Open Tetsujinfr opened 7 years ago
Hey,
You can feed any image into the network as follows:
Hi @TobyPDE!
I tried to follow the steps you mentioned here but the predictions only get 2 classes (related with light pink and black colors).
I also printed the outout prediction matrix and, from what I understood, it should paint the segmented image with another colors.
[[[2 2 2 ..., 3 2 3]
[2 2 2 ..., 3 2 3]
[2 2 2 ..., 3 3 3]
...,
[9 9 9 ..., 0 0 0]
[8 9 9 ..., 0 0 0]
[0 0 9 ..., 0 0 0]]]
I made same changes at the create_color_label_image function on dltools/utility.py and it solved my problem.
With the best regards
@BBarbosa Hi, Can you show me your code here ?
@TobyPDE I have tried it . but it seems not work
Hi @daicoolb! These are the files I have changed:
I'm using OpenCV 3.3 because it gave me problems trying to load a video file with OpenCV 2.4. Download files from here frrn.zip
@BBarbosa Thanks for your changes! I have used your new scripts to predict my own data. The "predictions[0]" has now 3 dimensions instead of 2. For example: (1, 800, 1280) vs (800, 1280) Why is that? And with the same input images, if using "mypredict.py" I got different results from what "predict.py" did. It seems the "image" you feed to "pred_fn(image)" is not the same "batch[0]" the author feeds to "val_fn(batch[0], batch[1])".
@hans41 you're welcome. I hope it helped in some useful way.
Technically, (1,800,1280) and (800,1280) are equivalent.
With regard to the inputs images, i had problems feeding images to the network with the author's provider implementation. So, I managed to adapt one for my own following what @TobyPDE described here . The outputs may differ from mypredict.py to predict.py once there may be some pre-processing operations skipped.
This in an example of what I got. It works really good even in an different angle from what it was trained for.
First, thanks for the new update. I do not have access to Cityscape, how can I use the pre trained models on other images? Not sure how to transform pics and how to input them to the net properly.
thanks
Tets