OCRmyPDF | OCRmyPDF adds an OCR text layer | Computer Vision library

https://github.com/dylan-profiler/visions/tree/v0.7.1/src/visions

But it's no longer available in v0.7.2

https://github.com/dylan-profiler/visions/tree/v0.7.2/src/visions

It also appears that the pandas_profiling project has been updated, the file summary.py no longer tries to do this import.

In summary: use visions version v0.7.1 or upgrade pandas_profiling.

https://issuetracker.google.com/154156890

I would recommend you to comment on the Public issue tracker and indicate that "you are affected to this issue" to gain visibility and push for get this change done.

Other that that I'm unsure is that can be implemented locally.

Now, to make all pixels transparent outside of the contour, you could do something like this:

1. Create a mask image but setting all pixels inside the contour white and black outside (set background to black and fill the path with white).
2. Use CIBlendWithMask filter where:
   • inputBackgroundImage is a fully transparent (clear) image
   • inputImage is the original frame
   • inputMaskImage is the mask you created above

The output of that filter will give you the image with all pixels outside the contour fully transparent. And now you can use the CIKMeans filter with it as described at the beginning.

BTW, if you want to play with every single of the 230 filters out there check this app out: https://apps.apple.com/us/app/filter-magic/id1594986951

CIFilters can only work with CIImages. So the mask image has to be a CIImage as well. One way to do that is to create a CGImage from CAShapeLayer containing the mask and then create CIImage out of it. Here is how the code could look like:

First, a prerequisite, you need to check the languages that are supported by language-based correction...

Look at supportedRecognitionLanguages for VNRecognizeTextRequestRevision2 for “accurate” recognition, and it would appear that the supported languages are:

That's what I managed to get using contours:

Code:

For LabVIEW users who have the NI vision library installed, there are VIs that allow for the image data of an IMAQ image to be copied from a 2D array.

For single-channel images (U8, U16, I16, float) the VI is

Vision and Motion >> Vision Utilites >> Pixel Manipulation >> IMAQ ArrayToImage.vi

For multichannel images (RGB etc) the VI is

Vision and Motion >> Vision Utilites >> Color Utilities >> IMAQ ArrayColorToImage.vi

Example 1

An example of using the IMAQ ArrayToImage.vi is shown in the snippet below where U16 data is read from a binary file and written to a Greyscale U16 type IMAQ image. Please note, if the file has been created by other software than LabVIEW then it is likely that it will have to be read in little-endian format which is specified for the Read From Binary File.vi

Example 2

A similar process can be used when some driver DLL call is used to get the image data as a buffer. For example, if the driver has a function capture(unsigned short * buffer) then the following technique could be employed where a correctly sized array is initialized before the function call using the initialize array primitive.