asciimath format, and the mathml format. We define custom
# math delimiters for the text result so that the math is surrounded
# by dollar signs ("$").
#
#array to store the LaTex code into a final string
latex_code=[]
r1 = mathpix.latex({
'src': mathpix.image_uri('new_rotated1.png'),
'ocr': ['math', 'text'],
'skip_recrop': True,
'formats': ['text', 'latex_styled'],
'format_options': {
'text': {
'transforms': ['rm_spaces', 'rm_newlines'],
'math_delims': ['$', '$']
},
'latex_styled': {
'transforms': ['rm_spaces'],
'math_delim': ['$$','$$']}
}
})
r2 = mathpix.latex({
'src': mathpix.image_uri('new_rotated2.png'),
#!/usr/bin/env python3
import mathpix
import json
#
# Simple example of calling Mathpix OCR with ../images/algebra.jpg.
#
# We use the default ocr (math-only) and a single return format, latex_simplified.
#
# If you expect the image to be math and want to examine the result
# as math then the return format should either be latex_simplified,
# asciimath, or mathml. If you want to see the text in the image then
# you should include 'ocr': ['math', 'text'] as an argument and
# the return format should be either text or latex_styled
# (depending on whether you want to use the result as a paragraph or an equation).
#
r = mathpix.latex({
'src': mathpix.image_uri('../images/algebra.jpg'),
'formats': ['latex_simplified']
})
print(json.dumps(r, indent=4, sort_keys=True))
assert (r['latex_simplified'] == '12 + 5 x - 8 = 12 x - 10')