A script to run prediction on any (unlabeled) image. And some small addition to Utils. #7
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Added Test on any unlabeled image. Couple extra checks and a function added to Utils.
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Thank you very much. I will review shortly. |
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I tested the custom demo script for arbitrary image, and it works well after some slight modifications. Please modify the script slightly based on my comment so that I will approve the merge. In addition, do also include some arbitrary images. I was using this image for test. Please also add brief guidance on how to use this script in the README.md for the public. Thank you very much for your contribution.
test_demo_any.py
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| model_filename = 'resnet_101_0.6959.ckpt' | ||
| image_filename='data/datasets/MyImg/JPEGImages/00.jpg' | ||
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| channel_means = save_load_means(means_filename='channel_means1.npz',image_filenames=None, recalculate=False) |
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Here we should use channel_means.npz instead of channel_means1.npz for compatibility with the current model settings.
test_demo_any.py
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| demo_dir = 'data/demos/deeplab/MyImg/' | ||
| models_dir = 'data/models/deeplab/resnet_101_voc2012/' | ||
| model_filename = 'resnet_101_0.6959.ckpt' | ||
| image_filename='data/datasets/MyImg/JPEGImages/00.jpg' |
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Please support multiple images in an image directory. A for loop will just work.
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I've done changes you proposed and committed in my repo. How do I proceed? Should I create a new pull request? (Sorry I' m new to github :)
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Thanks pinaxe1, no new pull request is required.
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Thank you very much for the contribution. I tested your program and it ran fine. I provided some suggestions and comments on the code. Please let me know if you have time or you are willing to further modify. Otherwise, I will just accept the pull request, and probably modify by my own in the near future, if I can find some time :) You attention and effort are appreciated.
| files = glob(demo_dir+'*.jpg') | ||
| for image_filename in files: | ||
| filename=osp.basename(image_filename).split('.')[0] | ||
| image = read_image(image_filename=image_filename) |
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read_image uses OpenCV which does support multiple different image formats. We should not restrict the image format to jpg.
| deeplab = DeepLab('resnet_101', training=False) | ||
| deeplab.load(osp.join(models_dir, model_filename)) | ||
| files = glob(demo_dir+'*.jpg') | ||
| for image_filename in files: |
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Try to use trange from tqdm to get progress bar. I believe you will like it.
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| if __name__ == '__main__': | ||
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| demo_dir = 'data/demos/deeplab/resnet_101_voc2012/' |
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Probably we can create a directory called custom_dir for new images and their predictions specifically. Royalty-free images could be downloaded from Pixabay.
| @@ -171,6 +172,12 @@ Image| Label | Prediction | | |||
| :-------------------------:|:-------------------------:|:-------------------------: | |||
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| ## Running demo on your own images:<br> | |||
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I am revising it slightly here.
Custom Demo
Just put some images into custom_dir and run the following command in the terminal:
$ python test_any_image.py
Results will be written into same folder. Make sure that proper model trained and a checkpoint is saved in models_dir. See the script for details.
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| if __name__ == '__main__': | ||
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| demo_dir = 'data/demos/deeplab/resnet_101_voc2012/' |
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The best way to do this with some customized arguments is probably to use argparse. Check some examples such as my train.py.
I think it would be the best course of actions. Because I'm more into quick dirty hacks than a proper programming :( You may put this requirements into ToDo so maybe someone (or even me) will make those sooner than you. :) |
I will accept the merge request then. Thank you very much for the contribution! |
Thank You. I greatly appreciate this DeepLab implementation of yours. It is really neat and clean and well documented. Thank You wery much. W.B.R. Paul. |
Added a function Single_demo to Utils to perform prediction on unlabeled images.
Added test_any_img script for same purpose.
WBR Paul