Using ffmpeg you can extract all key frames using the following code: ffmpeg -vf select="eq(pict_type\,PICT_TYPE_I)" -i yourvideo.mp4 -vsync 2 -s 160x90 -f image2 thumbnails-%02d.jpeg What follows -vf in a ffmpeg command line is a Filtergraph description. The select filter selects frames to pass in output.
So, in 2004, D.Lowe, University of British Columbia, came up with a new algorithm, Scale Invariant Feature Transform (SIFT) in his paper, Distinctive Image Features from Scale-Invariant Keypoints, which extract keypoints and compute its descriptors. (This paper is easy to understand and considered to be best material available on SIFT.
Key frame extraction using histogram difference python code
We use the inbuilt absdiff() to find the absolute difference between consecutive frames. Now, I have some code that will show us what the difference is. Before that, you must understand that OpenCv video and image frames are just numpy arrays that contain the values of all the pixels in the image or video.
Image Classification in Python with Visual Bag of Words (VBoW) Part 1. Part 2. Part 1: Feature Generation with SIFT Why we need to generate features. Raw pixel data is hard to use for machine learning, and for comparing images in general. A digital image in its simplest form is just a matrix of pixel intensity values.
Histogram can be created using the hist() function in R programming language. This function takes in a vector of values for which the histogram is plotted.. Let us use the built-in dataset airquality which has Daily air quality measurements in New York, May to September 1973.