{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "
\n", "\n", "# CSCI 3240U --- Computer Vision I\n", "\n", "## Lab 1 --- Python Setup and Image I/O\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": [ "## Import required packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Your solution here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load and display the image shown below (`data/test.jpg`)\n", "\n", "
\n", " \n", " \n", "
\n", " data/test.jpg\n", "
" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Your solution here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Print image height and width and the number of channels" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Your solution here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert image to grayscale\n", "\n", "After conversion, the image will look something like this.\n", "\n", "
\n", " \n", " \n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Your solution here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Print the height, width and the number of channels of the grayscale image" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Your solution here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save the grayscale image to file `test_gray.jpg`" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Your solution here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Display the first frame from the video `data/traffic-short.mp4`\n", "\n", "1. Open a video stream.\n", "2. Read in the first frame.\n", "3. Read in the second frame.\n", "4. Find the differences between the two frames.\n", "5. Display the first frame, the second frame and the differences as seen below.\n", "6. Close the video stream\n", "\n", "
\n", " \n", " \n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Your solution here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Capture a frame from webcam\n", "\n", "1. Open a camera stream.\n", "2. Read in one frame.\n", "3. Read in another frame.\n", "4. Find the differences between the two frames.\n", "5. Display the first frame, the second frame and the differences as seen below (your frames will be different).\n", "6. Close the video stream\n", "\n", "
\n", " \n", " \n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Your solution here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", " \n", " \n", "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.9.17" } }, "nbformat": 4, "nbformat_minor": 4 }