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Fastest way to use Vision API on 10,000+ images with python


Fastest way to use Vision API on 10,000+ images with python

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Fastest way to use Vision API on 10,000+ images with python
Tag : python , By : 03B
Date : November 29 2020, 12:01 PM

Any of those help Doing more in parallel would get it done faster. You can also include more than 1 image in a single annotate request. For python see the batch_annotate_images method.
This has an example of what a single request looks like. You would use an array of requests with batch_annotate_images.

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Is Approximate Nearest Neighbour the fastest feature matching in Computer Vision?


Tag : development , By : Techspirit
Date : March 29 2020, 07:55 AM
it should still fix some issue I'd say that Euclidean distnace based nearest neighbor would be the easiest to implement, but not necessarily the fastest.
I'd agree that approximate nearest neighbor or 'best bin first' would be the quickest at identifying which image in your background set most closely resembles the probe image.

Fastest way to process images from web in python?


Tag : python , By : Fenix Drakken
Date : March 29 2020, 07:55 AM
this one helps. Since it's getting the images that takes the longest time, why not use threading(or Gevent) to get those images concurrently, throw the results in a task queue, and process when they are ready.
And add cache for images with the same url...

Google cloud vision not accepting base64 encoded images python


Tag : python , By : yogan
Date : March 29 2020, 07:55 AM
wish of those help I don't have any experience with Google Cloud Vision, however after looking at their documentation and examples, my feeling is that the linked documentation page about base64 encoding of image data is for the case when you create and send the HTTP requests on your own, without using vision.ImageAnnotatorClient. The latter seems to encode the image data automatically, hence in your example double encoding is applied. Therefore I believe that you should remove the encoding step from your code:
from google.cloud import vision
import base64
client = vision.ImageAnnotatorClient()
image_path ='8720911950_91828a2aeb_b.jpg'
with open(image_path, 'rb') as image:
    content = image.read()
    response = client.annotate_image({'image': {'content': content}, 'features': [{'type': vision.enums.Feature.Type.LABEL_DETECTION}],})
    print(response)

404 error using Python with Computer Vision API analyzing web-based images


Tag : python , By : kuba53280
Date : March 29 2020, 07:55 AM
will be helpful for those in need File this under "never mind". The problem was due to a missing slash after the url. I was copying the link from Azure, but it doesn't add it automatically and I missed it in the code.

fastest Python native way to manipulate images?


Tag : python , By : arbeitandy
Date : March 29 2020, 07:55 AM
wish help you to fix your issue This is too long for a single comment. So I am putting it here.
Note that I am not an expert on any of these tools, other than ImageMagick, but have dabbled a little with each.
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