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Automatic Tags Generation

The aim of the project was to generate captions/tags for a given video which could be further used for recommendation or categorization. Generating tags for a video has a lot of applications.

Darknet, an open source neural network framework written in C and CUDA is used for this task. The algorithm is simple and works in the following way.

  • Initially the audio of the video is converted into text.
  • Then using darknet we find the key objects present in the video by dividing it into sequence of image frames.
  • Then Topic Modelling using LDA is done on the text obtained from the audio.
  • Then the best tags from both audio and video are chosen and presented.

Note : Many APIs are used to achieve our result. You can find all the packages installed by referring to the import commands in the file automaticTagging.py

All the funcitons are written in a single script - automaticTagging.py. This can be imported and used.

Steps to use :

Initially install all the required packages required by referring to automaticTagging.py.

Open Makefile.
Then Accordingly set flags to 1, as per system specifications

GPU=0          
CUDNN=0    
OPENCV=0
OPENMP=0
DEBUG=0

Run make.
Now darknet can be used. For more information refer README_TO_CONFIGURE.md

The functions can be used as follows : First import the package written

import automaticTagging

The video used here is for demonstration purpose only.

# The format of the file argument and
# file path should specify the same format.
data =
automaticTagging.process_speech_to_text("data/video.mp4","mp4")

data contains the text obtained from the speech.

# The data is unicoded string
# (str(<something>)) is sufficient if Python 3.
# The threshold for NLP model can be specified
# for it. The default is 0.5
audio_tags =
automaticTagging.get_tags_for_audio(data,threshold=0.5)
# The argument is the path to video file.
# The number of frames that can be skipped can be specified as n_frames.
# The default is 25. i.e. automaticTagging.get_tags_for_video('data/video.mp4',n_frames=25).
# If it is 1 nothing is skipped.
video_tags =
automaticTagging.get_tags_for_video('data/video.mp4')

The code is tested only on CPU due to lack of resources. And on CPU all the functions take a lot of time to process but the tags obtained are reasonable.