Deskew method errors out when running in docker(linux)

[uday-felix]

I am ending up with an error when I use the below code when its run in docker.

try:
     with skew(blob=img_png) as img_png_1:           
         img_png_1.deskew(0.5 * img_png_1.quantum_range)
         pil_image = Image.open(io.BytesIO(img_png_1.make_blob("png")))            
         img_png = image_to_byte_array(pil_image)            
     log.info("Corrected the Skewness for %s", file_name)
 except Exception as e:
     log.exception(f'Exception:{e} occured while Corrected the Skewness for {file_name}')
     pass

Error Message: wand.exceptions.WandRuntimeError: MagickReadImage returns false, but did not raise ImageMagick exception. This can occur when a delegate is missing, or returns EXIT_SUCCESS without generating a raster.

When I run this in local windows, I don't see any error, but failed in docker(linux). It keeps working for few images and fails for few. Please advice.

[emcconville]

The error is generic, and occurs when the decoder delegate is unable to read the image. Usually occurs when there's not enough resources to continue operations. Looking at your code, you maybe able to reduce the memory usage. Try removing everything but deskew, or write blob data to disk to save resources.

[uday-felix]

Thanks @emcconville . Also I have edited the policy.xml file which has improved.

[uday-felix]

@emcconville Am still facing the issue after increasing the values in policy.xml.
As am using this is production, I have 1000 + files going through the above code in multi thread at the same time and this kicks in the error.
Can you suggest something ?

@edit:

I have changed my code, removed this part of code from the above pil_image = Image.open(io.BytesIO(img_png_1.make_blob("png")))

after the try block I have a code to save the image which was converted to bytes.

[emcconville]

Can you suggest something ?

Look into queue-based task solution. AWS's SQS/lambda or Celery, but there are hundreds of other distributed messaging systems.

I have 1000 + files going through the above code in multi thread at the same time and this kicks in the error.

That's probably the issue. With a queued system, you can just add more workers, or flag/reject tasks that fail.