Closed delnasay closed 5 years ago
Hi! So one problem is that the image gets resized to a square image and then looks quite distorted. This shape is approximated by a slight rotation of the original model.
You can use the fucntion extract_square_patch() from dataset.py to create an undistorted image crop from a tight bounding box. This should improve the result. Also you could map the texture on the model to improve results.
For this thin object, maybe even a segmentation is sufficient?
Hi!
I trained the AAE with a cad leaf model, which I set VERTEX_SCALE: 80, AUXILIARY_MASK: True, BOOTSTRAP_RATIO: 10. The training image is showed below:![training image](https://github.com/delnasay/LeafModel/blob/master/training_images_29999.png?raw=true)
But when I test in images with![origin image](https://github.com/delnasay/LeafModel/blob/master/20190530090431.png?raw=true)
aae_image.py
, the results are not correct. For example, this is one of the images I used:The predict image is![pred image](https://github.com/delnasay/LeafModel/blob/master/20190530090431_pred.png?raw=true)
It is obviously that the predict result is not correct. Could you give me some suggestion for this? Thanks a lot.