Optical Flow Method

This method enables optical flow calculation using two input images (supported formats include .jpg, .bmp, .bif) to analyze object motion in visual scenes. Optical flow represents the apparent motion of objects between consecutive frames caused by relative movement between the observer and scene. The implementation typically involves gradient-based algorithms (like Lucas-Kanade) or deep learning approaches (such as FlowNet) to compute motion vectors for each pixel. Key processing steps include: 1) Image preprocessing for noise reduction and normalization, 2) Feature point detection using methods like Shi-Tomasi corner detection, 3) Motion vector calculation through iterative optimization minimizing brightness constancy error. The method provides motion direction and velocity estimates through displacement vectors between corresponding points across frames. With simple two-image input, it delivers accurate optical flow results with efficient computation using OpenCV functions like calcOpticalFlowPyrLK() for pyramid-based implementation or createOptFlow_DualTVL1() for variational methods.