Extracting the detected boxes
See original GitHub issue@YoungminBaek Now that Craft have detected the boxes and saved them into a .txt
file, can you add a script to extract/crop the detected boxes into images.
Issue Analytics
- State:
- Created 4 years ago
- Comments:6
Top Results From Across the Web
extract the images inside the bounding boxes #23 - GitHub
I am still confused about extracting Bounding boxes as a separate image, ... image_np) # Visualization of the results of a detection.
Read more >Bounding box extraction using rHOG - ResearchGate
Download scientific diagram | Bounding box extraction using rHOG: extracting oblique shaped ... A robust algorithm for detecting people in overhead views.
Read more >The extractDetectedObjects Action - SAS Help Center
The extractDetectedObjects action enables you to extract the object detection results from the SAS Deep Learning Toolkit. The extraction can take place in ......
Read more >Object detection: Bounding box regression with Keras ...
In this tutorial you will learn how to train a custom deep learning model to perform object detection via bounding box regression with...
Read more >Extraction of images within a bounding box using Tensorflow ...
Below is a snippet from the Tensorflow Object Detection API sample that I am trying to change. I was trying to extract the...
Read more >
Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free
Top Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
This is trivial, You can use something like this:
Hi,
I also need to export the detected boxes from the Colab notebook, but I don’t understand how to do it.
With the code suggested by @akarazniewicz, I get this error:
What am I doing wrong?
Thank you so much!