{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "
\n", "\n", "# CSCI 3240U --- Computer Vision I\n", "\n", "## Lab 8 --- Homography and Image Stitching\n", "\n", "Faisal Z. Qureshi \n", "Faculty of Science, Ontario Tech University \n", "Oshawa ON Canada \n", "\n", "\n", "*Fall 2026*\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Copyright information\n", "\n", "© Faisal Qureshi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## License\n", "\n", "\"Creative
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outline\n", "\n", "Consider the following image pair\n", "\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "\n", "You are asked to pick at least four correspondences in both images. E.g., you can pick four points $(x^l_i,y^l_i)$ in the left image and then you can pick the corresponding locations $(x^r_i,y^r_i)$ in the right image in the same order. Here $i > 3$. Given these correspondences, estimate the homography matrix between the two set of images, and use this matrix to stitch the two images into a single image." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Picking locations in an image\n", "\n", "Use the following code to pick locations on an image as seen below\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "Modify the code as needed to record the $(x,y)$ locations." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "%matplotlib tk\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "import cv2 as cv\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "class PointPicker:\n", " def __init__(self, imgplot, img, color=(255,0,0), radius=20, thickness=20):\n", " self.imgplot = imgplot\n", " self.img = img\n", " self.color = color\n", " self.radius = radius\n", " self.thickness = thickness\n", " self.cid = imgplot.figure.canvas.mpl_connect('button_press_event', self)\n", " \n", " def __call__(self, event):\n", " if event.inaxes != self.imgplot.axes: \n", " return\n", " ix = event.xdata\n", " iy = event.ydata\n", " print(f'x={ix}, y={iy}')\n", " self.img = cv.circle(self.img, (int(ix), int(iy)), self.radius, self.color, self.thickness)\n", " \n", " imgplot.set_array(self.img)\n", " self.imgplot.figure.canvas.draw()\n", "\n", "img=cv.imread('1-left.jpeg') \n", " \n", "fig = plt.figure(figsize=(10,10))\n", "ax = fig.add_subplot(111)\n", "imgplot = plt.imshow(img)\n", "point_picker = PointPicker(imgplot, img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task\n", "\n", "- Load left image (`1-left.jpeg`) and pick at least four points in order.\n", "- Load right image (`1-right.jpeg`) and pick the corresponding locations.\n", "- Use the correspondences to estimate *homography*.\n", "- Use the homography to stitch the images together.\n", "\n", "### Some outdoor photography\n", "\n", "- The above image exhibits strong parallex. You may consider going for a walk and taking an image pair of your favorite neighbourhood landscape. Try not to translate the camera, just rotate in place.\n", "\n", "### Useful information\n", "\n", "- [Least squares fitting](http://csundergrad.science.uoit.ca/courses/cv-notes/notebooks/15-least-squares.html)\n", "- [RANSAC](http://csundergrad.science.uoit.ca/courses/cv-notes/notebooks/17-ransac.html)\n", "- [Homography](http://csundergrad.science.uoit.ca/courses/cv-notes/notebooks/19-homography.html)\n", "- [Image sampling](http://csundergrad.science.uoit.ca/courses/cv-notes/notebooks/10-image-sampling.html)\n", "\n", "### Bonus\n", "\n", "- Try stitching on different image pairs.\n", "- Can you stich more than one images to create a panorama?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Submission\n", "\n", "Include code and stiched image single jupyter notebook. *Submit via canvas.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
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" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }