πŸ“š
DLIP
  • Introduction
  • Prerequisite
  • Image Processing Basics
    • Notes
      • Thresholding
      • Spatial Filtering
      • Masking with Bitwise Operation
      • Model n Calibration
    • Tutorial
      • Tutorial: Install OpenCV C++
      • Tutorial: Create OpenCV Project
      • Tutorial: C++ basics
      • Tutorial: OpenCV Basics
      • Tutorial: Image Watch for Debugging
      • Tutorial: Spatial Filter
      • Tutorial: Thresholding and Morphology
      • Tutorial: Camera Calibration
      • Tutorial: Color Image Processing
      • Tutorial: Edge Line Circle Detection
      • Tutorial: Corner Detection and Optical Flow
      • Tutorial: OpenCV C++ Cheatsheet
      • Tutorial: Installation for Py OpenCV
      • Tutorial: OpenCv (Python) Basics
    • LAB
      • Lab Report Template
      • Lab Report Grading Criteria
      • LAB Report Instruction
      • LAB: Grayscale Image Segmentation
        • LAB: Grayscale Image Segmentation -Gear
        • LAB: Grayscale Image Segmentation - Bolt and Nut
      • LAB: Color Image Segmentation
        • LAB: Facial Temperature Measurement with IR images
        • LAB: Magic Cloak
      • LAB: Straight Lane Detection and Departure Warning
      • LAB: Dimension Measurement with 2D camera
      • LAB: Tension Detection of Rolling Metal Sheet
  • Deep Learning for Perception
    • Notes
      • Lane Detection with Deep Learning
      • Overview of Deep Learning
        • Object Detection
        • Deep Learning Basics: Introduction
        • Deep Learning State of the Art
        • CNN, Object Detection
      • Perceptron
      • Activation Function
      • Optimization
      • Convolution
      • CNN Overview
      • Evaluation Metric
      • LossFunction Regularization
      • Bias vs Variance
      • BottleNeck Unit
      • Object Detection
      • DL Techniques
        • Technical Strategy by A.Ng
    • Tutorial - PyTorch
      • Tutorial: Install PyTorch
      • Tutorial: Python Numpy
      • Tutorial: PyTorch Tutorial List
      • Tutorial: PyTorch Example Code
      • Tutorial: Tensorboard in Pytorch
      • Tutorial: YOLO in PyTorch
        • Tutorial: Yolov8 in PyTorch
        • Tutorial: Train Yolo v8 with custom dataset
          • Tutorial: Train Yolo v5 with custom dataset
        • Tutorial: Yolov5 in Pytorch (VS code)
        • Tutorial: Yolov3 in Keras
    • LAB
      • Assignment: CNN Classification
      • Assignment: Object Detection
      • LAB: CNN Object Detection 1
      • LAB: CNN Object Detection 2
      • LAB Grading Criteria
    • Tutorial- Keras
      • Train Dataset
      • Train custom dataset
      • Test model
      • LeNet-5 Tutorial
      • AlexNet Tutorial
      • VGG Tutorial
      • ResNet Tutorial
    • Resource
      • Online Lecture
      • Programming tutorial
      • Books
      • Hardware
      • Dataset
      • Useful sites
  • Must Read Papers
    • AlexNet
    • VGG
    • ResNet
    • R-CNN, Fast-RCNN, Faster-RCNN
    • YOLOv1-3
    • Inception
    • MobileNet
    • SSD
    • ShuffleNet
    • Recent Methods
  • DLIP Project
    • Report Template
    • DLIP 2021 Projects
      • Digital Door Lock Control with Face Recognition
      • People Counting with YOLOv4 and DeepSORT
      • Eye Blinking Detection Alarm
      • Helmet-Detection Using YOLO-V5
      • Mask Detection using YOLOv5
      • Parking Space Management
      • Vehicle, Pedestrian Detection with IR Image
      • Drum Playing Detection
      • Turtle neck measurement program using OpenPose
    • DLIP 2022 Projects
      • BakeryCashier
      • Virtual Mouse
      • Sudoku Program with Hand gesture
      • Exercise Posture Assistance System
      • People Counting Embedded System
      • Turtle neck measurement program using OpenPose
    • DLIP Past Projects
  • Installation Guide
    • Installation Guide for Pytorch
      • Installation Guide 2021
    • Anaconda
    • CUDA cuDNN
      • CUDA 10.2
    • OpenCV
      • OpenCV Install and Setup
        • OpenCV 3.4.13 with VS2019
        • OpenCV3.4.7 VS2017
        • MacOS OpenCV C++ in XCode
      • Python OpenCV
      • MATLAB-OpenCV
    • Framework
      • Keras
      • TensorFlow
        • Cheat Sheet
        • Tutorial
      • PyTorch
    • IDE
      • Visual Studio Community
      • Google Codelab
      • Visual Studio Code
        • Python with VS Code
        • Notebook with VS Code
        • C++ with VS Code
      • Jupyter Notebook
        • Install
        • How to use
    • Ubuntu
      • Ubuntu 18.04 Installation
      • Ubuntu Installation using Docker in Win10
      • Ubuntu Troubleshooting
    • ROS
  • Programming
    • Python_Numpy
      • Python Tutorial - Tips
      • Python Tutorial - For Loop
      • Python Tutorial - List Tuple, Dic, Set
    • Markdown
      • Example: API documentation
    • Github
      • Create account
      • Tutorial: Github basic
      • Tutorial: Github Desktop
    • Keras
      • Tutorial Keras
      • Cheat Sheet
    • PyTorch
      • Cheat Sheet
      • Autograd in PyTorch
      • Simple ConvNet
      • MNIST using LeNet
      • Train ConvNet using CIFAR10
  • Resources
    • Useful Resources
    • Github
Powered by GitBook
On this page
  • File Submission
  • Comment on Source Code

Was this helpful?

  1. Image Processing Basics
  2. LAB

LAB Report Instruction

File Submission

If the submitted program does not run, it will not be graded. γ€€

Please follow the file names as specified below

If the submitted file name differs even by one character, you will get penalty points.

  • File name:

    • DLIP_LAB1_yourID_yourName.* (*.cpp, *.pdf, *.zip, etc..)

    • Example: DLIP_LAB1_21900123_GilDongHong.*

The following files must be included in the zip file.

  • Submit all files as one zip file.

    • Example: DLIP_LAB1_21900123_GilDongHong.zip

(a) Report

  • *.pdf and *.md files

if you attached a local image to the md file, please also submit the image used in .

(b) Program code and data file

  • *.cpp, *.h

Do not submit the entire folder of solutions and projects

NO Submit: *.sin, *.vcxproj, Property sheet files γ€€ etc

(c) Image/data file

  • images must be in the 'project folder' , the same folder as the main source file

  • Example)

    • src = imread("Lab_GrayScale_TestImage.jpg"); (O)

    • src = imread("../images/Lab_GrayScale_TestImage.jpg"); (X) γ€€

Comment on Source Code

Please make the main() function as concise with high readability.

  • It's not a good idea to write all of your algorithms within the main() function

  • Modulize your algorithms as functions.

  • You can define your functions within your library/header

Write comments to briefly describe what each function/line does

  • It is a good practice to describe the code with comments.

β€» λ°˜λ“œμ‹œ μ œμΆœνŒŒμΌμ„ λ‹€μš΄λ‘œλ“œ λ°›μ•„ μƒˆλ‘œμš΄ ν”„λ‘œμ νŠΈμ—μ„œ μ •μƒμ μœΌλ‘œ κ΅¬λ™λ˜λŠ”μ§€ 확인 λ°”λžλ‹ˆλ‹€.β€» μ œμΆœν•œ ν”„λ‘œκ·Έλž¨μ΄ μ½”λ”© μƒμ˜ 문제둜 싀행이 λ˜μ§€ μ•ŠλŠ” 경우, μ±„μ λŒ€μƒμ—μ„œ μ œμ™Έλ©λ‹ˆλ‹€

1) 제좜과제의 톡일성을 μœ„ν•΄ 제좜 파일λͺ… 및 ν™•μž₯자 ν˜•νƒœλ₯Ό μ•„λž˜μ™€ 같이 μ§€μ •ν•˜μ˜€λ‹ˆ λ°˜λ“œμ‹œ μ—„μˆ˜ν•˜μ‹œκΈ° λ°”λžλ‹ˆλ‹€. 제좜된 파일λͺ…이 문자 ν•˜λ‚˜λΌλ„ λ‹€λ₯Ό 경우 κ°μ ν•˜κ² μŠ΅λ‹ˆλ‹€.

- 파일λͺ…: DLIP_LAB1_ν•™λ²ˆ_μ„±λͺ… (λ³΅μ‚¬ν•˜λŠ” 것이 κ°€μž₯ ν™•μ‹€ν•©λ‹ˆλ‹€)(μ˜ˆμ‹œ: DLIP_LAB1_21900123_홍길동)

- ν™•μž₯자: zip 으둜 μ••μΆ•ν•˜μ—¬ 제좜 β€» 전년도 과제 감점 사둀 : ν•™λ²ˆ ν‘œκΈ° 였λ₯˜ / 과제번호 ν‘œκΈ° 였λ₯˜ / 타 ν™•μž₯자둜 μ••μΆ• λ“±

2) μ œμΆœν•˜λŠ” μ••μΆ•νŒŒμΌ λ‚΄μ—λŠ” μ•„λž˜ νŒŒμΌλ“€μ΄ ν¬ν•¨λ˜μ–΄μ•Ό ν•©λ‹ˆλ‹€.

- λ³΄κ³ μ„œ(.pdf 및 .md 파일 λͺ¨λ‘ 제좜 / Typora 파일->내보내기λ₯Ό 톡해 pdf 생성 κ°€λŠ₯)

Β· mdνŒŒμΌμ— local imageλ₯Ό μ²¨λΆ€ν•œ 경우 μ‚¬μš©λœ image도 μ œμΆœλ°”λžλ‹ˆλ‹€.

- κ³Όμ œμˆ˜ν–‰μ— μ‚¬μš©λœ ν”„λ‘œκ·Έλž¨ μ½”λ“œ(.c/cpp 및 *.h)

- ν”„λ‘œκ·Έλž¨ λΉŒλ“œλ₯Ό μœ„ν•œ μ΄λ―Έμ§€νŒŒμΌ (Lab_GrayScale_TestImage.jpg)

β€» 제좜 κΈˆμ§€ λͺ©λ‘(μ ˆλŒ€ μ œμΆœν•˜μ§€ λ§ˆμ„Έμš”)

  • a) κ³Όμ œμ—μ„œ μˆ˜ν–‰ν•œ μ†”λ£¨μ…˜ 및 ν”„λ‘œμ νŠΈ 전체 폴더

  • b) μ†”λ£¨μ…˜ 파일(*.sin), ν”„λ‘œμ νŠΈ 파일(*.vcxproj)

  • c) μ†μ„±μ‹œνŠΈ 파일

3) ν”„λ‘œκ·Έλž¨ μ œμΆœμ‹œ imread() 경둜λ₯Ό 좔가적인 μƒλŒ€κ²½λ‘œκ°€ 없도둝 μ§€μ • λ°”λžλ‹ˆλ‹€.

- ν•™μƒλ§ˆλ‹€ 이미지λ₯Ό λΆˆλŸ¬μ˜€κΈ°μœ„ν•œ 각기 λ‹€λ₯Έ μƒλŒ€κ²½λ‘œλ₯Ό μ œκ°€ 일일이 νŒŒμ•…/μˆ˜μ •ν•˜μ—¬ λΉŒλ“œν•  수 μ—†μŠ΅λ‹ˆλ‹€. - λ”°λΌμ„œ μ œμΆœμ‹œμ—λŠ” μ•„λž˜μ˜ μ˜ˆμ‹œμ™€ 같이 'ν”„λ‘œμ νŠΈ 폴더'에 μžˆλŠ” μ΄λ―Έμ§€λ‘œ λΉŒλ“œκ°€λŠ₯ν•˜λ„λ‘ ν˜‘μ‘° λΆ€νƒλ“œλ¦½λ‹ˆλ‹€.

  • μ˜ˆμ‹œ) src = imread("Lab_GrayScale_TestImage.jpg"); (O)

  • src = imread("../../images/Lab_GrayScale_TestImage.jpg"); (X)

4) main 문은 μ΅œλŒ€ν•œ κΉ”λ”ν•˜κ²Œ μž‘μ„±ν•˜μ—¬ μ œμΆœλ°”λžλ‹ˆλ‹€.

- λ©”μΈν•¨μˆ˜ λ‚΄μ—μ„œ λͺ¨λ“  μ•Œκ³ λ¦¬μ¦˜μ„ μˆ˜ν–‰ν•˜λŠ” 것은 μ’‹μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.

- κ°€λŠ₯ν•œ 각 κΈ°λŠ₯λ³„λ‘œ ν•¨μˆ˜λ₯Ό λ§Œλ“€κ³  λ©”μΈν•¨μˆ˜λŠ” 그것듀을 μ’…ν•©μ μœΌλ‘œ μ΄μš©ν•œ ν˜•νƒœλ‘œ μ œμΆœλ°”λžλ‹ˆλ‹€

5) 주석은 각 ν•¨μˆ˜λ³„λ‘œ μ–΄λ–€ κΈ°λŠ₯을 μˆ˜ν–‰ν•˜λŠ”μ§€ κ°„λ‹¨νžˆ μž‘μ„±λ°”λΌλ©°, 본인이 νŠΉμ΄ν•œ μ•Œκ³ λ¦¬μ¦˜μ„ κ΅¬μƒν•˜μ—¬ ν•¨μˆ˜λ₯Ό κ΅¬ν˜„ν–ˆμ„ 경우 line by line으둜 μƒμ„Ένžˆ κΈ°μž¬λ°”λžλ‹ˆλ‹€.γ€€

β€» λ°˜λ“œμ‹œ μ œμΆœνŒŒμΌμ„ λ‹€μš΄λ‘œλ“œ λ°›μ•„ μƒˆλ‘œμš΄ ν”„λ‘œμ νŠΈμ—μ„œ μ •μƒμ μœΌλ‘œ κ΅¬λ™λ˜λŠ”μ§€ 확인 λ°”λžλ‹ˆλ‹€.

β€» μ œμΆœν•œ ν”„λ‘œκ·Έλž¨μ΄ μ½”λ”©μƒμ˜ 문제둜 싀행이 λ˜μ§€ μ•ŠλŠ” 경우, μ±„μ λŒ€μƒμ—μ„œ μ œμ™Έλ©λ‹ˆλ‹€.

PreviousLab Report Grading CriteriaNextLAB: Grayscale Image Segmentation

Last updated 2 years ago

Was this helpful?