Added optimal flow

Author: sevdaimany

OpticalFlow_try.ipynb

@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d7f2c5dd",
+   "metadata": {},
+   "source": [
+    "# Optical Flow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e3c585f",
+   "metadata": {},
+   "source": [
+    "</br><div align=\"center\"> <b> <font size=\"3\" color='green'>cv2.calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, winSize, maxLevel)</font></b> </div>\n",
+    "<b>prevImg: </b>first 8-bit input image. </br>\n",
+    "<b>nextImg: </b>second input image. </br>\n",
+    "<b>prevPts: </b>vector of 2D points for which the flow needs to be found.</br>\n",
+    "<b>nextPts: </b>vector of 2D points for which the flow needs to be found.</br>\n",
+    "<b>winSize: </b> size of the search window at each pyramid level.</br>\n",
+    "<b>maxLevel: </b> 0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf3e22a6",
+   "metadata": {},
+   "source": [
+    "</br><div align=\"center\"> <b> <font size=\"3\" color='green'> cv2.calcOpticalFlowFarneback(prev, next, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma)</font></b> </div>\n",
+    "\n",
+    "<b>prev: </b> First input image in 8-bit single channel format.\n",
+    "\n",
+    "next : Second input image of same type and same size as prev.\n",
+    "\n",
+    "pyr_scale : parameter specifying the image scale to build pyramids for each image (scale < 1). A classic pyramid is of \n",
+    "generally 0.5 scale, every new layer added, it is halved to the previous one.\n",
+    "\n",
+    "levels : levels=1 says, there are no extra layers (only the initial image) . It is the number of pyramid layers including the first image.\n",
+    "\n",
+    "winsize : It is the average window size, larger the size, the more robust the algorithm is to noise, and provide fast motion detection, though gives blurred motion fields.\n",
+    "\n",
+    "iterations : Number of iterations to be performed at each pyramid level.\n",
+    "\n",
+    "poly_n : It is typically 5 or 7, it is the size of the pixel neighbourhood which is used to find polynomial expansion between the pixels.\n",
+    "\n",
+    "poly_sigma : standard deviation of the gaussian that is for derivatives to be smooth as the basis of the polynomial expansion. It can be 1.1 for poly= 5 and 1.5 for poly= 7."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}