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Incorrect padding error while importing base 64 images

By
E.M.
on 12/8/15, 11:25 AM 2,100 views

I am trying to import an image and I am getting the following error:

 

Unknown error during import: <class 'binascii.Error'>: Incorrect padding


I have read that this bug has been there in the past, I am running

Versión 8.0-20150726


 The base64 coded image is as follows (it can be checked easily in

http://codebeautify.org/base64-to-image-converter ,
it seems to be a valid base64 encoding):

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

how are you trying to import it?

Axel Mendoza
on 12/8/15, 1:03 PM

it is part of "image" column in a CSV. It works perfectly with other images. I am trying to guess that it might be related to image size or to number of records or something like that.

E.M.
on 12/8/15, 1:10 PM

I strongly suspect that this is due to image size, which is the maximum image size you can upload via CSV? Is it possible to increase it?

E.M.
on 12/10/15, 5:25 PM
1
Axel Mendoza
On 12/8/15, 2:07 PM

The thing is using csv you don't have the option to specify the field node type="base64" that will encode your image data using base64, better do it using xml to load the data.


Take an example

<record id="ax" model="res.country">
<field name="name">Åland Islands</field>
<field name="code">ax</field>
<field name="image" type="base64" file="base/static/img/country_flags/ax.png"></field>
<field name="currency_id" ref="EUR"/>
</record>

Or put directly the base64 data

<record id="ax" model="res.country">

<field name="name">Åland Islands</field>

<field name="code">ax</field>

<field name="image" type="base64">iVBORw0KGgoAAAANSUhEUgAAAH4AAABmCAIAAACCxLIpAABAa0lEQVR4nO29d7SlV3EnWrXDF0++

OXVO6lZo5dBIAsmASCILY5KxwWDjsf0A28PMcg7MPAYPg+3BAwaDkUAIBAKBAkq0cg6tTupudbp9

870nf3GHen+cVlsw0nqjVkN7vUetXr3uueuec/b+fbVr1676VW0kIvilnAxhJ3sA//+VX0J/0uSX</field>

<field name="currency_id" ref="EUR"/>

</record>

To load this kind of xml data just define it in an xml file defined in the data key of the __openerp__.py dict, like you do for views, actions and menus, it's the same, just records fields tuples that you are inserting in the record model

Where can I find an example for importing via XML?

E.M.
on 12/23/15, 4:26 AM

I put an example in the answer update since I cannot put it in comments

Axel Mendoza
on 12/23/15, 4:44 AM

Is it possible to include the whole base64 stream/string directly rather than pointing to an external file? And how do I import that XML file (never tried it)?

E.M.
on 12/23/15, 5:45 AM

Check the answer again since I update it with another example and how to load it

Axel Mendoza
on 12/23/15, 6:12 AM

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Asked: 12/8/15, 11:25 AM
Seen: 2100 times
Last updated: 12/30/15, 5:14 PM