This repo is an implementation of Efficient Video Generation on Complex Datasets

Note: For more detail, please look up requirements.txt

sudo apt install ffmpeg # important package
chmod u+x scripts/data_prepare.sh
scripts/data_prepare.sh <dataset_path>

scripts/train_model.sh <runing_mode> <dataset_path>

Process UCF-101

• Step 1: Download dataset
• Step 2: Convert from avi to jpg files using: python utils/video_jpg_ucf101_hmdb51.py avi_video_directory jpg_video_directory
• Step 3: Generate n_frames files using: python utils/n_frames_ucf101_hmdb51.py jpg_video_directory
• Step 4: Generate annotation file in json format similar to ActivityNet using: python utils/ucf101_json.py annotation_dir_path

Note: To change the number of class:

• Modify classInd.txt to contain the expected class(es). For example: 1 ApplyEyeMakeup 2 ApplyLipstick 3 Archery
• Run step 4 only
• The code in dataloader automatically skips the unsed videos.