"Magic" frame project implementation for AIT-Budapest's Computer Vision Applications for Digital Cinema.
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"Magic" frame project implementation for AIT-Budapest's Computer Vision Applications for Digital Cinema.


path-to-file/imgpro input/0000000.jpg output/0000000.jpg -magic <NUMBER_OF_IMAGES> <FIRST_IMAGE_NUMBER> Example: src/imgpro input/smallHD/0000118.jpg output/0000118.jpg -magic 60 118

Step 1: Setup

Iterate through images and call the correct functions on them NOTE: Coded based on the name format of jpg files provided by professor (e.g. 0000000.jpg, 0000001.jpg, etc).

Step 2: Detect and store the "frame" boundary information


  • Detect feature points (Harris Corner Detector) and determine which corners belong to the trackers on the corners of the frame.
  • Store the coordinates of the frame corners (center of trackers, calculated by taking the average of the coordinates of the trackers' feature points). NOTE: Tracker detector algorithm based on the trackers that we designed; looks for patches of color around the feature points and uses RGB color thresholds to determine whether or not the points are part of the tracker).

Step 3: Extract image fragment

Extract and store the frozen image from the first frame. magicExtractFrozen()

Step 4: Local search

Local search in the next frame of the .jpg image sequence to find the new position of the four saved points.

Step 5: Inverse warping

Calculate inverse transformation between next frame and current frame and replace a portion of the image with the saved image. magicReplaceFrameContent(nextImage)

About the skeleton code:

In C++ and provided by the professor. Defined methods read and write jpg images and allow us to overwrite pixel values. Image processing was written by students. Professor: Gergely Vass

Methods added for this project:

R2Image::magicFeature() --> feature detection based on assignments from throughout the semester. Called on a single image only. R2Image::clusters(x,y) --> given coordinates (x,y) of a feature point, check if surrounding pixels form clusters of color similar to the trackers we created. Return true if clusters of red/blue/green. R2Image::findShiftedFrame(nextImage, prev_frame) --> given frame coordinates prev_frame, returns the new coordinates of the frame in the nextImage, running a faster local search to locate the feature points R2Image::magicReplaceFrameContent(nextImage, shifted_frame) --> takes in the next image and replaces the pixels using the coordinates of the shifted frame calculated from findShiftedFrame R2Image::magicExtractFrozen() --> Detects and stores the iformation of the first frame