For aggressive HD resolution removal try no_duplicates=0.98 and hash_size=8.For 1920x1080 (HD) video with minimal duplicate removal try no_duplicates=0.99 and hash_size=32.Requires testing the two parameters: no_duplicates and hash_size based on video size and frame similarity.For r=1, its 1 FPS, and r=2, its 0.5 FPS. Sample one frame every r seconds (-interval) or 1/r FPS.For example: If there are 10 frames, -skip=2 will sample frames 1,3,5,7,9 and -num_frame=4 will sample frames 1,4,7,10. Uniformly sample n frames (-num_frame).Keep one of frame every k frames (default 1, i.e.Resize to specific height & width (-height -width).Resize the shorter edge and keep aspect ratio (the longer edge adapts) (-short).LMDB integrates better with Tensorpack, but reading from it is less flexible (though much much faster than HDF5).Reading from HDF5 is convenient, if you do not plan to use Tensorpack, which does not support HDF5 well currently, always choose HDF5.The database to use is either LMDB or HDF5, choose one according to:.This is important for subsequent deep learning tasks. Note the -qscale:v 2 option in vid2frame.py.

The frames are in JPEG format, with JPEG quality ~95.Both LMDB and HDF5 are key-value storage, the keys are in the format of video_name/frame_id (assuming there are no two videos with the same name). The frames will be stored as strings of their binary content, i.e.

Usage: vid2frame.py -db_name DB_NAME -db_type

(Optional) Use Tensorpack dataflow to accelerate reading from the database.

Store extracted frames into a database, LMDB or HDF5.

For deep learning and computer vision, a good quality of images (JPEG quality around 95) is required.

Extract JPEG frames using ffmpeg but ignores the JPEG quality.

Extract all frames using ffmpeg and write to disk.

Decode all frames (using scikit-video) and store them into a LARGE.

Storing millions of frames on disk makes subsequent processing SLOW. This is a fork and combination of a number of projects:Įxtracting frames from large video datasets (usually 10k ~ 100k, hundreds of GBs on disk) is tedious, automate it. Such that you can quite easily extract 100 different frames from a video, or if you scrapeĪ large number of images from the net you can resize and dedupe before placing them into an image database. An easy-to-use tool to extract frames from video and store into database.īasically, this is a python wrapper of ffmpeg which additionally stores the frames into database.Īdditionally, some basic capabilities for detecting duplicates and deduplication has been added

Python ffmpeg extract frames from video