small update

ensure int for bbox lukas-blecher#122

README.md

@@ -1,6 +1,6 @@
 # pix2tex - LaTeX OCR
 
-[![GitHub](https://img.shields.io/github/license/lukas-blecher/LaTeX-OCR)](https://github.com/lukas-blecher/LaTeX-OCR) [![PyPI](https://img.shields.io/pypi/v/pix2tex?logo=pypi)](https://pypi.org/project/pix2tex) [![PyPI - Downloads](https://img.shields.io/pypi/dm/pix2tex?logo=pypi)](https://pypi.org/project/pix2tex) [![GitHub all releases](https://img.shields.io/github/downloads/lukas-blecher/LaTeX-OCR/total?color=blue&logo=github)](https://github.com/lukas-blecher/LaTeX-OCR/releases) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lukas-blecher/LaTeX-OCR/blob/master/notebooks/LaTeX_OCR_test.ipynb) 
+[![GitHub](https://img.shields.io/github/license/lukas-blecher/LaTeX-OCR)](https://github.com/lukas-blecher/LaTeX-OCR) [![PyPI](https://img.shields.io/pypi/v/pix2tex?logo=pypi)](https://pypi.org/project/pix2tex) [![PyPI - Downloads](https://img.shields.io/pypi/dm/pix2tex?logo=pypi)](https://pypi.org/project/pix2tex) [![GitHub all releases](https://img.shields.io/github/downloads/lukas-blecher/LaTeX-OCR/total?color=blue&logo=github)](https://github.com/lukas-blecher/LaTeX-OCR/releases) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lukas-blecher/LaTeX-OCR/blob/main/notebooks/LaTeX_OCR_test.ipynb) 
 
 The goal of this project is to create a learning based system that takes an image of a math formula and returns corresponding LaTeX code. 
 
@@ -15,7 +15,7 @@ Install the package `pix2tex`:
 pip install pix2tex
 ```
 
-Model checkpoints will be automatically downloaded.
+Model checkpoints will be downloaded automatically.
 
 There are two ways to get a prediction from an image. 
 1. You can use the command line tool by calling `pix2tex_cli`. Here you can parse already existing images from the disk and images in your clipboard.
@@ -33,7 +33,7 @@ The model works best with images of smaller resolution. That's why I added a pre
 
 Always double check the result carefully. You can try to redo the prediction with an other resolution if the answer was wrong.
 
-## Training the model [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lukas-blecher/LaTeX-OCR/blob/master/notebooks/LaTeX_OCR_training.ipynb)
+## Training the model [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lukas-blecher/LaTeX-OCR/blob/main/notebooks/LaTeX_OCR_training.ipynb)
 
 1. First we need to combine the images with their ground truth labels. I wrote a dataset class (which needs further improving) that saves the relative paths to the images with the LaTeX code they were rendered with. To generate the dataset pickle file run 
 

pix2tex/gui.py

@@ -280,10 +280,10 @@ def mouseReleaseEvent(self, event):
         startPos = self.startPos
         endPos = self.mouse.position
 
-        x1 = min(startPos[0], endPos[0])
-        y1 = min(startPos[1], endPos[1])
-        x2 = max(startPos[0], endPos[0])
-        y2 = max(startPos[1], endPos[1])
+        x1 = int(min(startPos[0], endPos[0]))
+        y1 = int(min(startPos[1], endPos[1]))
+        x2 = int(max(startPos[0], endPos[0]))
+        y2 = int(max(startPos[1], endPos[1]))
 
         self.repaint()
         QApplication.processEvents()

setup.py

@@ -9,13 +9,13 @@
 
 setuptools.setup(
     name='pix2tex',
-    version='0.0.8',
+    version='0.0.10',
     description="pix2tex: Using a ViT to convert images of equations into LaTeX code.",
     long_description=long_description,
     long_description_content_type='text/markdown',
     author='Lukas Blecher',
     author_email='[email protected]',
-    url='https://lukas-blecher.github.io/LaTeX-OCR/',
+    url='https://github.com/lukas-blecher/LaTeX-OCR/',
     license='MIT',
     keywords=[
         'artificial intelligence',