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Thank you for sharing the code for the 'Boosting Crowd Counting via Multifaceted Attention' paper.
I'm intended to use it for initial step in a crowd forecasting project.
I was able to execute test.py file and see the mae and mse results 😊
Now, I want to be able to given an input image get its density map. Checking the paper I think it's an output of the Regression Decoder, but I couldn't found it in the code, so would you please point me how to achieve this?
Big thank you in advance.
The text was updated successfully, but these errors were encountered:
Hello @LoraLinH <https://github.com/LoraLinH> :)
Thank you for sharing the code for the 'Boosting Crowd Counting via
Multifaceted Attention' paper.
I'm intended to use it for initial step in a *crowd forecasting project*.
I was able to execute test.py file and see the mae and mse results 😊
Now, I want to be able to given an input image get its density map.
Checking the paper I think it's an output of the Regression Decoder, but I
couldn't found it in the code, so would you please point me how to achieve
this?
Big thank you in advance.
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I've manage to write such output into an image for the very first iteration, this output is expected right?
Code to generate the image:
outputs = model(inputs)[0] # a tensor of [1,1,117,156] and values between 0-1
outImg = outputs[0][0].detach().cpu().numpy()
cv2.imwrite("test.png", outImg*255)
Hello @LoraLinH :)
Thank you for sharing the code for the 'Boosting Crowd Counting via Multifaceted Attention' paper.
I'm intended to use it for initial step in a crowd forecasting project.
I was able to execute
test.py
file and see the mae and mse results 😊Now, I want to be able to given an input image get its density map. Checking the paper I think it's an output of the Regression Decoder, but I couldn't found it in the code, so would you please point me how to achieve this?
Big thank you in advance.
The text was updated successfully, but these errors were encountered: