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View Code? Open in Web Editor NEWAction Proposals generated by deep models
License: MIT License
Action Proposals generated by deep models
License: MIT License
The function ReadImageSequenceToVolumeDatum
on C3D is hard-coded to read images with format %s/%06d.jpg
therefore we should dump frames of videos accordingly.
We need a module to interact with activity-net dataset. I would recommend to make something similar to thumos14_helper.Thumos14
that generate the CSV-files required to interact with the annotations.
Some videos of Thumos14 are incorrectly annotated e.g. video_validation_0000364
and video_validation_0000856
. In order to avoid unpleasant surprises, it's useful to drop rows of the data-frame where t-init
or t-end
< video-duration
idx_drop = ((df['video-duration'] < df['t-init']) | (df['video-duration'] < df['t-end'])).nonzero()[0]
df.drop(idx_drop, inplace=True)
We are interested on retrieve c3d features associated with a segment of interest. Given a video segment, a feature stride, a folder with features of the video.
Handle multiple process on the same GPU may hurt the run-time performance of each process. In that order of ideas, it is useful to dispatch several tasks to the same GPU in serial mode.
This loop assumes that the DataFrame is contiguous i.e. index from 0
to df.shape[0]
.
Bug: confidence matrix is not dumped by create_dataset therefore it is not possible to train a model
I wrote this function replicating the idea of the cvpr-paper. It would be great to refine it in the following aspects:
t_size
.cov_ratio_per_segment
.RATIO_INTERVALS
i
is iterating over videos while score.shape[0]
corresponds to the number of segments for all the videos. My fault ๐Note: if t_size
is closer to l_size
is possible that not enough segments are generated to satisfy a uniform distribution on RATIO_INTERVALS
. We should decide how to overcome it.
Apparently there is a bug in lasagne
or theano
that produces as error if the input of the LSTM is an array of 3-dimesions and sizes of 1 in the first dimension array.shape = (1, X, X)
.
Python program similar to bash-source used in c3d feature extraction example.
I don't understand how much proposals are generated in all in one video.During training, only one stream is processed in one video,so the overall number of proposals is K.Is that true?
compute_priors
function on data_generation
module is a little bit convoluted. The matching of priors to segments should be on other function because we will end-up duplicating code if all the segments of the dataset are not used to compute priors. That is the case of ActivityNet where there is an explicit validation set.
Similar refactoring should be done on tool create_dataset.py
hi,
i am geting "no such a file or directory" message after runing tools programm batch_frame_extraction.py
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